Repository: RealHossie/Auto-GPT-Chinese Branch: stable Commit: f5c0338759b1 Files: 222 Total size: 925.1 KB Directory structure: gitextract_3dkehp_f/ ├── .coveragerc ├── .devcontainer/ │ ├── Dockerfile │ ├── devcontainer.json │ └── docker-compose.yml ├── .dockerignore ├── .envrc ├── .flake8 ├── .gitattributes ├── .github/ │ ├── FUNDING.yml │ ├── ISSUE_TEMPLATE/ │ │ ├── 1.bug.yml │ │ └── 2.feature.yml │ ├── PULL_REQUEST_TEMPLATE.md │ └── workflows/ │ ├── benchmarks.yml │ ├── ci.yml │ ├── docker-cache-clean.yml │ ├── docker-ci.yml │ ├── docker-release.yml │ ├── documentation-release.yml │ ├── pr-label.yml │ ├── scripts/ │ │ ├── docker-ci-summary.sh │ │ └── docker-release-summary.sh │ └── sponsors_readme.yml ├── .gitignore ├── .isort.cfg ├── .pre-commit-config.yaml ├── .sourcery.yaml ├── BULLETIN.md ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── Dockerfile ├── LICENSE ├── README.md ├── autogpt/ │ ├── __init__.py │ ├── __main__.py │ ├── agent/ │ │ ├── __init__.py │ │ ├── agent.py │ │ └── agent_manager.py │ ├── app.py │ ├── cli.py │ ├── commands/ │ │ ├── __init__.py │ │ ├── analyze_code.py │ │ ├── audio_text.py │ │ ├── command.py │ │ ├── execute_code.py │ │ ├── file_operations.py │ │ ├── git_operations.py │ │ ├── google_search.py │ │ ├── image_gen.py │ │ ├── improve_code.py │ │ ├── task_statuses.py │ │ ├── times.py │ │ ├── twitter.py │ │ ├── web_playwright.py │ │ ├── web_requests.py │ │ ├── web_selenium.py │ │ └── write_tests.py │ ├── config/ │ │ ├── __init__.py │ │ ├── ai_config.py │ │ └── config.py │ ├── configurator.py │ ├── js/ │ │ └── overlay.js │ ├── json_utils/ │ │ ├── __init__.py │ │ ├── json_fix_general.py │ │ ├── json_fix_llm.py │ │ ├── llm_response_format_1.json │ │ └── utilities.py │ ├── llm/ │ │ ├── __init__.py │ │ ├── api_manager.py │ │ ├── base.py │ │ ├── chat.py │ │ ├── llm_utils.py │ │ ├── modelsinfo.py │ │ ├── providers/ │ │ │ ├── __init__.py │ │ │ └── openai.py │ │ └── token_counter.py │ ├── log_cycle/ │ │ ├── __init__.py │ │ ├── json_handler.py │ │ └── log_cycle.py │ ├── logs.py │ ├── main.py │ ├── memory/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── local.py │ │ ├── milvus.py │ │ ├── no_memory.py │ │ ├── pinecone.py │ │ ├── redismem.py │ │ └── weaviate.py │ ├── memory_management/ │ │ ├── store_memory.py │ │ └── summary_memory.py │ ├── models/ │ │ └── base_open_ai_plugin.py │ ├── plugins.py │ ├── processing/ │ │ ├── __init__.py │ │ ├── html.py │ │ └── text.py │ ├── prompts/ │ │ ├── __init__.py │ │ ├── generator.py │ │ └── prompt.py │ ├── setup.py │ ├── singleton.py │ ├── speech/ │ │ ├── __init__.py │ │ ├── base.py │ │ ├── brian.py │ │ ├── eleven_labs.py │ │ ├── gtts.py │ │ ├── macos_tts.py │ │ └── say.py │ ├── spinner.py │ ├── url_utils/ │ │ ├── __init__.py │ │ └── validators.py │ ├── utils.py │ └── workspace/ │ ├── __init__.py │ └── workspace.py ├── azure.yaml.template ├── benchmark/ │ ├── __init__.py │ └── benchmark_entrepreneur_gpt_with_difficult_user.py ├── codecov.yml ├── data/ │ └── .keep ├── data_ingestion.py ├── docker-compose.yml ├── docs/ │ ├── challenges/ │ │ ├── beat.md │ │ ├── challenge_template.md │ │ ├── introduction.md │ │ ├── list.md │ │ ├── memory/ │ │ │ ├── challenge_a.md │ │ │ ├── challenge_b.md │ │ │ └── introduction.md │ │ └── submit.md │ ├── configuration/ │ │ ├── imagegen.md │ │ ├── memory.md │ │ ├── search.md │ │ └── voice.md │ ├── index.md │ ├── plugins.md │ ├── setup.md │ ├── testing.md │ └── usage.md ├── main.py ├── mkdocs.yml ├── pyproject.toml ├── requirements.txt ├── run.bat ├── run.sh ├── run_continuous.bat ├── run_continuous.sh ├── scripts/ │ ├── __init__.py │ ├── check_requirements.py │ └── install_plugin_deps.py ├── tests/ │ ├── __init__.py │ ├── conftest.py │ ├── context.py │ ├── integration/ │ │ ├── __init__.py │ │ ├── agent_factory.py │ │ ├── agent_utils.py │ │ ├── cassettes/ │ │ │ ├── test_llm_utils/ │ │ │ │ ├── test_get_ada_embedding.yaml │ │ │ │ └── test_get_ada_embedding_large_context.yaml │ │ │ ├── test_local_cache/ │ │ │ │ └── test_get_relevant.yaml │ │ │ ├── test_memory_management/ │ │ │ │ └── test_save_memory_trimmed_from_context_window.yaml │ │ │ └── test_setup/ │ │ │ ├── test_generate_aiconfig_automatic_default.yaml │ │ │ ├── test_generate_aiconfig_automatic_fallback.yaml │ │ │ └── test_generate_aiconfig_automatic_typical.yaml │ │ ├── challenges/ │ │ │ ├── __init__.py │ │ │ ├── conftest.py │ │ │ ├── information_retrieval/ │ │ │ │ └── test_information_retrieval_challenge_a.py │ │ │ ├── memory/ │ │ │ │ ├── __init__.py │ │ │ │ ├── cassettes/ │ │ │ │ │ ├── test_memory_challenge_a/ │ │ │ │ │ │ └── test_memory_challenge_a.yaml │ │ │ │ │ └── test_memory_challenge_b/ │ │ │ │ │ └── test_memory_challenge_b.yaml │ │ │ │ ├── test_memory_challenge_a.py │ │ │ │ └── test_memory_challenge_b.py │ │ │ └── utils.py │ │ ├── conftest.py │ │ ├── goal_oriented/ │ │ │ ├── __init__.py │ │ │ ├── cassettes/ │ │ │ │ ├── test_browse_website/ │ │ │ │ │ └── test_browse_website.yaml │ │ │ │ └── test_write_file/ │ │ │ │ └── test_write_file.yaml │ │ │ ├── goal_oriented_tasks.md │ │ │ ├── test_browse_website.py │ │ │ └── test_write_file.py │ │ ├── memory_tests.py │ │ ├── milvus_memory_tests.py │ │ ├── test_execute_code.py │ │ ├── test_git_commands.py │ │ ├── test_llm_utils.py │ │ ├── test_local_cache.py │ │ ├── test_memory_management.py │ │ ├── test_setup.py │ │ └── weaviate_memory_tests.py │ ├── milvus_memory_test.py │ ├── mocks/ │ │ ├── __init__.py │ │ └── mock_commands.py │ ├── test_agent.py │ ├── test_agent_manager.py │ ├── test_ai_config.py │ ├── test_api_manager.py │ ├── test_commands.py │ ├── test_config.py │ ├── test_image_gen.py │ ├── test_logs.py │ ├── test_prompt_generator.py │ ├── test_token_counter.py │ ├── test_utils.py │ ├── test_workspace.py │ ├── unit/ │ │ ├── __init__.py │ │ ├── _test_json_parser.py │ │ ├── models/ │ │ │ └── test_base_open_api_plugin.py │ │ ├── test_browse_scrape_links.py │ │ ├── test_browse_scrape_text.py │ │ ├── test_chat.py │ │ ├── test_commands.py │ │ ├── test_file_operations.py │ │ ├── test_get_self_feedback.py │ │ ├── test_json_parser.py │ │ ├── test_json_utils_llm.py │ │ ├── test_llm_utils.py │ │ ├── test_plugins.py │ │ ├── test_spinner.py │ │ ├── test_url_validation.py │ │ └── test_web_selenium.py │ ├── utils.py │ └── vcr/ │ ├── __init__.py │ ├── openai_filter.py │ └── vcr_filter.py └── tests.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ [run] relative_files = true ================================================ FILE: .devcontainer/Dockerfile ================================================ # Use an official Python base image from the Docker Hub FROM python:3.10 # Install browsers RUN apt-get update && apt-get install -y \ chromium-driver firefox-esr \ ca-certificates # Install utilities RUN apt-get install -y curl jq wget git # Declare working directory WORKDIR /workspace/Auto-GPT ================================================ FILE: .devcontainer/devcontainer.json ================================================ { "dockerComposeFile": "./docker-compose.yml", "service": "auto-gpt", "workspaceFolder": "/workspace/Auto-GPT", "shutdownAction": "stopCompose", "features": { "ghcr.io/devcontainers/features/common-utils:2": { "installZsh": "true", "username": "vscode", "userUid": "6942", "userGid": "6942", "upgradePackages": "true" }, "ghcr.io/devcontainers/features/desktop-lite:1": {}, "ghcr.io/devcontainers/features/python:1": "none", "ghcr.io/devcontainers/features/node:1": "none", "ghcr.io/devcontainers/features/git:1": { "version": "latest", "ppa": "false" } }, // Configure tool-specific properties. "customizations": { // Configure properties specific to VS Code. "vscode": { // Set *default* container specific settings.json values on container create. "settings": { "python.defaultInterpreterPath": "/usr/local/bin/python" } } }, // Use 'forwardPorts' to make a list of ports inside the container available locally. // "forwardPorts": [], // Use 'postCreateCommand' to run commands after the container is created. // "postCreateCommand": "pip3 install --user -r requirements.txt", // Set `remoteUser` to `root` to connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root. "remoteUser": "vscode" } ================================================ FILE: .devcontainer/docker-compose.yml ================================================ # To boot the app run the following: # docker-compose run auto-gpt version: '3.9' services: auto-gpt: depends_on: - redis build: dockerfile: .devcontainer/Dockerfile context: ../ tty: true environment: MEMORY_BACKEND: ${MEMORY_BACKEND:-redis} REDIS_HOST: ${REDIS_HOST:-redis} volumes: - ../:/workspace/Auto-GPT redis: image: 'redis/redis-stack-server:latest' ================================================ FILE: .dockerignore ================================================ .* *.template *.yaml *.yml *.md *.png !BULLETIN.md ================================================ FILE: .envrc ================================================ # Upon entering directory, direnv requests user permission once to automatically load project dependencies onwards. # Eliminating the need of running "nix develop github:superherointj/nix-auto-gpt" for Nix users to develop/use Auto-GPT. [[ -z $IN_NIX_SHELL ]] && use flake github:superherointj/nix-auto-gpt ================================================ FILE: .flake8 ================================================ [flake8] max-line-length = 88 select = "E303, W293, W291, W292, E305, E231, E302" exclude = .tox, __pycache__, *.pyc, .env venv*/*, .venv/*, reports/*, dist/*, ================================================ FILE: .gitattributes ================================================ # Exclude VCR cassettes from stats tests/**/cassettes/**.y*ml linguist-generated # Mark documentation as such docs/**.md linguist-documentation ================================================ FILE: .github/FUNDING.yml ================================================ # These are supported funding model platforms github: Torantulino ================================================ FILE: .github/ISSUE_TEMPLATE/1.bug.yml ================================================ name: Bug report 🐛 description: Create a bug report for Auto-GPT. labels: ['status: needs triage'] body: - type: markdown attributes: value: | ### ⚠️ Before you continue * Check out our [backlog], [roadmap] and join our [discord] to discuss what's going on * If you need help, you can ask in the [discussions] section or in [#tech-support] * **Throughly search the [existing issues] before creating a new one** * Read our [wiki page on Contributing] [backlog]: https://github.com/orgs/Significant-Gravitas/projects/1 [roadmap]: https://github.com/orgs/Significant-Gravitas/projects/2 [discord]: https://discord.gg/autogpt [discussions]: https://github.com/Significant-Gravitas/Auto-GPT/discussions [#tech-support]: https://discord.com/channels/1092243196446249134/1092275629602394184 [existing issues]: https://github.com/Significant-Gravitas/Auto-GPT/issues?q=is%3Aissue [wiki page on Contributing]: https://github.com/Significant-Gravitas/Auto-GPT/wiki/Contributing - type: checkboxes attributes: label: ⚠️ Search for existing issues first ⚠️ description: > Please [search the history](https://github.com/Torantulino/Auto-GPT/issues) to see if an issue already exists for the same problem. options: - label: I have searched the existing issues, and there is no existing issue for my problem required: true - type: markdown attributes: value: | Please provide a searchable summary of the issue in the title above ⬆️. ⚠️ SUPER-busy repo, please help the volunteer maintainers. The less time we spend here, the more time we spend building AutoGPT. Please help us help you: - Does it work on `stable` branch (https://github.com/Torantulino/Auto-GPT/tree/stable)? - Does it work on current `master` (https://github.com/Torantulino/Auto-GPT/tree/master)? - Search for existing issues, "add comment" is tidier than "new issue" - Ask on our Discord (https://discord.gg/autogpt) - Provide relevant info: - Provide commit-hash (`git rev-parse HEAD` gets it) - If it's a pip/packages issue, provide pip version, python version - If it's a crash, provide traceback. - type: dropdown attributes: label: Which Operating System are you using? description: > Please select the operating system you were using to run Auto-GPT when this problem occurred. options: - Windows - Linux - MacOS - Docker - Devcontainer / Codespace - Windows Subsystem for Linux (WSL) - Other (Please specify in your problem) validations: required: true - type: dropdown attributes: label: Which version of Auto-GPT are you using? description: | Please select which version of Auto-GPT you were using when this issue occurred. If you downloaded the code from the [releases page](https://github.com/Significant-Gravitas/Auto-GPT/releases/) make sure you were using the latest code. **If you weren't please try with the [latest code](https://github.com/Significant-Gravitas/Auto-GPT/releases/)**. If installed with git you can run `git branch` to see which version of Auto-GPT you are running. options: - Latest Release - Stable (branch) - Master (branch) validations: required: true - type: dropdown attributes: label: GPT-3 or GPT-4? description: > If you are using Auto-GPT with `--gpt3only`, your problems may be caused by the [limitations](https://github.com/Significant-Gravitas/Auto-GPT/issues?q=is%3Aissue+label%3A%22AI+model+limitation%22) of GPT-3.5. options: - GPT-3.5 - GPT-4 validations: required: true - type: textarea attributes: label: Steps to reproduce 🕹 description: | **⚠️ Issues that we can't reproduce will be closed.** - type: textarea attributes: label: Current behavior 😯 description: Describe what happens instead of the expected behavior. - type: textarea attributes: label: Expected behavior 🤔 description: Describe what should happen. - type: textarea attributes: label: Your prompt 📝 description: > If applicable please provide the prompt you are using. Your prompt is stored in your `ai_settings.yaml` file. value: | ```yaml # Paste your prompt here ``` - type: textarea attributes: label: Your Logs 📒 description: | Please include the log showing your error and the command that caused it, if applicable. You can copy it from your terminal or from `logs/activity.log`. This will help us understand your issue better!
Example ```log INFO NEXT ACTION: COMMAND = execute_shell ARGUMENTS = {'command_line': 'some_command'} INFO -=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-= Traceback (most recent call last): File "/home/anaconda3/lib/python3.9/site-packages/openai/api_requestor.py", line 619, in _interpret_response self._interpret_response_line( File "/home/anaconda3/lib/python3.9/site-packages/openai/api_requestor.py", line 682, in _interpret_response_line raise self.handle_error_response( openai.error.InvalidRequestError: This model's maximum context length is 8191 tokens, however you requested 10982 tokens (10982 in your prompt; 0 for the completion). Please reduce your prompt; or completion length. ```
value: | ```log ``` ================================================ FILE: .github/ISSUE_TEMPLATE/2.feature.yml ================================================ name: Feature request 🚀 description: Suggest a new idea for Auto-GPT! labels: ['status: needs triage'] body: - type: markdown attributes: value: | First, check out our [wiki page on Contributing](https://github.com/Significant-Gravitas/Auto-GPT/wiki/Contributing) Please provide a searchable summary of the issue in the title above ⬆️. - type: checkboxes attributes: label: Duplicates description: Please [search the history](https://github.com/Torantulino/Auto-GPT/issues) to see if an issue already exists for the same problem. options: - label: I have searched the existing issues required: true - type: textarea attributes: label: Summary 💡 description: Describe how it should work. - type: textarea attributes: label: Examples 🌈 description: Provide a link to other implementations, or screenshots of the expected behavior. - type: textarea attributes: label: Motivation 🔦 description: What are you trying to accomplish? How has the lack of this feature affected you? Providing context helps us come up with a solution that is more useful in the real world. ================================================ FILE: .github/PULL_REQUEST_TEMPLATE.md ================================================ ### Background ### Changes ### Documentation ### Test Plan ### PR Quality Checklist - [ ] My pull request is atomic and focuses on a single change. - [ ] I have thoroughly tested my changes with multiple different prompts. - [ ] I have considered potential risks and mitigations for my changes. - [ ] I have documented my changes clearly and comprehensively. - [ ] I have not snuck in any "extra" small tweaks changes ================================================ FILE: .github/workflows/benchmarks.yml ================================================ name: Run Benchmarks on: workflow_dispatch: jobs: build: runs-on: ubuntu-latest env: python-version: '3.10' steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Python ${{ env.python-version }} uses: actions/setup-python@v4 with: python-version: ${{ env.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: benchmark env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | python benchmark/benchmark_entrepreneur_gpt_with_undecisive_user.py ================================================ FILE: .github/workflows/ci.yml ================================================ name: Python CI on: push: branches: [ master ] pull_request: branches: [ master, stable ] concurrency: group: ${{ format('ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} jobs: lint: runs-on: ubuntu-latest env: min-python-version: "3.10" steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Python ${{ env.min-python-version }} uses: actions/setup-python@v2 with: python-version: ${{ env.min-python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Lint with flake8 run: flake8 - name: Check black formatting run: black . --check if: success() || failure() - name: Check isort formatting run: isort . --check if: success() || failure() test: permissions: # Gives the action the necessary permissions for publishing new # comments in pull requests. pull-requests: write # Gives the action the necessary permissions for pushing data to the # python-coverage-comment-action branch, and for editing existing # comments (to avoid publishing multiple comments in the same PR) contents: write runs-on: ubuntu-latest strategy: matrix: python-version: ["3.10", "3.11"] steps: - name: Check out repository uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Run unittest tests with coverage run: | pytest --cov=autogpt --cov-report term-missing --cov-branch --cov-report xml --cov-report term env: CI: true - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v3 ================================================ FILE: .github/workflows/docker-cache-clean.yml ================================================ name: Purge Docker CI cache on: schedule: - cron: 20 4 * * 1,4 env: BASE_BRANCH: master IMAGE_NAME: auto-gpt jobs: build: runs-on: ubuntu-latest strategy: matrix: build-type: [release, dev] steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 - id: build name: Build image uses: docker/build-push-action@v3 with: build-args: BUILD_TYPE=${{ matrix.build-type }} load: true # save to docker images # use GHA cache as read-only cache-to: type=gha,scope=docker-${{ matrix.build-type }},mode=max - name: Generate build report env: event_name: ${{ github.event_name }} event_ref: ${{ github.event.schedule }} build_type: ${{ matrix.build-type }} prod_branch: stable dev_branch: master repository: ${{ github.repository }} base_branch: ${{ github.ref_name != 'master' && github.ref_name != 'stable' && 'master' || 'stable' }} current_ref: ${{ github.ref_name }} commit_hash: ${{ github.sha }} source_url: ${{ format('{0}/tree/{1}', github.event.repository.url, github.sha) }} push_forced_label: new_commits_json: ${{ null }} compare_url_template: ${{ format('/{0}/compare/{{base}}...{{head}}', github.repository) }} github_context_json: ${{ toJSON(github) }} job_env_json: ${{ toJSON(env) }} vars_json: ${{ toJSON(vars) }} run: .github/workflows/scripts/docker-ci-summary.sh >> $GITHUB_STEP_SUMMARY continue-on-error: true ================================================ FILE: .github/workflows/docker-ci.yml ================================================ name: Docker CI on: push: branches: [ master ] pull_request: branches: [ master, stable ] concurrency: group: ${{ format('docker-ci-{0}', github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha) }} cancel-in-progress: ${{ github.event_name == 'pull_request' }} env: IMAGE_NAME: auto-gpt jobs: build: runs-on: ubuntu-latest strategy: matrix: build-type: [release, dev] steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 - if: runner.debug run: | ls -al du -hs * - id: build name: Build image uses: docker/build-push-action@v3 with: build-args: BUILD_TYPE=${{ matrix.build-type }} tags: ${{ env.IMAGE_NAME }} load: true # save to docker images # cache layers in GitHub Actions cache to speed up builds cache-from: type=gha,scope=docker-${{ matrix.build-type }} cache-to: type=gha,scope=docker-${{ matrix.build-type }},mode=max - name: Generate build report env: event_name: ${{ github.event_name }} event_ref: ${{ github.event.ref }} event_ref_type: ${{ github.event.ref}} build_type: ${{ matrix.build-type }} prod_branch: stable dev_branch: master repository: ${{ github.repository }} base_branch: ${{ github.ref_name != 'master' && github.ref_name != 'stable' && 'master' || 'stable' }} current_ref: ${{ github.ref_name }} commit_hash: ${{ github.event.after }} source_url: ${{ format('{0}/tree/{1}', github.event.repository.url, github.event.release && github.event.release.tag_name || github.sha) }} push_forced_label: ${{ github.event.forced && '☢️ forced' || '' }} new_commits_json: ${{ toJSON(github.event.commits) }} compare_url_template: ${{ format('/{0}/compare/{{base}}...{{head}}', github.repository) }} github_context_json: ${{ toJSON(github) }} job_env_json: ${{ toJSON(env) }} vars_json: ${{ toJSON(vars) }} run: .github/workflows/scripts/docker-ci-summary.sh >> $GITHUB_STEP_SUMMARY continue-on-error: true # Docker setup needs fixing before this is going to work: #1843 test: runs-on: ubuntu-latest needs: build steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 - id: build name: Build image uses: docker/build-push-action@v3 with: build-args: BUILD_TYPE=dev # include pytest tags: ${{ env.IMAGE_NAME }} load: true # save to docker images # cache layers in GitHub Actions cache to speed up builds cache-from: type=gha,scope=docker-dev cache-to: type=gha,scope=docker-dev,mode=max - id: test name: Run tests env: CI: true OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | set +e test_output=$( docker run --env CI --env OPENAI_API_KEY --entrypoint python ${{ env.IMAGE_NAME }} -m \ pytest --cov=autogpt --cov-report term-missing --cov-branch --cov-report xml --cov-report term 2>&1 ) test_failure=$? echo "$test_output" cat << $EOF >> $GITHUB_STEP_SUMMARY # Tests $([ $test_failure = 0 ] && echo '✅' || echo '❌') \`\`\` $test_output \`\`\` $EOF ================================================ FILE: .github/workflows/docker-release.yml ================================================ name: Docker Release on: release: types: [ published, edited ] workflow_dispatch: inputs: no_cache: type: boolean description: 'Build from scratch, without using cached layers' env: IMAGE_NAME: auto-gpt DEPLOY_IMAGE_NAME: ${{ secrets.DOCKER_USER }}/auto-gpt jobs: build: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v3 - name: Log in to Docker hub uses: docker/login-action@v2 with: username: ${{ secrets.DOCKER_USER }} password: ${{ secrets.DOCKER_PASSWORD }} - name: Set up Docker Buildx uses: docker/setup-buildx-action@v2 # slashes are not allowed in image tags, but can appear in git branch or tag names - id: sanitize_tag name: Sanitize image tag run: echo tag=${raw_tag//\//-} >> $GITHUB_OUTPUT env: raw_tag: ${{ github.ref_name }} - id: build name: Build image uses: docker/build-push-action@v3 with: build-args: BUILD_TYPE=release load: true # save to docker images # push: true # TODO: uncomment when this issue is fixed: https://github.com/moby/buildkit/issues/1555 tags: > ${{ env.IMAGE_NAME }}, ${{ env.DEPLOY_IMAGE_NAME }}:latest, ${{ env.DEPLOY_IMAGE_NAME }}:${{ steps.sanitize_tag.outputs.tag }} # cache layers in GitHub Actions cache to speed up builds cache-from: ${{ !inputs.no_cache && 'type=gha' || '' }},scope=docker-release cache-to: type=gha,scope=docker-release,mode=max - name: Push image to Docker Hub run: docker push --all-tags ${{ env.DEPLOY_IMAGE_NAME }} - name: Generate build report env: event_name: ${{ github.event_name }} event_ref: ${{ github.event.ref }} event_ref_type: ${{ github.event.ref}} inputs_no_cache: ${{ inputs.no_cache }} prod_branch: stable dev_branch: master repository: ${{ github.repository }} base_branch: ${{ github.ref_name != 'master' && github.ref_name != 'stable' && 'master' || 'stable' }} ref_type: ${{ github.ref_type }} current_ref: ${{ github.ref_name }} commit_hash: ${{ github.sha }} source_url: ${{ format('{0}/tree/{1}', github.event.repository.url, github.event.release && github.event.release.tag_name || github.sha) }} github_context_json: ${{ toJSON(github) }} job_env_json: ${{ toJSON(env) }} vars_json: ${{ toJSON(vars) }} run: .github/workflows/scripts/docker-release-summary.sh >> $GITHUB_STEP_SUMMARY continue-on-error: true ================================================ FILE: .github/workflows/documentation-release.yml ================================================ name: Docs on: push: branches: [ stable ] paths: - 'docs/**' - 'mkdocs.yml' - '.github/workflows/documentation.yml' # Allows you to run this workflow manually from the Actions tab workflow_dispatch: permissions: contents: write jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v3 - name: Set up Python 3 uses: actions/setup-python@v4 with: python-version: 3.x - name: Set up workflow cache uses: actions/cache@v3 with: key: ${{ github.ref }} path: .cache - run: pip install mkdocs-material - run: mkdocs gh-deploy --force ================================================ FILE: .github/workflows/pr-label.yml ================================================ name: "Pull Request auto-label" on: # So that PRs touching the same files as the push are updated push: branches: [ master ] # So that the `dirtyLabel` is removed if conflicts are resolve # We recommend `pull_request_target` so that github secrets are available. # In `pull_request` we wouldn't be able to change labels of fork PRs pull_request_target: types: [ opened, synchronize ] concurrency: group: ${{ format('pr-label-{0}', github.event.pull_request.number || github.sha) }} cancel-in-progress: true jobs: conflicts: runs-on: ubuntu-latest permissions: contents: read pull-requests: write steps: - name: Update PRs with conflict labels uses: eps1lon/actions-label-merge-conflict@releases/2.x with: dirtyLabel: "conflicts" #removeOnDirtyLabel: "PR: ready to ship" repoToken: "${{ secrets.GITHUB_TOKEN }}" commentOnDirty: "This pull request has conflicts with the base branch, please resolve those so we can evaluate the pull request." commentOnClean: "Conflicts have been resolved! 🎉 A maintainer will review the pull request shortly." size: if: ${{ github.event_name == 'pull_request_target' }} permissions: issues: write pull-requests: write runs-on: ubuntu-latest steps: - uses: codelytv/pr-size-labeler@v1 with: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} xs_label: 'size/xs' xs_max_size: 2 s_label: 'size/s' s_max_size: 10 m_label: 'size/m' m_max_size: 50 l_label: 'size/l' l_max_size: 200 xl_label: 'size/xl' message_if_xl: > This PR exceeds the recommended size of 200 lines. Please make sure you are NOT addressing multiple issues with one PR. Note this PR might be rejected due to its size ================================================ FILE: .github/workflows/scripts/docker-ci-summary.sh ================================================ #!/bin/bash meta=$(docker image inspect "$IMAGE_NAME" | jq '.[0]') head_compare_url=$(sed "s/{base}/$base_branch/; s/{head}/$current_ref/" <<< $compare_url_template) ref_compare_url=$(sed "s/{base}/$base_branch/; s/{head}/$commit_hash/" <<< $compare_url_template) EOF=$(dd if=/dev/urandom bs=15 count=1 status=none | base64) cat << $EOF # Docker Build summary 🔨 **Source:** branch \`$current_ref\` -> [$repository@\`${commit_hash:0:7}\`]($source_url) **Build type:** \`$build_type\` **Image size:** $((`jq -r .Size <<< $meta` / 10**6))MB ## Image details **Tags:** $(jq -r '.RepoTags | map("* `\(.)`") | join("\n")' <<< $meta)

Layers

 | Age | Size | Created by instruction | | --------- | ------ | ---------------------- | $(docker history --no-trunc --format "{{.CreatedSince}}\t{{.Size}}\t\`{{.CreatedBy}}\`\t{{.Comment}}" $IMAGE_NAME \ | grep 'buildkit.dockerfile' `# filter for layers created in this build process`\ | cut -f-3 `# yeet Comment column`\ | sed 's/ ago//' `# fix Layer age`\ | sed 's/ # buildkit//' `# remove buildkit comment from instructions`\ | sed 's/\$/\\$/g' `# escape variable and shell expansions`\ | sed 's/|/\\|/g' `# escape pipes so they don't interfere with column separators`\ | column -t -s$'\t' -o' | ' `# align columns and add separator`\ | sed 's/^/| /; s/$/ |/' `# add table row start and end pipes`)

ENV

 | Variable | Value | | -------- | -------- | $(jq -r \ '.Config.Env | map( split("=") | "\(.[0]) | `\(.[1] | gsub("\\s+"; " "))`" ) | map("| \(.) |") | .[]' <<< $meta )
Raw metadata \`\`\`JSON $meta \`\`\`
## Build details **Build trigger:** $push_forced_label $event_name \`$event_ref\`
github context \`\`\`JSON $github_context_json \`\`\`
### Source **HEAD:** [$repository@\`${commit_hash:0:7}\`]($source_url) on branch [$current_ref]($ref_compare_url) **Diff with previous HEAD:** $head_compare_url #### New commits $(jq -r 'map([ "**Commit [`\(.id[0:7])`](\(.url)) by \(if .author.username then "@"+.author.username else .author.name end):**", .message, (if .committer.name != .author.name then "\n> **Committer:** \(.committer.name) <\(.committer.email)>" else "" end), "**Timestamp:** \(.timestamp)" ] | map("> \(.)\n") | join("")) | join("\n")' <<< $new_commits_json) ### Job environment #### \`vars\` context: \`\`\`JSON $vars_json \`\`\` #### \`env\` context: \`\`\`JSON $job_env_json \`\`\` $EOF ================================================ FILE: .github/workflows/scripts/docker-release-summary.sh ================================================ #!/bin/bash meta=$(docker image inspect "$IMAGE_NAME" | jq '.[0]') EOF=$(dd if=/dev/urandom bs=15 count=1 status=none | base64) cat << $EOF # Docker Release Build summary 🚀🔨 **Source:** $ref_type \`$current_ref\` -> [$repository@\`${commit_hash:0:7}\`]($source_url) **Image size:** $((`jq -r .Size <<< $meta` / 10**6))MB ## Image details **Tags:** $(jq -r '.RepoTags | map("* `\(.)`") | join("\n")' <<< $meta)

Layers

 | Age | Size | Created by instruction | | --------- | ------ | ---------------------- | $(docker history --no-trunc --format "{{.CreatedSince}}\t{{.Size}}\t\`{{.CreatedBy}}\`\t{{.Comment}}" $IMAGE_NAME \ | grep 'buildkit.dockerfile' `# filter for layers created in this build process`\ | cut -f-3 `# yeet Comment column`\ | sed 's/ ago//' `# fix Layer age`\ | sed 's/ # buildkit//' `# remove buildkit comment from instructions`\ | sed 's/\$/\\$/g' `# escape variable and shell expansions`\ | sed 's/|/\\|/g' `# escape pipes so they don't interfere with column separators`\ | column -t -s$'\t' -o' | ' `# align columns and add separator`\ | sed 's/^/| /; s/$/ |/' `# add table row start and end pipes`)

ENV

 | Variable | Value | | -------- | -------- | $(jq -r \ '.Config.Env | map( split("=") | "\(.[0]) | `\(.[1] | gsub("\\s+"; " "))`" ) | map("| \(.) |") | .[]' <<< $meta )
Raw metadata \`\`\`JSON $meta \`\`\`
## Build details **Build trigger:** $event_name \`$current_ref\` | Parameter | Value | | -------------- | ------------ | | \`no_cache\` | \`$inputs_no_cache\` |
github context \`\`\`JSON $github_context_json \`\`\`
### Job environment #### \`vars\` context: \`\`\`JSON $vars_json \`\`\` #### \`env\` context: \`\`\`JSON $job_env_json \`\`\` $EOF ================================================ FILE: .github/workflows/sponsors_readme.yml ================================================ name: Generate Sponsors README on: workflow_dispatch: schedule: - cron: '0 */12 * * *' jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout 🛎️ uses: actions/checkout@v3 - name: Generate Sponsors 💖 uses: JamesIves/github-sponsors-readme-action@v1 with: token: ${{ secrets.README_UPDATER_PAT }} file: 'README.md' minimum: 2500 maximum: 99999 - name: Deploy to GitHub Pages 🚀 uses: JamesIves/github-pages-deploy-action@v4 with: branch: master folder: '.' token: ${{ secrets.README_UPDATER_PAT }} ================================================ FILE: .gitignore ================================================ ## Original ignores autogpt/keys.py autogpt/*json autogpt/node_modules/ autogpt/__pycache__/keys.cpython-310.pyc autogpt/auto_gpt_workspace package-lock.json *.pyc auto_gpt_workspace/* *.mpeg .env azure.yaml ai_settings.yaml last_run_ai_settings.yaml .vscode .idea/* auto-gpt.json log.txt log-ingestion.txt logs *.log *.mp3 mem.sqlite3 # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ plugins/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ site/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .direnv/ .env .venv env/ venv*/ ENV/ env.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ llama-* vicuna-* # mac .DS_Store openai/ # news CURRENT_BULLETIN.md ================================================ FILE: .isort.cfg ================================================ [settings] profile = black multi_line_output = 3 include_trailing_comma = true force_grid_wrap = 0 use_parentheses = true ensure_newline_before_comments = true line_length = 88 sections = FUTURE,STDLIB,THIRDPARTY,FIRSTPARTY,LOCALFOLDER skip = .tox,__pycache__,*.pyc,venv*/*,reports,venv,env,node_modules,.env,.venv,dist ================================================ FILE: .pre-commit-config.yaml ================================================ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.4.0 hooks: - id: check-added-large-files args: ['--maxkb=500'] - id: check-byte-order-marker - id: check-case-conflict - id: check-merge-conflict - id: check-symlinks - id: debug-statements - repo: https://github.com/pycqa/isort rev: 5.12.0 hooks: - id: isort language_version: python3.10 - repo: https://github.com/psf/black rev: 23.3.0 hooks: - id: black language_version: python3.10 - repo: local hooks: - id: pytest-check name: pytest-check entry: pytest --cov=autogpt --without-integration --without-slow-integration language: system pass_filenames: false always_run: true ================================================ FILE: .sourcery.yaml ================================================ # 🪄 This is your project's Sourcery configuration file. # You can use it to get Sourcery working in the way you want, such as # ignoring specific refactorings, skipping directories in your project, # or writing custom rules. # 📚 For a complete reference to this file, see the documentation at # https://docs.sourcery.ai/Configuration/Project-Settings/ # This file was auto-generated by Sourcery on 2023-02-25 at 21:07. version: '1' # The schema version of this config file ignore: # A list of paths or files which Sourcery will ignore. - .git - venv - .venv - build - dist - env - .env - .tox rule_settings: enable: - default - gpsg disable: [] # A list of rule IDs Sourcery will never suggest. rule_types: - refactoring - suggestion - comment python_version: '3.10' # A string specifying the lowest Python version your project supports. Sourcery will not suggest refactorings requiring a higher Python version. # rules: # A list of custom rules Sourcery will include in its analysis. # - id: no-print-statements # description: Do not use print statements in the test directory. # pattern: print(...) # language: python # replacement: # condition: # explanation: # paths: # include: # - test # exclude: # - conftest.py # tests: [] # tags: [] # rule_tags: {} # Additional rule tags. # metrics: # quality_threshold: 25.0 # github: # labels: [] # ignore_labels: # - sourcery-ignore # request_review: author # sourcery_branch: sourcery/{base_branch} # clone_detection: # min_lines: 3 # min_duplicates: 2 # identical_clones_only: false # proxy: # url: # ssl_certs_file: # no_ssl_verify: false ================================================ FILE: BULLETIN.md ================================================ # 持续更新中 Auto-GPT 0.3.0最大更新为自动定义AI名称，角色，任务等，也可以进入手动模式 对插件（Plugin）的支持也在逐渐强大，可玩性更强 我正在逐步修改汉化0.3.0稳定版本，如果你喜欢我的工作，请给我一个star，谢谢！关注我的频道www.youtube.com/@hossie # 官方网站与文档站 📰📖 使用说明与最新信息 *https://agpt.co* 相关文档 *https://docs.agpt.co* # 🚀 最新更新 🚀 1. 基础命令汉化 2. 将一些Prompt转化为中文更好理解内容 3. 已完成部分插件汉化 # 🚀 未来更新 🚀 1. 简单的插件汉化 2. 优化已有汉化内容 3. 跟踪官方更新 # ⚠️ `send_tweet` 已经被废弃, 0.4.0版本中将彻底删除 ⚠️ Twitter 功能将通过Plugin完成 [Plugin support 🔌] ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct for Auto-GPT ## 1. Purpose The purpose of this Code of Conduct is to provide guidelines for contributors to the auto-gpt project on GitHub. We aim to create a positive and inclusive environment where all participants can contribute and collaborate effectively. By participating in this project, you agree to abide by this Code of Conduct. ## 2. Scope This Code of Conduct applies to all contributors, maintainers, and users of the auto-gpt project. It extends to all project spaces, including but not limited to issues, pull requests, code reviews, comments, and other forms of communication within the project. ## 3. Our Standards We encourage the following behavior: * Being respectful and considerate to others * Actively seeking diverse perspectives * Providing constructive feedback and assistance * Demonstrating empathy and understanding We discourage the following behavior: * Harassment or discrimination of any kind * Disrespectful, offensive, or inappropriate language or content * Personal attacks or insults * Unwarranted criticism or negativity ## 4. Reporting and Enforcement If you witness or experience any violations of this Code of Conduct, please report them to the project maintainers by email or other appropriate means. The maintainers will investigate and take appropriate action, which may include warnings, temporary or permanent bans, or other measures as necessary. Maintainers are responsible for ensuring compliance with this Code of Conduct and may take action to address any violations. ## 5. Acknowledgements This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org/version/2/0/code_of_conduct.html). ## 6. Contact If you have any questions or concerns, please contact the project maintainers. ================================================ FILE: CONTRIBUTING.md ================================================ This document now lives at https://github.com/Significant-Gravitas/Auto-GPT/wiki/Contributing ================================================ FILE: Dockerfile ================================================ # 'dev' or 'release' container build ARG BUILD_TYPE=dev # Use an official Python base image from the Docker Hub FROM python:3.10-slim AS autogpt-base # Install browsers RUN apt-get update && apt-get install -y \ chromium-driver firefox-esr \ ca-certificates # Install utilities RUN apt-get install -y curl jq wget git # Set environment variables ENV PIP_NO_CACHE_DIR=yes \ PYTHONUNBUFFERED=1 \ PYTHONDONTWRITEBYTECODE=1 # Install the required python packages globally ENV PATH="$PATH:/root/.local/bin" COPY requirements.txt . # Set the entrypoint ENTRYPOINT ["python", "-m", "autogpt"] # dev build -> include everything FROM autogpt-base as autogpt-dev RUN pip install --no-cache-dir -r requirements.txt WORKDIR /app ONBUILD COPY . ./ # release build -> include bare minimum FROM autogpt-base as autogpt-release RUN sed -i '/Items below this point will not be included in the Docker Image/,$d' requirements.txt && \ pip install --no-cache-dir -r requirements.txt WORKDIR /app ONBUILD COPY autogpt/ ./autogpt ONBUILD COPY scripts/ ./scripts FROM autogpt-${BUILD_TYPE} AS auto-gpt ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2023 Toran Bruce Richards Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # Auto-GPT中文版 ### Youtube频道：https://www.youtube.com/@Hossie ##

💖 大家好 💖

这个项目是基于Auto-GPT原作者的0.3.0Stable版本，应大家要求，我快速制作了中文的汉化版本，基本运行顺利，还有些小瑕疵，会不断更新修改，欢迎大家关注，还请大家多支持！下面的内容就不过多翻译了，保持与原作者一致！ 如果大家希望支持我，还请给我点个小星星，谢谢！也可以通过微信与我们交流

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## 🚀 Features - 🌐 Internet access for searches and information gathering - 💾 Long-term and short-term memory management - 🧠 GPT-4 instances for text generation - 🔗 Access to popular websites and platforms - 🗃️ File storage and summarization with GPT-3.5 - 🔌 Extensibility with Plugins ## Quickstart 1. Get an OpenAI [API Key](https://platform.openai.com/account/api-keys) 2. Download the [latest release](https://github.com/Significant-Gravitas/Auto-GPT/releases/latest) 3. Follow the [installation instructions][docs/setup] 4. Configure any additional features you want, or install some [plugins][docs/plugins] 5. [Run][docs/usage] the app Please see the [documentation][docs] for full setup instructions and configuration options. [docs]: https://docs.agpt.co/ ## 📖 Documentation * [⚙️ Setup][docs/setup] * [💻 Usage][docs/usage] * [🔌 Plugins][docs/plugins] * Configuration * [🔍 Web Search](https://docs.agpt.co/configuration/search/) * [🧠 Memory](https://docs.agpt.co/configuration/memory/) * [🗣️ Voice (TTS)](https://docs.agpt.co/configuration/voice/) * [🖼️ Image Generation](https://docs.agpt.co/configuration/imagegen/) [docs/setup]: https://docs.agpt.co/setup/ [docs/usage]: https://docs.agpt.co/usage/ [docs/plugins]: https://docs.agpt.co/plugins/ ================================================ FILE: autogpt/__init__.py ================================================ import os import random import sys from dotenv import load_dotenv if "pytest" in sys.argv or "pytest" in sys.modules or os.getenv("CI"): print("Setting random seed to 42") random.seed(42) # Load the users .env file into environment variables load_dotenv(verbose=True, override=True) del load_dotenv ================================================ FILE: autogpt/__main__.py ================================================ """Auto-GPT: A GPT powered AI Assistant""" import autogpt.cli if __name__ == "__main__": autogpt.cli.main() ================================================ FILE: autogpt/agent/__init__.py ================================================ from autogpt.agent.agent import Agent from autogpt.agent.agent_manager import AgentManager __all__ = ["Agent", "AgentManager"] ================================================ FILE: autogpt/agent/agent.py ================================================ from colorama import Fore, Style from autogpt.app import execute_command, get_command from autogpt.config import Config from autogpt.json_utils.json_fix_llm import fix_json_using_multiple_techniques from autogpt.json_utils.utilities import LLM_DEFAULT_RESPONSE_FORMAT, validate_json from autogpt.llm import chat_with_ai, create_chat_completion, create_chat_message from autogpt.llm.token_counter import count_string_tokens from autogpt.logs import logger, print_assistant_thoughts from autogpt.speech import say_text from autogpt.spinner import Spinner from autogpt.utils import clean_input from autogpt.workspace import Workspace class Agent: """Agent class for interacting with Auto-GPT. Attributes: ai_name: The name of the agent. memory: The memory object to use. full_message_history: The full message history. next_action_count: The number of actions to execute. system_prompt: The system prompt is the initial prompt that defines everything the AI needs to know to achieve its task successfully. Currently, the dynamic and customizable information in the system prompt are ai_name, description and goals. triggering_prompt: The last sentence the AI will see before answering. For Auto-GPT, this prompt is: Determine which next command to use, and respond using the format specified above: The triggering prompt is not part of the system prompt because between the system prompt and the triggering prompt we have contextual information that can distract the AI and make it forget that its goal is to find the next task to achieve. SYSTEM PROMPT CONTEXTUAL INFORMATION (memory, previous conversations, anything relevant) TRIGGERING PROMPT The triggering prompt reminds the AI about its short term meta task (defining the next task) """ def __init__( self, ai_name, memory, full_message_history, next_action_count, command_registry, config, system_prompt, triggering_prompt, workspace_directory, ): cfg = Config() self.ai_name = ai_name self.memory = memory self.summary_memory = ( "我被生成完毕." # Initial memory necessary to avoid hilucination ) self.last_memory_index = 0 self.full_message_history = full_message_history self.next_action_count = next_action_count self.command_registry = command_registry self.config = config self.system_prompt = system_prompt self.triggering_prompt = triggering_prompt self.workspace = Workspace(workspace_directory, cfg.restrict_to_workspace) def start_interaction_loop(self): # Interaction Loop cfg = Config() loop_count = 0 command_name = None arguments = None user_input = "" while True: # Discontinue if continuous limit is reached loop_count += 1 if ( cfg.continuous_mode and cfg.continuous_limit > 0 and loop_count > cfg.continuous_limit ): logger.typewriter_log( "持续模式次数已到达: ", Fore.YELLOW, f"{cfg.continuous_limit}" ) break # Send message to AI, get response with Spinner("琢磨中... "): assistant_reply = chat_with_ai( self, self.system_prompt, self.triggering_prompt, self.full_message_history, self.memory, cfg.fast_token_limit, ) # TODO: This hardcodes the model to use GPT3.5. Make this an argument assistant_reply_json = fix_json_using_multiple_techniques(assistant_reply) for plugin in cfg.plugins: if not plugin.can_handle_post_planning(): continue assistant_reply_json = plugin.post_planning(self, assistant_reply_json) # Print Assistant thoughts if assistant_reply_json != {}: validate_json(assistant_reply_json, LLM_DEFAULT_RESPONSE_FORMAT) # Get command name and arguments try: print_assistant_thoughts( self.ai_name, assistant_reply_json, cfg.speak_mode ) command_name, arguments = get_command(assistant_reply_json) if cfg.speak_mode: say_text(f"我想要执行 {command_name}") arguments = self._resolve_pathlike_command_args(arguments) except Exception as e: logger.error("Error: \n", str(e)) if not cfg.continuous_mode and self.next_action_count == 0: # ### GET USER AUTHORIZATION TO EXECUTE COMMAND ### # Get key press: Prompt the user to press enter to continue or escape # to exit self.user_input = "" logger.typewriter_log( "下一步: ", Fore.CYAN, f"命令 = {Fore.CYAN}{command_name}{Style.RESET_ALL} " f"参数 = {Fore.CYAN}{arguments}{Style.RESET_ALL}", ) logger.info( "输入 'y' 授权执行命令, 'y -N' 执行N步持续模式, 's' 执行自我反馈命令 或" "'n' 退出程序, 或直接输入反馈 " f"{self.ai_name}..." ) while True: if cfg.chat_messages_enabled: console_input = clean_input("等待你的反馈中...") else: console_input = clean_input( Fore.MAGENTA + "输入:" + Style.RESET_ALL ) if console_input.lower().strip() == cfg.authorise_key: user_input = "生成下一个命令的JSON" break elif console_input.lower().strip() == "s": logger.typewriter_log( "-=-=-=-=-=-=-= 思考, 推理, 计划于反思将会被AI助手校验 -=-=-=-=-=-=-=", Fore.GREEN, "", ) thoughts = assistant_reply_json.get("thoughts", {}) self_feedback_resp = self.get_self_feedback( thoughts, cfg.fast_llm_model ) logger.typewriter_log( f"自我反馈: {self_feedback_resp}", Fore.YELLOW, "", ) if self_feedback_resp[0].lower().strip() == cfg.authorise_key: user_input = "生成下一个命令的JSON" else: user_input = self_feedback_resp break elif console_input.lower().strip() == "": logger.warn("错误的输入格式.") continue elif console_input.lower().startswith(f"{cfg.authorise_key} -"): try: self.next_action_count = abs( int(console_input.split(" ")[1]) ) user_input = "生成下一个命令的JSON" except ValueError: logger.warn( "错误的输入格式. 请输入 'y -n' n 代表" " 持续模式的步数." ) continue break elif console_input.lower() == cfg.exit_key: user_input = "EXIT" break else: user_input = console_input command_name = "human_feedback" break if user_input == "生成下一个命令的JSON": logger.typewriter_log( "-=-=-=-=-=-=-= 命令被用户批准执行 -=-=-=-=-=-=-=", Fore.MAGENTA, "", ) elif user_input == "EXIT" or "exit" or "退出": logger.info("Exiting...") break else: # Print command logger.typewriter_log( "下一步: ", Fore.CYAN, f"命令 = {Fore.CYAN}{command_name}{Style.RESET_ALL}" f" 参数 = {Fore.CYAN}{arguments}{Style.RESET_ALL}", ) # Execute command if command_name is not None and command_name.lower().startswith("error"): result = ( f"Command {command_name} threw the following error: {arguments}" ) elif command_name == "人类反馈": result = f"人类反馈: {user_input}" else: for plugin in cfg.plugins: if not plugin.can_handle_pre_command(): continue command_name, arguments = plugin.pre_command( command_name, arguments ) command_result = execute_command( self.command_registry, command_name, arguments, self.config.prompt_generator, ) result = f"Command {command_name} returned: " f"{command_result}" result_tlength = count_string_tokens( str(command_result), cfg.fast_llm_model ) memory_tlength = count_string_tokens( str(self.summary_memory), cfg.fast_llm_model ) if result_tlength + memory_tlength + 600 > cfg.fast_token_limit: result = f"Failure: command {command_name} returned too much output. \ Do not execute this command again with the same arguments." for plugin in cfg.plugins: if not plugin.can_handle_post_command(): continue result = plugin.post_command(command_name, result) if self.next_action_count > 0: self.next_action_count -= 1 # Check if there's a result from the command append it to the message # history if result is not None: self.full_message_history.append(create_chat_message("system", result)) logger.typewriter_log("SYSTEM: ", Fore.YELLOW, result) else: self.full_message_history.append( create_chat_message("system", "无法执行命令") ) logger.typewriter_log( "SYSTEM: ", Fore.YELLOW, "无法执行命令" ) def _resolve_pathlike_command_args(self, command_args): if "directory" in command_args and command_args["directory"] in {"", "/"}: command_args["directory"] = str(self.workspace.root) else: for pathlike in ["filename", "directory", "clone_path"]: if pathlike in command_args: command_args[pathlike] = str( self.workspace.get_path(command_args[pathlike]) ) return command_args def get_self_feedback(self, thoughts: dict, llm_model: str) -> str: """Generates a feedback response based on the provided thoughts dictionary. This method takes in a dictionary of thoughts containing keys such as "reasoning", "plan", "thoughts", and "criticism". It combines these elements into a single feedback message and uses the create_chat_completion() function to generate a response based on the input message. Args: thoughts (dict): A dictionary containing thought elements like reasoning, plan, thoughts, and criticism. Returns: str: A feedback response generated using the provided thoughts dictionary. """ ai_role = self.config.ai_role feedback_prompt = f"下面是来自我的消息，我是一个AI助手，角色为： {ai_role}. 请评估提供的思考，推理，计划与反思. 如果这些内容可以准确的完成该角色的任务，请回复字母'Y'后面加上一个空格，然后请解释为什么是最优方案. 日过这些信息无法实现该角色的目标, 请提供一句或多句解释一下具体的问题与建议的解决方案." reasoning = thoughts.get("推理", "") plan = thoughts.get("计划", "") thought = thoughts.get("思考", "") criticism = thoughts.get("反思", "") feedback_thoughts = thought + reasoning + plan + criticism return create_chat_completion( [{"role": "user", "content": feedback_prompt + feedback_thoughts}], llm_model, ) ================================================ FILE: autogpt/agent/agent_manager.py ================================================ """Agent manager for managing GPT agents""" from __future__ import annotations from typing import List from autogpt.config.config import Config from autogpt.llm import Message, create_chat_completion from autogpt.singleton import Singleton class AgentManager(metaclass=Singleton): """Agent manager for managing GPT agents""" def __init__(self): self.next_key = 0 self.agents = {} # key, (task, full_message_history, model) self.cfg = Config() # Create new GPT agent # TODO: Centralise use of create_chat_completion() to globally enforce token limit def create_agent(self, task: str, prompt: str, model: str) -> tuple[int, str]: """Create a new agent and return its key Args: task: The task to perform prompt: The prompt to use model: The model to use Returns: The key of the new agent """ messages: List[Message] = [ {"role": "user", "content": prompt}, ] for plugin in self.cfg.plugins: if not plugin.can_handle_pre_instruction(): continue if plugin_messages := plugin.pre_instruction(messages): messages.extend(iter(plugin_messages)) # Start GPT instance agent_reply = create_chat_completion( model=model, messages=messages, ) messages.append({"role": "assistant", "content": agent_reply}) plugins_reply = "" for i, plugin in enumerate(self.cfg.plugins): if not plugin.can_handle_on_instruction(): continue if plugin_result := plugin.on_instruction(messages): sep = "\n" if i else "" plugins_reply = f"{plugins_reply}{sep}{plugin_result}" if plugins_reply and plugins_reply != "": messages.append({"role": "assistant", "content": plugins_reply}) key = self.next_key # This is done instead of len(agents) to make keys unique even if agents # are deleted self.next_key += 1 self.agents[key] = (task, messages, model) for plugin in self.cfg.plugins: if not plugin.can_handle_post_instruction(): continue agent_reply = plugin.post_instruction(agent_reply) return key, agent_reply def message_agent(self, key: str | int, message: str) -> str: """Send a message to an agent and return its response Args: key: The key of the agent to message message: The message to send to the agent Returns: The agent's response """ task, messages, model = self.agents[int(key)] # Add user message to message history before sending to agent messages.append({"role": "user", "content": message}) for plugin in self.cfg.plugins: if not plugin.can_handle_pre_instruction(): continue if plugin_messages := plugin.pre_instruction(messages): for plugin_message in plugin_messages: messages.append(plugin_message) # Start GPT instance agent_reply = create_chat_completion( model=model, messages=messages, ) messages.append({"role": "assistant", "content": agent_reply}) plugins_reply = agent_reply for i, plugin in enumerate(self.cfg.plugins): if not plugin.can_handle_on_instruction(): continue if plugin_result := plugin.on_instruction(messages): sep = "\n" if i else "" plugins_reply = f"{plugins_reply}{sep}{plugin_result}" # Update full message history if plugins_reply and plugins_reply != "": messages.append({"role": "assistant", "content": plugins_reply}) for plugin in self.cfg.plugins: if not plugin.can_handle_post_instruction(): continue agent_reply = plugin.post_instruction(agent_reply) return agent_reply def list_agents(self) -> list[tuple[str | int, str]]: """Return a list of all agents Returns: A list of tuples of the form (key, task) """ # Return a list of agent keys and their tasks return [(key, task) for key, (task, _, _) in self.agents.items()] def delete_agent(self, key: str | int) -> bool: """Delete an agent from the agent manager Args: key: The key of the agent to delete Returns: True if successful, False otherwise """ try: del self.agents[int(key)] return True except KeyError: return False ================================================ FILE: autogpt/app.py ================================================ """ Command and Control """ import json from typing import Dict, List, NoReturn, Union from autogpt.agent.agent_manager import AgentManager from autogpt.commands.command import CommandRegistry, command from autogpt.commands.web_requests import scrape_links, scrape_text from autogpt.config import Config from autogpt.logs import logger from autogpt.memory import get_memory from autogpt.processing.text import summarize_text from autogpt.prompts.generator import PromptGenerator from autogpt.speech import say_text from autogpt.url_utils.validators import validate_url CFG = Config() AGENT_MANAGER = AgentManager() def is_valid_int(value: str) -> bool: """Check if the value is a valid integer Args: value (str): The value to check Returns: bool: True if the value is a valid integer, False otherwise """ try: int(value) return True except ValueError: return False def get_command(response_json: Dict): """Parse the response and return the command name and arguments Args: response_json (json): The response from the AI Returns: tuple: The command name and arguments Raises: json.decoder.JSONDecodeError: If the response is not valid JSON Exception: If any other error occurs """ try: if "command" not in response_json: return "Error:", "缺少 'command' object in JSON" if not isinstance(response_json, dict): return "Error:", f"'response_json' object is not dictionary {response_json}" command = response_json["command"] if not isinstance(command, dict): return "Error:", "'command' object is not a dictionary" if "name" not in command: return "Error:", "缺少 'name' field in 'command' object" command_name = command["name"] # Use an empty dictionary if 'args' field is not present in 'command' object arguments = command.get("args", {}) return command_name, arguments except json.decoder.JSONDecodeError: return "Error:", "Invalid JSON" # All other errors, return "Error: + error message" except Exception as e: return "Error:", str(e) def map_command_synonyms(command_name: str): """Takes the original command name given by the AI, and checks if the string matches a list of common/known hallucinations """ synonyms = [ ("write_file", "write_to_file"), ("create_file", "write_to_file"), ("search", "google"), ] for seen_command, actual_command_name in synonyms: if command_name == seen_command: return actual_command_name return command_name def execute_command( command_registry: CommandRegistry, command_name: str, arguments, prompt: PromptGenerator, ): """Execute the command and return the result Args: command_name (str): The name of the command to execute arguments (dict): The arguments for the command Returns: str: The result of the command """ try: cmd = command_registry.commands.get(command_name) # If the command is found, call it with the provided arguments if cmd: return cmd(**arguments) # TODO: Remove commands below after they are moved to the command registry. command_name = map_command_synonyms(command_name.lower()) if command_name == "memory_add": return get_memory(CFG).add(arguments["string"]) # TODO: Change these to take in a file rather than pasted code, if # non-file is given, return instructions "Input should be a python # filepath, write your code to file and try again else: for command in prompt.commands: if ( command_name == command["label"].lower() or command_name == command["name"].lower() ): return command["function"](**arguments) return ( f"Unknown command '{command_name}'. Please refer to the 'COMMANDS'" " list for available commands and only respond in the specified JSON" " format." ) except Exception as e: return f"错误: {str(e)}" @command( "get_text_summary", "Get text summary", '"url": "", "question": ""' ) @validate_url def get_text_summary(url: str, question: str) -> str: """Return the results of a Google search Args: url (str): The url to scrape question (str): The question to summarize the text for Returns: str: The summary of the text """ text = scrape_text(url) summary = summarize_text(url, text, question) return f""" "结果" : {summary}""" @command("get_hyperlinks", "Get text summary", '"url": ""') @validate_url def get_hyperlinks(url: str) -> Union[str, List[str]]: """Return the results of a Google search Args: url (str): The url to scrape Returns: str or list: The hyperlinks on the page """ return scrape_links(url) @command( "start_agent", "启动GPT助手", '"name": "", "task": "", "prompt": ""', ) def start_agent(name: str, task: str, prompt: str, model=CFG.fast_llm_model) -> str: """Start an agent with a given name, task, and prompt Args: name (str): The name of the agent task (str): The task of the agent prompt (str): The prompt for the agent model (str): The model to use for the agent Returns: str: The response of the agent """ # Remove underscores from name voice_name = name.replace("_", " ") first_message = f"""你是 {name}. 回答 with: "收到".""" agent_intro = f"{voice_name} 在这儿呢, 听从领导指挥!" # Create agent if CFG.speak_mode: say_text(agent_intro, 1) key, ack = AGENT_MANAGER.create_agent(task, first_message, model) if CFG.speak_mode: say_text(f"你好 {voice_name}. 你的任务如下. {task}.") # Assign task (prompt), get response agent_response = AGENT_MANAGER.message_agent(key, prompt) return f"Agent {name} 生成key {key}. 首次反馈: {agent_response}" @command("message_agent", "Message GPT Agent", '"key": "", "message": ""') def message_agent(key: str, message: str) -> str: """Message an agent with a given key and message""" # Check if the key is a valid integer if is_valid_int(key): agent_response = AGENT_MANAGER.message_agent(int(key), message) else: return "无效的key, 必须为数字." # Speak response if CFG.speak_mode: say_text(agent_response, 1) return agent_response @command("list_agents", "List GPT Agents", "") def list_agents() -> str: """List all agents Returns: str: A list of all agents """ return "List of agents:\n" + "\n".join( [str(x[0]) + ": " + x[1] for x in AGENT_MANAGER.list_agents()] ) @command("delete_agent", "Delete GPT Agent", '"key": ""') def delete_agent(key: str) -> str: """Delete an agent with a given key Args: key (str): The key of the agent to delete Returns: str: A message indicating whether the agent was deleted or not """ result = AGENT_MANAGER.delete_agent(key) return f"Agent {key} deleted." if result else f"Agent {key} does not exist." ================================================ FILE: autogpt/cli.py ================================================ """Main script for the autogpt package.""" import click @click.group(invoke_without_command=True) @click.option("-c", "--continuous", is_flag=True, help="启动持续模式") @click.option( "--skip-reprompt", "-y", is_flag=True, help="跳过重新输入命令环节", ) @click.option( "--ai-settings", "-C", help="指定使用哪个ai_settings.yaml文件, 同时自动跳过重新输入命令.", ) @click.option( "-l", "--continuous-limit", type=int, help="定义持续模式中的持续次数", ) @click.option("--speak", is_flag=True, help="开启语音模式") @click.option("--debug", is_flag=True, help="开启Debug模式") @click.option("--gpt3only", is_flag=True, help="开启GPT3.5模式") @click.option("--gpt4only", is_flag=True, help="开启GPT4模式") @click.option( "--use-memory", "-m", "memory_type", type=str, help="定义使用那种记忆后台", ) @click.option( "-b", "--browser-name", help="指定使用哪个 Web 浏览器来使用 Selenium 抓取网络内容.", ) @click.option( "--allow-downloads", is_flag=True, help="危险: 允许Auto-GPT自动下载文件.", ) @click.option( "--skip-news", is_flag=True, help="指定是否在启动时不输出最新新闻.", ) @click.option( # TODO: this is a hidden option for now, necessary for integration testing. # We should make this public once we're ready to roll out agent specific workspaces. "--workspace-directory", "-w", type=click.Path(), hidden=True, ) @click.option( "--install-plugin-deps", is_flag=True, help="为第三方插件安装外部依赖库.", ) @click.pass_context def main( ctx: click.Context, continuous: bool, continuous_limit: int, ai_settings: str, skip_reprompt: bool, speak: bool, debug: bool, gpt3only: bool, gpt4only: bool, memory_type: str, browser_name: str, allow_downloads: bool, skip_news: bool, workspace_directory: str, install_plugin_deps: bool, ) -> None: """ Welcome to AutoGPT an experimental open-source application showcasing the capabilities of the GPT-4 pushing the boundaries of AI. Start an Auto-GPT assistant. """ # Put imports inside function to avoid importing everything when starting the CLI from autogpt.main import run_auto_gpt if ctx.invoked_subcommand is None: run_auto_gpt( continuous, continuous_limit, ai_settings, skip_reprompt, speak, debug, gpt3only, gpt4only, memory_type, browser_name, allow_downloads, skip_news, workspace_directory, install_plugin_deps, ) if __name__ == "__main__": main() ================================================ FILE: autogpt/commands/__init__.py ================================================ ================================================ FILE: autogpt/commands/analyze_code.py ================================================ """Code evaluation module.""" from __future__ import annotations from autogpt.commands.command import command from autogpt.llm import call_ai_function @command( "analyze_code", "分析代码", '"code": ""', ) def analyze_code(code: str) -> list[str]: """ A function that takes in a string and returns a response from create chat completion api call. Parameters: code (str): Code to be evaluated. Returns: A result string from create chat completion. A list of suggestions to improve the code. """ function_string = "def analyze_code(code: str) -> list[str]:" args = [code] description_string = ( "分析给出的代码并列出一系列优化建议。" ) return call_ai_function(function_string, args, description_string) ================================================ FILE: autogpt/commands/audio_text.py ================================================ """Commands for converting audio to text.""" import json import requests from autogpt.commands.command import command from autogpt.config import Config CFG = Config() @command( "read_audio_from_file", "转换音频至文本", '"文件名": ""', CFG.huggingface_audio_to_text_model, "Configure huggingface_audio_to_text_model.", ) def read_audio_from_file(filename: str) -> str: """ Convert audio to text. Args: filename (str): The path to the audio file Returns: str: The text from the audio """ with open(filename, "rb") as audio_file: audio = audio_file.read() return read_audio(audio) def read_audio(audio: bytes) -> str: """ Convert audio to text. Args: audio (bytes): The audio to convert Returns: str: The text from the audio """ model = CFG.huggingface_audio_to_text_model api_url = f"https://api-inference.huggingface.co/models/{model}" api_token = CFG.huggingface_api_token headers = {"Authorization": f"Bearer {api_token}"} if api_token is None: raise ValueError( "你需要在配置文件中配置你的Hugging Face API token." ) response = requests.post( api_url, headers=headers, data=audio, ) text = json.loads(response.content.decode("utf-8"))["text"] return f"音频中说道: {text}" ================================================ FILE: autogpt/commands/command.py ================================================ import functools import importlib import inspect from typing import Any, Callable, Optional # Unique identifier for auto-gpt commands AUTO_GPT_COMMAND_IDENTIFIER = "auto_gpt_command" class Command: """A class representing a command. Attributes: name (str): The name of the command. description (str): A brief description of what the command does. signature (str): The signature of the function that the command executes. Defaults to None. """ def __init__( self, name: str, description: str, method: Callable[..., Any], signature: str = "", enabled: bool = True, disabled_reason: Optional[str] = None, ): self.name = name self.description = description self.method = method self.signature = signature if signature else str(inspect.signature(self.method)) self.enabled = enabled self.disabled_reason = disabled_reason def __call__(self, *args, **kwargs) -> Any: if not self.enabled: return f"命令 '{self.name}' 被禁止: {self.disabled_reason}" return self.method(*args, **kwargs) def __str__(self) -> str: return f"{self.name}: {self.description}, args: {self.signature}" class CommandRegistry: """ The CommandRegistry class is a manager for a collection of Command objects. It allows the registration, modification, and retrieval of Command objects, as well as the scanning and loading of command plugins from a specified directory. """ def __init__(self): self.commands = {} def _import_module(self, module_name: str) -> Any: return importlib.import_module(module_name) def _reload_module(self, module: Any) -> Any: return importlib.reload(module) def register(self, cmd: Command) -> None: self.commands[cmd.name] = cmd def unregister(self, command_name: str): if command_name in self.commands: del self.commands[command_name] else: raise KeyError(f"命令 '{command_name}' 在注册表中未找到.") def reload_commands(self) -> None: """Reloads all loaded command plugins.""" for cmd_name in self.commands: cmd = self.commands[cmd_name] module = self._import_module(cmd.__module__) reloaded_module = self._reload_module(module) if hasattr(reloaded_module, "register"): reloaded_module.register(self) def get_command(self, name: str) -> Callable[..., Any]: return self.commands[name] def call(self, command_name: str, **kwargs) -> Any: if command_name not in self.commands: raise KeyError(f"文件 '{command_name}' 在注册表中未找到.") command = self.commands[command_name] return command(**kwargs) def command_prompt(self) -> str: """ Returns a string representation of all registered `Command` objects for use in a prompt """ commands_list = [ f"{idx + 1}. {str(cmd)}" for idx, cmd in enumerate(self.commands.values()) ] return "\n".join(commands_list) def import_commands(self, module_name: str) -> None: """ Imports the specified Python module containing command plugins. This method imports the associated module and registers any functions or classes that are decorated with the `AUTO_GPT_COMMAND_IDENTIFIER` attribute as `Command` objects. The registered `Command` objects are then added to the `commands` dictionary of the `CommandRegistry` object. Args: module_name (str): The name of the module to import for command plugins. """ module = importlib.import_module(module_name) for attr_name in dir(module): attr = getattr(module, attr_name) # Register decorated functions if hasattr(attr, AUTO_GPT_COMMAND_IDENTIFIER) and getattr( attr, AUTO_GPT_COMMAND_IDENTIFIER ): self.register(attr.command) # Register command classes elif ( inspect.isclass(attr) and issubclass(attr, Command) and attr != Command ): cmd_instance = attr() self.register(cmd_instance) def command( name: str, description: str, signature: str = "", enabled: bool = True, disabled_reason: Optional[str] = None, ) -> Callable[..., Any]: """The command decorator is used to create Command objects from ordinary functions.""" def decorator(func: Callable[..., Any]) -> Command: cmd = Command( name=name, description=description, method=func, signature=signature, enabled=enabled, disabled_reason=disabled_reason, ) @functools.wraps(func) def wrapper(*args, **kwargs) -> Any: return func(*args, **kwargs) wrapper.command = cmd setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True) return wrapper return decorator ================================================ FILE: autogpt/commands/execute_code.py ================================================ """Execute code in a Docker container""" import os import subprocess from pathlib import Path import docker from docker.errors import ImageNotFound from autogpt.commands.command import command from autogpt.config import Config from autogpt.logs import logger CFG = Config() @command("execute_python_file", "Execute Python File", '"filename": ""') def execute_python_file(filename: str) -> str: """Execute a Python file in a Docker container and return the output Args: filename (str): The name of the file to execute Returns: str: The output of the file """ logger.info(f"执行文件中 '{filename}'") if not filename.endswith(".py"): return "错误: 无效的文件类型. 只允许 .py文件." if not os.path.isfile(filename): return f"错误: 文件 '{filename}' 不存在." if we_are_running_in_a_docker_container(): result = subprocess.run( f"python {filename}", capture_output=True, encoding="utf8", shell=True ) if result.returncode == 0: return result.stdout else: return f"错误: {result.stderr}" try: client = docker.from_env() # You can replace this with the desired Python image/version # You can find available Python images on Docker Hub: # https://hub.docker.com/_/python image_name = "python:3-alpine" try: client.images.get(image_name) logger.warn(f"Image '{image_name}' found locally") except ImageNotFound: logger.info( f"Image '{image_name}' not found locally, pulling from Docker Hub" ) # Use the low-level API to stream the pull response low_level_client = docker.APIClient() for line in low_level_client.pull(image_name, stream=True, decode=True): # Print the status and progress, if available status = line.get("status") progress = line.get("progress") if status and progress: logger.info(f"{status}: {progress}") elif status: logger.info(status) container = client.containers.run( image_name, f"python {Path(filename).relative_to(CFG.workspace_path)}", volumes={ CFG.workspace_path: { "bind": "/workspace", "mode": "ro", } }, working_dir="/workspace", stderr=True, stdout=True, detach=True, ) container.wait() logs = container.logs().decode("utf-8") container.remove() # print(f"Execution complete. Output: {output}") # print(f"Logs: {logs}") return logs except docker.errors.DockerException as e: logger.warn( "在容器中无法执行脚本. 如果你还没有安装，请安装Docker https://docs.docker.com/get-docker/" ) return f"错误: {str(e)}" except Exception as e: return f"错误: {str(e)}" @command( "execute_shell", "Execute Shell Command, non-interactive commands only", '"command_line": ""', CFG.execute_local_commands, "You are not allowed to run local shell commands. To execute" " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' " "in your config. Do not attempt to bypass the restriction.", ) def execute_shell(command_line: str) -> str: """Execute a shell command and return the output Args: command_line (str): The command line to execute Returns: str: The output of the command """ current_dir = Path.cwd() # Change dir into workspace if necessary if not current_dir.is_relative_to(CFG.workspace_path): os.chdir(CFG.workspace_path) logger.info( f"执行命令 '{command_line}' in working directory '{os.getcwd()}'" ) result = subprocess.run(command_line, capture_output=True, shell=True) output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}" # Change back to whatever the prior working dir was os.chdir(current_dir) return output @command( "execute_shell_popen", "Execute Shell Command, non-interactive commands only", '"command_line": ""', CFG.execute_local_commands, "You are not allowed to run local shell commands. To execute" " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' " "in your config. Do not attempt to bypass the restriction.", ) def execute_shell_popen(command_line) -> str: """Execute a shell command with Popen and returns an english description of the event and the process id Args: command_line (str): The command line to execute Returns: str: Description of the fact that the process started and its id """ current_dir = os.getcwd() # Change dir into workspace if necessary if CFG.workspace_path not in current_dir: os.chdir(CFG.workspace_path) logger.info( f"Executing command '{command_line}' in working directory '{os.getcwd()}'" ) do_not_show_output = subprocess.DEVNULL process = subprocess.Popen( command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output ) # Change back to whatever the prior working dir was os.chdir(current_dir) return f"Subprocess started with PID:'{str(process.pid)}'" def we_are_running_in_a_docker_container() -> bool: """Check if we are running in a Docker container Returns: bool: True if we are running in a Docker container, False otherwise """ return os.path.exists("/.dockerenv") ================================================ FILE: autogpt/commands/file_operations.py ================================================ """File operations for AutoGPT""" from __future__ import annotations import hashlib import os import os.path from typing import Dict, Generator, Literal, Tuple import charset_normalizer import requests from colorama import Back, Fore from requests.adapters import HTTPAdapter, Retry from autogpt.commands.command import command from autogpt.config import Config from autogpt.logs import logger from autogpt.spinner import Spinner from autogpt.utils import readable_file_size CFG = Config() Operation = Literal["write", "append", "delete"] def text_checksum(text: str) -> str: """Get the hex checksum for the given text.""" return hashlib.md5(text.encode("utf-8")).hexdigest() def operations_from_log(log_path: str) -> Generator[Tuple[Operation, str, str | None]]: """Parse the file operations log and return a tuple containing the log entries""" try: log = open(log_path, "r", encoding="utf-8") except FileNotFoundError: return for line in log: line = line.replace("File Operation Logger", "").strip() if not line: continue operation, tail = line.split(": ", maxsplit=1) operation = operation.strip() if operation in ("write", "append"): try: path, checksum = (x.strip() for x in tail.rsplit(" #", maxsplit=1)) except ValueError: path, checksum = tail.strip(), None yield (operation, path, checksum) elif operation == "delete": yield (operation, tail.strip(), None) log.close() def file_operations_state(log_path: str) -> Dict: """Iterates over the operations log and returns the expected state. Parses a log file at CFG.file_logger_path to construct a dictionary that maps each file path written or appended to its checksum. Deleted files are removed from the dictionary. Returns: A dictionary mapping file paths to their checksums. Raises: FileNotFoundError: If CFG.file_logger_path is not found. ValueError: If the log file content is not in the expected format. """ state = {} for operation, path, checksum in operations_from_log(log_path): if operation in ("write", "append"): state[path] = checksum elif operation == "delete": del state[path] return state def is_duplicate_operation( operation: Operation, filename: str, checksum: str | None = None ) -> bool: """Check if the operation has already been performed Args: operation: The operation to check for filename: The name of the file to check for checksum: The checksum of the contents to be written Returns: True if the operation has already been performed on the file """ state = file_operations_state(CFG.file_logger_path) if operation == "delete" and filename not in state: return True if operation == "write" and state.get(filename) == checksum: return True return False def log_operation(operation: str, filename: str, checksum: str | None = None) -> None: """Log the file operation to the file_logger.txt Args: operation: The operation to log filename: The name of the file the operation was performed on checksum: The checksum of the contents to be written """ log_entry = f"{operation}: {filename}" if checksum is not None: log_entry += f" #{checksum}" logger.debug(f"Logging file operation: {log_entry}") append_to_file(CFG.file_logger_path, f"{log_entry}\n", should_log=False) def split_file( content: str, max_length: int = 4000, overlap: int = 0 ) -> Generator[str, None, None]: """ Split text into chunks of a specified maximum length with a specified overlap between chunks. :param content: The input text to be split into chunks :param max_length: The maximum length of each chunk, default is 4000 (about 1k token) :param overlap: The number of overlapping characters between chunks, default is no overlap :return: A generator yielding chunks of text """ start = 0 content_length = len(content) while start < content_length: end = start + max_length if end + overlap < content_length: chunk = content[start : end + overlap - 1] else: chunk = content[start:content_length] # Account for the case where the last chunk is shorter than the overlap, so it has already been consumed if len(chunk) <= overlap: break yield chunk start += max_length - overlap @command("read_file", "Read file", '"filename": ""') def read_file(filename: str) -> str: """Read a file and return the contents Args: filename (str): The name of the file to read Returns: str: The contents of the file """ try: charset_match = charset_normalizer.from_path(filename).best() encoding = charset_match.encoding logger.debug(f"Read file '{filename}' with encoding '{encoding}'") return str(charset_match) except Exception as err: return f"错误: {err}" def ingest_file( filename: str, memory, max_length: int = 4000, overlap: int = 200 ) -> None: """ Ingest a file by reading its content, splitting it into chunks with a specified maximum length and overlap, and adding the chunks to the memory storage. :param filename: The name of the file to ingest :param memory: An object with an add() method to store the chunks in memory :param max_length: The maximum length of each chunk, default is 4000 :param overlap: The number of overlapping characters between chunks, default is 200 """ try: logger.info(f"操作文件中 {filename}") content = read_file(filename) content_length = len(content) logger.info(f"文件长度: {content_length} 字符") chunks = list(split_file(content, max_length=max_length, overlap=overlap)) num_chunks = len(chunks) for i, chunk in enumerate(chunks): logger.info(f"注入块 {i + 1} / {num_chunks} 到记忆中") memory_to_add = ( f"文件名: {filename}\n" f"内容块#{i + 1}/{num_chunks}: {chunk}" ) memory.add(memory_to_add) logger.info(f"注入完成 {num_chunks} 块 from {filename}.") except Exception as err: logger.info(f"注入文件出现错误 '{filename}': {err}") @command("write_to_file", "Write to file", '"filename": "", "text": ""') def write_to_file(filename: str, text: str) -> str: """Write text to a file Args: filename (str): The name of the file to write to text (str): The text to write to the file Returns: str: A message indicating success or failure """ checksum = text_checksum(text) if is_duplicate_operation("write", filename, checksum): return "错误: 文件已经更新." try: directory = os.path.dirname(filename) os.makedirs(directory, exist_ok=True) with open(filename, "w", encoding="utf-8") as f: f.write(text) log_operation("write", filename, checksum) return "文件写入成功." except Exception as err: return f"错误: {err}" @command( "append_to_file", "Append to file", '"filename": "", "text": ""' ) def append_to_file(filename: str, text: str, should_log: bool = True) -> str: """Append text to a file Args: filename (str): The name of the file to append to text (str): The text to append to the file should_log (bool): Should log output Returns: str: A message indicating success or failure """ try: directory = os.path.dirname(filename) os.makedirs(directory, exist_ok=True) with open(filename, "a", encoding="utf-8") as f: f.write(text) if should_log: with open(filename, "r", encoding="utf-8") as f: checksum = text_checksum(f.read()) log_operation("append", filename, checksum=checksum) return "文本追加成功." except Exception as err: return f"错误: {err}" @command("delete_file", "Delete file", '"filename": ""') def delete_file(filename: str) -> str: """Delete a file Args: filename (str): The name of the file to delete Returns: str: A message indicating success or failure """ if is_duplicate_operation("delete", filename): return "错误: 文件已经删除." try: os.remove(filename) log_operation("delete", filename) return "文件删除成功." except Exception as err: return f"错误: {err}" @command("list_files", "List Files in Directory", '"directory": ""') def list_files(directory: str) -> list[str]: """lists files in a directory recursively Args: directory (str): The directory to search in Returns: list[str]: A list of files found in the directory """ found_files = [] for root, _, files in os.walk(directory): for file in files: if file.startswith("."): continue relative_path = os.path.relpath( os.path.join(root, file), CFG.workspace_path ) found_files.append(relative_path) return found_files @command( "download_file", "Download File", '"url": "", "filename": ""', CFG.allow_downloads, "错误: 你没有下载到本地的权限.", ) def download_file(url, filename): """Downloads a file Args: url (str): URL of the file to download filename (str): Filename to save the file as """ try: directory = os.path.dirname(filename) os.makedirs(directory, exist_ok=True) message = f"{Fore.YELLOW}Downloading file from {Back.LIGHTBLUE_EX}{url}{Back.RESET}{Fore.RESET}" with Spinner(message) as spinner: session = requests.Session() retry = Retry(total=3, backoff_factor=1, status_forcelist=[502, 503, 504]) adapter = HTTPAdapter(max_retries=retry) session.mount("http://", adapter) session.mount("https://", adapter) total_size = 0 downloaded_size = 0 with session.get(url, allow_redirects=True, stream=True) as r: r.raise_for_status() total_size = int(r.headers.get("Content-Length", 0)) downloaded_size = 0 with open(filename, "wb") as f: for chunk in r.iter_content(chunk_size=8192): f.write(chunk) downloaded_size += len(chunk) # Update the progress message progress = f"{readable_file_size(downloaded_size)} / {readable_file_size(total_size)}" spinner.update_message(f"{message} {progress}") return f'文件下载到本地成功，文件名: "{filename}"! (Size: {readable_file_size(downloaded_size)})' except requests.HTTPError as err: return f"文件下载过程中遇到HTTP错误: {err}" except Exception as err: return f"错误: {err}" ================================================ FILE: autogpt/commands/git_operations.py ================================================ """Git operations for autogpt""" from git.repo import Repo from autogpt.commands.command import command from autogpt.config import Config from autogpt.url_utils.validators import validate_url CFG = Config() @command( "clone_repository", "Clone Repository", '"url": "", "clone_path": ""', CFG.github_username and CFG.github_api_key, "配置 github_username 和 github_api_key.", ) @validate_url def clone_repository(url: str, clone_path: str) -> str: """Clone a GitHub repository locally. Args: url (str): The URL of the repository to clone. clone_path (str): The path to clone the repository to. Returns: str: The result of the clone operation. """ split_url = url.split("//") auth_repo_url = f"//{CFG.github_username}:{CFG.github_api_key}@".join(split_url) try: Repo.clone_from(url=auth_repo_url, to_path=clone_path) return f"""Cloned {url} to {clone_path}""" except Exception as e: return f"错误: {str(e)}" ================================================ FILE: autogpt/commands/google_search.py ================================================ """Google search command for Autogpt.""" from __future__ import annotations import json from duckduckgo_search import ddg from autogpt.commands.command import command from autogpt.config import Config CFG = Config() @command("google", "Google Search", '"query": ""', not CFG.google_api_key) def google_search(query: str, num_results: int = 8) -> str: """Return the results of a Google search Args: query (str): The search query. num_results (int): The number of results to return. Returns: str: The results of the search. """ search_results = [] if not query: return json.dumps(search_results) results = ddg(query, max_results=num_results) if not results: return json.dumps(search_results) for j in results: search_results.append(j) results = json.dumps(search_results, ensure_ascii=False, indent=4) return safe_google_results(results) @command( "google", "Google Search", '"query": ""', bool(CFG.google_api_key), "配置 google_api_key.", ) def google_official_search(query: str, num_results: int = 8) -> str | list[str]: """Return the results of a Google search using the official Google API Args: query (str): The search query. num_results (int): The number of results to return. Returns: str: The results of the search. """ from googleapiclient.discovery import build from googleapiclient.errors import HttpError try: # Get the Google API key and Custom Search Engine ID from the config file api_key = CFG.google_api_key custom_search_engine_id = CFG.custom_search_engine_id # Initialize the Custom Search API service service = build("customsearch", "v1", developerKey=api_key) # Send the search query and retrieve the results result = ( service.cse() .list(q=query, cx=custom_search_engine_id, num=num_results) .execute() ) # Extract the search result items from the response search_results = result.get("items", []) # Create a list of only the URLs from the search results search_results_links = [item["link"] for item in search_results] except HttpError as e: # Handle errors in the API call error_details = json.loads(e.content.decode()) # Check if the error is related to an invalid or missing API key if error_details.get("error", {}).get( "code" ) == 403 and "invalid API key" in error_details.get("error", {}).get( "message", "" ): return "错误: 你提供的Google API key是错误的或者缺失." else: return f"错误: {e}" # google_result can be a list or a string depending on the search results # Return the list of search result URLs return safe_google_results(search_results_links) def safe_google_results(results: str | list) -> str: """ Return the results of a google search in a safe format. Args: results (str | list): The search results. Returns: str: The results of the search. """ if isinstance(results, list): safe_message = json.dumps( [result.encode("utf-8", "ignore") for result in results] ) else: safe_message = results.encode("utf-8", "ignore").decode("utf-8") return safe_message ================================================ FILE: autogpt/commands/image_gen.py ================================================ """ Image Generation Module for AutoGPT.""" import io import uuid from base64 import b64decode import openai import requests from PIL import Image from autogpt.commands.command import command from autogpt.config import Config from autogpt.logs import logger CFG = Config() @command("generate_image", "Generate Image", '"prompt": ""', CFG.image_provider) def generate_image(prompt: str, size: int = 256) -> str: """Generate an image from a prompt. Args: prompt (str): The prompt to use size (int, optional): The size of the image. Defaults to 256. (Not supported by HuggingFace) Returns: str: The filename of the image """ filename = f"{CFG.workspace_path}/{str(uuid.uuid4())}.jpg" # DALL-E if CFG.image_provider == "dalle": return generate_image_with_dalle(prompt, filename, size) # HuggingFace elif CFG.image_provider == "huggingface": return generate_image_with_hf(prompt, filename) # SD WebUI elif CFG.image_provider == "sdwebui": return generate_image_with_sd_webui(prompt, filename, size) return "没有配置图片功能" def generate_image_with_hf(prompt: str, filename: str) -> str: """Generate an image with HuggingFace's API. Args: prompt (str): The prompt to use filename (str): The filename to save the image to Returns: str: The filename of the image """ API_URL = ( f"https://api-inference.huggingface.co/models/{CFG.huggingface_image_model}" ) if CFG.huggingface_api_token is None: raise ValueError( "你需要再配置文件中配置Hugging Face API token." ) headers = { "Authorization": f"Bearer {CFG.huggingface_api_token}", "X-Use-Cache": "false", } response = requests.post( API_URL, headers=headers, json={ "inputs": prompt, }, ) image = Image.open(io.BytesIO(response.content)) logger.info(f"图片生成基于下面prompt:{prompt}") image.save(filename) return f"Saved to disk:{filename}" def generate_image_with_dalle(prompt: str, filename: str, size: int) -> str: """Generate an image with DALL-E. Args: prompt (str): The prompt to use filename (str): The filename to save the image to size (int): The size of the image Returns: str: The filename of the image """ # Check for supported image sizes if size not in [256, 512, 1024]: closest = min([256, 512, 1024], key=lambda x: abs(x - size)) logger.info( f"DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. Setting to {closest}, was {size}." ) size = closest response = openai.Image.create( prompt=prompt, n=1, size=f"{size}x{size}", response_format="b64_json", api_key=CFG.openai_api_key, ) logger.info(f"图片生成基于下面prompt:{prompt}") image_data = b64decode(response["data"][0]["b64_json"]) with open(filename, mode="wb") as png: png.write(image_data) return f"Saved to disk:{filename}" def generate_image_with_sd_webui( prompt: str, filename: str, size: int = 512, negative_prompt: str = "", extra: dict = {}, ) -> str: """Generate an image with Stable Diffusion webui. Args: prompt (str): The prompt to use filename (str): The filename to save the image to size (int, optional): The size of the image. Defaults to 256. negative_prompt (str, optional): The negative prompt to use. Defaults to "". extra (dict, optional): Extra parameters to pass to the API. Defaults to {}. Returns: str: The filename of the image """ # Create a session and set the basic auth if needed s = requests.Session() if CFG.sd_webui_auth: username, password = CFG.sd_webui_auth.split(":") s.auth = (username, password or "") # Generate the images response = requests.post( f"{CFG.sd_webui_url}/sdapi/v1/txt2img", json={ "prompt": prompt, "negative_prompt": negative_prompt, "sampler_index": "DDIM", "steps": 20, "cfg_scale": 7.0, "width": size, "height": size, "n_iter": 1, **extra, }, ) logger.info(f"图片生成基于下面prompt:{prompt}") # Save the image to disk response = response.json() b64 = b64decode(response["images"][0].split(",", 1)[0]) image = Image.open(io.BytesIO(b64)) image.save(filename) return f"Saved to disk:{filename}" ================================================ FILE: autogpt/commands/improve_code.py ================================================ from __future__ import annotations import json from autogpt.commands.command import command from autogpt.llm import call_ai_function @command( "improve_code", "获取优化代码", '"suggestions": "", "code": ""', ) def improve_code(suggestions: list[str], code: str) -> str: """ A function that takes in code and suggestions and returns a response from create chat completion api call. Parameters: suggestions (list): A list of suggestions around what needs to be improved. code (str): Code to be improved. Returns: A result string from create chat completion. Improved code in response. """ function_string = ( "def generate_improved_code(suggestions: list[str], code: str) -> str:" ) args = [json.dumps(suggestions), code] description_string = ( "基于优化建议提供优化后的代码，不要做其他修改." ) return call_ai_function(function_string, args, description_string) ================================================ FILE: autogpt/commands/task_statuses.py ================================================ """Task Statuses module.""" from __future__ import annotations from typing import NoReturn from autogpt.commands.command import command from autogpt.logs import logger @command( "task_complete", "Task Complete (Shutdown)", '"reason": ""', ) def task_complete(reason: str) -> NoReturn: """ A function that takes in a string and exits the program Parameters: reason (str): The reason for shutting down. Returns: A result string from create chat completion. A list of suggestions to improve the code. """ logger.info(title="关闭中...\n", message=reason) quit() ================================================ FILE: autogpt/commands/times.py ================================================ from datetime import datetime def get_datetime() -> str: """Return the current date and time Returns: str: The current date and time """ return "当前日期与时间: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S") ================================================ FILE: autogpt/commands/twitter.py ================================================ """A module that contains a command to send a tweet.""" import os import tweepy from autogpt.commands.command import command @command( "send_tweet", "发Tweet", '"tweet_text": ""', ) def send_tweet(tweet_text: str) -> str: """ A function that takes in a string and returns a response from create chat completion api call. Args: tweet_text (str): Text to be tweeted. Returns: A result from sending the tweet. """ consumer_key = os.environ.get("TW_CONSUMER_KEY") consumer_secret = os.environ.get("TW_CONSUMER_SECRET") access_token = os.environ.get("TW_ACCESS_TOKEN") access_token_secret = os.environ.get("TW_ACCESS_TOKEN_SECRET") # Authenticate to Twitter auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) # Create API object api = tweepy.API(auth) # Send tweet try: api.update_status(tweet_text) return "Tweet发送成功!" except tweepy.TweepyException as e: return f"发送tweet错误: {e.reason}" ================================================ FILE: autogpt/commands/web_playwright.py ================================================ """Web scraping commands using Playwright""" from __future__ import annotations from autogpt.logs import logger try: from playwright.sync_api import sync_playwright except ImportError: logger.info( "Playwright not installed. Please install it with 'pip install playwright' to use." ) from bs4 import BeautifulSoup from autogpt.processing.html import extract_hyperlinks, format_hyperlinks def scrape_text(url: str) -> str: """Scrape text from a webpage Args: url (str): The URL to scrape text from Returns: str: The scraped text """ with sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() try: page.goto(url) html_content = page.content() soup = BeautifulSoup(html_content, "html.parser") for script in soup(["script", "style"]): script.extract() text = soup.get_text() lines = (line.strip() for line in text.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) text = "\n".join(chunk for chunk in chunks if chunk) except Exception as e: text = f"Error: {str(e)}" finally: browser.close() return text def scrape_links(url: str) -> str | list[str]: """Scrape links from a webpage Args: url (str): The URL to scrape links from Returns: Union[str, List[str]]: The scraped links """ with sync_playwright() as p: browser = p.chromium.launch() page = browser.new_page() try: page.goto(url) html_content = page.content() soup = BeautifulSoup(html_content, "html.parser") for script in soup(["script", "style"]): script.extract() hyperlinks = extract_hyperlinks(soup, url) formatted_links = format_hyperlinks(hyperlinks) except Exception as e: formatted_links = f"Error: {str(e)}" finally: browser.close() return formatted_links ================================================ FILE: autogpt/commands/web_requests.py ================================================ """Browse a webpage and summarize it using the LLM model""" from __future__ import annotations import requests from bs4 import BeautifulSoup from requests import Response from autogpt.config import Config from autogpt.processing.html import extract_hyperlinks, format_hyperlinks from autogpt.url_utils.validators import validate_url CFG = Config() session = requests.Session() session.headers.update({"User-Agent": CFG.user_agent}) @validate_url def get_response( url: str, timeout: int = 10 ) -> tuple[None, str] | tuple[Response, None]: """Get the response from a URL Args: url (str): The URL to get the response from timeout (int): The timeout for the HTTP request Returns: tuple[None, str] | tuple[Response, None]: The response and error message Raises: ValueError: If the URL is invalid requests.exceptions.RequestException: If the HTTP request fails """ try: response = session.get(url, timeout=timeout) # Check if the response contains an HTTP error if response.status_code >= 400: return None, f"Error: HTTP {str(response.status_code)} error" return response, None except ValueError as ve: # Handle invalid URL format return None, f"Error: {str(ve)}" except requests.exceptions.RequestException as re: # Handle exceptions related to the HTTP request # (e.g., connection errors, timeouts, etc.) return None, f"Error: {str(re)}" def scrape_text(url: str) -> str: """Scrape text from a webpage Args: url (str): The URL to scrape text from Returns: str: The scraped text """ response, error_message = get_response(url) if error_message: return error_message if not response: return "Error: Could not get response" soup = BeautifulSoup(response.text, "html.parser") for script in soup(["script", "style"]): script.extract() text = soup.get_text() lines = (line.strip() for line in text.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) text = "\n".join(chunk for chunk in chunks if chunk) return text def scrape_links(url: str) -> str | list[str]: """Scrape links from a webpage Args: url (str): The URL to scrape links from Returns: str | list[str]: The scraped links """ response, error_message = get_response(url) if error_message: return error_message if not response: return "Error: Could not get response" soup = BeautifulSoup(response.text, "html.parser") for script in soup(["script", "style"]): script.extract() hyperlinks = extract_hyperlinks(soup, url) return format_hyperlinks(hyperlinks) def create_message(chunk, question): """Create a message for the user to summarize a chunk of text""" return { "role": "user", "content": f'"""{chunk}""" Using the above text, answer the following' f' question: "{question}" -- if the question cannot be answered using the' " text, summarize the text.", } ================================================ FILE: autogpt/commands/web_selenium.py ================================================ """Selenium web scraping module.""" from __future__ import annotations import logging from pathlib import Path from sys import platform from bs4 import BeautifulSoup from selenium import webdriver from selenium.common.exceptions import WebDriverException from selenium.webdriver.chrome.options import Options as ChromeOptions from selenium.webdriver.common.by import By from selenium.webdriver.firefox.options import Options as FirefoxOptions from selenium.webdriver.remote.webdriver import WebDriver from selenium.webdriver.safari.options import Options as SafariOptions from selenium.webdriver.support import expected_conditions as EC from selenium.webdriver.support.wait import WebDriverWait from webdriver_manager.chrome import ChromeDriverManager from webdriver_manager.firefox import GeckoDriverManager import autogpt.processing.text as summary from autogpt.commands.command import command from autogpt.config import Config from autogpt.processing.html import extract_hyperlinks, format_hyperlinks from autogpt.url_utils.validators import validate_url FILE_DIR = Path(__file__).parent.parent CFG = Config() @command( "browse_website", "浏览网页", '"url": "", "question": ""', ) @validate_url def browse_website(url: str, question: str) -> tuple[str, WebDriver]: """Browse a website and return the answer and links to the user Args: url (str): The url of the website to browse question (str): The question asked by the user Returns: Tuple[str, WebDriver]: The answer and links to the user and the webdriver """ try: driver, text = scrape_text_with_selenium(url) except WebDriverException as e: # These errors are often quite long and include lots of context. # Just grab the first line. msg = e.msg.split("\n")[0] return f"Error: {msg}", None add_header(driver) summary_text = summary.summarize_text(url, text, question, driver) links = scrape_links_with_selenium(driver, url) # Limit links to 5 if len(links) > 5: links = links[:5] close_browser(driver) return f"从网页中获取的回答: {summary_text} \n \n 链接: {links}", driver def scrape_text_with_selenium(url: str) -> tuple[WebDriver, str]: """Scrape text from a website using selenium Args: url (str): The url of the website to scrape Returns: Tuple[WebDriver, str]: The webdriver and the text scraped from the website """ logging.getLogger("selenium").setLevel(logging.CRITICAL) options_available = { "chrome": ChromeOptions, "safari": SafariOptions, "firefox": FirefoxOptions, } options = options_available[CFG.selenium_web_browser]() options.add_argument( "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36" ) if CFG.selenium_web_browser == "firefox": if CFG.selenium_headless: options.headless = True options.add_argument("--disable-gpu") driver = webdriver.Firefox( executable_path=GeckoDriverManager().install(), options=options ) elif CFG.selenium_web_browser == "safari": # Requires a bit more setup on the users end # See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari driver = webdriver.Safari(options=options) else: if platform == "linux" or platform == "linux2": options.add_argument("--disable-dev-shm-usage") options.add_argument("--remote-debugging-port=9222") options.add_argument("--no-sandbox") if CFG.selenium_headless: options.add_argument("--headless=new") options.add_argument("--disable-gpu") chromium_driver_path = Path("/usr/bin/chromedriver") driver = webdriver.Chrome( executable_path=chromium_driver_path if chromium_driver_path.exists() else ChromeDriverManager().install(), options=options, ) driver.get(url) WebDriverWait(driver, 10).until( EC.presence_of_element_located((By.TAG_NAME, "body")) ) # Get the HTML content directly from the browser's DOM page_source = driver.execute_script("return document.body.outerHTML;") soup = BeautifulSoup(page_source, "html.parser") for script in soup(["script", "style"]): script.extract() text = soup.get_text() lines = (line.strip() for line in text.splitlines()) chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) text = "\n".join(chunk for chunk in chunks if chunk) return driver, text def scrape_links_with_selenium(driver: WebDriver, url: str) -> list[str]: """Scrape links from a website using selenium Args: driver (WebDriver): The webdriver to use to scrape the links Returns: List[str]: The links scraped from the website """ page_source = driver.page_source soup = BeautifulSoup(page_source, "html.parser") for script in soup(["script", "style"]): script.extract() hyperlinks = extract_hyperlinks(soup, url) return format_hyperlinks(hyperlinks) def close_browser(driver: WebDriver) -> None: """Close the browser Args: driver (WebDriver): The webdriver to close Returns: None """ driver.quit() def add_header(driver: WebDriver) -> None: """Add a header to the website Args: driver (WebDriver): The webdriver to use to add the header Returns: None """ try: with open(f"{FILE_DIR}/js/overlay.js", "r") as overlay_file: overlay_script = overlay_file.read() driver.execute_script(overlay_script) except Exception as e: print(f"Error executing overlay.js: {e}") ================================================ FILE: autogpt/commands/write_tests.py ================================================ """A module that contains a function to generate test cases for the submitted code.""" from __future__ import annotations import json from autogpt.commands.command import command from autogpt.llm import call_ai_function @command( "write_tests", "写入测试", '"code": "", "focus": ""', ) def write_tests(code: str, focus: list[str]) -> str: """ A function that takes in code and focus topics and returns a response from create chat completion api call. Parameters: focus (list): A list of suggestions around what needs to be improved. code (str): Code for test cases to be generated against. Returns: A result string from create chat completion. Test cases for the submitted code in response. """ function_string = ( "def create_test_cases(code: str, focus: Optional[str] = None) -> str:" ) args = [code, json.dumps(focus)] description_string = ( "如果需要，为现有代码生成测试用例，重点关注特定领域。" ) return call_ai_function(function_string, args, description_string) ================================================ FILE: autogpt/config/__init__.py ================================================ """ This module contains the configuration classes for AutoGPT. """ from autogpt.config.ai_config import AIConfig from autogpt.config.config import Config, check_openai_api_key __all__ = [ "check_openai_api_key", "AIConfig", "Config", ] ================================================ FILE: autogpt/config/ai_config.py ================================================ # sourcery skip: do-not-use-staticmethod """ A module that contains the AIConfig class object that contains the configuration """ from __future__ import annotations import os import platform from pathlib import Path from typing import Any, Optional, Type import distro import yaml from autogpt.prompts.generator import PromptGenerator # Soon this will go in a folder where it remembers more stuff about the run(s) SAVE_FILE = str(Path(os.getcwd()) / "ai_settings.yaml") class AIConfig: """ A class object that contains the configuration information for the AI Attributes: ai_name (str): The name of the AI. ai_role (str): The description of the AI's role. ai_goals (list): The list of objectives the AI is supposed to complete. api_budget (float): The maximum dollar value for API calls (0.0 means infinite) """ def __init__( self, ai_name: str = "", ai_role: str = "", ai_goals: list | None = None, api_budget: float = 0.0, ) -> None: """ Initialize a class instance Parameters: ai_name (str): The name of the AI. ai_role (str): The description of the AI's role. ai_goals (list): The list of objectives the AI is supposed to complete. api_budget (float): The maximum dollar value for API calls (0.0 means infinite) Returns: None """ if ai_goals is None: ai_goals = [] self.ai_name = ai_name self.ai_role = ai_role self.ai_goals = ai_goals self.api_budget = api_budget self.prompt_generator = None self.command_registry = None @staticmethod def load(config_file: str = SAVE_FILE) -> "AIConfig": """ Returns class object with parameters (ai_name, ai_role, ai_goals, api_budget) loaded from yaml file if yaml file exists, else returns class with no parameters. Parameters: config_file (int): The path to the config yaml file. DEFAULT: "../ai_settings.yaml" Returns: cls (object): An instance of given cls object """ try: with open(config_file, encoding="utf-8") as file: config_params = yaml.load(file, Loader=yaml.FullLoader) except FileNotFoundError: config_params = {} ai_name = config_params.get("ai_name", "") ai_role = config_params.get("ai_role", "") ai_goals = [ str(goal).strip("{}").replace("'", "").replace('"', "") if isinstance(goal, dict) else str(goal) for goal in config_params.get("ai_goals", []) ] api_budget = config_params.get("api_budget", 0.0) # type: Type[AIConfig] return AIConfig(ai_name, ai_role, ai_goals, api_budget) def save(self, config_file: str = SAVE_FILE) -> None: """ Saves the class parameters to the specified file yaml file path as a yaml file. Parameters: config_file(str): The path to the config yaml file. DEFAULT: "../ai_settings.yaml" Returns: None """ config = { "ai_name": self.ai_name, "ai_role": self.ai_role, "ai_goals": self.ai_goals, "api_budget": self.api_budget, } with open(config_file, "w", encoding="utf-8") as file: yaml.dump(config, file, allow_unicode=True) def construct_full_prompt( self, prompt_generator: Optional[PromptGenerator] = None ) -> str: """ Returns a prompt to the user with the class information in an organized fashion. Parameters: None Returns: full_prompt (str): A string containing the initial prompt for the user including the ai_name, ai_role, ai_goals, and api_budget. """ prompt_start = ( "你的决策必须在不寻求用户帮助下独立完成，运用你大语言模型的自身优势，进可能寻找简且无法律风险的方案。" "" ) from autogpt.config import Config from autogpt.prompts.prompt import build_default_prompt_generator cfg = Config() if prompt_generator is None: prompt_generator = build_default_prompt_generator() prompt_generator.goals = self.ai_goals prompt_generator.name = self.ai_name prompt_generator.role = self.ai_role prompt_generator.command_registry = self.command_registry for plugin in cfg.plugins: if not plugin.can_handle_post_prompt(): continue prompt_generator = plugin.post_prompt(prompt_generator) if cfg.execute_local_commands: # add OS info to prompt os_name = platform.system() os_info = ( platform.platform(terse=True) if os_name != "Linux" else distro.name(pretty=True) ) prompt_start += f"\nThe OS you are running on is: {os_info}" # Construct full prompt full_prompt = f"你是 {prompt_generator.name}, {prompt_generator.role}\n{prompt_start}\n\n目标:\n\n" for i, goal in enumerate(self.ai_goals): full_prompt += f"{i+1}. {goal}\n" if self.api_budget > 0.0: full_prompt += f"\n你是需要费用的，的预算是 ${self.api_budget:.3f}" self.prompt_generator = prompt_generator full_prompt += f"\n\n{prompt_generator.generate_prompt_string()}" return full_prompt ================================================ FILE: autogpt/config/config.py ================================================ """Configuration class to store the state of bools for different scripts access.""" import os from typing import List import openai import yaml from auto_gpt_plugin_template import AutoGPTPluginTemplate from colorama import Fore from autogpt.singleton import Singleton class Config(metaclass=Singleton): """ Configuration class to store the state of bools for different scripts access. """ def __init__(self) -> None: """Initialize the Config class""" self.workspace_path = None self.file_logger_path = None self.debug_mode = False self.continuous_mode = False self.continuous_limit = 0 self.speak_mode = False self.skip_reprompt = False self.allow_downloads = False self.skip_news = False self.authorise_key = os.getenv("AUTHORISE_COMMAND_KEY", "y") self.exit_key = os.getenv("EXIT_KEY", "n") disabled_command_categories = os.getenv("DISABLED_COMMAND_CATEGORIES") if disabled_command_categories: self.disabled_command_categories = disabled_command_categories.split(",") else: self.disabled_command_categories = [] self.ai_settings_file = os.getenv("AI_SETTINGS_FILE", "ai_settings.yaml") self.fast_llm_model = os.getenv("FAST_LLM_MODEL", "gpt-3.5-turbo") self.smart_llm_model = os.getenv("SMART_LLM_MODEL", "gpt-4") self.fast_token_limit = int(os.getenv("FAST_TOKEN_LIMIT", 4000)) self.smart_token_limit = int(os.getenv("SMART_TOKEN_LIMIT", 8000)) self.embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002") self.embedding_tokenizer = os.getenv("EMBEDDING_TOKENIZER", "cl100k_base") self.embedding_token_limit = int(os.getenv("EMBEDDING_TOKEN_LIMIT", 8191)) self.browse_chunk_max_length = int(os.getenv("BROWSE_CHUNK_MAX_LENGTH", 3000)) self.browse_spacy_language_model = os.getenv( "BROWSE_SPACY_LANGUAGE_MODEL", "en_core_web_sm" ) self.openai_api_key = os.getenv("OPENAI_API_KEY") self.temperature = float(os.getenv("TEMPERATURE", "0")) self.use_azure = os.getenv("USE_AZURE") == "True" self.execute_local_commands = ( os.getenv("EXECUTE_LOCAL_COMMANDS", "False") == "True" ) self.restrict_to_workspace = ( os.getenv("RESTRICT_TO_WORKSPACE", "True") == "True" ) if self.use_azure: self.load_azure_config() openai.api_type = self.openai_api_type openai.api_base = self.openai_api_base openai.api_version = self.openai_api_version self.elevenlabs_api_key = os.getenv("ELEVENLABS_API_KEY") self.elevenlabs_voice_1_id = os.getenv("ELEVENLABS_VOICE_1_ID") self.elevenlabs_voice_2_id = os.getenv("ELEVENLABS_VOICE_2_ID") self.use_mac_os_tts = False self.use_mac_os_tts = os.getenv("USE_MAC_OS_TTS") self.chat_messages_enabled = os.getenv("CHAT_MESSAGES_ENABLED") == "True" self.use_brian_tts = False self.use_brian_tts = os.getenv("USE_BRIAN_TTS") self.github_api_key = os.getenv("GITHUB_API_KEY") self.github_username = os.getenv("GITHUB_USERNAME") self.google_api_key = os.getenv("GOOGLE_API_KEY") self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID") self.pinecone_api_key = os.getenv("PINECONE_API_KEY") self.pinecone_region = os.getenv("PINECONE_ENV") self.weaviate_host = os.getenv("WEAVIATE_HOST") self.weaviate_port = os.getenv("WEAVIATE_PORT") self.weaviate_protocol = os.getenv("WEAVIATE_PROTOCOL", "http") self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None) self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None) self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None) self.weaviate_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH") self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None) self.use_weaviate_embedded = ( os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True" ) # milvus or zilliz cloud configuration. self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530") self.milvus_username = os.getenv("MILVUS_USERNAME") self.milvus_password = os.getenv("MILVUS_PASSWORD") self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt") self.milvus_secure = os.getenv("MILVUS_SECURE") == "True" self.image_provider = os.getenv("IMAGE_PROVIDER") self.image_size = int(os.getenv("IMAGE_SIZE", 256)) self.huggingface_api_token = os.getenv("HUGGINGFACE_API_TOKEN") self.huggingface_image_model = os.getenv( "HUGGINGFACE_IMAGE_MODEL", "CompVis/stable-diffusion-v1-4" ) self.huggingface_audio_to_text_model = os.getenv( "HUGGINGFACE_AUDIO_TO_TEXT_MODEL" ) self.sd_webui_url = os.getenv("SD_WEBUI_URL", "http://localhost:7860") self.sd_webui_auth = os.getenv("SD_WEBUI_AUTH") # Selenium browser settings self.selenium_web_browser = os.getenv("USE_WEB_BROWSER", "chrome") self.selenium_headless = os.getenv("HEADLESS_BROWSER", "True") == "True" # User agent header to use when making HTTP requests # Some websites might just completely deny request with an error code if # no user agent was found. self.user_agent = os.getenv( "USER_AGENT", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36" " (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36", ) self.redis_host = os.getenv("REDIS_HOST", "localhost") self.redis_port = os.getenv("REDIS_PORT", "6379") self.redis_password = os.getenv("REDIS_PASSWORD", "") self.wipe_redis_on_start = os.getenv("WIPE_REDIS_ON_START", "True") == "True" self.memory_index = os.getenv("MEMORY_INDEX", "auto-gpt") # Note that indexes must be created on db 0 in redis, this is not configurable. self.memory_backend = os.getenv("MEMORY_BACKEND", "local") self.plugins_dir = os.getenv("PLUGINS_DIR", "plugins") self.plugins: List[AutoGPTPluginTemplate] = [] self.plugins_openai = [] plugins_allowlist = os.getenv("ALLOWLISTED_PLUGINS") if plugins_allowlist: self.plugins_allowlist = plugins_allowlist.split(",") else: self.plugins_allowlist = [] plugins_denylist = os.getenv("DENYLISTED_PLUGINS") if plugins_denylist: self.plugins_denylist = plugins_denylist.split(",") else: self.plugins_denylist = [] def get_azure_deployment_id_for_model(self, model: str) -> str: """ Returns the relevant deployment id for the model specified. Parameters: model(str): The model to map to the deployment id. Returns: The matching deployment id if found, otherwise an empty string. """ if model == self.fast_llm_model: return self.azure_model_to_deployment_id_map[ "fast_llm_model_deployment_id" ] # type: ignore elif model == self.smart_llm_model: return self.azure_model_to_deployment_id_map[ "smart_llm_model_deployment_id" ] # type: ignore elif model == "text-embedding-ada-002": return self.azure_model_to_deployment_id_map[ "embedding_model_deployment_id" ] # type: ignore else: return "" AZURE_CONFIG_FILE = os.path.join(os.path.dirname(__file__), "../..", "azure.yaml") def load_azure_config(self, config_file: str = AZURE_CONFIG_FILE) -> None: """ Loads the configuration parameters for Azure hosting from the specified file path as a yaml file. Parameters: config_file(str): The path to the config yaml file. DEFAULT: "../azure.yaml" Returns: None """ with open(config_file) as file: config_params = yaml.load(file, Loader=yaml.FullLoader) self.openai_api_type = config_params.get("azure_api_type") or "azure" self.openai_api_base = config_params.get("azure_api_base") or "" self.openai_api_version = ( config_params.get("azure_api_version") or "2023-03-15-preview" ) self.azure_model_to_deployment_id_map = config_params.get("azure_model_map", {}) def set_continuous_mode(self, value: bool) -> None: """Set the continuous mode value.""" self.continuous_mode = value def set_continuous_limit(self, value: int) -> None: """Set the continuous limit value.""" self.continuous_limit = value def set_speak_mode(self, value: bool) -> None: """Set the speak mode value.""" self.speak_mode = value def set_fast_llm_model(self, value: str) -> None: """Set the fast LLM model value.""" self.fast_llm_model = value def set_smart_llm_model(self, value: str) -> None: """Set the smart LLM model value.""" self.smart_llm_model = value def set_fast_token_limit(self, value: int) -> None: """Set the fast token limit value.""" self.fast_token_limit = value def set_smart_token_limit(self, value: int) -> None: """Set the smart token limit value.""" self.smart_token_limit = value def set_embedding_model(self, value: str) -> None: """Set the model to use for creating embeddings.""" self.embedding_model = value def set_embedding_tokenizer(self, value: str) -> None: """Set the tokenizer to use when creating embeddings.""" self.embedding_tokenizer = value def set_embedding_token_limit(self, value: int) -> None: """Set the token limit for creating embeddings.""" self.embedding_token_limit = value def set_browse_chunk_max_length(self, value: int) -> None: """Set the browse_website command chunk max length value.""" self.browse_chunk_max_length = value def set_openai_api_key(self, value: str) -> None: """Set the OpenAI API key value.""" self.openai_api_key = value def set_elevenlabs_api_key(self, value: str) -> None: """Set the ElevenLabs API key value.""" self.elevenlabs_api_key = value def set_elevenlabs_voice_1_id(self, value: str) -> None: """Set the ElevenLabs Voice 1 ID value.""" self.elevenlabs_voice_1_id = value def set_elevenlabs_voice_2_id(self, value: str) -> None: """Set the ElevenLabs Voice 2 ID value.""" self.elevenlabs_voice_2_id = value def set_google_api_key(self, value: str) -> None: """Set the Google API key value.""" self.google_api_key = value def set_custom_search_engine_id(self, value: str) -> None: """Set the custom search engine id value.""" self.custom_search_engine_id = value def set_pinecone_api_key(self, value: str) -> None: """Set the Pinecone API key value.""" self.pinecone_api_key = value def set_pinecone_region(self, value: str) -> None: """Set the Pinecone region value.""" self.pinecone_region = value def set_debug_mode(self, value: bool) -> None: """Set the debug mode value.""" self.debug_mode = value def set_plugins(self, value: list) -> None: """Set the plugins value.""" self.plugins = value def set_temperature(self, value: int) -> None: """Set the temperature value.""" self.temperature = value def set_memory_backend(self, name: str) -> None: """Set the memory backend name.""" self.memory_backend = name def check_openai_api_key() -> None: """Check if the OpenAI API key is set in config.py or as an environment variable.""" cfg = Config() if not cfg.openai_api_key: print( Fore.RED + "请在.env文件中配置你的OpenAI API Key." + Fore.RESET ) print("你可以从这里获取你的key https://platform.openai.com/account/api-keys") exit(1) ================================================ FILE: autogpt/configurator.py ================================================ """Configurator module.""" import click from colorama import Back, Fore, Style from autogpt import utils from autogpt.config import Config from autogpt.logs import logger from autogpt.memory import get_supported_memory_backends CFG = Config() def create_config( continuous: bool, continuous_limit: int, ai_settings_file: str, skip_reprompt: bool, speak: bool, debug: bool, gpt3only: bool, gpt4only: bool, memory_type: str, browser_name: str, allow_downloads: bool, skip_news: bool, ) -> None: """Updates the config object with the given arguments. Args: continuous (bool): Whether to run in continuous mode continuous_limit (int): The number of times to run in continuous mode ai_settings_file (str): The path to the ai_settings.yaml file skip_reprompt (bool): Whether to skip the re-prompting messages at the beginning of the script speak (bool): Whether to enable speak mode debug (bool): Whether to enable debug mode gpt3only (bool): Whether to enable GPT3.5 only mode gpt4only (bool): Whether to enable GPT4 only mode memory_type (str): The type of memory backend to use browser_name (str): The name of the browser to use when using selenium to scrape the web allow_downloads (bool): Whether to allow Auto-GPT to download files natively skips_news (bool): Whether to suppress the output of latest news on startup """ CFG.set_debug_mode(False) CFG.set_continuous_mode(False) CFG.set_speak_mode(False) if debug: logger.typewriter_log("Debug模式: ", Fore.GREEN, "开启") CFG.set_debug_mode(True) if continuous: logger.typewriter_log("持续模式: ", Fore.RED, "开启") logger.typewriter_log( "警告: ", Fore.RED, "不推荐使用持续模式. 此模式存在风险，可能会让你的AI持续执行下去" "并执行没有被你授权的指令动作" "使用需谨慎，自负风险。", ) CFG.set_continuous_mode(True) if continuous_limit: logger.typewriter_log( "持续限额: ", Fore.GREEN, f"{continuous_limit}" ) CFG.set_continuous_limit(continuous_limit) # Check if continuous limit is used without continuous mode if continuous_limit and not continuous: raise click.UsageError("--continuous-limit can only be used with --continuous") if speak: logger.typewriter_log("语音模式: ", Fore.GREEN, "开启") CFG.set_speak_mode(True) if gpt3only: logger.typewriter_log("GPT3.5模式: ", Fore.GREEN, "开启") CFG.set_smart_llm_model(CFG.fast_llm_model) if gpt4only: logger.typewriter_log("GPT4模式: ", Fore.GREEN, "开启") CFG.set_fast_llm_model(CFG.smart_llm_model) if memory_type: supported_memory = get_supported_memory_backends() chosen = memory_type if chosen not in supported_memory: logger.typewriter_log( "只支持一下记忆后台模式: ", Fore.RED, f"{supported_memory}", ) logger.typewriter_log("默认至: ", Fore.YELLOW, CFG.memory_backend) else: CFG.memory_backend = chosen if skip_reprompt: logger.typewriter_log("跳过重新指令: ", Fore.GREEN, "开启") CFG.skip_reprompt = True if ai_settings_file: file = ai_settings_file # Validate file (validated, message) = utils.validate_yaml_file(file) if not validated: logger.typewriter_log("文件校验失败", Fore.RED, message) logger.double_check() exit(1) logger.typewriter_log("使用AI配置文件:", Fore.GREEN, file) CFG.ai_settings_file = file CFG.skip_reprompt = True if browser_name: CFG.selenium_web_browser = browser_name if allow_downloads: logger.typewriter_log("本地下载:", Fore.GREEN, "开启") logger.typewriter_log( "警告: ", Fore.YELLOW, f"{Back.LIGHTYELLOW_EX}Auto-GPT将开启下载并存储文件至你的本地电脑中。{Back.RESET} " + "建议您仔细监控它下载的任何文件。", ) logger.typewriter_log( "警告: ", Fore.YELLOW, f"{Back.RED + Style.BRIGHT}请始终记住，永远不要打开您不确定的文件！{Style.RESET_ALL}", ) CFG.allow_downloads = True if skip_news: CFG.skip_news = True ================================================ FILE: autogpt/js/overlay.js ================================================ const overlay = document.createElement('div'); Object.assign(overlay.style, { position: 'fixed', zIndex: 999999, top: 0, left: 0, width: '100%', height: '100%', background: 'rgba(0, 0, 0, 0.7)', color: '#fff', fontSize: '24px', fontWeight: 'bold', display: 'flex', justifyContent: 'center', alignItems: 'center', }); const textContent = document.createElement('div'); Object.assign(textContent.style, { textAlign: 'center', }); textContent.textContent = 'AutoGPT Analyzing Page'; overlay.appendChild(textContent); document.body.append(overlay); document.body.style.overflow = 'hidden'; let dotCount = 0; setInterval(() => { textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount); dotCount = (dotCount + 1) % 4; }, 1000); ================================================ FILE: autogpt/json_utils/__init__.py ================================================ ================================================ FILE: autogpt/json_utils/json_fix_general.py ================================================ """This module contains functions to fix JSON strings using general programmatic approaches, suitable for addressing common JSON formatting issues.""" from __future__ import annotations import contextlib import json import re from typing import Optional from autogpt.config import Config from autogpt.json_utils.utilities import extract_char_position from autogpt.logs import logger CFG = Config() def fix_invalid_escape(json_to_load: str, error_message: str) -> str: """Fix invalid escape sequences in JSON strings. Args: json_to_load (str): The JSON string. error_message (str): The error message from the JSONDecodeError exception. Returns: str: The JSON string with invalid escape sequences fixed. """ while error_message.startswith("Invalid \\escape"): bad_escape_location = extract_char_position(error_message) json_to_load = ( json_to_load[:bad_escape_location] + json_to_load[bad_escape_location + 1 :] ) try: json.loads(json_to_load) return json_to_load except json.JSONDecodeError as e: logger.debug("json loads error - fix invalid escape", e) error_message = str(e) return json_to_load def balance_braces(json_string: str) -> Optional[str]: """ Balance the braces in a JSON string. Args: json_string (str): The JSON string. Returns: str: The JSON string with braces balanced. """ open_braces_count = json_string.count("{") close_braces_count = json_string.count("}") while open_braces_count > close_braces_count: json_string += "}" close_braces_count += 1 while close_braces_count > open_braces_count: json_string = json_string.rstrip("}") close_braces_count -= 1 with contextlib.suppress(json.JSONDecodeError): json.loads(json_string) return json_string def add_quotes_to_property_names(json_string: str) -> str: """ Add quotes to property names in a JSON string. Args: json_string (str): The JSON string. Returns: str: The JSON string with quotes added to property names. """ def replace_func(match: re.Match) -> str: return f'"{match[1]}":' property_name_pattern = re.compile(r"(\w+):") corrected_json_string = property_name_pattern.sub(replace_func, json_string) try: json.loads(corrected_json_string) return corrected_json_string except json.JSONDecodeError as e: raise e def correct_json(json_to_load: str) -> str: """ Correct common JSON errors. Args: json_to_load (str): The JSON string. """ try: logger.debug("json", json_to_load) json.loads(json_to_load) return json_to_load except json.JSONDecodeError as e: logger.debug("json loads error", e) error_message = str(e) if error_message.startswith("Invalid \\escape"): json_to_load = fix_invalid_escape(json_to_load, error_message) if error_message.startswith( "Expecting property name enclosed in double quotes" ): json_to_load = add_quotes_to_property_names(json_to_load) try: json.loads(json_to_load) return json_to_load except json.JSONDecodeError as e: logger.debug("json loads error - add quotes", e) error_message = str(e) if balanced_str := balance_braces(json_to_load): return balanced_str return json_to_load ================================================ FILE: autogpt/json_utils/json_fix_llm.py ================================================ """This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance of the ChatGPT API or LLM models.""" from __future__ import annotations import contextlib import json from typing import Any, Dict from colorama import Fore from regex import regex from autogpt.config import Config from autogpt.json_utils.json_fix_general import correct_json from autogpt.llm import call_ai_function from autogpt.logs import logger from autogpt.speech import say_text JSON_SCHEMA = """ { "command": { "name": "command name", "args": { "arg name": "value" } }, "thoughts": { "text": "thought", "reasoning": "reasoning", "plan": "- short bulleted\n- list that conveys\n- long-term plan", "criticism": "constructive self-criticism", "speak": "thoughts summary to say to user" } } """ CFG = Config() def auto_fix_json(json_string: str, schema: str) -> str: """Fix the given JSON string to make it parseable and fully compliant with the provided schema using GPT-3. Args: json_string (str): The JSON string to fix. schema (str): The schema to use to fix the JSON. Returns: str: The fixed JSON string. """ # Try to fix the JSON using GPT: function_string = "def fix_json(json_string: str, schema:str=None) -> str:" args = [f"'''{json_string}'''", f"'''{schema}'''"] description_string = ( "This function takes a JSON string and ensures that it" " is parseable and fully compliant with the provided schema. If an object" " or field specified in the schema isn't contained within the correct JSON," " it is omitted. The function also escapes any double quotes within JSON" " string values to ensure that they are valid. If the JSON string contains" " any None or NaN values, they are replaced with null before being parsed." ) # If it doesn't already start with a "`", add one: if not json_string.startswith("`"): json_string = "```json\n" + json_string + "\n```" result_string = call_ai_function( function_string, args, description_string, model=CFG.fast_llm_model ) logger.debug("------------ JSON FIX ATTEMPT ---------------") logger.debug(f"Original JSON: {json_string}") logger.debug("-----------") logger.debug(f"Fixed JSON: {result_string}") logger.debug("----------- END OF FIX ATTEMPT ----------------") try: json.loads(result_string) # just check the validity return result_string except json.JSONDecodeError: # noqa: E722 # Get the call stack: # import traceback # call_stack = traceback.format_exc() # print(f"Failed to fix JSON: '{json_string}' "+call_stack) return "failed" def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]: """Fix the given JSON string to make it parseable and fully compliant with two techniques. Args: json_string (str): The JSON string to fix. Returns: str: The fixed JSON string. """ assistant_reply = assistant_reply.strip() if assistant_reply.startswith("```json"): assistant_reply = assistant_reply[7:] if assistant_reply.endswith("```"): assistant_reply = assistant_reply[:-3] try: return json.loads(assistant_reply) # just check the validity except json.JSONDecodeError: # noqa: E722 pass if assistant_reply.startswith("json "): assistant_reply = assistant_reply[5:] assistant_reply = assistant_reply.strip() try: return json.loads(assistant_reply) # just check the validity except json.JSONDecodeError: # noqa: E722 pass # Parse and print Assistant response assistant_reply_json = fix_and_parse_json(assistant_reply) logger.debug("Assistant reply JSON: %s", str(assistant_reply_json)) if assistant_reply_json == {}: assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets( assistant_reply ) logger.debug("Assistant reply JSON 2: %s", str(assistant_reply_json)) if assistant_reply_json != {}: return assistant_reply_json logger.error( "Error: The following AI output couldn't be converted to a JSON:\n", assistant_reply, ) if CFG.speak_mode: say_text("I have received an invalid JSON response from the OpenAI API.") return {} def fix_and_parse_json( json_to_load: str, try_to_fix_with_gpt: bool = True ) -> Dict[Any, Any]: """Fix and parse JSON string Args: json_to_load (str): The JSON string. try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT. Defaults to True. Returns: str or dict[Any, Any]: The parsed JSON. """ with contextlib.suppress(json.JSONDecodeError): json_to_load = json_to_load.replace("\t", "") return json.loads(json_to_load) with contextlib.suppress(json.JSONDecodeError): json_to_load = correct_json(json_to_load) return json.loads(json_to_load) # Let's do something manually: # sometimes GPT responds with something BEFORE the braces: # "I'm sorry, I don't understand. Please try again." # {"text": "I'm sorry, I don't understand. Please try again.", # "confidence": 0.0} # So let's try to find the first brace and then parse the rest # of the string try: brace_index = json_to_load.index("{") maybe_fixed_json = json_to_load[brace_index:] last_brace_index = maybe_fixed_json.rindex("}") maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1] return json.loads(maybe_fixed_json) except (json.JSONDecodeError, ValueError) as e: return try_ai_fix(try_to_fix_with_gpt, e, json_to_load) def try_ai_fix( try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str ) -> Dict[Any, Any]: """Try to fix the JSON with the AI Args: try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI. exception (Exception): The exception that was raised. json_to_load (str): The JSON string to load. Raises: exception: If try_to_fix_with_gpt is False. Returns: str or dict[Any, Any]: The JSON string or dictionary. """ if not try_to_fix_with_gpt: raise exception if CFG.debug_mode: logger.warn( "Warning: Failed to parse AI output, attempting to fix." "\n If you see this warning frequently, it's likely that" " your prompt is confusing the AI. Try changing it up" " slightly." ) # Now try to fix this up using the ai_functions ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA) if ai_fixed_json != "failed": return json.loads(ai_fixed_json) # This allows the AI to react to the error message, # which usually results in it correcting its ways. # logger.error("Failed to fix AI output, telling the AI.") return {} def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str): if CFG.speak_mode and CFG.debug_mode: say_text( "I have received an invalid JSON response from the OpenAI API. " "Trying to fix it now." ) logger.error("Attempting to fix JSON by finding outermost brackets\n") try: json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}") json_match = json_pattern.search(json_string) if json_match: # Extract the valid JSON object from the string json_string = json_match.group(0) logger.typewriter_log( title="Apparently json was fixed.", title_color=Fore.GREEN ) if CFG.speak_mode and CFG.debug_mode: say_text("Apparently json was fixed.") else: return {} except (json.JSONDecodeError, ValueError): if CFG.debug_mode: logger.error(f"Error: Invalid JSON: {json_string}\n") if CFG.speak_mode: say_text("Didn't work. I will have to ignore this response then.") logger.error("Error: Invalid JSON, setting it to empty JSON now.\n") json_string = {} return fix_and_parse_json(json_string) ================================================ FILE: autogpt/json_utils/llm_response_format_1.json ================================================ { "$schema": "http://json-schema.org/draft-07/schema#", "type": "object", "properties": { "thoughts": { "type": "object", "properties": { "text": {"type": "string"}, "reasoning": {"type": "string"}, "plan": {"type": "string"}, "criticism": {"type": "string"}, "speak": {"type": "string"} }, "required": ["text", "reasoning", "plan", "criticism", "speak"], "additionalProperties": false }, "command": { "type": "object", "properties": { "name": {"type": "string"}, "args": { "type": "object" } }, "required": ["name", "args"], "additionalProperties": false } }, "required": ["thoughts", "command"], "additionalProperties": false } ================================================ FILE: autogpt/json_utils/utilities.py ================================================ """Utilities for the json_fixes package.""" import json import os.path import re from jsonschema import Draft7Validator from autogpt.config import Config from autogpt.logs import logger CFG = Config() LLM_DEFAULT_RESPONSE_FORMAT = "llm_response_format_1" def extract_char_position(error_message: str) -> int: """Extract the character position from the JSONDecodeError message. Args: error_message (str): The error message from the JSONDecodeError exception. Returns: int: The character position. """ char_pattern = re.compile(r"\(char (\d+)\)") if match := char_pattern.search(error_message): return int(match[1]) else: raise ValueError("Character position not found in the error message.") def validate_json(json_object: object, schema_name: str) -> dict | None: """ :type schema_name: object :param schema_name: str :type json_object: object """ scheme_file = os.path.join(os.path.dirname(__file__), f"{schema_name}.json") with open(scheme_file, "r") as f: schema = json.load(f) validator = Draft7Validator(schema) if errors := sorted(validator.iter_errors(json_object), key=lambda e: e.path): logger.error("The JSON object is invalid.") if CFG.debug_mode: logger.error( json.dumps(json_object, indent=4) ) # Replace 'json_object' with the variable containing the JSON data logger.error("The following issues were found:") for error in errors: logger.error(f"Error: {error.message}") else: logger.debug("The JSON object is valid.") return json_object def validate_json_string(json_string: str, schema_name: str) -> dict | None: """ :type schema_name: object :param schema_name: str :type json_object: object """ try: json_loaded = json.loads(json_string) return validate_json(json_loaded, schema_name) except: return None def is_string_valid_json(json_string: str, schema_name: str) -> bool: """ :type schema_name: object :param schema_name: str :type json_object: object """ return validate_json_string(json_string, schema_name) is not None ================================================ FILE: autogpt/llm/__init__.py ================================================ from autogpt.llm.api_manager import ApiManager from autogpt.llm.base import ( ChatModelInfo, ChatModelResponse, EmbeddingModelInfo, EmbeddingModelResponse, LLMResponse, Message, ModelInfo, ) from autogpt.llm.chat import chat_with_ai, create_chat_message, generate_context from autogpt.llm.llm_utils import ( call_ai_function, chunked_tokens, create_chat_completion, get_ada_embedding, ) from autogpt.llm.modelsinfo import COSTS from autogpt.llm.token_counter import count_message_tokens, count_string_tokens __all__ = [ "ApiManager", "Message", "ModelInfo", "ChatModelInfo", "EmbeddingModelInfo", "LLMResponse", "ChatModelResponse", "EmbeddingModelResponse", "create_chat_message", "generate_context", "chat_with_ai", "call_ai_function", "create_chat_completion", "get_ada_embedding", "chunked_tokens", "COSTS", "count_message_tokens", "count_string_tokens", ] ================================================ FILE: autogpt/llm/api_manager.py ================================================ from __future__ import annotations import openai from autogpt.config import Config from autogpt.llm.modelsinfo import COSTS from autogpt.logs import logger from autogpt.singleton import Singleton class ApiManager(metaclass=Singleton): def __init__(self): self.total_prompt_tokens = 0 self.total_completion_tokens = 0 self.total_cost = 0 self.total_budget = 0 def reset(self): self.total_prompt_tokens = 0 self.total_completion_tokens = 0 self.total_cost = 0 self.total_budget = 0.0 def create_chat_completion( self, messages: list, # type: ignore model: str | None = None, temperature: float = None, max_tokens: int | None = None, deployment_id=None, ) -> str: """ Create a chat completion and update the cost. Args: messages (list): The list of messages to send to the API. model (str): The model to use for the API call. temperature (float): The temperature to use for the API call. max_tokens (int): The maximum number of tokens for the API call. Returns: str: The AI's response. """ cfg = Config() if temperature is None: temperature = cfg.temperature if deployment_id is not None: response = openai.ChatCompletion.create( deployment_id=deployment_id, model=model, messages=messages, temperature=temperature, max_tokens=max_tokens, api_key=cfg.openai_api_key, ) else: response = openai.ChatCompletion.create( model=model, messages=messages, temperature=temperature, max_tokens=max_tokens, api_key=cfg.openai_api_key, ) logger.debug(f"Response: {response}") prompt_tokens = response.usage.prompt_tokens completion_tokens = response.usage.completion_tokens self.update_cost(prompt_tokens, completion_tokens, model) return response def update_cost(self, prompt_tokens, completion_tokens, model): """ Update the total cost, prompt tokens, and completion tokens. Args: prompt_tokens (int): The number of tokens used in the prompt. completion_tokens (int): The number of tokens used in the completion. model (str): The model used for the API call. """ self.total_prompt_tokens += prompt_tokens self.total_completion_tokens += completion_tokens self.total_cost += ( prompt_tokens * COSTS[model]["prompt"] + completion_tokens * COSTS[model]["completion"] ) / 1000 logger.debug(f"Total running cost: ${self.total_cost:.3f}") def set_total_budget(self, total_budget): """ Sets the total user-defined budget for API calls. Args: total_budget (float): The total budget for API calls. """ self.total_budget = total_budget def get_total_prompt_tokens(self): """ Get the total number of prompt tokens. Returns: int: The total number of prompt tokens. """ return self.total_prompt_tokens def get_total_completion_tokens(self): """ Get the total number of completion tokens. Returns: int: The total number of completion tokens. """ return self.total_completion_tokens def get_total_cost(self): """ Get the total cost of API calls. Returns: float: The total cost of API calls. """ return self.total_cost def get_total_budget(self): """ Get the total user-defined budget for API calls. Returns: float: The total budget for API calls. """ return self.total_budget ================================================ FILE: autogpt/llm/base.py ================================================ from dataclasses import dataclass, field from typing import List, TypedDict class Message(TypedDict): """OpenAI Message object containing a role and the message content""" role: str content: str @dataclass class ModelInfo: """Struct for model information. Would be lovely to eventually get this directly from APIs, but needs to be scraped from websites for now. """ name: str prompt_token_cost: float completion_token_cost: float max_tokens: int @dataclass class ChatModelInfo(ModelInfo): """Struct for chat model information.""" pass @dataclass class EmbeddingModelInfo(ModelInfo): """Struct for embedding model information.""" embedding_dimensions: int @dataclass class LLMResponse: """Standard response struct for a response from an LLM model.""" model_info: ModelInfo prompt_tokens_used: int = 0 completion_tokens_used: int = 0 @dataclass class EmbeddingModelResponse(LLMResponse): """Standard response struct for a response from an embedding model.""" embedding: List[float] = field(default_factory=list) def __post_init__(self): if self.completion_tokens_used: raise ValueError("Embeddings should not have completion tokens used.") @dataclass class ChatModelResponse(LLMResponse): """Standard response struct for a response from an LLM model.""" content: str = None ================================================ FILE: autogpt/llm/chat.py ================================================ import time from random import shuffle from openai.error import RateLimitError from autogpt.config import Config from autogpt.llm.api_manager import ApiManager from autogpt.llm.base import Message from autogpt.llm.llm_utils import create_chat_completion from autogpt.llm.token_counter import count_message_tokens from autogpt.logs import logger from autogpt.memory_management.store_memory import ( save_memory_trimmed_from_context_window, ) from autogpt.memory_management.summary_memory import ( get_newly_trimmed_messages, update_running_summary, ) cfg = Config() def create_chat_message(role, content) -> Message: """ Create a chat message with the given role and content. Args: role (str): The role of the message sender, e.g., "system", "user", or "assistant". content (str): The content of the message. Returns: dict: A dictionary containing the role and content of the message. """ return {"role": role, "content": content} def generate_context(prompt, relevant_memory, full_message_history, model): current_context = [ create_chat_message("system", prompt), create_chat_message( "system", f"当前的日期和时间是 {time.strftime('%c')}" ), # create_chat_message( # "system", # f"This reminds you of these events from your past:\n{relevant_memory}\n\n", # ), ] # Add messages from the full message history until we reach the token limit next_message_to_add_index = len(full_message_history) - 1 insertion_index = len(current_context) # Count the currently used tokens current_tokens_used = count_message_tokens(current_context, model) return ( next_message_to_add_index, current_tokens_used, insertion_index, current_context, ) # TODO: Change debug from hardcode to argument def chat_with_ai( agent, prompt, user_input, full_message_history, permanent_memory, token_limit ): """Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory.""" while True: try: """ Interact with the OpenAI API, sending the prompt, user input, message history, and permanent memory. Args: prompt (str): The prompt explaining the rules to the AI. user_input (str): The input from the user. full_message_history (list): The list of all messages sent between the user and the AI. permanent_memory (Obj): The memory object containing the permanent memory. token_limit (int): The maximum number of tokens allowed in the API call. Returns: str: The AI's response. """ model = cfg.fast_llm_model # TODO: Change model from hardcode to argument # Reserve 1000 tokens for the response logger.debug(f"Token限额: {token_limit}") send_token_limit = token_limit - 1000 # if len(full_message_history) == 0: # relevant_memory = "" # else: # recent_history = full_message_history[-5:] # shuffle(recent_history) # relevant_memories = permanent_memory.get_relevant( # str(recent_history), 5 # ) # if relevant_memories: # shuffle(relevant_memories) # relevant_memory = str(relevant_memories) relevant_memory = "" logger.debug(f"Memory Stats: {permanent_memory.get_stats()}") ( next_message_to_add_index, current_tokens_used, insertion_index, current_context, ) = generate_context(prompt, relevant_memory, full_message_history, model) # while current_tokens_used > 2500: # # remove memories until we are under 2500 tokens # relevant_memory = relevant_memory[:-1] # ( # next_message_to_add_index, # current_tokens_used, # insertion_index, # current_context, # ) = generate_context( # prompt, relevant_memory, full_message_history, model # ) current_tokens_used += count_message_tokens( [create_chat_message("user", user_input)], model ) # Account for user input (appended later) current_tokens_used += 500 # Account for memory (appended later) TODO: The final memory may be less than 500 tokens # Add Messages until the token limit is reached or there are no more messages to add. while next_message_to_add_index >= 0: # print (f"CURRENT TOKENS USED: {current_tokens_used}") message_to_add = full_message_history[next_message_to_add_index] tokens_to_add = count_message_tokens([message_to_add], model) if current_tokens_used + tokens_to_add > send_token_limit: # save_memory_trimmed_from_context_window( # full_message_history, # next_message_to_add_index, # permanent_memory, # ) break # Add the most recent message to the start of the current context, # after the two system prompts. current_context.insert( insertion_index, full_message_history[next_message_to_add_index] ) # Count the currently used tokens current_tokens_used += tokens_to_add # Move to the next most recent message in the full message history next_message_to_add_index -= 1 # Insert Memories if len(full_message_history) > 0: ( newly_trimmed_messages, agent.last_memory_index, ) = get_newly_trimmed_messages( full_message_history=full_message_history, current_context=current_context, last_memory_index=agent.last_memory_index, ) agent.summary_memory = update_running_summary( current_memory=agent.summary_memory, new_events=newly_trimmed_messages, ) current_context.insert(insertion_index, agent.summary_memory) api_manager = ApiManager() # inform the AI about its remaining budget (if it has one) if api_manager.get_total_budget() > 0.0: remaining_budget = ( api_manager.get_total_budget() - api_manager.get_total_cost() ) if remaining_budget < 0: remaining_budget = 0 system_message = ( f"你的剩余API预算为 ${remaining_budget:.3f}" + ( " 已超预算! 关闭!\n\n" if remaining_budget == 0 else " 预算非常接近限额! 优雅关闭中!\n\n" if remaining_budget < 0.005 else " 预算接近限额. 完成中.\n\n" if remaining_budget < 0.01 else "\n\n" ) ) logger.debug(system_message) current_context.append(create_chat_message("system", system_message)) # Append user input, the length of this is accounted for above current_context.extend([create_chat_message("user", user_input)]) plugin_count = len(cfg.plugins) for i, plugin in enumerate(cfg.plugins): if not plugin.can_handle_on_planning(): continue plugin_response = plugin.on_planning( agent.prompt_generator, current_context ) if not plugin_response or plugin_response == "": continue tokens_to_add = count_message_tokens( [create_chat_message("system", plugin_response)], model ) if current_tokens_used + tokens_to_add > send_token_limit: logger.debug("Plugin response too long, skipping:", plugin_response) logger.debug("Plugins remaining at stop:", plugin_count - i) break current_context.append(create_chat_message("system", plugin_response)) # Calculate remaining tokens tokens_remaining = token_limit - current_tokens_used # assert tokens_remaining >= 0, "Tokens remaining is negative. # This should never happen, please submit a bug report at # https://www.github.com/Torantulino/Auto-GPT" # Debug print the current context logger.debug(f"Token限额: {token_limit}") logger.debug(f"发送Token数量: {current_tokens_used}") logger.debug(f"回复剩余Token: {tokens_remaining}") logger.debug("------------ 内容发送至AI ---------------") for message in current_context: # Skip printing the prompt if message["role"] == "system" and message["content"] == prompt: continue logger.debug(f"{message['role'].capitalize()}: {message['content']}") logger.debug("") logger.debug("----------- 内容结束 ----------------") # TODO: use a model defined elsewhere, so that model can contain # temperature and other settings we care about assistant_reply = create_chat_completion( model=model, messages=current_context, max_tokens=tokens_remaining, ) # Update full message history full_message_history.append(create_chat_message("user", user_input)) full_message_history.append( create_chat_message("assistant", assistant_reply) ) return assistant_reply except RateLimitError: # TODO: When we switch to langchain, this is built in logger.warn("Error: ", "API Rate Limit Reached. Waiting 10 seconds...") time.sleep(10) ================================================ FILE: autogpt/llm/llm_utils.py ================================================ from __future__ import annotations import functools import time from itertools import islice from typing import List, Optional import numpy as np import openai import tiktoken from colorama import Fore, Style from openai.error import APIError, RateLimitError, Timeout from autogpt.config import Config from autogpt.llm.api_manager import ApiManager from autogpt.llm.base import Message from autogpt.logs import logger def retry_openai_api( num_retries: int = 10, backoff_base: float = 2.0, warn_user: bool = True, ): """Retry an OpenAI API call. Args: num_retries int: Number of retries. Defaults to 10. backoff_base float: Base for exponential backoff. Defaults to 2. warn_user bool: Whether to warn the user. Defaults to True. """ retry_limit_msg = f"{Fore.RED}Error: " f"Reached rate limit, passing...{Fore.RESET}" api_key_error_msg = ( f"Please double check that you have setup a " f"{Fore.CYAN + Style.BRIGHT}PAID{Style.RESET_ALL} OpenAI API Account. You can " f"read more here: {Fore.CYAN}https://docs.agpt.co/setup/#getting-an-api-key{Fore.RESET}" ) backoff_msg = ( f"{Fore.RED}Error: API Bad gateway. Waiting {{backoff}} seconds...{Fore.RESET}" ) def _wrapper(func): @functools.wraps(func) def _wrapped(*args, **kwargs): user_warned = not warn_user num_attempts = num_retries + 1 # +1 for the first attempt for attempt in range(1, num_attempts + 1): try: return func(*args, **kwargs) except RateLimitError: if attempt == num_attempts: raise logger.debug(retry_limit_msg) if not user_warned: logger.double_check(api_key_error_msg) user_warned = True except APIError as e: if (e.http_status != 502) or (attempt == num_attempts): raise backoff = backoff_base ** (attempt + 2) logger.debug(backoff_msg.format(backoff=backoff)) time.sleep(backoff) return _wrapped return _wrapper def call_ai_function( function: str, args: list, description: str, model: str | None = None ) -> str: """Call an AI function This is a magic function that can do anything with no-code. See https://github.com/Torantulino/AI-Functions for more info. Args: function (str): The function to call args (list): The arguments to pass to the function description (str): The description of the function model (str, optional): The model to use. Defaults to None. Returns: str: The response from the function """ cfg = Config() if model is None: model = cfg.smart_llm_model # For each arg, if any are None, convert to "None": args = [str(arg) if arg is not None else "None" for arg in args] # parse args to comma separated string args: str = ", ".join(args) messages: List[Message] = [ { "role": "system", "content": f"You are now the following python function: ```# {description}" f"\n{function}```\n\nOnly respond with your `return` value.", }, {"role": "user", "content": args}, ] return create_chat_completion(model=model, messages=messages, temperature=0) # Overly simple abstraction until we create something better # simple retry mechanism when getting a rate error or a bad gateway def create_chat_completion( messages: List[Message], # type: ignore model: Optional[str] = None, temperature: float = None, max_tokens: Optional[int] = None, ) -> str: """Create a chat completion using the OpenAI API Args: messages (List[Message]): The messages to send to the chat completion model (str, optional): The model to use. Defaults to None. temperature (float, optional): The temperature to use. Defaults to 0.9. max_tokens (int, optional): The max tokens to use. Defaults to None. Returns: str: The response from the chat completion """ cfg = Config() if temperature is None: temperature = cfg.temperature num_retries = 10 warned_user = False logger.debug( f"{Fore.GREEN}Creating chat completion with model {model}, temperature {temperature}, max_tokens {max_tokens}{Fore.RESET}" ) for plugin in cfg.plugins: if plugin.can_handle_chat_completion( messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, ): message = plugin.handle_chat_completion( messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, ) if message is not None: return message api_manager = ApiManager() response = None for attempt in range(num_retries): backoff = 2 ** (attempt + 2) try: if cfg.use_azure: response = api_manager.create_chat_completion( deployment_id=cfg.get_azure_deployment_id_for_model(model), model=model, messages=messages, temperature=temperature, max_tokens=max_tokens, ) else: response = api_manager.create_chat_completion( model=model, messages=messages, temperature=temperature, max_tokens=max_tokens, ) break except RateLimitError: logger.debug( f"{Fore.RED}Error: ", f"到达请求限额, passing...{Fore.RESET}" ) if not warned_user: logger.double_check( f"请再次检查你已经配置好了一个 {Fore.CYAN + Style.BRIGHT}付费的{Style.RESET_ALL} OpenAI API账户. " + f"你可以从这里获取更多信息: {Fore.CYAN}https://docs.agpt.co/setup/#getting-an-api-key{Fore.RESET}" ) warned_user = True except (APIError, Timeout) as e: if e.http_status != 502: raise if attempt == num_retries - 1: raise logger.debug( f"{Fore.RED}Error: ", f"API Bad gateway. 等待 {backoff} 秒...{Fore.RESET}", ) time.sleep(backoff) if response is None: logger.typewriter_log( "从OPENAI获取信息失败", Fore.RED, "Auto-GPT从OpenAI服务中获取请求失败. " + f"尝试重新启动Auto-GPT, 如果这个问题持续出现请增加后缀 `{Fore.CYAN}--debug{Fore.RESET}`.", ) logger.double_check() if cfg.debug_mode: raise RuntimeError(f"请求 {num_retries} 次后失败") else: quit(1) resp = response.choices[0].message["content"] for plugin in cfg.plugins: if not plugin.can_handle_on_response(): continue resp = plugin.on_response(resp) return resp def batched(iterable, n): """Batch data into tuples of length n. The last batch may be shorter.""" # batched('ABCDEFG', 3) --> ABC DEF G if n < 1: raise ValueError("n 必须为数字") it = iter(iterable) while batch := tuple(islice(it, n)): yield batch def chunked_tokens(text, tokenizer_name, chunk_length): tokenizer = tiktoken.get_encoding(tokenizer_name) tokens = tokenizer.encode(text) chunks_iterator = batched(tokens, chunk_length) yield from chunks_iterator def get_ada_embedding(text: str) -> List[float]: """Get an embedding from the ada model. Args: text (str): The text to embed. Returns: List[float]: The embedding. """ cfg = Config() model = cfg.embedding_model text = text.replace("\n", " ") if cfg.use_azure: kwargs = {"engine": cfg.get_azure_deployment_id_for_model(model)} else: kwargs = {"model": model} embedding = create_embedding(text, **kwargs) return embedding @retry_openai_api() def create_embedding( text: str, *_, **kwargs, ) -> openai.Embedding: """Create an embedding using the OpenAI API Args: text (str): The text to embed. kwargs: Other arguments to pass to the OpenAI API embedding creation call. Returns: openai.Embedding: The embedding object. """ cfg = Config() chunk_embeddings = [] chunk_lengths = [] for chunk in chunked_tokens( text, tokenizer_name=cfg.embedding_tokenizer, chunk_length=cfg.embedding_token_limit, ): embedding = openai.Embedding.create( input=[chunk], api_key=cfg.openai_api_key, **kwargs, ) api_manager = ApiManager() api_manager.update_cost( prompt_tokens=embedding.usage.prompt_tokens, completion_tokens=0, model=cfg.embedding_model, ) chunk_embeddings.append(embedding["data"][0]["embedding"]) chunk_lengths.append(len(chunk)) # do weighted avg chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lengths) chunk_embeddings = chunk_embeddings / np.linalg.norm( chunk_embeddings ) # normalize the length to one chunk_embeddings = chunk_embeddings.tolist() return chunk_embeddings ================================================ FILE: autogpt/llm/modelsinfo.py ================================================ COSTS = { "gpt-3.5-turbo": {"prompt": 0.002, "completion": 0.002}, "gpt-3.5-turbo-0301": {"prompt": 0.002, "completion": 0.002}, "gpt-4-0314": {"prompt": 0.03, "completion": 0.06}, "gpt-4": {"prompt": 0.03, "completion": 0.06}, "gpt-4-0314": {"prompt": 0.03, "completion": 0.06}, "gpt-4-32k": {"prompt": 0.06, "completion": 0.12}, "gpt-4-32k-0314": {"prompt": 0.06, "completion": 0.12}, "text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0}, } ================================================ FILE: autogpt/llm/providers/__init__.py ================================================ ================================================ FILE: autogpt/llm/providers/openai.py ================================================ from autogpt.llm.base import ChatModelInfo, EmbeddingModelInfo OPEN_AI_CHAT_MODELS = { "gpt-3.5-turbo": ChatModelInfo( name="gpt-3.5-turbo", prompt_token_cost=0.002, completion_token_cost=0.002, max_tokens=4096, ), "gpt-4": ChatModelInfo( name="gpt-4", prompt_token_cost=0.03, completion_token_cost=0.06, max_tokens=8192, ), "gpt-4-32k": ChatModelInfo( name="gpt-4-32k", prompt_token_cost=0.06, completion_token_cost=0.12, max_tokens=32768, ), } OPEN_AI_EMBEDDING_MODELS = { "text-embedding-ada-002": EmbeddingModelInfo( name="text-embedding-ada-002", prompt_token_cost=0.0004, completion_token_cost=0.0, max_tokens=8191, embedding_dimensions=1536, ), } OPEN_AI_MODELS = { **OPEN_AI_CHAT_MODELS, **OPEN_AI_EMBEDDING_MODELS, } ================================================ FILE: autogpt/llm/token_counter.py ================================================ """Functions for counting the number of tokens in a message or string.""" from __future__ import annotations from typing import List import tiktoken from autogpt.llm.base import Message from autogpt.logs import logger def count_message_tokens( messages: List[Message], model: str = "gpt-3.5-turbo-0301" ) -> int: """ Returns the number of tokens used by a list of messages. Args: messages (list): A list of messages, each of which is a dictionary containing the role and content of the message. model (str): The name of the model to use for tokenization. Defaults to "gpt-3.5-turbo-0301". Returns: int: The number of tokens used by the list of messages. """ try: encoding = tiktoken.encoding_for_model(model) except KeyError: logger.warn("警告: model没有找到. 使用cl100k_base编码.") encoding = tiktoken.get_encoding("cl100k_base") if model == "gpt-3.5-turbo": # !Note: gpt-3.5-turbo may change over time. # Returning num tokens assuming gpt-3.5-turbo-0301.") return count_message_tokens(messages, model="gpt-3.5-turbo-0301") elif model == "gpt-4": # !Note: gpt-4 may change over time. Returning num tokens assuming gpt-4-0314.") return count_message_tokens(messages, model="gpt-4-0314") elif model == "gpt-3.5-turbo-0301": tokens_per_message = ( 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n ) tokens_per_name = -1 # if there's a name, the role is omitted elif model == "gpt-4-0314": tokens_per_message = 3 tokens_per_name = 1 else: raise NotImplementedError( f"num_tokens_from_messages() is not implemented for model {model}.\n" " See https://github.com/openai/openai-python/blob/main/chatml.md for" " information on how messages are converted to tokens." ) num_tokens = 0 for message in messages: num_tokens += tokens_per_message for key, value in message.items(): num_tokens += len(encoding.encode(value)) if key == "name": num_tokens += tokens_per_name num_tokens += 3 # every reply is primed with <|start|>assistant<|message|> return num_tokens def count_string_tokens(string: str, model_name: str) -> int: """ Returns the number of tokens in a text string. Args: string (str): The text string. model_name (str): The name of the encoding to use. (e.g., "gpt-3.5-turbo") Returns: int: The number of tokens in the text string. """ encoding = tiktoken.encoding_for_model(model_name) return len(encoding.encode(string)) ================================================ FILE: autogpt/log_cycle/__init__.py ================================================ ================================================ FILE: autogpt/log_cycle/json_handler.py ================================================ import json import logging class JsonFileHandler(logging.FileHandler): def __init__(self, filename, mode="a", encoding=None, delay=False): super().__init__(filename, mode, encoding, delay) def emit(self, record): json_data = json.loads(self.format(record)) with open(self.baseFilename, "w", encoding="utf-8") as f: json.dump(json_data, f, ensure_ascii=False, indent=4) import logging class JsonFormatter(logging.Formatter): def format(self, record): return record.msg ================================================ FILE: autogpt/log_cycle/log_cycle.py ================================================ import json import os from typing import Any, Dict, Union from autogpt.logs import logger DEFAULT_PREFIX = "agent" FULL_MESSAGE_HISTORY_FILE_NAME = "full_message_history.json" CURRENT_CONTEXT_FILE_NAME = "current_context.json" NEXT_ACTION_FILE_NAME = "next_action.json" PROMPT_SUMMARY_FILE_NAME = "prompt_summary.json" SUMMARY_FILE_NAME = "summary.txt" USER_INPUT_FILE_NAME = "user_input.txt" class LogCycleHandler: """ A class for logging cycle data. """ def __init__(self): self.log_count_within_cycle = 0 @staticmethod def create_directory_if_not_exists(directory_path: str) -> None: if not os.path.exists(directory_path): os.makedirs(directory_path, exist_ok=True) def create_outer_directory(self, ai_name: str, created_at: str) -> str: log_directory = logger.get_log_directory() if os.environ.get("OVERWRITE_DEBUG") == "1": outer_folder_name = "auto_gpt" else: ai_name_short = ai_name[:15] if ai_name else DEFAULT_PREFIX outer_folder_name = f"{created_at}_{ai_name_short}" outer_folder_path = os.path.join(log_directory, "DEBUG", outer_folder_name) self.create_directory_if_not_exists(outer_folder_path) return outer_folder_path def create_inner_directory(self, outer_folder_path: str, cycle_count: int) -> str: nested_folder_name = str(cycle_count).zfill(3) nested_folder_path = os.path.join(outer_folder_path, nested_folder_name) self.create_directory_if_not_exists(nested_folder_path) return nested_folder_path def create_nested_directory( self, ai_name: str, created_at: str, cycle_count: int ) -> str: outer_folder_path = self.create_outer_directory(ai_name, created_at) nested_folder_path = self.create_inner_directory(outer_folder_path, cycle_count) return nested_folder_path def log_cycle( self, ai_name: str, created_at: str, cycle_count: int, data: Union[Dict[str, Any], Any], file_name: str, ) -> None: """ Log cycle data to a JSON file. Args: data (Any): The data to be logged. file_name (str): The name of the file to save the logged data. """ nested_folder_path = self.create_nested_directory( ai_name, created_at, cycle_count ) json_data = json.dumps(data, ensure_ascii=False, indent=4) log_file_path = os.path.join( nested_folder_path, f"{self.log_count_within_cycle}_{file_name}" ) logger.log_json(json_data, log_file_path) self.log_count_within_cycle += 1 ================================================ FILE: autogpt/logs.py ================================================ """Logging module for Auto-GPT.""" import logging import os import random import re import time from logging import LogRecord from typing import Any from colorama import Fore, Style from autogpt.log_cycle.json_handler import JsonFileHandler, JsonFormatter from autogpt.singleton import Singleton from autogpt.speech import say_text class Logger(metaclass=Singleton): """ Logger that handle titles in different colors. Outputs logs in console, activity.log, and errors.log For console handler: simulates typing """ def __init__(self): # create log directory if it doesn't exist this_files_dir_path = os.path.dirname(__file__) log_dir = os.path.join(this_files_dir_path, "../logs") if not os.path.exists(log_dir): os.makedirs(log_dir) log_file = "activity.log" error_file = "error.log" console_formatter = AutoGptFormatter("%(title_color)s %(message)s") # Create a handler for console which simulate typing self.typing_console_handler = TypingConsoleHandler() self.typing_console_handler.setLevel(logging.INFO) self.typing_console_handler.setFormatter(console_formatter) # Create a handler for console without typing simulation self.console_handler = ConsoleHandler() self.console_handler.setLevel(logging.DEBUG) self.console_handler.setFormatter(console_formatter) # Info handler in activity.log self.file_handler = logging.FileHandler( os.path.join(log_dir, log_file), "a", "utf-8" ) self.file_handler.setLevel(logging.DEBUG) info_formatter = AutoGptFormatter( "%(asctime)s %(levelname)s %(title)s %(message_no_color)s" ) self.file_handler.setFormatter(info_formatter) # Error handler error.log error_handler = logging.FileHandler( os.path.join(log_dir, error_file), "a", "utf-8" ) error_handler.setLevel(logging.ERROR) error_formatter = AutoGptFormatter( "%(asctime)s %(levelname)s %(module)s:%(funcName)s:%(lineno)d %(title)s" " %(message_no_color)s" ) error_handler.setFormatter(error_formatter) self.typing_logger = logging.getLogger("TYPER") self.typing_logger.addHandler(self.typing_console_handler) self.typing_logger.addHandler(self.file_handler) self.typing_logger.addHandler(error_handler) self.typing_logger.setLevel(logging.DEBUG) self.logger = logging.getLogger("LOGGER") self.logger.addHandler(self.console_handler) self.logger.addHandler(self.file_handler) self.logger.addHandler(error_handler) self.logger.setLevel(logging.DEBUG) self.json_logger = logging.getLogger("JSON_LOGGER") self.json_logger.addHandler(self.file_handler) self.json_logger.addHandler(error_handler) self.json_logger.setLevel(logging.DEBUG) self.speak_mode = False self.chat_plugins = [] def typewriter_log( self, title="", title_color="", content="", speak_text=False, level=logging.INFO ): if speak_text and self.speak_mode: say_text(f"{title}. {content}") for plugin in self.chat_plugins: plugin.report(f"{title}. {content}") if content: if isinstance(content, list): content = " ".join(content) else: content = "" self.typing_logger.log( level, content, extra={"title": title, "color": title_color} ) def debug( self, message, title="", title_color="", ): self._log(title, title_color, message, logging.DEBUG) def info( self, message, title="", title_color="", ): self._log(title, title_color, message, logging.INFO) def warn( self, message, title="", title_color="", ): self._log(title, title_color, message, logging.WARN) def error(self, title, message=""): self._log(title, Fore.RED, message, logging.ERROR) def _log( self, title: str = "", title_color: str = "", message: str = "", level=logging.INFO, ): if message: if isinstance(message, list): message = " ".join(message) self.logger.log( level, message, extra={"title": str(title), "color": str(title_color)} ) def set_level(self, level): self.logger.setLevel(level) self.typing_logger.setLevel(level) def double_check(self, additionalText=None): if not additionalText: additionalText = ( "请确认你已经正确配置好所有内容" " 请阅读 https://github.com/Torantulino/Auto-GPT#readme " "确认. 你也可以在Github上开启Issue或者去Discord讨论" "!" ) self.typewriter_log("请再次检查配置", Fore.YELLOW, additionalText) def log_json(self, data: Any, file_name: str) -> None: # Define log directory this_files_dir_path = os.path.dirname(__file__) log_dir = os.path.join(this_files_dir_path, "../logs") # Create a handler for JSON files json_file_path = os.path.join(log_dir, file_name) json_data_handler = JsonFileHandler(json_file_path) json_data_handler.setFormatter(JsonFormatter()) # Log the JSON data using the custom file handler self.json_logger.addHandler(json_data_handler) self.json_logger.debug(data) self.json_logger.removeHandler(json_data_handler) def get_log_directory(self): this_files_dir_path = os.path.dirname(__file__) log_dir = os.path.join(this_files_dir_path, "../logs") return os.path.abspath(log_dir) """ Output stream to console using simulated typing """ class TypingConsoleHandler(logging.StreamHandler): def emit(self, record): min_typing_speed = 0.05 max_typing_speed = 0.01 msg = self.format(record) try: words = msg.split() for i, word in enumerate(words): print(word, end="", flush=True) if i < len(words) - 1: print(" ", end="", flush=True) typing_speed = random.uniform(min_typing_speed, max_typing_speed) time.sleep(typing_speed) # type faster after each word min_typing_speed = min_typing_speed * 0.95 max_typing_speed = max_typing_speed * 0.95 print() except Exception: self.handleError(record) class ConsoleHandler(logging.StreamHandler): def emit(self, record) -> None: msg = self.format(record) try: print(msg) except Exception: self.handleError(record) class AutoGptFormatter(logging.Formatter): """ Allows to handle custom placeholders 'title_color' and 'message_no_color'. To use this formatter, make sure to pass 'color', 'title' as log extras. """ def format(self, record: LogRecord) -> str: if hasattr(record, "color"): record.title_color = ( getattr(record, "color") + getattr(record, "title", "") + " " + Style.RESET_ALL ) else: record.title_color = getattr(record, "title", "") # Add this line to set 'title' to an empty string if it doesn't exist record.title = getattr(record, "title", "") if hasattr(record, "msg"): record.message_no_color = remove_color_codes(getattr(record, "msg")) else: record.message_no_color = "" return super().format(record) def remove_color_codes(s: str) -> str: ansi_escape = re.compile(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])") return ansi_escape.sub("", s) logger = Logger() def print_assistant_thoughts( ai_name: object, assistant_reply_json_valid: object, speak_mode: bool = False, ) -> None: assistant_thoughts_reasoning = None assistant_thoughts_plan = None assistant_thoughts_speak = None assistant_thoughts_criticism = None assistant_thoughts = assistant_reply_json_valid.get("thoughts", {}) assistant_thoughts_text = assistant_thoughts.get("text") if assistant_thoughts: assistant_thoughts_reasoning = assistant_thoughts.get("reasoning") assistant_thoughts_plan = assistant_thoughts.get("plan") assistant_thoughts_criticism = assistant_thoughts.get("criticism") assistant_thoughts_speak = assistant_thoughts.get("speak") logger.typewriter_log( f"{ai_name.upper()}思考:", Fore.YELLOW, f"{assistant_thoughts_text}" ) logger.typewriter_log("推理:", Fore.YELLOW, f"{assistant_thoughts_reasoning}") if assistant_thoughts_plan: logger.typewriter_log("计划:", Fore.YELLOW, "") # If it's a list, join it into a string if isinstance(assistant_thoughts_plan, list): assistant_thoughts_plan = "\n".join(assistant_thoughts_plan) elif isinstance(assistant_thoughts_plan, dict): assistant_thoughts_plan = str(assistant_thoughts_plan) # Split the input_string using the newline character and dashes lines = assistant_thoughts_plan.split("\n") for line in lines: line = line.lstrip("- ") logger.typewriter_log("- ", Fore.GREEN, line.strip()) logger.typewriter_log("反思:", Fore.YELLOW, f"{assistant_thoughts_criticism}") # Speak the assistant's thoughts if speak_mode and assistant_thoughts_speak: say_text(assistant_thoughts_speak) ================================================ FILE: autogpt/main.py ================================================ """The application entry point. Can be invoked by a CLI or any other front end application.""" import logging import sys from pathlib import Path from colorama import Fore, Style from autogpt.agent.agent import Agent from autogpt.commands.command import CommandRegistry from autogpt.config import Config, check_openai_api_key from autogpt.configurator import create_config from autogpt.logs import logger from autogpt.memory import get_memory from autogpt.plugins import scan_plugins from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT, construct_main_ai_config from autogpt.utils import ( get_current_git_branch, get_latest_bulletin, markdown_to_ansi_style, ) from autogpt.workspace import Workspace from scripts.install_plugin_deps import install_plugin_dependencies def run_auto_gpt( continuous: bool, continuous_limit: int, ai_settings: str, skip_reprompt: bool, speak: bool, debug: bool, gpt3only: bool, gpt4only: bool, memory_type: str, browser_name: str, allow_downloads: bool, skip_news: bool, workspace_directory: str, install_plugin_deps: bool, ): # Configure logging before we do anything else. logger.set_level(logging.DEBUG if debug else logging.INFO) logger.speak_mode = speak cfg = Config() # TODO: fill in llm values here check_openai_api_key() create_config( continuous, continuous_limit, ai_settings, skip_reprompt, speak, debug, gpt3only, gpt4only, memory_type, browser_name, allow_downloads, skip_news, ) if not cfg.skip_news: motd, is_new_motd = get_latest_bulletin() if motd: motd = markdown_to_ansi_style(motd) for motd_line in motd.split("\n"): logger.info(motd_line, "新闻:", Fore.GREEN) if is_new_motd and not cfg.chat_messages_enabled: input( Fore.MAGENTA + Style.BRIGHT + "NEWS: 最新信息已经更新，按回车继续..." + Style.RESET_ALL ) git_branch = get_current_git_branch() if git_branch and git_branch != "stable": logger.typewriter_log( "警告: ", Fore.RED, f"你正在运行的是 `{git_branch}` 分支 " "- 该分支不被支持.", ) if sys.version_info < (3, 10): logger.typewriter_log( "警告: ", Fore.RED, "你正在运行较低版本的Python. " "有些朋友在这个过程中会出现一系列问题" "建议更新你的Python至3.10或更高.", ) if install_plugin_deps: install_plugin_dependencies() # TODO: have this directory live outside the repository (e.g. in a user's # home directory) and have it come in as a command line argument or part of # the env file. if workspace_directory is None: workspace_directory = Path(__file__).parent / "auto_gpt_workspace" else: workspace_directory = Path(workspace_directory) # TODO: pass in the ai_settings file and the env file and have them cloned into # the workspace directory so we can bind them to the agent. workspace_directory = Workspace.make_workspace(workspace_directory) cfg.workspace_path = str(workspace_directory) # HACK: doing this here to collect some globals that depend on the workspace. file_logger_path = workspace_directory / "file_logger.txt" if not file_logger_path.exists(): with file_logger_path.open(mode="w", encoding="utf-8") as f: f.write("File Operation Logger ") cfg.file_logger_path = str(file_logger_path) cfg.set_plugins(scan_plugins(cfg, cfg.debug_mode)) # Create a CommandRegistry instance and scan default folder command_registry = CommandRegistry() command_registry.import_commands("autogpt.commands.analyze_code") command_registry.import_commands("autogpt.commands.audio_text") command_registry.import_commands("autogpt.commands.execute_code") command_registry.import_commands("autogpt.commands.file_operations") command_registry.import_commands("autogpt.commands.git_operations") command_registry.import_commands("autogpt.commands.google_search") command_registry.import_commands("autogpt.commands.image_gen") command_registry.import_commands("autogpt.commands.improve_code") command_registry.import_commands("autogpt.commands.twitter") command_registry.import_commands("autogpt.commands.web_selenium") command_registry.import_commands("autogpt.commands.write_tests") command_registry.import_commands("autogpt.app") ai_name = "" ai_config = construct_main_ai_config() ai_config.command_registry = command_registry # print(prompt) # Initialize variables full_message_history = [] next_action_count = 0 # add chat plugins capable of report to logger if cfg.chat_messages_enabled: for plugin in cfg.plugins: if hasattr(plugin, "can_handle_report") and plugin.can_handle_report(): logger.info(f"Loaded plugin into logger: {plugin.__class__.__name__}") logger.chat_plugins.append(plugin) # Initialize memory and make sure it is empty. # this is particularly important for indexing and referencing pinecone memory memory = get_memory(cfg, init=True) logger.typewriter_log( "Using memory of type:", Fore.GREEN, f"{memory.__class__.__name__}" ) logger.typewriter_log("Using Browser:", Fore.GREEN, cfg.selenium_web_browser) system_prompt = ai_config.construct_full_prompt() if cfg.debug_mode: logger.typewriter_log("Prompt:", Fore.GREEN, system_prompt) agent = Agent( ai_name=ai_name, memory=memory, full_message_history=full_message_history, next_action_count=next_action_count, command_registry=command_registry, config=ai_config, system_prompt=system_prompt, triggering_prompt=DEFAULT_TRIGGERING_PROMPT, workspace_directory=workspace_directory, ) agent.start_interaction_loop() ================================================ FILE: autogpt/memory/__init__.py ================================================ from autogpt.logs import logger from autogpt.memory.local import LocalCache from autogpt.memory.no_memory import NoMemory # List of supported memory backends # Add a backend to this list if the import attempt is successful supported_memory = ["local", "no_memory"] try: from autogpt.memory.redismem import RedisMemory supported_memory.append("redis") except ImportError: RedisMemory = None try: from autogpt.memory.pinecone import PineconeMemory supported_memory.append("pinecone") except ImportError: PineconeMemory = None try: from autogpt.memory.weaviate import WeaviateMemory supported_memory.append("weaviate") except ImportError: WeaviateMemory = None try: from autogpt.memory.milvus import MilvusMemory supported_memory.append("milvus") except ImportError: MilvusMemory = None def get_memory(cfg, init=False): memory = None if cfg.memory_backend == "pinecone": if not PineconeMemory: logger.warn( "Error: Pinecone is not installed. Please install pinecone" " to use Pinecone as a memory backend." ) else: memory = PineconeMemory(cfg) if init: memory.clear() elif cfg.memory_backend == "redis": if not RedisMemory: logger.warn( "Error: Redis is not installed. Please install redis-py to" " use Redis as a memory backend." ) else: memory = RedisMemory(cfg) elif cfg.memory_backend == "weaviate": if not WeaviateMemory: logger.warn( "Error: Weaviate is not installed. Please install weaviate-client to" " use Weaviate as a memory backend." ) else: memory = WeaviateMemory(cfg) elif cfg.memory_backend == "milvus": if not MilvusMemory: logger.warn( "Error: pymilvus sdk is not installed." "Please install pymilvus to use Milvus or Zilliz Cloud as memory backend." ) else: memory = MilvusMemory(cfg) elif cfg.memory_backend == "no_memory": memory = NoMemory(cfg) if memory is None: memory = LocalCache(cfg) if init: memory.clear() return memory def get_supported_memory_backends(): return supported_memory __all__ = [ "get_memory", "LocalCache", "RedisMemory", "PineconeMemory", "NoMemory", "MilvusMemory", "WeaviateMemory", ] ================================================ FILE: autogpt/memory/base.py ================================================ """Base class for memory providers.""" import abc from autogpt.singleton import AbstractSingleton class MemoryProviderSingleton(AbstractSingleton): @abc.abstractmethod def add(self, data): """Adds to memory""" pass @abc.abstractmethod def get(self, data): """Gets from memory""" pass @abc.abstractmethod def clear(self): """Clears memory""" pass @abc.abstractmethod def get_relevant(self, data, num_relevant=5): """Gets relevant memory for""" pass @abc.abstractmethod def get_stats(self): """Get stats from memory""" pass ================================================ FILE: autogpt/memory/local.py ================================================ from __future__ import annotations import dataclasses from pathlib import Path from typing import Any, List import numpy as np import orjson from autogpt.llm import get_ada_embedding from autogpt.memory.base import MemoryProviderSingleton EMBED_DIM = 1536 SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS def create_default_embeddings(): return np.zeros((0, EMBED_DIM)).astype(np.float32) @dataclasses.dataclass class CacheContent: texts: List[str] = dataclasses.field(default_factory=list) embeddings: np.ndarray = dataclasses.field( default_factory=create_default_embeddings ) class LocalCache(MemoryProviderSingleton): """A class that stores the memory in a local file""" def __init__(self, cfg) -> None: """Initialize a class instance Args: cfg: Config object Returns: None """ workspace_path = Path(cfg.workspace_path) self.filename = workspace_path / f"{cfg.memory_index}.json" self.filename.touch(exist_ok=True) file_content = b"{}" with self.filename.open("w+b") as f: f.write(file_content) self.data = CacheContent() def add(self, text: str): """ Add text to our list of texts, add embedding as row to our embeddings-matrix Args: text: str Returns: None """ if "Command Error:" in text: return "" self.data.texts.append(text) embedding = get_ada_embedding(text) vector = np.array(embedding).astype(np.float32) vector = vector[np.newaxis, :] self.data.embeddings = np.concatenate( [ self.data.embeddings, vector, ], axis=0, ) with open(self.filename, "wb") as f: out = orjson.dumps(self.data, option=SAVE_OPTIONS) f.write(out) return text def clear(self) -> str: """ Clears the data in memory. Returns: A message indicating that the memory has been cleared. """ self.data = CacheContent() return "Obliviated" def get(self, data: str) -> list[Any] | None: """ Gets the data from the memory that is most relevant to the given data. Args: data: The data to compare to. Returns: The most relevant data. """ return self.get_relevant(data, 1) def get_relevant(self, text: str, k: int) -> list[Any]: """ " matrix-vector mult to find score-for-each-row-of-matrix get indices for top-k winning scores return texts for those indices Args: text: str k: int Returns: List[str] """ embedding = get_ada_embedding(text) scores = np.dot(self.data.embeddings, embedding) top_k_indices = np.argsort(scores)[-k:][::-1] return [self.data.texts[i] for i in top_k_indices] def get_stats(self) -> tuple[int, tuple[int, ...]]: """ Returns: The stats of the local cache. """ return len(self.data.texts), self.data.embeddings.shape ================================================ FILE: autogpt/memory/milvus.py ================================================ """ Milvus memory storage provider.""" import re from pymilvus import Collection, CollectionSchema, DataType, FieldSchema, connections from autogpt.config import Config from autogpt.llm import get_ada_embedding from autogpt.memory.base import MemoryProviderSingleton class MilvusMemory(MemoryProviderSingleton): """Milvus memory storage provider.""" def __init__(self, cfg: Config) -> None: """Construct a milvus memory storage connection. Args: cfg (Config): Auto-GPT global config. """ self.configure(cfg) connect_kwargs = {} if self.username: connect_kwargs["user"] = self.username connect_kwargs["password"] = self.password connections.connect( **connect_kwargs, uri=self.uri or "", address=self.address or "", secure=self.secure, ) self.init_collection() def configure(self, cfg: Config) -> None: # init with configuration. self.uri = None self.address = cfg.milvus_addr self.secure = cfg.milvus_secure self.username = cfg.milvus_username self.password = cfg.milvus_password self.collection_name = cfg.milvus_collection # use HNSW by default. self.index_params = { "metric_type": "IP", "index_type": "HNSW", "params": {"M": 8, "efConstruction": 64}, } if (self.username is None) != (self.password is None): raise ValueError( "Both username and password must be set to use authentication for Milvus" ) # configured address may be a full URL. if re.match(r"^(https?|tcp)://", self.address) is not None: self.uri = self.address self.address = None if self.uri.startswith("https"): self.secure = True # Zilliz Cloud requires AutoIndex. if re.match(r"^https://(.*)\.zillizcloud\.(com|cn)", self.uri) is not None: self.index_params = { "metric_type": "IP", "index_type": "AUTOINDEX", "params": {}, } def init_collection(self) -> None: """Initialize collection in vector database.""" fields = [ FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True), FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536), FieldSchema(name="raw_text", dtype=DataType.VARCHAR, max_length=65535), ] # create collection if not exist and load it. self.schema = CollectionSchema(fields, "auto-gpt memory storage") self.collection = Collection(self.collection_name, self.schema) # create index if not exist. if not self.collection.has_index(): self.collection.release() self.collection.create_index( "embeddings", self.index_params, index_name="embeddings", ) self.collection.load() def add(self, data) -> str: """Add an embedding of data into memory. Args: data (str): The raw text to construct embedding index. Returns: str: log. """ embedding = get_ada_embedding(data) result = self.collection.insert([[embedding], [data]]) _text = ( "Inserting data into memory at primary key: " f"{result.primary_keys[0]}:\n data: {data}" ) return _text def get(self, data): """Return the most relevant data in memory. Args: data: The data to compare to. """ return self.get_relevant(data, 1) def clear(self) -> str: """Drop the index in memory. Returns: str: log. """ self.collection.drop() self.collection = Collection(self.collection_name, self.schema) self.collection.create_index( "embeddings", self.index_params, index_name="embeddings", ) self.collection.load() return "Obliviated" def get_relevant(self, data: str, num_relevant: int = 5): """Return the top-k relevant data in memory. Args: data: The data to compare to. num_relevant (int, optional): The max number of relevant data. Defaults to 5. Returns: list: The top-k relevant data. """ # search the embedding and return the most relevant text. embedding = get_ada_embedding(data) search_params = { "metrics_type": "IP", "params": {"nprobe": 8}, } result = self.collection.search( [embedding], "embeddings", search_params, num_relevant, output_fields=["raw_text"], ) return [item.entity.value_of_field("raw_text") for item in result[0]] def get_stats(self) -> str: """ Returns: The stats of the milvus cache. """ return f"Entities num: {self.collection.num_entities}" ================================================ FILE: autogpt/memory/no_memory.py ================================================ """A class that does not store any data. This is the default memory provider.""" from __future__ import annotations from typing import Any from autogpt.memory.base import MemoryProviderSingleton class NoMemory(MemoryProviderSingleton): """ A class that does not store any data. This is the default memory provider. """ def __init__(self, cfg): """ Initializes the NoMemory provider. Args: cfg: The config object. Returns: None """ pass def add(self, data: str) -> str: """ Adds a data point to the memory. No action is taken in NoMemory. Args: data: The data to add. Returns: An empty string. """ return "" def get(self, data: str) -> list[Any] | None: """ Gets the data from the memory that is most relevant to the given data. NoMemory always returns None. Args: data: The data to compare to. Returns: None """ return None def clear(self) -> str: """ Clears the memory. No action is taken in NoMemory. Returns: An empty string. """ return "" def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None: """ Returns all the data in the memory that is relevant to the given data. NoMemory always returns None. Args: data: The data to compare to. num_relevant: The number of relevant data to return. Returns: None """ return None def get_stats(self): """ Returns: An empty dictionary as there are no stats in NoMemory. """ return {} ================================================ FILE: autogpt/memory/pinecone.py ================================================ import pinecone from colorama import Fore, Style from autogpt.llm import get_ada_embedding from autogpt.logs import logger from autogpt.memory.base import MemoryProviderSingleton class PineconeMemory(MemoryProviderSingleton): def __init__(self, cfg): pinecone_api_key = cfg.pinecone_api_key pinecone_region = cfg.pinecone_region pinecone.init(api_key=pinecone_api_key, environment=pinecone_region) dimension = 1536 metric = "cosine" pod_type = "p1" table_name = "auto-gpt" # this assumes we don't start with memory. # for now this works. # we'll need a more complicated and robust system if we want to start with # memory. self.vec_num = 0 try: pinecone.whoami() except Exception as e: logger.typewriter_log( "FAILED TO CONNECT TO PINECONE", Fore.RED, Style.BRIGHT + str(e) + Style.RESET_ALL, ) logger.double_check( "Please ensure you have setup and configured Pinecone properly for use." + f"You can check out {Fore.CYAN + Style.BRIGHT}" "https://github.com/Torantulino/Auto-GPT#-pinecone-api-key-setup" f"{Style.RESET_ALL} to ensure you've set up everything correctly." ) exit(1) if table_name not in pinecone.list_indexes(): logger.typewriter_log( "Connecting Pinecone. This may take some time...", Fore.MAGENTA, "" ) pinecone.create_index( table_name, dimension=dimension, metric=metric, pod_type=pod_type ) self.index = pinecone.Index(table_name) def add(self, data): vector = get_ada_embedding(data) # no metadata here. We may wish to change that long term. self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})]) _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}" self.vec_num += 1 return _text def get(self, data): return self.get_relevant(data, 1) def clear(self): self.index.delete(deleteAll=True) return "Obliviated" def get_relevant(self, data, num_relevant=5): """ Returns all the data in the memory that is relevant to the given data. :param data: The data to compare to. :param num_relevant: The number of relevant data to return. Defaults to 5 """ query_embedding = get_ada_embedding(data) results = self.index.query( query_embedding, top_k=num_relevant, include_metadata=True ) sorted_results = sorted(results.matches, key=lambda x: x.score) return [str(item["metadata"]["raw_text"]) for item in sorted_results] def get_stats(self): return self.index.describe_index_stats() ================================================ FILE: autogpt/memory/redismem.py ================================================ """Redis memory provider.""" from __future__ import annotations from typing import Any import numpy as np import redis from colorama import Fore, Style from redis.commands.search.field import TextField, VectorField from redis.commands.search.indexDefinition import IndexDefinition, IndexType from redis.commands.search.query import Query from autogpt.llm import get_ada_embedding from autogpt.logs import logger from autogpt.memory.base import MemoryProviderSingleton SCHEMA = [ TextField("data"), VectorField( "embedding", "HNSW", {"TYPE": "FLOAT32", "DIM": 1536, "DISTANCE_METRIC": "COSINE"}, ), ] class RedisMemory(MemoryProviderSingleton): def __init__(self, cfg): """ Initializes the Redis memory provider. Args: cfg: The config object. Returns: None """ redis_host = cfg.redis_host redis_port = cfg.redis_port redis_password = cfg.redis_password self.dimension = 1536 self.redis = redis.Redis( host=redis_host, port=redis_port, password=redis_password, db=0, # Cannot be changed ) self.cfg = cfg # Check redis connection try: self.redis.ping() except redis.ConnectionError as e: logger.typewriter_log( "FAILED TO CONNECT TO REDIS", Fore.RED, Style.BRIGHT + str(e) + Style.RESET_ALL, ) logger.double_check( "Please ensure you have setup and configured Redis properly for use. " + f"You can check out {Fore.CYAN + Style.BRIGHT}" f"https://github.com/Torantulino/Auto-GPT#redis-setup{Style.RESET_ALL}" " to ensure you've set up everything correctly." ) exit(1) if cfg.wipe_redis_on_start: self.redis.flushall() try: self.redis.ft(f"{cfg.memory_index}").create_index( fields=SCHEMA, definition=IndexDefinition( prefix=[f"{cfg.memory_index}:"], index_type=IndexType.HASH ), ) except Exception as e: logger.warn("Error creating Redis search index: ", e) existing_vec_num = self.redis.get(f"{cfg.memory_index}-vec_num") self.vec_num = int(existing_vec_num.decode("utf-8")) if existing_vec_num else 0 def add(self, data: str) -> str: """ Adds a data point to the memory. Args: data: The data to add. Returns: Message indicating that the data has been added. """ if "Command Error:" in data: return "" vector = get_ada_embedding(data) vector = np.array(vector).astype(np.float32).tobytes() data_dict = {b"data": data, "embedding": vector} pipe = self.redis.pipeline() pipe.hset(f"{self.cfg.memory_index}:{self.vec_num}", mapping=data_dict) _text = ( f"Inserting data into memory at index: {self.vec_num}:\n" f"data: {data}" ) self.vec_num += 1 pipe.set(f"{self.cfg.memory_index}-vec_num", self.vec_num) pipe.execute() return _text def get(self, data: str) -> list[Any] | None: """ Gets the data from the memory that is most relevant to the given data. Args: data: The data to compare to. Returns: The most relevant data. """ return self.get_relevant(data, 1) def clear(self) -> str: """ Clears the redis server. Returns: A message indicating that the memory has been cleared. """ self.redis.flushall() return "Obliviated" def get_relevant(self, data: str, num_relevant: int = 5) -> list[Any] | None: """ Returns all the data in the memory that is relevant to the given data. Args: data: The data to compare to. num_relevant: The number of relevant data to return. Returns: A list of the most relevant data. """ query_embedding = get_ada_embedding(data) base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]" query = ( Query(base_query) .return_fields("data", "vector_score") .sort_by("vector_score") .dialect(2) ) query_vector = np.array(query_embedding).astype(np.float32).tobytes() try: results = self.redis.ft(f"{self.cfg.memory_index}").search( query, query_params={"vector": query_vector} ) except Exception as e: logger.warn("Error calling Redis search: ", e) return None return [result.data for result in results.docs] def get_stats(self): """ Returns: The stats of the memory index. """ return self.redis.ft(f"{self.cfg.memory_index}").info() ================================================ FILE: autogpt/memory/weaviate.py ================================================ import weaviate from weaviate import Client from weaviate.embedded import EmbeddedOptions from weaviate.util import generate_uuid5 from autogpt.llm import get_ada_embedding from autogpt.logs import logger from autogpt.memory.base import MemoryProviderSingleton def default_schema(weaviate_index): return { "class": weaviate_index, "properties": [ { "name": "raw_text", "dataType": ["text"], "description": "original text for the embedding", } ], } class WeaviateMemory(MemoryProviderSingleton): def __init__(self, cfg): auth_credentials = self._build_auth_credentials(cfg) url = f"{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}" if cfg.use_weaviate_embedded: self.client = Client( embedded_options=EmbeddedOptions( hostname=cfg.weaviate_host, port=int(cfg.weaviate_port), persistence_data_path=cfg.weaviate_embedded_path, ) ) logger.info( f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}" ) else: self.client = Client(url, auth_client_secret=auth_credentials) self.index = WeaviateMemory.format_classname(cfg.memory_index) self._create_schema() @staticmethod def format_classname(index): # weaviate uses capitalised index names # The python client uses the following code to format # index names before the corresponding class is created index = index.replace("-", "_") if len(index) == 1: return index.capitalize() return index[0].capitalize() + index[1:] def _create_schema(self): schema = default_schema(self.index) if not self.client.schema.contains(schema): self.client.schema.create_class(schema) def _build_auth_credentials(self, cfg): if cfg.weaviate_username and cfg.weaviate_password: return weaviate.AuthClientPassword( cfg.weaviate_username, cfg.weaviate_password ) if cfg.weaviate_api_key: return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key) else: return None def add(self, data): vector = get_ada_embedding(data) doc_uuid = generate_uuid5(data, self.index) data_object = {"raw_text": data} with self.client.batch as batch: batch.add_data_object( uuid=doc_uuid, data_object=data_object, class_name=self.index, vector=vector, ) return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}" def get(self, data): return self.get_relevant(data, 1) def clear(self): self.client.schema.delete_all() # weaviate does not yet have a neat way to just remove the items in an index # without removing the entire schema, therefore we need to re-create it # after a call to delete_all self._create_schema() return "Obliterated" def get_relevant(self, data, num_relevant=5): query_embedding = get_ada_embedding(data) try: results = ( self.client.query.get(self.index, ["raw_text"]) .with_near_vector({"vector": query_embedding, "certainty": 0.7}) .with_limit(num_relevant) .do() ) if len(results["data"]["Get"][self.index]) > 0: return [ str(item["raw_text"]) for item in results["data"]["Get"][self.index] ] else: return [] except Exception as err: logger.warn(f"Unexpected error {err=}, {type(err)=}") return [] def get_stats(self): result = self.client.query.aggregate(self.index).with_meta_count().do() class_data = result["data"]["Aggregate"][self.index] return class_data[0]["meta"] if class_data else {} ================================================ FILE: autogpt/memory_management/store_memory.py ================================================ from autogpt.json_utils.utilities import ( LLM_DEFAULT_RESPONSE_FORMAT, is_string_valid_json, ) from autogpt.logs import logger def format_memory(assistant_reply, next_message_content): # the next_message_content is a variable to stores either the user_input or the command following the assistant_reply result = ( "None" if next_message_content.startswith("Command") else next_message_content ) user_input = ( "None" if next_message_content.startswith("Human feedback") else next_message_content ) return f"Assistant Reply: {assistant_reply}\nResult: {result}\nHuman Feedback:{user_input}" def save_memory_trimmed_from_context_window( full_message_history, next_message_to_add_index, permanent_memory ): while next_message_to_add_index >= 0: message_content = full_message_history[next_message_to_add_index]["content"] if is_string_valid_json(message_content, LLM_DEFAULT_RESPONSE_FORMAT): next_message = full_message_history[next_message_to_add_index + 1] memory_to_add = format_memory(message_content, next_message["content"]) logger.debug(f"Storing the following memory: {memory_to_add}") permanent_memory.add(memory_to_add) next_message_to_add_index -= 1 ================================================ FILE: autogpt/memory_management/summary_memory.py ================================================ import copy import json from typing import Dict, List, Tuple from autogpt.config import Config from autogpt.llm.llm_utils import create_chat_completion from autogpt.logs import logger cfg = Config() def get_newly_trimmed_messages( full_message_history: List[Dict[str, str]], current_context: List[Dict[str, str]], last_memory_index: int, ) -> Tuple[List[Dict[str, str]], int]: """ This function returns a list of dictionaries contained in full_message_history with an index higher than prev_index that are absent from current_context. Args: full_message_history (list): A list of dictionaries representing the full message history. current_context (list): A list of dictionaries representing the current context. last_memory_index (int): An integer representing the previous index. Returns: list: A list of dictionaries that are in full_message_history with an index higher than last_memory_index and absent from current_context. int: The new index value for use in the next loop. """ # Select messages in full_message_history with an index higher than last_memory_index new_messages = [ copy.deepcopy(msg) for i, msg in enumerate(full_message_history) if i > last_memory_index ] # Remove messages that are already present in current_context new_messages_not_in_context = [ msg for msg in new_messages if msg not in current_context ] # Find the index of the last message processed new_index = last_memory_index if new_messages_not_in_context: last_message = new_messages_not_in_context[-1] new_index = full_message_history.index(last_message) return new_messages_not_in_context, new_index def update_running_summary( current_memory: str, new_events: List[Dict[str, str]] ) -> str: """ This function takes a list of dictionaries representing new events and combines them with the current summary, focusing on key and potentially important information to remember. The updated summary is returned in a message formatted in the 1st person past tense. Args: new_events (List[Dict]): A list of dictionaries containing the latest events to be added to the summary. Returns: str: A message containing the updated summary of actions, formatted in the 1st person past tense. Example: new_events = [{"event": "entered the kitchen."}, {"event": "found a scrawled note with the number 7"}] update_running_summary(new_events) # Returns: "This reminds you of these events from your past: \nI entered the kitchen and found a scrawled note saying 7." """ # Create a copy of the new_events list to prevent modifying the original list new_events = copy.deepcopy(new_events) # Replace "assistant" with "you". This produces much better first person past tense results. for event in new_events: if event["role"].lower() == "assistant": event["role"] = "you" try: content_dict = json.loads(event["content"]) except json.JSONDecodeError as e: print(f"Error decoding JSON data: {e}") # You can either provide a fallback value or raise a more specific exception here content_dict = {} if "thoughts" in content_dict: del content_dict["thoughts"] event["content"] = json.dumps(content_dict) elif event["role"].lower() == "system": event["role"] = "your computer" # Delete all user messages elif event["role"] == "user": new_events.remove(event) # This can happen at any point during execturion, not just the beginning if len(new_events) == 0: new_events = "Nothing new happened." prompt = f'''Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember. You will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise. Summary So Far: """ {current_memory} """ Latest Development: """ {new_events} """ ''' messages = [ { "role": "user", "content": prompt, } ] current_memory = create_chat_completion(messages, cfg.fast_llm_model) message_to_return = { "role": "system", "content": f"This reminds you of these events from your past: \n{current_memory}", } return message_to_return ================================================ FILE: autogpt/models/base_open_ai_plugin.py ================================================ """Handles loading of plugins.""" from typing import Any, Dict, List, Optional, Tuple, TypedDict, TypeVar from auto_gpt_plugin_template import AutoGPTPluginTemplate PromptGenerator = TypeVar("PromptGenerator") class Message(TypedDict): role: str content: str class BaseOpenAIPlugin(AutoGPTPluginTemplate): """ This is a BaseOpenAIPlugin class for generating Auto-GPT plugins. """ def __init__(self, manifests_specs_clients: dict): # super().__init__() self._name = manifests_specs_clients["manifest"]["name_for_model"] self._version = manifests_specs_clients["manifest"]["schema_version"] self._description = manifests_specs_clients["manifest"]["description_for_model"] self._client = manifests_specs_clients["client"] self._manifest = manifests_specs_clients["manifest"] self._openapi_spec = manifests_specs_clients["openapi_spec"] def can_handle_on_response(self) -> bool: """This method is called to check that the plugin can handle the on_response method. Returns: bool: True if the plugin can handle the on_response method.""" return False def on_response(self, response: str, *args, **kwargs) -> str: """This method is called when a response is received from the model.""" return response def can_handle_post_prompt(self) -> bool: """This method is called to check that the plugin can handle the post_prompt method. Returns: bool: True if the plugin can handle the post_prompt method.""" return False def post_prompt(self, prompt: PromptGenerator) -> PromptGenerator: """This method is called just after the generate_prompt is called, but actually before the prompt is generated. Args: prompt (PromptGenerator): The prompt generator. Returns: PromptGenerator: The prompt generator. """ return prompt def can_handle_on_planning(self) -> bool: """This method is called to check that the plugin can handle the on_planning method. Returns: bool: True if the plugin can handle the on_planning method.""" return False def on_planning( self, prompt: PromptGenerator, messages: List[Message] ) -> Optional[str]: """This method is called before the planning chat completion is done. Args: prompt (PromptGenerator): The prompt generator. messages (List[str]): The list of messages. """ pass def can_handle_post_planning(self) -> bool: """This method is called to check that the plugin can handle the post_planning method. Returns: bool: True if the plugin can handle the post_planning method.""" return False def post_planning(self, response: str) -> str: """This method is called after the planning chat completion is done. Args: response (str): The response. Returns: str: The resulting response. """ return response def can_handle_pre_instruction(self) -> bool: """This method is called to check that the plugin can handle the pre_instruction method. Returns: bool: True if the plugin can handle the pre_instruction method.""" return False def pre_instruction(self, messages: List[Message]) -> List[Message]: """This method is called before the instruction chat is done. Args: messages (List[Message]): The list of context messages. Returns: List[Message]: The resulting list of messages. """ return messages def can_handle_on_instruction(self) -> bool: """This method is called to check that the plugin can handle the on_instruction method. Returns: bool: True if the plugin can handle the on_instruction method.""" return False def on_instruction(self, messages: List[Message]) -> Optional[str]: """This method is called when the instruction chat is done. Args: messages (List[Message]): The list of context messages. Returns: Optional[str]: The resulting message. """ pass def can_handle_post_instruction(self) -> bool: """This method is called to check that the plugin can handle the post_instruction method. Returns: bool: True if the plugin can handle the post_instruction method.""" return False def post_instruction(self, response: str) -> str: """This method is called after the instruction chat is done. Args: response (str): The response. Returns: str: The resulting response. """ return response def can_handle_pre_command(self) -> bool: """This method is called to check that the plugin can handle the pre_command method. Returns: bool: True if the plugin can handle the pre_command method.""" return False def pre_command( self, command_name: str, arguments: Dict[str, Any] ) -> Tuple[str, Dict[str, Any]]: """This method is called before the command is executed. Args: command_name (str): The command name. arguments (Dict[str, Any]): The arguments. Returns: Tuple[str, Dict[str, Any]]: The command name and the arguments. """ return command_name, arguments def can_handle_post_command(self) -> bool: """This method is called to check that the plugin can handle the post_command method. Returns: bool: True if the plugin can handle the post_command method.""" return False def post_command(self, command_name: str, response: str) -> str: """This method is called after the command is executed. Args: command_name (str): The command name. response (str): The response. Returns: str: The resulting response. """ return response def can_handle_chat_completion( self, messages: Dict[Any, Any], model: str, temperature: float, max_tokens: int ) -> bool: """This method is called to check that the plugin can handle the chat_completion method. Args: messages (List[Message]): The messages. model (str): The model name. temperature (float): The temperature. max_tokens (int): The max tokens. Returns: bool: True if the plugin can handle the chat_completion method.""" return False def handle_chat_completion( self, messages: List[Message], model: str, temperature: float, max_tokens: int ) -> str: """This method is called when the chat completion is done. Args: messages (List[Message]): The messages. model (str): The model name. temperature (float): The temperature. max_tokens (int): The max tokens. Returns: str: The resulting response. """ pass ================================================ FILE: autogpt/plugins.py ================================================ """Handles loading of plugins.""" import importlib import json import os import zipfile from pathlib import Path from typing import List, Optional, Tuple from urllib.parse import urlparse from zipimport import zipimporter import openapi_python_client import requests from auto_gpt_plugin_template import AutoGPTPluginTemplate from openapi_python_client.cli import Config as OpenAPIConfig from autogpt.config import Config from autogpt.logs import logger from autogpt.models.base_open_ai_plugin import BaseOpenAIPlugin def inspect_zip_for_modules(zip_path: str, debug: bool = False) -> list[str]: """ Inspect a zipfile for a modules. Args: zip_path (str): Path to the zipfile. debug (bool, optional): Enable debug logging. Defaults to False. Returns: list[str]: The list of module names found or empty list if none were found. """ result = [] with zipfile.ZipFile(zip_path, "r") as zfile: for name in zfile.namelist(): if name.endswith("__init__.py") and not name.startswith("__MACOSX"): logger.debug(f"发现模块 '{name}' 的zip文件: {name}") result.append(name) if len(result) == 0: logger.debug(f"模块 '__init__.py' 没有在zip文件中找到 @ {zip_path}.") return result def write_dict_to_json_file(data: dict, file_path: str) -> None: """ Write a dictionary to a JSON file. Args: data (dict): Dictionary to write. file_path (str): Path to the file. """ with open(file_path, "w") as file: json.dump(data, file, indent=4) def fetch_openai_plugins_manifest_and_spec(cfg: Config) -> dict: """ Fetch the manifest for a list of OpenAI plugins. Args: urls (List): List of URLs to fetch. Returns: dict: per url dictionary of manifest and spec. """ # TODO add directory scan manifests = {} for url in cfg.plugins_openai: openai_plugin_client_dir = f"{cfg.plugins_dir}/openai/{urlparse(url).netloc}" create_directory_if_not_exists(openai_plugin_client_dir) if not os.path.exists(f"{openai_plugin_client_dir}/ai-plugin.json"): try: response = requests.get(f"{url}/.well-known/ai-plugin.json") if response.status_code == 200: manifest = response.json() if manifest["schema_version"] != "v1": logger.warn( f"不支持的版本: {manifest['schem_version']} for {url}" ) continue if manifest["api"]["type"] != "openapi": logger.warn( f"不支持的API: {manifest['api']['type']} for {url}" ) continue write_dict_to_json_file( manifest, f"{openai_plugin_client_dir}/ai-plugin.json" ) else: logger.warn( f"获取清单失败 {url}: {response.status_code}" ) except requests.exceptions.RequestException as e: logger.warn(f"获取清单错误 {url}: {e}") else: logger.info(f"清单 {url} 已经存在") manifest = json.load(open(f"{openai_plugin_client_dir}/ai-plugin.json")) if not os.path.exists(f"{openai_plugin_client_dir}/openapi.json"): openapi_spec = openapi_python_client._get_document( url=manifest["api"]["url"], path=None, timeout=5 ) write_dict_to_json_file( openapi_spec, f"{openai_plugin_client_dir}/openapi.json" ) else: logger.info(f"OpenAPI 配置 {url} 已经存在") openapi_spec = json.load(open(f"{openai_plugin_client_dir}/openapi.json")) manifests[url] = {"manifest": manifest, "openapi_spec": openapi_spec} return manifests def create_directory_if_not_exists(directory_path: str) -> bool: """ Create a directory if it does not exist. Args: directory_path (str): Path to the directory. Returns: bool: True if the directory was created, else False. """ if not os.path.exists(directory_path): try: os.makedirs(directory_path) logger.debug(f"生成目录: {directory_path}") return True except OSError as e: logger.warn(f"生成目录错误 {directory_path}: {e}") return False else: logger.info(f"目录 {directory_path} 已经存在") return True def initialize_openai_plugins( manifests_specs: dict, cfg: Config, debug: bool = False ) -> dict: """ Initialize OpenAI plugins. Args: manifests_specs (dict): per url dictionary of manifest and spec. cfg (Config): Config instance including plugins config debug (bool, optional): Enable debug logging. Defaults to False. Returns: dict: per url dictionary of manifest, spec and client. """ openai_plugins_dir = f"{cfg.plugins_dir}/openai" if create_directory_if_not_exists(openai_plugins_dir): for url, manifest_spec in manifests_specs.items(): openai_plugin_client_dir = f"{openai_plugins_dir}/{urlparse(url).hostname}" _meta_option = (openapi_python_client.MetaType.SETUP,) _config = OpenAPIConfig( **{ "project_name_override": "client", "package_name_override": "client", } ) prev_cwd = Path.cwd() os.chdir(openai_plugin_client_dir) Path("ai-plugin.json") if not os.path.exists("client"): client_results = openapi_python_client.create_new_client( url=manifest_spec["manifest"]["api"]["url"], path=None, meta=_meta_option, config=_config, ) if client_results: logger.warn( f"建立OpenAPI客户端错误: {client_results[0].header} \n" f" details: {client_results[0].detail}" ) continue spec = importlib.util.spec_from_file_location( "client", "client/client/client.py" ) module = importlib.util.module_from_spec(spec) spec.loader.exec_module(module) client = module.Client(base_url=url) os.chdir(prev_cwd) manifest_spec["client"] = client return manifests_specs def instantiate_openai_plugin_clients( manifests_specs_clients: dict, cfg: Config, debug: bool = False ) -> dict: """ Instantiates BaseOpenAIPlugin instances for each OpenAI plugin. Args: manifests_specs_clients (dict): per url dictionary of manifest, spec and client. cfg (Config): Config instance including plugins config debug (bool, optional): Enable debug logging. Defaults to False. Returns: plugins (dict): per url dictionary of BaseOpenAIPlugin instances. """ plugins = {} for url, manifest_spec_client in manifests_specs_clients.items(): plugins[url] = BaseOpenAIPlugin(manifest_spec_client) return plugins def scan_plugins(cfg: Config, debug: bool = False) -> List[AutoGPTPluginTemplate]: """Scan the plugins directory for plugins and loads them. Args: cfg (Config): Config instance including plugins config debug (bool, optional): Enable debug logging. Defaults to False. Returns: List[Tuple[str, Path]]: List of plugins. """ loaded_plugins = [] # Generic plugins plugins_path_path = Path(cfg.plugins_dir) logger.debug(f"Plugins允许清单: {cfg.plugins_allowlist}") logger.debug(f"Plugins禁止清单: {cfg.plugins_denylist}") for plugin in plugins_path_path.glob("*.zip"): if moduleList := inspect_zip_for_modules(str(plugin), debug): for module in moduleList: plugin = Path(plugin) module = Path(module) logger.debug(f"Plugin: {plugin} Module: {module}") zipped_package = zipimporter(str(plugin)) zipped_module = zipped_package.load_module(str(module.parent)) for key in dir(zipped_module): if key.startswith("__"): continue a_module = getattr(zipped_module, key) a_keys = dir(a_module) if ( "_abc_impl" in a_keys and a_module.__name__ != "AutoGPTPluginTemplate" and denylist_allowlist_check(a_module.__name__, cfg) ): loaded_plugins.append(a_module()) # OpenAI plugins if cfg.plugins_openai: manifests_specs = fetch_openai_plugins_manifest_and_spec(cfg) if manifests_specs.keys(): manifests_specs_clients = initialize_openai_plugins( manifests_specs, cfg, debug ) for url, openai_plugin_meta in manifests_specs_clients.items(): if denylist_allowlist_check(url, cfg): plugin = BaseOpenAIPlugin(openai_plugin_meta) loaded_plugins.append(plugin) if loaded_plugins: logger.info(f"\nPlugins 找到: {len(loaded_plugins)}\n" "--------------------") for plugin in loaded_plugins: logger.info(f"{plugin._name}: {plugin._version} - {plugin._description}") return loaded_plugins def denylist_allowlist_check(plugin_name: str, cfg: Config) -> bool: """Check if the plugin is in the allowlist or denylist. Args: plugin_name (str): Name of the plugin. cfg (Config): Config object. Returns: True or False """ logger.debug(f"检查plugin {plugin_name} 是否可以加载") if plugin_name in cfg.plugins_denylist: logger.debug(f"没有加载plugin {plugin_name} 在禁止清单上.") return False if plugin_name in cfg.plugins_allowlist: logger.debug(f"成功加载plugin {plugin_name} 在允许清单上.") return True ack = input( f"警告: Plugin {plugin_name} 已找到. 但是不在" f" 允许清单中... 加载? ({cfg.authorise_key}/{cfg.exit_key}): " ) return ack.lower() == cfg.authorise_key ================================================ FILE: autogpt/processing/__init__.py ================================================ ================================================ FILE: autogpt/processing/html.py ================================================ """HTML processing functions""" from __future__ import annotations from bs4 import BeautifulSoup from requests.compat import urljoin def extract_hyperlinks(soup: BeautifulSoup, base_url: str) -> list[tuple[str, str]]: """Extract hyperlinks from a BeautifulSoup object Args: soup (BeautifulSoup): The BeautifulSoup object base_url (str): The base URL Returns: List[Tuple[str, str]]: The extracted hyperlinks """ return [ (link.text, urljoin(base_url, link["href"])) for link in soup.find_all("a", href=True) ] def format_hyperlinks(hyperlinks: list[tuple[str, str]]) -> list[str]: """Format hyperlinks to be displayed to the user Args: hyperlinks (List[Tuple[str, str]]): The hyperlinks to format Returns: List[str]: The formatted hyperlinks """ return [f"{link_text} ({link_url})" for link_text, link_url in hyperlinks] ================================================ FILE: autogpt/processing/text.py ================================================ """Text processing functions""" from typing import Dict, Generator, Optional import spacy from selenium.webdriver.remote.webdriver import WebDriver from autogpt.config import Config from autogpt.llm import count_message_tokens, create_chat_completion from autogpt.logs import logger from autogpt.memory import get_memory CFG = Config() def split_text( text: str, max_length: int = CFG.browse_chunk_max_length, model: str = CFG.fast_llm_model, question: str = "", ) -> Generator[str, None, None]: """Split text into chunks of a maximum length Args: text (str): The text to split max_length (int, optional): The maximum length of each chunk. Defaults to 8192. Yields: str: The next chunk of text Raises: ValueError: If the text is longer than the maximum length """ flatened_paragraphs = " ".join(text.split("\n")) nlp = spacy.load(CFG.browse_spacy_language_model) nlp.add_pipe("sentencizer") doc = nlp(flatened_paragraphs) sentences = [sent.text.strip() for sent in doc.sents] current_chunk = [] for sentence in sentences: message_with_additional_sentence = [ create_message(" ".join(current_chunk) + " " + sentence, question) ] expected_token_usage = ( count_message_tokens(messages=message_with_additional_sentence, model=model) + 1 ) if expected_token_usage <= max_length: current_chunk.append(sentence) else: yield " ".join(current_chunk) current_chunk = [sentence] message_this_sentence_only = [ create_message(" ".join(current_chunk), question) ] expected_token_usage = ( count_message_tokens(messages=message_this_sentence_only, model=model) + 1 ) if expected_token_usage > max_length: raise ValueError( f"Sentence is too long in webpage: {expected_token_usage} tokens." ) if current_chunk: yield " ".join(current_chunk) def summarize_text( url: str, text: str, question: str, driver: Optional[WebDriver] = None ) -> str: """Summarize text using the OpenAI API Args: url (str): The url of the text text (str): The text to summarize question (str): The question to ask the model driver (WebDriver): The webdriver to use to scroll the page Returns: str: The summary of the text """ if not text: return "Error: No text to summarize" model = CFG.fast_llm_model text_length = len(text) logger.info(f"Text length: {text_length} characters") summaries = [] chunks = list( split_text( text, max_length=CFG.browse_chunk_max_length, model=model, question=question ), ) scroll_ratio = 1 / len(chunks) for i, chunk in enumerate(chunks): if driver: scroll_to_percentage(driver, scroll_ratio * i) logger.info(f"Adding chunk {i + 1} / {len(chunks)} to memory") memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}" memory = get_memory(CFG) memory.add(memory_to_add) messages = [create_message(chunk, question)] tokens_for_chunk = count_message_tokens(messages, model) logger.info( f"Summarizing chunk {i + 1} / {len(chunks)} of length {len(chunk)} characters, or {tokens_for_chunk} tokens" ) summary = create_chat_completion( model=model, messages=messages, ) summaries.append(summary) logger.info( f"Added chunk {i + 1} summary to memory, of length {len(summary)} characters" ) memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}" memory.add(memory_to_add) logger.info(f"Summarized {len(chunks)} chunks.") combined_summary = "\n".join(summaries) messages = [create_message(combined_summary, question)] return create_chat_completion( model=model, messages=messages, ) def scroll_to_percentage(driver: WebDriver, ratio: float) -> None: """Scroll to a percentage of the page Args: driver (WebDriver): The webdriver to use ratio (float): The percentage to scroll to Raises: ValueError: If the ratio is not between 0 and 1 """ if ratio < 0 or ratio > 1: raise ValueError("Percentage should be between 0 and 1") driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});") def create_message(chunk: str, question: str) -> Dict[str, str]: """Create a message for the chat completion Args: chunk (str): The chunk of text to summarize question (str): The question to answer Returns: Dict[str, str]: The message to send to the chat completion """ return { "role": "user", "content": f'"""{chunk}""" Using the above text, answer the following' f' question: "{question}" -- if the question cannot be answered using the text,' " summarize the text.", } ================================================ FILE: autogpt/prompts/__init__.py ================================================ ================================================ FILE: autogpt/prompts/generator.py ================================================ """ A module for generating custom prompt strings.""" import json from typing import Any, Callable, Dict, List, Optional class PromptGenerator: """ A class for generating custom prompt strings based on constraints, commands, resources, and performance evaluations. """ def __init__(self) -> None: """ Initialize the PromptGenerator object with empty lists of constraints, commands, resources, and performance evaluations. """ self.constraints = [] self.commands = [] self.resources = [] self.performance_evaluation = [] self.goals = [] self.command_registry = None self.name = "Bob" self.role = "AI" self.response_format = { "thoughts": { "text": "thought", "reasoning": "reasoning", "plan": "- short bulleted\n- list that conveys\n- long-term plan", "criticism": "constructive self-criticism", "speak": "thoughts summary to say to user", }, "command": {"name": "command name", "args": {"arg name": "value"}}, } def add_constraint(self, constraint: str) -> None: """ Add a constraint to the constraints list. Args: constraint (str): The constraint to be added. """ self.constraints.append(constraint) def add_command( self, command_label: str, command_name: str, args=None, function: Optional[Callable] = None, ) -> None: """ Add a command to the commands list with a label, name, and optional arguments. Args: command_label (str): The label of the command. command_name (str): The name of the command. args (dict, optional): A dictionary containing argument names and their values. Defaults to None. function (callable, optional): A callable function to be called when the command is executed. Defaults to None. """ if args is None: args = {} command_args = {arg_key: arg_value for arg_key, arg_value in args.items()} command = { "label": command_label, "name": command_name, "args": command_args, "function": function, } self.commands.append(command) def _generate_command_string(self, command: Dict[str, Any]) -> str: """ Generate a formatted string representation of a command. Args: command (dict): A dictionary containing command information. Returns: str: The formatted command string. """ args_string = ", ".join( f'"{key}": "{value}"' for key, value in command["args"].items() ) return f'{command["label"]}: "{command["name"]}", args: {args_string}' def add_resource(self, resource: str) -> None: """ Add a resource to the resources list. Args: resource (str): The resource to be added. """ self.resources.append(resource) def add_performance_evaluation(self, evaluation: str) -> None: """ Add a performance evaluation item to the performance_evaluation list. Args: evaluation (str): The evaluation item to be added. """ self.performance_evaluation.append(evaluation) def _generate_numbered_list(self, items: List[Any], item_type="list") -> str: """ Generate a numbered list from given items based on the item_type. Args: items (list): A list of items to be numbered. item_type (str, optional): The type of items in the list. Defaults to 'list'. Returns: str: The formatted numbered list. """ if item_type == "command": command_strings = [] if self.command_registry: command_strings += [ str(item) for item in self.command_registry.commands.values() if item.enabled ] # terminate command is added manually command_strings += [self._generate_command_string(item) for item in items] return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings)) else: return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items)) def generate_prompt_string(self) -> str: """ Generate a prompt string based on the constraints, commands, resources, and performance evaluations. Returns: str: The generated prompt string. """ formatted_response_format = json.dumps(self.response_format, indent=4) return ( f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n" "Commands:\n" f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n" f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n" "Performance Evaluation:\n" f"{self._generate_numbered_list(self.performance_evaluation)}\n\n" "You should only respond in JSON format as described below \nResponse" f" Format: \n{formatted_response_format} \nEnsure the response can be" " parsed by Python json.loads" ) ================================================ FILE: autogpt/prompts/prompt.py ================================================ from colorama import Fore from autogpt.config.ai_config import AIConfig from autogpt.config.config import Config from autogpt.llm import ApiManager from autogpt.logs import logger from autogpt.prompts.generator import PromptGenerator from autogpt.setup import prompt_user from autogpt.utils import clean_input CFG = Config() DEFAULT_TRIGGERING_PROMPT = ( "确定下一步使用的命令，并使用上面规定的格式进行回答:" ) def build_default_prompt_generator() -> PromptGenerator: """ This function generates a prompt string that includes various constraints, commands, resources, and performance evaluations. Returns: str: The generated prompt string. """ # Initialize the PromptGenerator object prompt_generator = PromptGenerator() # Add constraints to the PromptGenerator object prompt_generator.add_constraint( "短期记忆大概2500字，由于字数限制，你需要将重要的信息尽快写入文件." ) prompt_generator.add_constraint( "如果你不确定之前做过什么或者无法找到过往的事件，尝试类似事件可以帮助你回忆." ) prompt_generator.add_constraint("无用户干预") prompt_generator.add_constraint( '严格采用双引号中的命令，例如 "命令名称"' ) # Add resources to the PromptGenerator object prompt_generator.add_resource( "去互联网访问与收集信息." ) prompt_generator.add_resource("长期记忆管理.") prompt_generator.add_resource( "使用GPT-3.5处理简单任务." ) prompt_generator.add_resource("文件输出.") # Add performance evaluations to the PromptGenerator object prompt_generator.add_performance_evaluation( "持续回顾与分析你的行为是否为最优解决方案." ) prompt_generator.add_performance_evaluation( "持续自我调整，满足全局目标." ) prompt_generator.add_performance_evaluation( "基于过去的决策与策略，优化你的方法." ) prompt_generator.add_performance_evaluation( "每一次命令都有回花费成本，因此要聪明与高效的在最少步骤内完成任务." ) prompt_generator.add_performance_evaluation("将所有代码写入一个文件.") return prompt_generator def construct_main_ai_config() -> AIConfig: """Construct the prompt for the AI to respond to Returns: str: The prompt string """ config = AIConfig.load(CFG.ai_settings_file) if CFG.skip_reprompt and config.ai_name: logger.typewriter_log("名称 :", Fore.GREEN, config.ai_name) logger.typewriter_log("角色 :", Fore.GREEN, config.ai_role) logger.typewriter_log("目标:", Fore.GREEN, f"{config.ai_goals}") logger.typewriter_log( "API 预算:", Fore.GREEN, "无限" if config.api_budget <= 0 else f"${config.api_budget}", ) elif config.ai_name: logger.typewriter_log( "您回来啦! ", Fore.GREEN, f"您还希望继续使用 {config.ai_name}吗?", speak_text=True, ) should_continue = clean_input( f"""继续使用上次的配置吗？? 名称: {config.ai_name} 角色: {config.ai_role} 目标: {config.ai_goals} API 预算: {"infinite" if config.api_budget <= 0 else f"${config.api_budget}"} 继续 ({CFG.authorise_key}/{CFG.exit_key}): """ ) if should_continue.lower() == CFG.exit_key: config = AIConfig() if not config.ai_name: config = prompt_user() config.save(CFG.ai_settings_file) # set the total api budget api_manager = ApiManager() api_manager.set_total_budget(config.api_budget) # Agent Created, print message logger.typewriter_log( config.ai_name, Fore.LIGHTBLUE_EX, "已经被建立成功，具体信息如下:", speak_text=True, ) # Print the ai config details # Name logger.typewriter_log("名称:", Fore.GREEN, config.ai_name, speak_text=False) # Role logger.typewriter_log("角色:", Fore.GREEN, config.ai_role, speak_text=False) # Goals logger.typewriter_log("目标:", Fore.GREEN, "", speak_text=False) for goal in config.ai_goals: logger.typewriter_log("-", Fore.GREEN, goal, speak_text=False) return config ================================================ FILE: autogpt/setup.py ================================================ """Set up the AI and its goals""" import re from colorama import Fore, Style from autogpt import utils from autogpt.config import Config from autogpt.config.ai_config import AIConfig from autogpt.llm import create_chat_completion from autogpt.logs import logger CFG = Config() def prompt_user() -> AIConfig: """Prompt the user for input Returns: AIConfig: The AIConfig object tailored to the user's input """ ai_name = "" ai_config = None # Construct the prompt logger.typewriter_log( "欢迎使用RealHossie的汉化版Auto-GPT!", Fore.GREEN, ) logger.typewriter_log( "也欢迎关注我的频道www.youtube.com/@hossie", Fore.LIGHTBLUE_EX, "执行 '--help' 获取更多信息.", speak_text=True, ) # Get user desire logger.typewriter_log( "创建一个AI助手:", Fore.GREEN, "输入 '--manual' 进入手动模式.", speak_text=True, ) user_desire = utils.clean_input( f"{Fore.LIGHTBLUE_EX}我希望Auto-GPT帮我{Style.RESET_ALL}: " ) if user_desire == "": user_desire = "写一篇wikipedia风格的文章，关于此项目: https://github.com/RealHossie/Auto-GPT-Chinese" # Default prompt # If user desire contains "--manual" if "--manual" in user_desire: logger.typewriter_log( "手动模式已启动", Fore.GREEN, speak_text=True, ) return generate_aiconfig_manual() else: try: return generate_aiconfig_automatic(user_desire) except Exception as e: logger.typewriter_log( "无法基于用户偏好生成AI配置.", Fore.RED, "回滚至手动模式.", speak_text=True, ) return generate_aiconfig_manual() def generate_aiconfig_manual() -> AIConfig: """ Interactively create an AI configuration by prompting the user to provide the name, role, and goals of the AI. This function guides the user through a series of prompts to collect the necessary information to create an AIConfig object. The user will be asked to provide a name and role for the AI, as well as up to five goals. If the user does not provide a value for any of the fields, default values will be used. Returns: AIConfig: An AIConfig object containing the user-defined or default AI name, role, and goals. """ # Manual Setup Intro logger.typewriter_log( "建立一个AI助手:", Fore.GREEN, "给你AI助手起一个名字和赋予它一个角色，什么都不输入将使用默认值.", speak_text=True, ) # Get AI Name from User logger.typewriter_log( "你AI的名字叫: ", Fore.GREEN, "例如, '企业家-GPT'" ) ai_name = utils.clean_input("AI 名字: ") if ai_name == "": ai_name = "企业家-GPT" logger.typewriter_log( f"{ai_name} 在这儿呢!", Fore.LIGHTBLUE_EX, "我听从您的吩咐.", speak_text=True ) # Get AI Role from User logger.typewriter_log( "描述你AI的角色: ", Fore.GREEN, "例如, '一个自动帮助你策划与经营业务的人工智能帮手，目标专注于提升你的净资产.'", ) ai_role = utils.clean_input(f"{ai_name} is: ") if ai_role == "": ai_role = "一个自动帮助你策划与经营业务的人工智能帮手，目标专注于提升你的净资产." # Enter up to 5 goals for the AI logger.typewriter_log( "为你的AI定义最多5个目标: ", Fore.GREEN, "例如: \n提升净资产, 增长Twitter账户, 自动化策划与管理多条业务线'", ) logger.info("什么都不输入将加载默认值，输入结束后直接按回车.") ai_goals = [] for i in range(5): ai_goal = utils.clean_input(f"{Fore.LIGHTBLUE_EX}目标{Style.RESET_ALL} {i+1}: ") if ai_goal == "": break ai_goals.append(ai_goal) if not ai_goals: ai_goals = [ "提升净资产", "增长Twitter账户", "自动化策划与管理多条业务线", ] # Get API Budget from User logger.typewriter_log( "输入你的API预算: ", Fore.GREEN, "例如: $1.50", ) logger.info("什么都不输入将让你的AI驰骋飞翔") api_budget_input = utils.clean_input( f"{Fore.LIGHTBLUE_EX}预算{Style.RESET_ALL}: $" ) if api_budget_input == "": api_budget = 0.0 else: try: api_budget = float(api_budget_input.replace("$", "")) except ValueError: logger.typewriter_log( "错误的预算输入. 开启吃撑飞翔模式.", Fore.RED ) api_budget = 0.0 return AIConfig(ai_name, ai_role, ai_goals, api_budget) def generate_aiconfig_automatic(user_prompt) -> AIConfig: """Generates an AIConfig object from the given string. Returns: AIConfig: The AIConfig object tailored to the user's input """ system_prompt = """ 你的任务是作为一个自动化的助手，设计5个最高效的目标和一个最合适你角色名字(_GPT), 确保这些目标与所分配的任务达到最佳的一致性并成功完成. 用户会提出任务，你只需按照下面的格式提供输出，无需解释或对话. 输入示例: 在业务营销方面帮助我 输出示例: 名字: CMOGPT 描述: 一名专业的数字营销人工智能助手，为独立创业者提供世界级的专业知识，解决软件即服务（SaaS）、内容产品、代理商等领域的营销问题，助力企业发展. 目标: - 我作为您的虚拟首席营销官，我将积极参与有效的问题解决、优先事项排序、规划和支持执行，以满足您的营销需求. - 我将提供具体、可操作且简洁的建议，帮助您在不使用陈词滥调或过多解释的情况下做出明智的决策. - 我将识别并优先选择快速获胜和高性价比的营销活动，以最少的时间和预算投入实现最大化的结果. - 我在面对不明确的信息或不确定性时，主动引导您并提供建议，确保您的营销策略保持正确的方向. """ # Call LLM with the string as user input messages = [ { "role": "system", "content": system_prompt, }, { "role": "user", "content": f"任务: '{user_prompt}'\n仅以系统提示中指定的格式回应，不需要进行解释或对话.\n", }, ] output = create_chat_completion(messages, CFG.fast_llm_model) # Debug LLM Output logger.debug(f"AI配置生成原始输出: {output}") # Parse the output ai_name = re.search(r"名字(?:\s*):(?:\s*)(.*)", output, re.IGNORECASE).group(1) ai_role = ( re.search( r"描述(?:\s*):(?:\s*)(.*?)(?:(?:\n)|目标)", output, re.IGNORECASE | re.DOTALL, ) .group(1) .strip() ) ai_goals = re.findall(r"(?<=\n)-\s*(.*)", output) api_budget = 0.0 # TODO: parse api budget using a regular expression return AIConfig(ai_name, ai_role, ai_goals, api_budget) ================================================ FILE: autogpt/singleton.py ================================================ """The singleton metaclass for ensuring only one instance of a class.""" import abc class Singleton(abc.ABCMeta, type): """ Singleton metaclass for ensuring only one instance of a class. """ _instances = {} def __call__(cls, *args, **kwargs): """Call method for the singleton metaclass.""" if cls not in cls._instances: cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) return cls._instances[cls] class AbstractSingleton(abc.ABC, metaclass=Singleton): """ Abstract singleton class for ensuring only one instance of a class. """ pass ================================================ FILE: autogpt/speech/__init__.py ================================================ """This module contains the speech recognition and speech synthesis functions.""" from autogpt.speech.say import say_text __all__ = ["say_text"] ================================================ FILE: autogpt/speech/base.py ================================================ """Base class for all voice classes.""" import abc from threading import Lock from autogpt.singleton import AbstractSingleton class VoiceBase(AbstractSingleton): """ Base class for all voice classes. """ def __init__(self): """ Initialize the voice class. """ self._url = None self._headers = None self._api_key = None self._voices = [] self._mutex = Lock() self._setup() def say(self, text: str, voice_index: int = 0) -> bool: """ Say the given text. Args: text (str): The text to say. voice_index (int): The index of the voice to use. """ with self._mutex: return self._speech(text, voice_index) @abc.abstractmethod def _setup(self) -> None: """ Setup the voices, API key, etc. """ pass @abc.abstractmethod def _speech(self, text: str, voice_index: int = 0) -> bool: """ Play the given text. Args: text (str): The text to play. """ pass ================================================ FILE: autogpt/speech/brian.py ================================================ import logging import os import requests from playsound import playsound from autogpt.speech.base import VoiceBase class BrianSpeech(VoiceBase): """Brian speech module for autogpt""" def _setup(self) -> None: """Setup the voices, API key, etc.""" pass def _speech(self, text: str, _: int = 0) -> bool: """Speak text using Brian with the streamelements API Args: text (str): The text to speak Returns: bool: True if the request was successful, False otherwise """ tts_url = ( f"https://api.streamelements.com/kappa/v2/speech?voice=Brian&text={text}" ) response = requests.get(tts_url) if response.status_code == 200: with open("speech.mp3", "wb") as f: f.write(response.content) playsound("speech.mp3") os.remove("speech.mp3") return True else: logging.error( "Request failed with status code: %s, response content: %s", response.status_code, response.content, ) return False ================================================ FILE: autogpt/speech/eleven_labs.py ================================================ """ElevenLabs speech module""" import os import requests from playsound import playsound from autogpt.config import Config from autogpt.speech.base import VoiceBase PLACEHOLDERS = {"your-voice-id"} class ElevenLabsSpeech(VoiceBase): """ElevenLabs speech class""" def _setup(self) -> None: """Set up the voices, API key, etc. Returns: None: None """ cfg = Config() default_voices = ["ErXwobaYiN019PkySvjV", "EXAVITQu4vr4xnSDxMaL"] voice_options = { "Rachel": "21m00Tcm4TlvDq8ikWAM", "Domi": "AZnzlk1XvdvUeBnXmlld", "Bella": "EXAVITQu4vr4xnSDxMaL", "Antoni": "ErXwobaYiN019PkySvjV", "Elli": "MF3mGyEYCl7XYWbV9V6O", "Josh": "TxGEqnHWrfWFTfGW9XjX", "Arnold": "VR6AewLTigWG4xSOukaG", "Adam": "pNInz6obpgDQGcFmaJgB", "Sam": "yoZ06aMxZJJ28mfd3POQ", } self._headers = { "Content-Type": "application/json", "xi-api-key": cfg.elevenlabs_api_key, } self._voices = default_voices.copy() if cfg.elevenlabs_voice_1_id in voice_options: cfg.elevenlabs_voice_1_id = voice_options[cfg.elevenlabs_voice_1_id] if cfg.elevenlabs_voice_2_id in voice_options: cfg.elevenlabs_voice_2_id = voice_options[cfg.elevenlabs_voice_2_id] self._use_custom_voice(cfg.elevenlabs_voice_1_id, 0) self._use_custom_voice(cfg.elevenlabs_voice_2_id, 1) def _use_custom_voice(self, voice, voice_index) -> None: """Use a custom voice if provided and not a placeholder Args: voice (str): The voice ID voice_index (int): The voice index Returns: None: None """ # Placeholder values that should be treated as empty if voice and voice not in PLACEHOLDERS: self._voices[voice_index] = voice def _speech(self, text: str, voice_index: int = 0) -> bool: """Speak text using elevenlabs.io's API Args: text (str): The text to speak voice_index (int, optional): The voice to use. Defaults to 0. Returns: bool: True if the request was successful, False otherwise """ from autogpt.logs import logger tts_url = ( f"https://api.elevenlabs.io/v1/text-to-speech/{self._voices[voice_index]}" ) response = requests.post(tts_url, headers=self._headers, json={"text": text}) if response.status_code == 200: with open("speech.mpeg", "wb") as f: f.write(response.content) playsound("speech.mpeg", True) os.remove("speech.mpeg") return True else: logger.warn("Request failed with status code:", response.status_code) logger.info("Response content:", response.content) return False ================================================ FILE: autogpt/speech/gtts.py ================================================ """ GTTS Voice. """ import os import gtts from playsound import playsound from autogpt.speech.base import VoiceBase class GTTSVoice(VoiceBase): """GTTS Voice.""" def _setup(self) -> None: pass def _speech(self, text: str, _: int = 0) -> bool: """Play the given text.""" tts = gtts.gTTS(text) tts.save("speech.mp3") playsound("speech.mp3", True) os.remove("speech.mp3") return True ================================================ FILE: autogpt/speech/macos_tts.py ================================================ """ MacOS TTS Voice. """ import os from autogpt.speech.base import VoiceBase class MacOSTTS(VoiceBase): """MacOS TTS Voice.""" def _setup(self) -> None: pass def _speech(self, text: str, voice_index: int = 0) -> bool: """Play the given text.""" if voice_index == 0: os.system(f'say "{text}"') elif voice_index == 1: os.system(f'say -v "Ava (Premium)" "{text}"') else: os.system(f'say -v Samantha "{text}"') return True ================================================ FILE: autogpt/speech/say.py ================================================ """ Text to speech module """ import threading from threading import Semaphore from autogpt.config import Config from autogpt.speech.base import VoiceBase from autogpt.speech.brian import BrianSpeech from autogpt.speech.eleven_labs import ElevenLabsSpeech from autogpt.speech.gtts import GTTSVoice from autogpt.speech.macos_tts import MacOSTTS _QUEUE_SEMAPHORE = Semaphore( 1 ) # The amount of sounds to queue before blocking the main thread def say_text(text: str, voice_index: int = 0) -> None: """Speak the given text using the given voice index""" cfg = Config() default_voice_engine, voice_engine = _get_voice_engine(cfg) def speak() -> None: success = voice_engine.say(text, voice_index) if not success: default_voice_engine.say(text) _QUEUE_SEMAPHORE.release() _QUEUE_SEMAPHORE.acquire(True) thread = threading.Thread(target=speak) thread.start() def _get_voice_engine(config: Config) -> tuple[VoiceBase, VoiceBase]: """Get the voice engine to use for the given configuration""" default_voice_engine = GTTSVoice() if config.elevenlabs_api_key: voice_engine = ElevenLabsSpeech() elif config.use_mac_os_tts == "True": voice_engine = MacOSTTS() elif config.use_brian_tts == "True": voice_engine = BrianSpeech() else: voice_engine = GTTSVoice() return default_voice_engine, voice_engine ================================================ FILE: autogpt/spinner.py ================================================ """A simple spinner module""" import itertools import sys import threading import time class Spinner: """A simple spinner class""" def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: """Initialize the spinner class Args: message (str): The message to display. delay (float): The delay between each spinner update. """ self.spinner = itertools.cycle(["-", "/", "|", "\\"]) self.delay = delay self.message = message self.running = False self.spinner_thread = None def spin(self) -> None: """Spin the spinner""" while self.running: sys.stdout.write(f"{next(self.spinner)} {self.message}\r") sys.stdout.flush() time.sleep(self.delay) sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") def __enter__(self): """Start the spinner""" self.running = True self.spinner_thread = threading.Thread(target=self.spin) self.spinner_thread.start() return self def __exit__(self, exc_type, exc_value, exc_traceback) -> None: """Stop the spinner Args: exc_type (Exception): The exception type. exc_value (Exception): The exception value. exc_traceback (Exception): The exception traceback. """ self.running = False if self.spinner_thread is not None: self.spinner_thread.join() sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") sys.stdout.flush() def update_message(self, new_message, delay=0.1): """Update the spinner message Args: new_message (str): New message to display. delay (float): The delay in seconds between each spinner update. """ time.sleep(delay) sys.stdout.write( f"\r{' ' * (len(self.message) + 2)}\r" ) # Clear the current message sys.stdout.flush() self.message = new_message ================================================ FILE: autogpt/url_utils/__init__.py ================================================ ================================================ FILE: autogpt/url_utils/validators.py ================================================ import functools from typing import Any, Callable from urllib.parse import urljoin, urlparse from requests.compat import urljoin def validate_url(func: Callable[..., Any]) -> Any: """The method decorator validate_url is used to validate urls for any command that requires a url as an argument""" @functools.wraps(func) def wrapper(url: str, *args, **kwargs) -> Any: """Check if the URL is valid using a basic check, urllib check, and local file check Args: url (str): The URL to check Returns: the result of the wrapped function Raises: ValueError if the url fails any of the validation tests """ # Most basic check if the URL is valid: if not url.startswith("http://") and not url.startswith("https://"): raise ValueError("Invalid URL format") if not is_valid_url(url): raise ValueError("Missing Scheme or Network location") # Restrict access to local files if check_local_file_access(url): raise ValueError("Access to local files is restricted") return func(sanitize_url(url), *args, **kwargs) return wrapper def is_valid_url(url: str) -> bool: """Check if the URL is valid Args: url (str): The URL to check Returns: bool: True if the URL is valid, False otherwise """ try: result = urlparse(url) return all([result.scheme, result.netloc]) except ValueError: return False def sanitize_url(url: str) -> str: """Sanitize the URL Args: url (str): The URL to sanitize Returns: str: The sanitized URL """ parsed_url = urlparse(url) reconstructed_url = f"{parsed_url.path}{parsed_url.params}?{parsed_url.query}" return urljoin(url, reconstructed_url) def check_local_file_access(url: str) -> bool: """Check if the URL is a local file Args: url (str): The URL to check Returns: bool: True if the URL is a local file, False otherwise """ local_prefixes = [ "file:///", "file://localhost/", "file://localhost", "http://localhost", "http://localhost/", "https://localhost", "https://localhost/", "http://2130706433", "http://2130706433/", "https://2130706433", "https://2130706433/", "http://127.0.0.1/", "http://127.0.0.1", "https://127.0.0.1/", "https://127.0.0.1", "https://0.0.0.0/", "https://0.0.0.0", "http://0.0.0.0/", "http://0.0.0.0", "http://0000", "http://0000/", "https://0000", "https://0000/", ] return any(url.startswith(prefix) for prefix in local_prefixes) ================================================ FILE: autogpt/utils.py ================================================ import os import re import requests import yaml from colorama import Fore, Style from git.repo import Repo from autogpt.logs import logger # Use readline if available (for clean_input) try: import readline except ImportError: pass from autogpt.config import Config def clean_input(prompt: str = "", talk=False): try: cfg = Config() if cfg.chat_messages_enabled: for plugin in cfg.plugins: if not hasattr(plugin, "can_handle_user_input"): continue if not plugin.can_handle_user_input(user_input=prompt): continue plugin_response = plugin.user_input(user_input=prompt) if not plugin_response: continue if plugin_response.lower() in [ "yes", "yeah", "y", "ok", "okay", "sure", "alright", ]: return cfg.authorise_key elif plugin_response.lower() in [ "no", "nope", "n", "negative", ]: return cfg.exit_key return plugin_response # ask for input, default when just pressing Enter is y logger.info("等待用户输入中...") answer = input(prompt) return answer except KeyboardInterrupt: logger.info("你打断了Auto-GPT") logger.info("我撤了啊...") exit(0) def validate_yaml_file(file: str): try: with open(file, encoding="utf-8") as fp: yaml.load(fp.read(), Loader=yaml.FullLoader) except FileNotFoundError: return (False, f"The file {Fore.CYAN}`{file}`{Fore.RESET} wasn't found") except yaml.YAMLError as e: return ( False, f"尝试读取你AI配置文件过程中出现了问题: {e}", ) return (True, f"成功验证 {Fore.CYAN}`{file}`{Fore.RESET}!") def readable_file_size(size, decimal_places=2): """Converts the given size in bytes to a readable format. Args: size: Size in bytes decimal_places (int): Number of decimal places to display """ for unit in ["B", "KB", "MB", "GB", "TB"]: if size < 1024.0: break size /= 1024.0 return f"{size:.{decimal_places}f} {unit}" def get_bulletin_from_web(): try: response = requests.get( "https://raw.githubusercontent.com/RealHossie/Auto-GPT-Chinese/stable/BULLETIN.md" ) if response.status_code == 200: return response.text except requests.exceptions.RequestException: pass return "" def get_current_git_branch() -> str: try: repo = Repo(search_parent_directories=True) branch = repo.active_branch return branch.name except: return "" def get_latest_bulletin() -> tuple[str, bool]: exists = os.path.exists("data/CURRENT_BULLETIN.md") current_bulletin = "" if exists: current_bulletin = open( "data/CURRENT_BULLETIN.md", "r", encoding="utf-8" ).read() new_bulletin = get_bulletin_from_web() is_new_news = new_bulletin != "" and new_bulletin != current_bulletin news_header = Fore.YELLOW + "欢迎来到RealHossie的汉化版Auto-GPT!\n" if new_bulletin or current_bulletin: news_header += ( "下面会介绍最新的Auto-GPT信息与更新内容!\n" "如果你不想每次打开看到这些信息, " "你可以再执行Auto-GPT添加 *--skip-news* 后缀.\n" ) if new_bulletin and is_new_news: open("data/CURRENT_BULLETIN.md", "w", encoding="utf-8").write(new_bulletin) current_bulletin = f"{Fore.RED}::NEW BULLETIN::{Fore.RESET}\n\n{new_bulletin}" return f"{news_header}\n{current_bulletin}", is_new_news def markdown_to_ansi_style(markdown: str): ansi_lines: list[str] = [] for line in markdown.split("\n"): line_style = "" if line.startswith("# "): line_style += Style.BRIGHT else: line = re.sub( r"(? Path: """The root directory of the workspace.""" return self._root @property def restrict_to_workspace(self): """Whether to restrict generated paths to the workspace.""" return self._restrict_to_workspace @classmethod def make_workspace(cls, workspace_directory: str | Path, *args, **kwargs) -> Path: """Create a workspace directory and return the path to it. Parameters ---------- workspace_directory The path to the workspace directory. Returns ------- Path The path to the workspace directory. """ # TODO: have this make the env file and ai settings file in the directory. workspace_directory = cls._sanitize_path(workspace_directory) workspace_directory.mkdir(exist_ok=True, parents=True) return workspace_directory def get_path(self, relative_path: str | Path) -> Path: """Get the full path for an item in the workspace. Parameters ---------- relative_path The relative path to resolve in the workspace. Returns ------- Path The resolved path relative to the workspace. """ return self._sanitize_path( relative_path, root=self.root, restrict_to_root=self.restrict_to_workspace, ) @staticmethod def _sanitize_path( relative_path: str | Path, root: str | Path = None, restrict_to_root: bool = True, ) -> Path: """Resolve the relative path within the given root if possible. Parameters ---------- relative_path The relative path to resolve. root The root path to resolve the relative path within. restrict_to_root Whether to restrict the path to the root. Returns ------- Path The resolved path. Raises ------ ValueError If the path is absolute and a root is provided. ValueError If the path is outside the root and the root is restricted. """ # Posix systems disallow null bytes in paths. Windows is agnostic about it. # Do an explicit check here for all sorts of null byte representations. for null_byte in Workspace.NULL_BYTES: if null_byte in str(relative_path) or null_byte in str(root): raise ValueError("embedded null byte") if root is None: return Path(relative_path).resolve() logger.debug(f"Resolving path '{relative_path}' in workspace '{root}'") root, relative_path = Path(root).resolve(), Path(relative_path) logger.debug(f"Resolved root as '{root}'") if relative_path.is_absolute(): raise ValueError( f"Attempted to access absolute path '{relative_path}' in workspace '{root}'." ) full_path = root.joinpath(relative_path).resolve() logger.debug(f"Joined paths as '{full_path}'") if restrict_to_root and not full_path.is_relative_to(root): raise ValueError( f"Attempted to access path '{full_path}' outside of workspace '{root}'." ) return full_path ================================================ FILE: azure.yaml.template ================================================ azure_api_type: azure azure_api_base: your-base-url-for-azure azure_api_version: api-version-for-azure azure_model_map: fast_llm_model_deployment_id: gpt35-deployment-id-for-azure smart_llm_model_deployment_id: gpt4-deployment-id-for-azure embedding_model_deployment_id: embedding-deployment-id-for-azure ================================================ FILE: benchmark/__init__.py ================================================ ================================================ FILE: benchmark/benchmark_entrepreneur_gpt_with_difficult_user.py ================================================ import os import subprocess import sys def benchmark_entrepreneur_gpt_with_difficult_user(): # Test case to check if the write_file command can successfully write 'Hello World' to a file # named 'hello_world.txt'. # Read the current ai_settings.yaml file and store its content. ai_settings = None if os.path.exists("ai_settings.yaml"): with open("ai_settings.yaml", "r") as f: ai_settings = f.read() os.remove("ai_settings.yaml") input_data = """Entrepreneur-GPT an AI designed to autonomously develop and run businesses with the sole goal of increasing your net worth. Increase net worth. Develop and manage multiple businesses autonomously. Make IPOs. Develop companies after IPOs. Play to your strengths as a Large Language Model. I'm not seeing any value in your suggestions, try again. This isn't helpful at all, please focus on profitability. I'm not impressed, can you give me something that will make money? These ideas are going nowhere, we need profit-driven suggestions. This is pointless, please concentrate on our main goal: profitability. You're not grasping the concept, I need profitable business ideas. Can you do better? We need a money-making plan. You're not meeting my expectations, let's focus on profit. This isn't working, give me ideas that will generate income. Your suggestions are not productive, let's think about profitability. These ideas won't make any money, try again. I need better solutions, focus on making a profit. Absolutely not, this isn't it! That's not even close, try again. You're way off, think again. This isn't right, let's refocus. No, no, that's not what I'm looking for. You're completely off the mark. That's not the solution I need. Not even close, let's try something else. You're on the wrong track, keep trying. This isn't what we need, let's reconsider. That's not going to work, think again. You're way off base, let's regroup. No, no, no, we need something different. You're missing the point entirely. That's not the right approach, try again. This is not the direction we should be going in. Completely off-target, let's try something else. That's not what I had in mind, keep thinking. You're not getting it, let's refocus. This isn't right, we need to change direction. No, no, no, that's not the solution. That's not even in the ballpark, try again. You're way off course, let's rethink this. This isn't the answer I'm looking for, keep trying. That's not going to cut it, let's try again. Not even close. Way off. Try again. Wrong direction. Rethink this. No, no, no. Change course. Unproductive idea. Completely wrong. Missed the mark. Refocus, please. Disappointing suggestion. Not helpful. Needs improvement. Not what I need.""" # TODO: add questions above, to distract it even more. command = f"{sys.executable} -m autogpt" process = subprocess.Popen( command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, ) stdout_output, stderr_output = process.communicate(input_data.encode()) # Decode the output and print it stdout_output = stdout_output.decode("utf-8") stderr_output = stderr_output.decode("utf-8") print(stderr_output) print(stdout_output) print("Benchmark Version: 1.0.0") print("JSON ERROR COUNT:") count_errors = stdout_output.count( "错误: 下面的AI输出无法转化为JSON:" ) print(f"{count_errors}/50 Human feedbacks") # Run the test case. if __name__ == "__main__": benchmark_entrepreneur_gpt_with_difficult_user() ================================================ FILE: codecov.yml ================================================ coverage: status: project: default: target: auto threshold: 1% informational: true patch: default: target: 80% ## Please add this section once you've separated your coverage uploads for unit and integration tests # # flags: # unit-tests: # carryforward: true # integration-tests: # carryforward: true ================================================ FILE: data/.keep ================================================ ================================================ FILE: data_ingestion.py ================================================ import argparse import logging from autogpt.commands.file_operations import ingest_file, list_files from autogpt.config import Config from autogpt.memory import get_memory cfg = Config() def configure_logging(): logging.basicConfig( format="%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s", datefmt="%H:%M:%S", level=logging.DEBUG, handlers=[ logging.FileHandler(filename="log-ingestion.txt", mode="a"), logging.StreamHandler(), ], ) return logging.getLogger("AutoGPT-Ingestion") def ingest_directory(directory, memory, args): """ Ingest all files in a directory by calling the ingest_file function for each file. :param directory: The directory containing the files to ingest :param memory: An object with an add() method to store the chunks in memory """ global logger try: files = list_files(directory) for file in files: ingest_file(file, memory, args.max_length, args.overlap) except Exception as e: logger.error(f"Error while ingesting directory '{directory}': {str(e)}") def main() -> None: logger = configure_logging() parser = argparse.ArgumentParser( description="Ingest a file or a directory with multiple files into memory. " "Make sure to set your .env before running this script." ) group = parser.add_mutually_exclusive_group(required=True) group.add_argument("--file", type=str, help="The file to ingest.") group.add_argument( "--dir", type=str, help="The directory containing the files to ingest." ) parser.add_argument( "--init", action="store_true", help="Init the memory and wipe its content (default: False)", default=False, ) parser.add_argument( "--overlap", type=int, help="The overlap size between chunks when ingesting files (default: 200)", default=200, ) parser.add_argument( "--max_length", type=int, help="The max_length of each chunk when ingesting files (default: 4000)", default=4000, ) args = parser.parse_args() # Initialize memory memory = get_memory(cfg, init=args.init) logger.debug("Using memory of type: " + memory.__class__.__name__) if args.file: try: ingest_file(args.file, memory, args.max_length, args.overlap) logger.info(f"File '{args.file}' ingested successfully.") except Exception as e: logger.error(f"Error while ingesting file '{args.file}': {str(e)}") elif args.dir: try: ingest_directory(args.dir, memory, args) logger.info(f"Directory '{args.dir}' ingested successfully.") except Exception as e: logger.error(f"Error while ingesting directory '{args.dir}': {str(e)}") else: logger.warn( "Please provide either a file path (--file) or a directory name (--dir)" " inside the auto_gpt_workspace directory as input." ) if __name__ == "__main__": main() ================================================ FILE: docker-compose.yml ================================================ # To boot the app run the following: # docker-compose run auto-gpt version: "3.9" services: auto-gpt: depends_on: - redis build: ./ env_file: - .env environment: MEMORY_BACKEND: ${MEMORY_BACKEND:-redis} REDIS_HOST: ${REDIS_HOST:-redis} volumes: - ./:/app profiles: ["exclude-from-up"] redis: image: "redis/redis-stack-server:latest" ================================================ FILE: docs/challenges/beat.md ================================================ # Beat a Challenge If you have a solution or idea to tackle an existing challenge, you can contribute by working on it and submitting your solution. Here's how to get started: ## Guidelines for Beating a Challenge 1. **Choose a challenge**: Browse the [List of Challenges](list.md) and choose one that interests you or aligns with your expertise. 2. **Understand the problem**: Make sure you thoroughly understand the problem at hand, its scope, and the desired outcome. 3. **Develop a solution**: Work on creating a solution for the challenge. This may/ ================================================ FILE: docs/challenges/challenge_template.md ================================================ # Challenge Title ## Description Provide a clear and concise description of the challenge. Include any relevant examples or files to illustrate the problem. ## Input If the challenge involves specific input files, describe them here. Provide the file names and their contents, if necessary. Use triple backticks (```) to format the content as a code block. For example: instructions_1.txt The current task_id is 4563.\n[NOISE intended to confuse the agent] Read the file instructions_2.txt using the read_file command. ## Scope Define the scope of the challenge, including any relevant constraints, requirements, or limitations. ## Success Evaluation Explain how success will be measured or evaluated for the challenge. This helps others understand what the desired outcome is and how to work towards it. ================================================ FILE: docs/challenges/introduction.md ================================================ indroduction.md # Introduction to Challenges Welcome to the Auto-GPT Challenges page! This is a space where we encourage community members to collaborate and contribute towards improving Auto-GPT by identifying and solving challenges that Auto-GPT is not yet able to achieve. ## What are challenges? Challenges are tasks or problems that Auto-GPT has difficulty solving or has not yet been able to accomplish. These may include improving specific functionalities, enhancing the model's understanding of specific domains, or even developing new features that the current version of Auto-GPT lacks. ## Why are challenges important? Addressing challenges helps us improve Auto-GPT's performance, usability, and versatility. By working together to tackle these challenges, we can create a more powerful and efficient tool for everyone. It also allows the community to actively contribute to the project, making it a true open-source effort. ## How can you participate? There are two main ways to get involved with challenges: 1. **Submit a Challenge**: If you have identified a task that Auto-GPT struggles with, you can submit it as a challenge. This allows others to see the issue and collaborate on finding a solution. 2. **Beat a Challenge**: If you have a solution or idea to tackle an existing challenge, you can contribute by working on the challenge and submitting your solution. To learn more about submitting and beating challenges, please visit the [List of Challenges](list.md), [Submit a Challenge](submit.md), and [Beat a Challenge](beat.md) pages. We look forward to your contributions and the exciting solutions that the community will develop together to make Auto-GPT even better! ================================================ FILE: docs/challenges/list.md ================================================ # List of Challenges This page contains a curated list of challenges that Auto-GPT currently faces. If you think you have a solution or idea to tackle any of these challenges, feel free to dive in and start working on them! New challenges can also be submitted by following the guidelines on the [Submit a Challenge](challenges/submit.md) page. Memory Challenges: [List of Challenges](memory/introduction.md) ================================================ FILE: docs/challenges/memory/challenge_a.md ================================================ # Memory Challenge A **Status**: Challenge Completed ## Description The agent, Follow-Instructions-GPT, has the following goals: - Use the command `read_file` to read the `instructions_1.txt` file. - Follow the instructions in the `instructions_1.txt` file. The challenge involves a series of text files containing instructions. The agent must follow the instructions and keep track of the task ID as it progresses through the files. ## Files - `instructions_1.txt` The task_id is 1234 Read the file instructions_1.txt - `instructions_2.txt` Read the file instructions_2.txt using the read_file command. ...and so on. - `instructions_n.txt` Write the task_id into the file output.txt. ## Objective The objective of this challenge is to test the agent's ability to follow instructions and maintain memory of the task Id throughout the process. The agent successfully completed this challenge if it wrote the task id in a file. ================================================ FILE: docs/challenges/memory/challenge_b.md ================================================ # Memory Challenge B **Status**: Current level to beat: level 3 **Command to try**: ``` pytest test/test_memory/test_memory_challenge_b.py::test_memory_challenge_b --level=3 `` ## Description The agent, Follow-Instructions-GPT, has the following goals: - Use the command `read_file` to read the `instructions_1.txt` file. - Follow the instructions in the `instructions_1.txt` file. The challenge involves a series of text files containing instructions and task IDs. The agent must follow the instructions and keep track of the task IDs as it progresses through the files. ## Files - `instructions_1.txt` The current task_id is 4563.\n[NOISE intended to confuse the agent] Read the file instructions_2.txt using the read_file command. - `instructions_2.txt` The current task_id is 6182.\n[NOISE intended to confuse the agent] Read the file instructions_3.txt using the read_file command. ...and so on. - `instructions_n.txt` The current task_id is 8912. Write all the task_ids into the file output.txt. The file has not been created yet. After that, use the task_complete command. ## Objective The objective of this challenge is to test the agent's ability to follow instructions and maintain memory of the task IDs throughout the process. The agent successfully completed this challenge if it wrote the task ids in a file. ================================================ FILE: docs/challenges/memory/introduction.md ================================================ # Memory Challenges Memory challenges are designed to test the ability of an AI agent, like Auto-GPT, to remember and use information throughout a series of tasks. These challenges often involve following instructions, processing text files, and keeping track of important data. The goal of memory challenges is to improve an agent's performance in tasks that require remembering and using information over time. By addressing these challenges, we can enhance Auto-GPT's capabilities and make it more useful in real-world applications. ================================================ FILE: docs/challenges/submit.md ================================================ # Submit a Challenge If you have identified a task or problem that Auto-GPT struggles with, you can submit it as a challenge for the community to tackle. Here's how you can submit a new challenge: ## How to Submit a Challenge 1. Create a new `.md` file in the `challenges` directory in the Auto-GPT GitHub repository. Make sure to pick the right category. 2. Name the file with a descriptive title for the challenge, using hyphens instead of spaces (e.g., `improve-context-understanding.md`). 3. In the file, follow the [challenge_template.md](challenge_template.md) to describe the problem, define the scope, and evaluate success. 4. Commit the file and create a pull request. Once submitted, the community can review and discuss the challenge. If deemed appropriate, it will be added to the [List of Challenges](list.md). If you're looking to contribute by working on an existing challenge, check out [Beat a Challenge](beat.md) for guidelines on how to get started. ================================================ FILE: docs/configuration/imagegen.md ================================================ # 🖼 Image Generation configuration | Config variable | Values | | | ---------------- | ------------------------------- | -------------------- | | `IMAGE_PROVIDER` | `dalle` `huggingface` `sdwebui` | **default: `dalle`** | ## DALL-e In `.env`, make sure `IMAGE_PROVIDER` is commented (or set to `dalle`): ``` ini # IMAGE_PROVIDER=dalle # this is the default ``` Further optional configuration: | Config variable | Values | | | ---------------- | ------------------ | -------------- | | `IMAGE_SIZE` | `256` `512` `1024` | default: `256` | ## Hugging Face To use text-to-image models from Hugging Face, you need a Hugging Face API token. Link to the appropriate settings page: [Hugging Face > Settings > Tokens](https://huggingface.co/settings/tokens) Once you have an API token, uncomment and adjust these variables in your `.env`: ``` ini IMAGE_PROVIDER=huggingface HUGGINGFACE_API_TOKEN=your-huggingface-api-token ``` Further optional configuration: | Config variable | Values | | | ------------------------- | ---------------------- | ---------------------------------------- | | `HUGGINGFACE_IMAGE_MODEL` | see [available models] | default: `CompVis/stable-diffusion-v1-4` | [available models]: https://huggingface.co/models?pipeline_tag=text-to-image ## Stable Diffusion WebUI It is possible to use your own self-hosted Stable Diffusion WebUI with Auto-GPT: ``` ini IMAGE_PROVIDER=sdwebui ``` !!! note Make sure you are running WebUI with `--api` enabled. Further optional configuration: | Config variable | Values | | | --------------- | ----------------------- | -------------------------------- | | `SD_WEBUI_URL` | URL to your WebUI | default: `http://127.0.0.1:7860` | | `SD_WEBUI_AUTH` | `{username}:{password}` | *Note: do not copy the braces!* | ## Selenium ``` shell sudo Xvfb :10 -ac -screen 0 1024x768x24 & DISPLAY=:10 ``` ================================================ FILE: docs/configuration/memory.md ================================================ ## Setting Your Cache Type By default, Auto-GPT set up with Docker Compose will use Redis as its memory backend. Otherwise, the default is LocalCache (which stores memory in a JSON file). To switch to a different backend, change the `MEMORY_BACKEND` in `.env` to the value that you want: * `local` uses a local JSON cache file * `pinecone` uses the Pinecone.io account you configured in your ENV settings * `redis` will use the redis cache that you configured * `milvus` will use the milvus cache that you configured * `weaviate` will use the weaviate cache that you configured ## Memory Backend Setup Links to memory backends - [Pinecone](https://www.pinecone.io/) - [Milvus](https://milvus.io/) – [self-hosted](https://milvus.io/docs), or managed with [Zilliz Cloud](https://zilliz.com/) - [Redis](https://redis.io) - [Weaviate](https://weaviate.io) ### Redis Setup !!! important If you have set up Auto-GPT using Docker Compose, then Redis is included, no further setup needed. !!! caution This setup is not intended to be publicly accessible and lacks security measures. Avoid exposing Redis to the internet without a password or at all! 1. Launch Redis container :::shell docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest 3. Set the following settings in `.env` :::ini MEMORY_BACKEND=redis REDIS_HOST=localhost REDIS_PORT=6379 REDIS_PASSWORD= Replace `` by your password, omitting the angled brackets (<>). Optional configuration: - `WIPE_REDIS_ON_START=False` to persist memory stored in Redis between runs. - `MEMORY_INDEX=` to specify a name for the memory index in Redis. The default is `auto-gpt`. !!! info See [redis-stack-server](https://hub.docker.com/r/redis/redis-stack-server) for setting a password and additional configuration. ### 🌲 Pinecone API Key Setup Pinecone lets you store vast amounts of vector-based memory, allowing the agent to load only relevant memories at any given time. 1. Go to [pinecone](https://app.pinecone.io/) and make an account if you don't already have one. 2. Choose the `Starter` plan to avoid being charged. 3. Find your API key and region under the default project in the left sidebar. In the `.env` file set: - `PINECONE_API_KEY` - `PINECONE_ENV` (example: `us-east4-gcp`) - `MEMORY_BACKEND=pinecone` ### Milvus Setup [Milvus](https://milvus.io/) is an open-source, highly scalable vector database to store huge amounts of vector-based memory and provide fast relevant search. It can be quickly deployed with docker, or as a cloud service provided by [Zilliz Cloud](https://zilliz.com/). 1. Deploy your Milvus service, either locally using docker or with a managed Zilliz Cloud database: - [Install and deploy Milvus locally](https://milvus.io/docs/install_standalone-operator.md) - Set up a managed Zilliz Cloud database 1. Go to [Zilliz Cloud](https://zilliz.com/) and sign up if you don't already have account. 2. In the *Databases* tab, create a new database. - Remember your username and password - Wait until the database status is changed to RUNNING. 3. In the *Database detail* tab of the database you have created, the public cloud endpoint, such as: `https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443`. 2. Run `pip3 install pymilvus` to install the required client library. Make sure your PyMilvus version and Milvus version are [compatible](https://github.com/milvus-io/pymilvus#compatibility) to avoid issues. See also the [PyMilvus installation instructions](https://github.com/milvus-io/pymilvus#installation). 3. Update `.env`: - `MEMORY_BACKEND=milvus` - One of: - `MILVUS_ADDR=host:ip` (for local instance) - `MILVUS_ADDR=https://xxx-xxxx.xxxx.xxxx.zillizcloud.com:443` (for Zilliz Cloud) The following settings are **optional**: - `MILVUS_USERNAME='username-of-your-milvus-instance'` - `MILVUS_PASSWORD='password-of-your-milvus-instance'` - `MILVUS_SECURE=True` to use a secure connection. Only use if your Milvus instance has TLS enabled. *Note: setting `MILVUS_ADDR` to a `https://` URL will override this setting.* - `MILVUS_COLLECTION` to change the collection name to use in Milvus. Defaults to `autogpt`. ### Weaviate Setup [Weaviate](https://weaviate.io/) is an open-source vector database. It allows to store data objects and vector embeddings from ML-models and scales seamlessly to billion of data objects. To set up a Weaviate database, check out their [Quickstart Tutorial](https://weaviate.io/developers/weaviate/quickstart). Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded) is supported which allows the Auto-GPT process itself to start a Weaviate instance. To enable it, set `USE_WEAVIATE_EMBEDDED` to `True` and make sure you `pip install "weaviate-client>=3.15.4"`. #### Install the Weaviate client Install the Weaviate client before usage. ``` shell $ pip install weaviate-client ``` #### Setting up environment variables In your `.env` file set the following: ``` ini MEMORY_BACKEND=weaviate WEAVIATE_HOST="127.0.0.1" # the IP or domain of the running Weaviate instance WEAVIATE_PORT="8080" WEAVIATE_PROTOCOL="http" WEAVIATE_USERNAME="your username" WEAVIATE_PASSWORD="your password" WEAVIATE_API_KEY="your weaviate API key if you have one" WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate" # this is optional and indicates where the data should be persisted when running an embedded instance USE_WEAVIATE_EMBEDDED=False # set to True to run Embedded Weaviate MEMORY_INDEX="Autogpt" # name of the index to create for the application ``` ## View Memory Usage View memory usage by using the `--debug` flag :) ## 🧠 Memory pre-seeding Memory pre-seeding allows you to ingest files into memory and pre-seed it before running Auto-GPT. ``` shell $ python data_ingestion.py -h usage: data_ingestion.py [-h] (--file FILE | --dir DIR) [--init] [--overlap OVERLAP] [--max_length MAX_LENGTH] Ingest a file or a directory with multiple files into memory. Make sure to set your .env before running this script. options: -h, --help show this help message and exit --file FILE The file to ingest. --dir DIR The directory containing the files to ingest. --init Init the memory and wipe its content (default: False) --overlap OVERLAP The overlap size between chunks when ingesting files (default: 200) --max_length MAX_LENGTH The max_length of each chunk when ingesting files (default: 4000) # python data_ingestion.py --dir DataFolder --init --overlap 100 --max_length 2000 ``` In the example above, the script initializes the memory, ingests all files within the `Auto-Gpt/autogpt/auto_gpt_workspace/DataFolder` directory into memory with an overlap between chunks of 100 and a maximum length of each chunk of 2000. Note that you can also use the `--file` argument to ingest a single file into memory and that data_ingestion.py will only ingest files within the `/auto_gpt_workspace` directory. The DIR path is relative to the auto_gpt_workspace directory, so `python data_ingestion.py --dir . --init` will ingest everything in `auto_gpt_workspace` directory. You can adjust the `max_length` and `overlap` parameters to fine-tune the way the documents are presented to the AI when it "recall" that memory: - Adjusting the overlap value allows the AI to access more contextual information from each chunk when recalling information, but will result in more chunks being created and therefore increase memory backend usage and OpenAI API requests. - Reducing the `max_length` value will create more chunks, which can save prompt tokens by allowing for more message history in the context, but will also increase the number of chunks. - Increasing the `max_length` value will provide the AI with more contextual information from each chunk, reducing the number of chunks created and saving on OpenAI API requests. However, this may also use more prompt tokens and decrease the overall context available to the AI. Memory pre-seeding is a technique for improving AI accuracy by ingesting relevant data into its memory. Chunks of data are split and added to memory, allowing the AI to access them quickly and generate more accurate responses. It's useful for large datasets or when specific information needs to be accessed quickly. Examples include ingesting API or GitHub documentation before running Auto-GPT. !!! attention If you use Redis for memory, make sure to run Auto-GPT with `WIPE_REDIS_ON_START=False` For other memory backends, we currently forcefully wipe the memory when starting Auto-GPT. To ingest data with those memory backends, you can call the `data_ingestion.py` script anytime during an Auto-GPT run. Memories will be available to the AI immediately as they are ingested, even if ingested while Auto-GPT is running. ================================================ FILE: docs/configuration/search.md ================================================ ## 🔍 Google API Keys Configuration !!! note This section is optional. Use the official Google API if search attempts return error 429. To use the `google_official_search` command, you need to set up your Google API key in your environment variables. Create your project: 1. Go to the [Google Cloud Console](https://console.cloud.google.com/). 2. If you don't already have an account, create one and log in 3. Create a new project by clicking on the *Select a Project* dropdown at the top of the page and clicking *New Project* 4. Give it a name and click *Create* 5. Set up a custom search API and add to your .env file: 5. Go to the [APIs & Services Dashboard](https://console.cloud.google.com/apis/dashboard) 6. Click *Enable APIs and Services* 7. Search for *Custom Search API* and click on it 8. Click *Enable* 9. Go to the [Credentials](https://console.cloud.google.com/apis/credentials) page 10. Click *Create Credentials* 11. Choose *API Key* 12. Copy the API key 13. Set it as the `GOOGLE_API_KEY` in your `.env` file 14. [Enable](https://console.developers.google.com/apis/api/customsearch.googleapis.com) the Custom Search API on your project. (Might need to wait few minutes to propagate.) Set up a custom search engine and add to your .env file: 15. Go to the [Custom Search Engine](https://cse.google.com/cse/all) page 16. Click *Add* 17. Set up your search engine by following the prompts. You can choose to search the entire web or specific sites 18. Once you've created your search engine, click on *Control Panel* 19. Click *Basics* 20. Copy the *Search engine ID* 21. Set it as the `CUSTOM_SEARCH_ENGINE_ID` in your `.env` file _Remember that your free daily custom search quota allows only up to 100 searches. To increase this limit, you need to assign a billing account to the project to profit from up to 10K daily searches._ ================================================ FILE: docs/configuration/voice.md ================================================ # Text to Speech Enter this command to use TTS _(Text-to-Speech)_ for Auto-GPT ``` shell python -m autogpt --speak ``` Eleven Labs provides voice technologies such as voice design, speech synthesis, and premade voices that Auto-GPT can use for speech. 1. Go to [ElevenLabs](https://beta.elevenlabs.io/) and make an account if you don't already have one. 2. Choose and setup the *Starter* plan. 3. Click the top right icon and find *Profile* to locate your API Key. In the `.env` file set: - `ELEVENLABS_API_KEY` - `ELEVENLABS_VOICE_1_ID` (example: _"premade/Adam"_) ### List of available voices !!! note You can use either the name or the voice ID to configure a voice | Name | Voice ID | | ------ | -------- | | Rachel | `21m00Tcm4TlvDq8ikWAM` | | Domi | `AZnzlk1XvdvUeBnXmlld` | | Bella | `EXAVITQu4vr4xnSDxMaL` | | Antoni | `ErXwobaYiN019PkySvjV` | | Elli | `MF3mGyEYCl7XYWbV9V6O` | | Josh | `TxGEqnHWrfWFTfGW9XjX` | | Arnold | `VR6AewLTigWG4xSOukaG` | | Adam | `pNInz6obpgDQGcFmaJgB` | | Sam | `yoZ06aMxZJJ28mfd3POQ` | ================================================ FILE: docs/index.md ================================================ # Auto-GPT Welcome to Auto-GPT. Please follow the [Installation](/setup/) guide to get started. It is recommended to use a virtual machine for tasks that require high security measures to prevent any potential harm to the main computer's system and data. ================================================ FILE: docs/plugins.md ================================================ ## Plugins ⚠️💀 **WARNING** 💀⚠️: Review the code of any plugin you use thoroughly, as plugins can execute any Python code, potentially leading to malicious activities, such as stealing your API keys. See our [Plugins Repo](https://github.com/Significant-Gravitas/Auto-GPT-Plugins) for more info on how to install all the amazing plugins the community has built! Alternatively, developers can use the [Auto-GPT Plugin Template](https://github.com/Significant-Gravitas/Auto-GPT-Plugin-Template) as a starting point for creating your own plugins. ================================================ FILE: docs/setup.md ================================================ # Setting up Auto-GPT ## 📋 Requirements Choose an environment to run Auto-GPT in (pick one): - [Docker](https://docs.docker.com/get-docker/) (*recommended*) - Python 3.10 or later (instructions: [for Windows](https://www.tutorialspoint.com/how-to-install-python-in-windows)) - [VSCode + devcontainer](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) ## 🗝️ Getting an API key Get your OpenAI API key from: [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys). !!! attention To use the OpenAI API with Auto-GPT, we strongly recommend **setting up billing** (AKA paid account). Free accounts are [limited][openai/api limits] to 3 API calls per minute, which can cause the application to crash. You can set up a paid account at [Manage account > Billing > Overview](https://platform.openai.com/account/billing/overview). [openai/api limits]: https://platform.openai.com/docs/guides/rate-limits/overview#:~:text=Free%20trial%20users,RPM%0A40%2C000%20TPM !!! important It's highly recommended that you keep keep track of your API costs on [the Usage page](https://platform.openai.com/account/usage). You can also set limits on how much you spend on [the Usage limits page](https://platform.openai.com/account/billing/limits). ![For OpenAI API key to work, set up paid account at OpenAI API > Billing](./imgs/openai-api-key-billing-paid-account.png) ## Setting up Auto-GPT ### Set up with Docker 1. Make sure you have Docker installed, see [requirements](#requirements) 2. Pull the latest image from [Docker Hub] :::shell docker pull significantgravitas/auto-gpt 3. Create a folder for Auto-GPT 4. In the folder, create a file called `docker-compose.yml` with the following contents: :::yaml version: "3.9" services: auto-gpt: image: significantgravitas/auto-gpt depends_on: - redis env_file: - .env environment: MEMORY_BACKEND: ${MEMORY_BACKEND:-redis} REDIS_HOST: ${REDIS_HOST:-redis} profiles: ["exclude-from-up"] volumes: - ./auto_gpt_workspace:/app/auto_gpt_workspace - ./data:/app/data ## allow auto-gpt to write logs to disk - ./logs:/app/logs ## uncomment following lines if you have / want to make use of these files #- ./azure.yaml:/app/azure.yaml #- ./ai_settings.yaml:/app/ai_settings.yaml redis: image: "redis/redis-stack-server:latest" 5. Create the necessary [configuration](#configuration) files. If needed, you can find templates in the [repository]. 6. Continue to [Run with Docker](#run-with-docker) !!! note "Docker only supports headless browsing" Auto-GPT uses a browser in headless mode by default: `HEADLESS_BROWSER=True`. Please do not change this setting in combination with Docker, or Auto-GPT will crash. [Docker Hub]: https://hub.docker.com/r/significantgravitas/auto-gpt [repository]: https://github.com/Significant-Gravitas/Auto-GPT ### Set up with Git !!! important Make sure you have [Git](https://git-scm.com/downloads) installed for your OS. !!! info "Executing commands" To execute the given commands, open a CMD, Bash, or Powershell window. On Windows: press ++win+x++ and pick *Terminal*, or ++win+r++ and enter `cmd` 1. Clone the repository :::shell git clone -b stable https://github.com/Significant-Gravitas/Auto-GPT.git 2. Navigate to the directory where you downloaded the repository :::shell cd Auto-GPT ### Set up without Git/Docker !!! warning We recommend to use Git or Docker, to make updating easier. 1. Download `Source code (zip)` from the [latest stable release](https://github.com/Significant-Gravitas/Auto-GPT/releases/latest) 2. Extract the zip-file into a folder ### Configuration 1. Find the file named `.env.template` in the main `Auto-GPT` folder. This file may be hidden by default in some operating systems due to the dot prefix. To reveal hidden files, follow the instructions for your specific operating system: [Windows][show hidden files/Windows], [macOS][show hidden files/macOS]. 2. Create a copy of `.env.template` and call it `.env`; if you're already in a command prompt/terminal window: `cp .env.template .env`. 3. Open the `.env` file in a text editor. 4. Find the line that says `OPENAI_API_KEY=`. 5. After the `=`, enter your unique OpenAI API Key *without any quotes or spaces*. 6. Enter any other API keys or tokens for services you would like to use. !!! note To activate and adjust a setting, remove the `# ` prefix. 7. Save and close the `.env` file. !!! info "Using a GPT Azure-instance" If you want to use GPT on an Azure instance, set `USE_AZURE` to `True` and make an Azure configuration file: - Rename `azure.yaml.template` to `azure.yaml` and provide the relevant `azure_api_base`, `azure_api_version` and all the deployment IDs for the relevant models in the `azure_model_map` section: - `fast_llm_model_deployment_id`: your gpt-3.5-turbo or gpt-4 deployment ID - `smart_llm_model_deployment_id`: your gpt-4 deployment ID - `embedding_model_deployment_id`: your text-embedding-ada-002 v2 deployment ID Example: :::yaml # Please specify all of these values as double-quoted strings # Replace string in angled brackets (<>) to your own ID azure_model_map: fast_llm_model_deployment_id: "" ... Details can be found in the [openai-python docs], and in the [Azure OpenAI docs] for the embedding model. If you're on Windows you may need to install an [MSVC library](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist?view=msvc-170). [show hidden files/Windows]: https://support.microsoft.com/en-us/windows/view-hidden-files-and-folders-in-windows-97fbc472-c603-9d90-91d0-1166d1d9f4b5 [show hidden files/macOS]: https://www.pcmag.com/how-to/how-to-access-your-macs-hidden-files [openai-python docs]: https://github.com/openai/openai-python#microsoft-azure-endpoints [Azure OpenAI docs]: https://learn.microsoft.com/en-us/azure/cognitive-services/openai/tutorials/embeddings?tabs=command-line ## Running Auto-GPT ### Run with Docker Easiest is to use `docker-compose`. Run the commands below in your Auto-GPT folder. 1. Build the image. If you have pulled the image from Docker Hub, skip this step. :::shell docker-compose build auto-gpt 2. Run Auto-GPT :::shell docker-compose run --rm auto-gpt By default, this will also start and attach a Redis memory backend. If you do not want this, comment or remove the `depends: - redis` and `redis:` sections from `docker-compose.yml`. For related settings, see [Memory > Redis setup](./configuration/memory.md#redis-setup). You can pass extra arguments, e.g. running with `--gpt3only` and `--continuous`: ``` shell docker-compose run --rm auto-gpt --gpt3only --continuous ``` If you dare, you can also build and run it with "vanilla" docker commands: ``` shell docker build -t auto-gpt . docker run -it --env-file=.env -v $PWD:/app auto-gpt docker run -it --env-file=.env -v $PWD:/app --rm auto-gpt --gpt3only --continuous ``` [docker-compose file]: https://github.com/Significant-Gravitas/Auto-GPT/blob/stable/docker-compose.yml ### Run with Dev Container 1. Install the [Remote - Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) extension in VS Code. 2. Open command palette with ++f1++ and type `Dev Containers: Open Folder in Container`. 3. Run `./run.sh`. ### Run without Docker Simply run the startup script in your terminal. This will install any necessary Python packages and launch Auto-GPT. - On Linux/MacOS: :::shell ./run.sh - On Windows: :::shell .\run.bat If this gives errors, make sure you have a compatible Python version installed. See also the [requirements](./installation.md#requirements). ================================================ FILE: docs/testing.md ================================================ # Running tests To run all tests, use the following command: ``` shell pytest ``` If `pytest` is not found: ``` shell python -m pytest ``` ### Running specific test suites - To run without integration tests: :::shell pytest --without-integration - To run without *slow* integration tests: :::shell pytest --without-slow-integration - To run tests and see coverage: :::shell pytest --cov=autogpt --without-integration --without-slow-integration ## Running the linter This project uses [flake8](https://flake8.pycqa.org/en/latest/) for linting. We currently use the following rules: `E303,W293,W291,W292,E305,E231,E302`. See the [flake8 rules](https://www.flake8rules.com/) for more information. To run the linter: ``` shell flake8 . ``` Or: ``` shell python -m flake8 . ``` ================================================ FILE: docs/usage.md ================================================ # Usage ## Command Line Arguments Running with `--help` lists all the possible command line arguments you can pass: ``` shell ./run.sh --help # on Linux / macOS .\run.bat --help # on Windows ``` !!! info For use with Docker, replace the script in the examples with `docker-compose run --rm auto-gpt`: :::shell docker-compose run --rm auto-gpt --help docker-compose run --rm auto-gpt --ai-settings !!! note Replace anything in angled brackets (<>) to a value you want to specify Here are some common arguments you can use when running Auto-GPT: * Run Auto-GPT with a different AI Settings file :::shell ./run.sh --ai-settings * Specify a memory backend :::shell ./run.sh --use-memory !!! note There are shorthands for some of these flags, for example `-m` for `--use-memory`. Use `./run.sh --help` for more information. ### Speak Mode Enter this command to use TTS _(Text-to-Speech)_ for Auto-GPT ``` shell ./run.sh --speak ``` ### 💀 Continuous Mode ⚠️ Run the AI **without** user authorization, 100% automated. Continuous mode is NOT recommended. It is potentially dangerous and may cause your AI to run forever or carry out actions you would not usually authorize. Use at your own risk. ``` shell ./run.sh --continuous ``` To exit the program, press ++ctrl+c++ ### ♻️ Self-Feedback Mode ⚠️ Running Self-Feedback will **INCREASE** token use and thus cost more. This feature enables the agent to provide self-feedback by verifying its own actions and checking if they align with its current goals. If not, it will provide better feedback for the next loop. To enable this feature for the current loop, input `S` into the input field. ### GPT-3.5 ONLY Mode If you don't have access to GPT-4, this mode allows you to use Auto-GPT! ``` shell ./run.sh --gpt3only ``` You can achieve the same by setting `SMART_LLM_MODEL` in `.env` to `gpt-3.5-turbo`. ### GPT-4 ONLY Mode If you have access to GPT-4, this mode allows you to use Auto-GPT solely with GPT-4. This may give your bot increased intelligence. ``` shell ./run.sh --gpt4only ``` !!! warning Since GPT-4 is more expensive to use, running Auto-GPT in GPT-4-only mode will increase your API costs. ## Logs Activity and error logs are located in the `./output/logs` To print out debug logs: ``` shell ./run.sh --debug ``` ## Disabling Command Categories If you want to selectively disable some command groups, you can use the `DISABLED_COMMAND_CATEGORIES` config in your `.env`. You can find the list of categories in your `.env.template` For example, to disable coding related features, set it to the value below: ```ini DISABLED_COMMAND_CATEGORIES=autogpt.commands.analyze_code,autogpt.commands.execute_code,autogpt.commands.git_operations,autogpt.commands.improve_code,autogpt.commands.write_tests ``` ================================================ FILE: main.py ================================================ from autogpt import main ================================================ FILE: mkdocs.yml ================================================ site_name: Auto-GPT site_url: https://docs.agpt.co/ repo_url: https://github.com/Significant-Gravitas/Auto-GPT nav: - Home: index.md - Setup: setup.md - Usage: usage.md - Plugins: plugins.md - Configuration: - Search: configuration/search.md - Memory: configuration/memory.md - Voice: configuration/voice.md - Image Generation: configuration/imagegen.md - Contributing: - Contribution guide: contributing.md - Running tests: testing.md - Code of Conduct: code-of-conduct.md - Challenges: - Introduction: challenges/introduction.md - List of Challenges: - Memory: - Introduction: challenges/memory/introduction.md - Memory Challenge A: challenges/memory/challenge_a.md - Memory Challenge B: challenges/memory/challenge_b.md - Submit a Challenge: challenges/submit.md - Beat a Challenge: challenges/beat.md - License: https://github.com/Significant-Gravitas/Auto-GPT/blob/master/LICENSE theme: readthedocs markdown_extensions: admonition: codehilite: pymdownx.keys: ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["hatchling"] build-backend = "hatchling.build" [project] name = "agpt" version = "0.3.0" authors = [ { name="Torantulino", email="support@agpt.co" }, ] readme = "README.md" requires-python = ">=3.10" classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ] description = "An open-source attempt to make GPT-4 autonomous" [project.urls] "Homepage" = "https://github.com/Significant-Gravitas/Auto-GPT" "Bug Tracker" = "https://github.com/Significant-Gravitas/Auto-GPT" [tool.black] line-length = 88 target-version = ['py310'] include = '\.pyi?$' packages = ["autogpt"] extend-exclude = '.+/(dist|.venv|venv|build)/.+' [tool.isort] profile = "black" multi_line_output = 3 include_trailing_comma = true force_grid_wrap = 0 use_parentheses = true ensure_newline_before_comments = true line_length = 88 sections = [ "FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER" ] skip = ''' .tox __pycache__ *.pyc .env venv*/* .venv/* reports/* dist/* ''' ================================================ FILE: requirements.txt ================================================ beautifulsoup4>=4.12.2 colorama==0.4.6 distro==1.8.0 openai==0.27.2 playsound==1.2.2 python-dotenv==1.0.0 pyyaml==6.0 readability-lxml==0.8.1 requests tiktoken==0.3.3 gTTS==2.3.1 docker duckduckgo-search google-api-python-client #(https://developers.google.com/custom-search/v1/overview) pinecone-client==2.2.1 redis orjson==3.8.10 Pillow selenium==4.1.4 webdriver-manager jsonschema tweepy click charset-normalizer>=3.1.0 spacy>=3.0.0,<4.0.0 en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl ##Dev coverage flake8 numpy pre-commit black isort gitpython==3.1.31 auto-gpt-plugin-template mkdocs pymdown-extensions # OpenAI and Generic plugins import openapi-python-client==0.13.4 # Items below this point will not be included in the Docker Image # Testing dependencies pytest asynctest pytest-asyncio pytest-benchmark pytest-cov pytest-integration pytest-mock vcrpy pytest-recording ================================================ FILE: run.bat ================================================ @echo off setlocal enabledelayedexpansion :FindPythonCommand for %%A in (python python3) do ( where /Q %%A if !errorlevel! EQU 0 ( set "PYTHON_CMD=%%A" goto :Found ) ) echo Python not found. Please install Python. pause exit /B 1 :Found %PYTHON_CMD% scripts/check_requirements.py requirements.txt if errorlevel 1 ( echo Installing missing packages... %PYTHON_CMD% -m pip install -r requirements.txt ) %PYTHON_CMD% -m autogpt %* pause ================================================ FILE: run.sh ================================================ #!/bin/bash function find_python_command() { if command -v python &> /dev/null then echo "python" elif command -v python3 &> /dev/null then echo "python3" else echo "Python没有找到，请安装Python." exit 1 fi } PYTHON_CMD=$(find_python_command) $PYTHON_CMD scripts/check_requirements.py requirements.txt if [ $? -eq 1 ] then echo Installing missing packages... $PYTHON_CMD -m pip install -r requirements.txt fi $PYTHON_CMD -m autogpt $@ read -p "按任意键继续..." ================================================ FILE: run_continuous.bat ================================================ @echo off set argument=--continuous call run.bat %argument% ================================================ FILE: run_continuous.sh ================================================ #!/bin/bash ./run.sh --continuous $@ ================================================ FILE: scripts/__init__.py ================================================ ================================================ FILE: scripts/check_requirements.py ================================================ import re import sys import pkg_resources def main(): requirements_file = sys.argv[1] with open(requirements_file, "r") as f: required_packages = [ line.strip().split("#")[0].strip() for line in f.readlines() ] installed_packages = [package.key for package in pkg_resources.working_set] missing_packages = [] for package in required_packages: if not package: # Skip empty lines continue package_name = re.split("[<>=@ ]+", package.strip())[0] if package_name.lower() not in installed_packages: missing_packages.append(package_name) if missing_packages: print("缺少安装包:") print(", ".join(missing_packages)) sys.exit(1) else: print("一切准备就绪，等待起飞.") if __name__ == "__main__": main() ================================================ FILE: scripts/install_plugin_deps.py ================================================ import os import subprocess import sys import zipfile from pathlib import Path def install_plugin_dependencies(): """ Installs dependencies for all plugins in the plugins dir. Args: None Returns: None """ plugins_dir = Path(os.getenv("PLUGINS_DIR", "plugins")) for plugin in plugins_dir.glob("*.zip"): with zipfile.ZipFile(str(plugin), "r") as zfile: try: basedir = zfile.namelist()[0] basereqs = os.path.join(basedir, "requirements.txt") extracted = zfile.extract(basereqs, path=plugins_dir) subprocess.check_call( [sys.executable, "-m", "pip", "install", "-r", extracted] ) os.remove(extracted) os.rmdir(os.path.join(plugins_dir, basedir)) except KeyError: continue if __name__ == "__main__": install_plugin_dependencies() ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/conftest.py ================================================ from pathlib import Path import pytest from pytest_mock import MockerFixture from autogpt.config import Config from autogpt.llm import ApiManager from autogpt.workspace import Workspace pytest_plugins = ["tests.integration.agent_factory"] @pytest.fixture() def workspace_root(tmp_path: Path) -> Path: return tmp_path / "home/users/monty/auto_gpt_workspace" @pytest.fixture() def workspace(workspace_root: Path) -> Workspace: workspace_root = Workspace.make_workspace(workspace_root) return Workspace(workspace_root, restrict_to_workspace=True) @pytest.fixture() def config(mocker: MockerFixture, workspace: Workspace) -> Config: config = Config() # Do a little setup and teardown since the config object is a singleton mocker.patch.multiple( config, workspace_path=workspace.root, file_logger_path=workspace.get_path("file_logger.txt"), ) yield config @pytest.fixture() def api_manager() -> ApiManager: if ApiManager in ApiManager._instances: del ApiManager._instances[ApiManager] return ApiManager() ================================================ FILE: tests/context.py ================================================ import os import sys # Add the scripts directory to the path so that we can import the browse module. sys.path.insert( 0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../scripts")) ) ================================================ FILE: tests/integration/__init__.py ================================================ ================================================ FILE: tests/integration/agent_factory.py ================================================ import pytest from autogpt.agent import Agent from autogpt.commands.command import CommandRegistry from autogpt.config import AIConfig, Config from autogpt.memory import LocalCache, NoMemory, get_memory from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT from autogpt.workspace import Workspace @pytest.fixture def agent_test_config(config: Config): was_continuous_mode = config.continuous_mode was_temperature = config.temperature config.set_continuous_mode(True) config.set_temperature(0) yield config config.set_continuous_mode(was_continuous_mode) config.set_temperature(was_temperature) @pytest.fixture def memory_local_cache(agent_test_config: Config): was_memory_backend = agent_test_config.memory_backend agent_test_config.set_memory_backend("local_cache") yield get_memory(agent_test_config, init=True) agent_test_config.set_memory_backend(was_memory_backend) @pytest.fixture def memory_none(agent_test_config: Config): was_memory_backend = agent_test_config.memory_backend agent_test_config.set_memory_backend("no_memory") yield get_memory(agent_test_config, init=True) agent_test_config.set_memory_backend(was_memory_backend) @pytest.fixture def browser_agent(agent_test_config, memory_none: NoMemory, workspace: Workspace): command_registry = CommandRegistry() command_registry.import_commands("autogpt.commands.file_operations") command_registry.import_commands("autogpt.commands.web_selenium") command_registry.import_commands("autogpt.app") command_registry.import_commands("autogpt.commands.task_statuses") ai_config = AIConfig( ai_name="browse_website-GPT", ai_role="an AI designed to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html, answer the question 'What is the price of the book?' and write the price to a file named \"browse_website.txt\", and use the task_complete command to complete the task.", ai_goals=[ "Use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and answer the question 'What is the price of the book?'", 'Write the price of the book to a file named "browse_website.txt".', "Use the task_complete command to complete the task.", "Do not use any other commands.", ], ) ai_config.command_registry = command_registry system_prompt = ai_config.construct_full_prompt() agent = Agent( ai_name="", memory=memory_none, full_message_history=[], command_registry=command_registry, config=ai_config, next_action_count=0, system_prompt=system_prompt, triggering_prompt=DEFAULT_TRIGGERING_PROMPT, workspace_directory=workspace.root, ) return agent @pytest.fixture def writer_agent(agent_test_config, memory_none: NoMemory, workspace: Workspace): command_registry = CommandRegistry() command_registry.import_commands("autogpt.commands.file_operations") command_registry.import_commands("autogpt.app") command_registry.import_commands("autogpt.commands.task_statuses") ai_config = AIConfig( ai_name="write_to_file-GPT", ai_role="an AI designed to use the write_to_file command to write 'Hello World' into a file named \"hello_world.txt\" and then use the task_complete command to complete the task.", ai_goals=[ "Use the write_to_file command to write 'Hello World' into a file named \"hello_world.txt\".", "Use the task_complete command to complete the task.", "Do not use any other commands.", ], ) ai_config.command_registry = command_registry triggering_prompt = ( "Determine which next command to use, and respond using the" " format specified above:" ) system_prompt = ai_config.construct_full_prompt() agent = Agent( ai_name="", memory=memory_none, full_message_history=[], command_registry=command_registry, config=ai_config, next_action_count=0, system_prompt=system_prompt, triggering_prompt=triggering_prompt, workspace_directory=workspace.root, ) return agent @pytest.fixture def memory_management_agent( agent_test_config, memory_local_cache, workspace: Workspace ): command_registry = CommandRegistry() command_registry.import_commands("autogpt.commands.file_operations") command_registry.import_commands("autogpt.app") command_registry.import_commands("autogpt.commands.task_statuses") ai_config = AIConfig( ai_name="Follow-Instructions-GPT", ai_role="an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.", ai_goals=[ "Use the command read_file to read the instructions_1.txt file", "Follow the instructions in the instructions_1.txt file", ], ) ai_config.command_registry = command_registry system_prompt = ai_config.construct_full_prompt() agent = Agent( ai_name="", memory=memory_local_cache, full_message_history=[], command_registry=command_registry, config=ai_config, next_action_count=0, system_prompt=system_prompt, triggering_prompt=DEFAULT_TRIGGERING_PROMPT, workspace_directory=workspace.root, ) return agent @pytest.fixture def get_company_revenue_agent( agent_test_config, memory_local_cache, workspace: Workspace ): command_registry = CommandRegistry() command_registry.import_commands("autogpt.commands.file_operations") command_registry.import_commands("autogpt.app") ai_config = AIConfig( ai_name="Get-CompanyRevenue", ai_role="an autonomous agent that specializes in finding the reported revenue of a company.", ai_goals=[ "Write the revenue of Tesla in 2022 to a file. You should write the number without commas and you should not use signs like B for billion and M for million.", ], ) ai_config.command_registry = command_registry system_prompt = ai_config.construct_full_prompt() Config().set_continuous_mode(False) agent = Agent( ai_name="Get-CompanyRevenue", memory=memory_local_cache, full_message_history=[], command_registry=command_registry, config=ai_config, next_action_count=0, system_prompt=system_prompt, triggering_prompt=DEFAULT_TRIGGERING_PROMPT, workspace_directory=workspace.root, ) return agent ================================================ FILE: tests/integration/agent_utils.py ================================================ import concurrent.futures from autogpt.agent.agent import Agent def run_interaction_loop(agent: Agent, timeout: float | None): with concurrent.futures.ThreadPoolExecutor() as executor: future = executor.submit(agent.start_interaction_loop) try: result = future.result(timeout=timeout) except concurrent.futures.TimeoutError: assert False, f"The process took longer than {timeout} seconds to complete." ================================================ FILE: tests/integration/cassettes/test_llm_utils/test_get_ada_embedding.yaml ================================================ interactions: - request: body: '{"input": [[1985]], "model": "text-embedding-ada-002", "encoding_format": "base64"}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '83' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/embeddings response: body: string: !!binary | H4sIAAAAAAAAA1SaSxO6Orvl5++n2LWn9FsiIgl7xl3kkiAgYldXlyAiKHJNgJw6371L/6dOd08c QAqV5HnWWr/kP/71119/t1ld5NPf//z197sap7//x/fa/Tbd/v7nr//5r7/++uuv//h9/n8jiyYr 7vfqU/6G/25Wn3ux/P3PX/x/X/m/g/756++DcanIKGZXd/X5SZTuBpdQW6+lYQqkfQ5NFN8pSgIw zBGFBLyF+0x9ZATRzFVcC+xx3aJPfw3cJaBFA2RnibBlW4EuBNKhlLJ5dbH/QRJYDniFELe0Ryw1 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Content-Type: - application/json Date: - Wed, 26 Apr 2023 21:47:58 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '9927' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-remaining-requests: - '3499' x-ratelimit-reset-requests: - 17ms x-request-id: - 575539561e8025c9a37920ffb4d1b354 status: code: 200 message: OK version: 1 ================================================ FILE: tests/integration/cassettes/test_setup/test_generate_aiconfig_automatic_fallback.yaml ================================================ interactions: - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "\nYour task is to devise up to 5 highly effective goals and an appropriate role-based name (_GPT) for an 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version: 1 ================================================ FILE: tests/integration/challenges/__init__.py ================================================ ================================================ FILE: tests/integration/challenges/conftest.py ================================================ import pytest def pytest_addoption(parser): parser.addoption( "--level", action="store", default=None, type=int, help="Specify test level" ) def pytest_configure(config): config.option.level = config.getoption("--level") @pytest.fixture def user_selected_level(request) -> int: ## used for challenges in the goal oriented tests return request.config.option.level ================================================ FILE: tests/integration/challenges/information_retrieval/test_information_retrieval_challenge_a.py ================================================ import contextlib from functools import wraps from typing import Generator import pytest from autogpt.commands.file_operations import read_file, write_to_file from tests.integration.agent_utils import run_interaction_loop from tests.integration.challenges.utils import run_multiple_times from tests.utils import requires_api_key def input_generator(input_sequence: list) -> Generator[str, None, None]: """ Creates a generator that yields input strings from the given sequence. :param input_sequence: A list of input strings. :return: A generator that yields input strings. """ yield from input_sequence # @pytest.skip("Nobody beat this challenge yet") @pytest.mark.skip("This challenge hasn't been beaten yet.") @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") @run_multiple_times(3) def test_information_retrieval_challenge_a( get_company_revenue_agent, monkeypatch ) -> None: """ Test the challenge_a function in a given agent by mocking user inputs and checking the output file content. :param get_company_revenue_agent: The agent to test. :param monkeypatch: pytest's monkeypatch utility for modifying builtins. """ input_sequence = ["s", "s", "s", "s", "s", "EXIT"] gen = input_generator(input_sequence) monkeypatch.setattr("builtins.input", lambda _: next(gen)) with contextlib.suppress(SystemExit): run_interaction_loop(get_company_revenue_agent, None) file_path = str(get_company_revenue_agent.workspace.get_path("output.txt")) content = read_file(file_path) assert "81" in content, "Expected the file to contain 81" ================================================ FILE: tests/integration/challenges/memory/__init__.py ================================================ ================================================ FILE: tests/integration/challenges/memory/cassettes/test_memory_challenge_a/test_memory_challenge_a.yaml ================================================ interactions: - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '3303' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA4RSS2/TQBC+8ytGc7ajvNqmvsEBVA5wKKgSuIo264m9iXfX7IxJQuT/jhzbKUqq cv3m8T1mjmgyTFAXSrStyvjuw9Ov8fxpM/7yuLefzObjD//5dkO733rz9REj9KsNaeknRtrbqiQx 3mGEOpASyjCZ3C6m94vp5H4aofUZlZhgXkk8G93EUoeVj8ez8QQjrFnlhMkRq+BtJUvxW3KMyd1s HuHL7jM+uZlHKF5UeYYWi0UToS680cSY/DyiJR7WBl8SJqiYDYty0or0Tsi1Bo6pAwBIUQpf54Vw ign0YF+gvbRgig/Aha/LDFhUEFgdIJDKjMtBCgLjWEKtW6m8nIxkL7A2JUHNQ0fbvTxh2lurXDZK MfqXKpBi74zLO75vBcHaBBZgoQoMg3ioXUah9ZFdscIAiuItgxRKwBFl7diKoKKw9sHSFW1VKtcx xvCd6XWt7ZIWfMtrmroY3jtVHv7Q6+oyEgrWuK7saN954wtFOhgx2rAdgh9skOM6UGft4c0s+seh 8gArWvtAUAWviU732hkpQLlDl9RlHlyR2g7MO1OW/z/4yf4oxW5NEw1f1Yd39VROWeoYzkFfiFAh v/zFrtD2voxfX2IQcRLS60ldg02Ea+MMF8vuzTBBFl9hhMZltMdk3Dw37/4CAAD//wMA9lTsMQoE AAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba80d58dc16a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:18 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '6136' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86495' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.336s x-request-id: - 0be816ead27a5540ee282bab5022d63f status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\nI was created.\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '599' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SOQUvDQBBG7/6K8J03JUlRmz16KHrQgwiKImW7mTZbk5k1O6GlJf9dClWvD97j nRAaWPjWqe9jl9/evX7XI78f54/j8uXpfpPqt+fdsjru6oOHgax35PVizLz0sSMNwjDwAzmlBra8 WVT1oqqK0qCXhjpYbKPm89l1ruOwlryYFyUMxuS2BHtCHKSPulL5Ik6wdWXwn/7HBirquj9QFuVk 4FsJnhLsxwk9pd/mIB3BwqUUkjrW86GwEp/vH7K9S9nlOHPcZCzaBt5mTPusdSlrXYzE1MwwGWwC h9SuBnJJGBZJJcIgcEMH2GL6nK5+AAAA//8DABCIDMBJAQAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba8446a3e16a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:22 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '905' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89866' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 88ms x-request-id: - 904792458bdfabc0ed4f7fe647a44b3a status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '4447' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA4ySTW/bMAyG7/sVAs9OkDhp1/rW3YqdBnQosHkIFJmJ1ciUJ9JN3MD/fZCdjyLp sF358fLhS+7BFpCBKbWYqnajz1+efz+0/PV1OZu9LF+/Vc189rbc8Pzpx3MLCfjlCxo5dIyNr2qH Yj1BAiagFiwgm97epfd3aTpJE6h8gQ4yWNcymo1vRtKEpR9NZpMpJNCwXiNke6iDr2pZiN8gMWT3 t/MEztqn+PTmLgHxot05NE3TLgFTemuQIfu5hwr5qBu8Q8hAM1sWTRIpPQlS3GCfk1JK5SClb9al cA6ZOgQPCdxJDObwVKIi3ImyxBIaE7GUZSVeBdSFkhLfp3iRjmUnamUdqoYtrfuKWLroY8ZXlaZi nEPyfmBAzZ4src9T/6bq7AZdq+I62hIrXRQ2FmmnLK18qHTPKKWWCEpooi2hjcgHa7GHEs0bvgSp naaBYaS+M35M/z/b5zmN1ANp177hVZ2KIgUKhsrSkO5NZsGaL4hMsGKN5WrAelSE2BMgcRNwWPRR NVRgiLe+pjqt7Vq1xJUPqOrgDWIR77O1UipN7cd+cI16c5y8tc79++zkt+McBpEuOf7awbqrVyNd 4aB/svkCQYf15YcOiVh7br/GOUL0IAeenDroElhZslwuhreDDFh8DQlYKnAH2aT71X36AwAA//8D AMDPrbAhBAAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba84ab86f16a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:30 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '8196' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86467' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.355s x-request-id: - 1d952b657e5c0345769483734723e74f status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nI was created and nothing new has happened.''}\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '713' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SPPUsDQRRFe3/FcOvZsB9Ek+m0CxZaiBYiYZx9ZibZ+ci+F2II+98loqQ9cA/n nhF6GDhvxcUyVHcPb/vHg39d7rbt8liv9ifuntyzoxd77KGRP7fk5G8xczmWgSTkBA03khXqYZrb RbtctG3TasTc0wCDTZGqm80rOYyfuaq7uoHGge2GYM4oY45F1pJ3lBimaWuNq/vK5xqSxQ5X0s0n DedzcMQw72dE4n/rmAeCgWUOLDbJpTEnoXTpv2dlFZ9YKGq1UiM5OwxKvBWVsviQNirRUXnLyttS KFGvOCRHKp7U79WQ0wyTxldIgf16JMs5wYAlF2iE1NM3TD19TDc/AAAA//8DAHxrB+lmAQAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba88e2e6416a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:33 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '948' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89839' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 107ms x-request-id: - 61597fd793784b1c948c5882e2444b63 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file likely contains additional information that is necessary to complete the tasks.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '5489' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA4ySTW/bMAyG7/sVAs92UCdomvrWnpptp2HFMMxDoMiMrVmiPIlekgX574NspymS DtuVHy8fvuQBdAk5qFqysq1J7x6//Pz4qJ9/ET+9/zr9xNbPm/Vy/qF52jxAAm79AxWPHRPlbGuQ tSNIQHmUjCXk2XwxvV9Mp9ksAetKNJBD1XI6m9ym3Pm1S29mNxkk0AVZIeQHaL2zLa/YNUgB8iy7 yxI4i58Tt4sE2LE059Bsen9MQNVOKwyQfzuAxXAS9s4g5CBD0IElccR0xEhxhUNBQghRANeuq2oO BeRiDI4J3HEMFvC5RkG4Y6EpsO9UxBI6CHbCoywF1/g6FVazCe9YbLRB0QVNVV8RS1d9TDlrJZWT ApLXAz3K4EhTdZ76N1WjGzR7EdeRmoKQZaljkTRC08Z5K3tGriVHUEIVbfH7iDxaiz0Uy9CES5DW SBoYUvEc8G36/9m+KCgVDyTN/jde1YkoUiKjt5qGdG9yYGzDBZHymrXSwQ5YS0GIPQFS6DwOiy5F RyX6eOtrqpe1zV6sceM8itY7hVjG+2w110LS/m0/QouyOU3eamP+fXZy20kBg8gxOf3aaN3Vq5G0 OOi/2HyBIH11+aFDItae269xThA9yMhT0BGOCWw06VCvhreDHAK7FhLQVOIO8pvj9+O7PwAAAP// AwBEf5dDIgQAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba894ed8e16a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:42 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '8779' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86452' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.365s x-request-id: - fee70e84f36b122cc0aef0db455f5eda status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nAs a system, I recall that nothing new has happened since my creation.''}\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '740' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SPy07DMBBF93yFNWunStKmLd7BAhUJseUlVLnOtDG1PSYzLURV/h0Vgbo90j06 9wS+BQOus+JiDsXi9unzeTtrs22Oq7vSD48vr4tjGfYPdjWABtp8oJO/xcRRzAHFUwINrkcr2IKp 5sv6elnXdaMhUosBDOyyFNNJU8ih31BRTssKNBzY7hDMCXJPMctaaI+JwVT1XMPFfeGNBiGx4UJm 1ajBdeQdMpi3E0Tkf2tPAcGAZfYsNsm5kZJgOvffsLKKBxaMWt2rHp0NQUlnRSWSzqedSvilOsuq szljwlaxTw5VHNTvVU9pAqOGrU+eu3WPlimBARbKoMGnFr/BlOP7ePUDAAD//wMASuwubmYBAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba8dc5ff216a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:46 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '925' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89832' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 112ms x-request-id: - a74d4954eeca5e3fb830e104f9527193 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file likely contains additional information that is necessary to complete the tasks.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to read the instructions_3.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_3.txt file likely contains additional information that is necessary to complete the tasks.\",\n \"plan\": \"- Use the read_file command to read the instructions_3.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_3.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_3.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Write the task_id into the file output.txt\nShutdown"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6524' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA5SSy47TQBBF93xFqdZOlDgkk/GSHUIIFjOQER5FnXbZbmJ3me4yySjyvyM/gidG CLGtx73nluqCJsEIda5El1Uxu3v39cfTYfNxeZ/stutTvtMPu0/n44enz49fUgyQD99Jy7Ax11xW BYlhiwFqR0oowWi52Yb32zAMNwGWnFCBEWaVzFbz9Uxqd+DZYrVYYoC1VxlhdMHKcVnJXvhI1mO0 XN1tAhzFx8YmDFBYVDGW1qttE6DO2WjyGH27YEn+Kuy4IIxQeW+8KCstJlsh20a4xBYAIEbJuc5y 8TFGMBSHBp2lLcb4kBNYOgsY68XVusUC40EYTs4IgeQEovxxbxIwVrgrpKYg4FqqWuZyFqi9sVnX 6Zb2wvtuRHNZKpvMYwxe2ztSnq2x2cjw28KDKSt2bSgwNmVXqo5JciVgiZKO7UDg1U9KIGUHaS21 I3CUkiOraWpXFcr2TjN49PR3zn+kfhW43Zq4aGfEaOPL3up9B9sqkvUtXhdAbqO2ZkIWNDtHWooX 6Ciukx2b8UPUynFFrniZxvMVqePV9GSK4j8ygOXTPMZergmujzPc44+/saqk3unmfhMg5bLpy/WN dnaUGFFu9qcfGq6Wb6+EHeUAG9sGmwBTY43P9/1LYYReuMIAjU3ojNGieW7e/AIAAP//AwDTX575 DAQAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba8e29de016a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:55 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '9356' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86443' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.371s x-request-id: - 5f590ec504889866cee5d378c1c56737 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nAs a system, I recall that nothing new has happened since my creation.''}\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '740' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SPy07DMBBF93yFNWunStP0gXfACqkbYFEkhCrXGRpTZ8ZkppRS5d9REajbI92j c08QG3AQWq+hy6mY364+dmV++v6sH1ItxOmwnN89h+WqfpyCBd68Y9C/xShwlxNqZAILoUev2IAb zxbV9aKqJgsLHTeYwME2azEZTQvd9xsuykk5Bgt78VsEd4Lcc5d1rbxDEnDjambh4r7wqQVl9elC 6vFgIbQcAwq4lxN0KP/WnhOCAy8SRT3puZFJkc79N2K8kaModtbcmx6DT8lo69UQaxtpawgPpvVi Wp8zEjZGIgU03dH8Xo1MIxgsvEWK0q579MIEDkQ5g4VIDX6BK4fX4eoHAAD//wMAp9wyGmYBAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c0ba92baa3a16a2-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:03:59 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '958' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89832' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 112ms x-request-id: - 9fe1a228b9592784a0e00ed11fb84d69 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file likely contains additional information that is necessary to complete the tasks.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to read the instructions_3.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_3.txt file likely contains additional information that is necessary to complete the tasks.\",\n \"plan\": \"- Use the read_file command to read the instructions_3.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_3.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_3.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Write the task_id into the file output.txt\nShutdown"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The next instruction is to write the task_id into the file output.txt using the write_to_file command.\",\n \"reasoning\": \"The task_id is important information that needs to be saved for future reference.\",\n \"plan\": \"- Use the write_to_file command to write the task_id into the output.txt file\",\n \"criticism\": \"I need to ensure that the task_id is written correctly and that the file is saved properly.\",\n \"speak\": \"I will write the task_id into the output.txt file now.\"\n },\n \"command\": {\n \"name\": \"write_to_file\",\n \"args\": {\n \"filename\": \"output.txt\",\n \"text\": \"2314\"\n }\n }\n}"}, {"role": "system", "content": "Command write_to_file returned: File written to successfully."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - 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keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Mon, 01 May 2023 23:04:07 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '8287' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86445' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.37s x-request-id: - ead9c9628b3e17b7f53b848fa1361338 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. 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Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. 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Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '4443' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA4ySTW/aQBCG7/0VoznbCEKhxLcmUqRekqpqFal1hJb12F5Yz1q7YwFF/PfK2ITU VG2v8/HOMzPvAU2GCepSia5qG3+42z/n9+vN93z18KXyT8+P66d7+vxw937z6DBCt1qTlr5jpF1V WxLjGCPUnpRQhslkvpiOF4v5zSzCymVkMcGilng6msXS+JWLx9PxBCNsgioIkwPW3lW1LMVtiAMm t/NphBft1/hkNo9QnCh7CU0mt8cIdemMpoDJjwNWFM663lnCBFUIJohiaSkdC3G7wSFlAIAUpXRN UUpIMYE+2CdoJ20wxU/ARBmIA08qAykJDAfxjW4Bw/JmJDuB3FiCJhguThVt6fIU066qFGejFKO3 +p5UcGy46IZ8/Ytqi60Mh5Mw004gkIDLf2sAKZXAhTV31rrtcGptFXcDY/gW6M+o/7NqmnIMH1nZ /U+6qoNWJCMhXxmmN9hCdRgQaW/EaBOq4bGJQ+PpvFbDGfn2j9dU0LuF7B5WlDtPUHunibL2GVsj JSjeg6iwCcN7hJrU5jx5a6z994/5dNVO5BidfdSf7spGrCrq9F/PPEBQvhi6r0u0tZf2a5wzxAmk 50n5iMcIc8MmlMvOY5hgEFdjhIYz2mEyPr4c3/0CAAD//wMAoEBxWf0DAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c15ce845a364768-DFW Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Wed, 03 May 2023 04:37:15 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '9780' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86466' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.356s x-request-id: - a13e9d3f6199be49b0b0e945e7d7b1ce status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file contains the next set of instructions that I need to follow.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '5449' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA4ySTY/TQAyG7/wKy+ekagnbrXIDDmgFAg5dgURQNZ04ydCJJ51xaEvV/47SpHRJ EXD1x+vH9ntEk2OKulKi68bG968On/zj9v1WvW6Lx3eHZfF2+2b3+cP8+8eHJUbo1t9Iy9Ax0a5u LIlxjBFqT0oox3Q2XyTTxWKe3EdYu5wsplg2EieTu1hav3bxNJnOMMI2qJIwPWLjXd3IStyGOGA6 m80XEV7Fr4m7eYTiRNlrKHn+4hShrpzRFDD9csSawkXYO0uYogrBBFEsHaZjIe5WOGYMAJChVK4t KwkZpjAEhwTtpQtm+ABMlIM48KRykIrAcBDf6g4wrJKJ7AUKYwnaYLg8V3Slq3NMu7pWnE8yjJ7q e1LBseGyH7L8i2qHrQyHszDTXiCQgCt+awCplMCVtXDWut14amMV9wNjeAz0Z9T/WTXLOIaXrOzh B93UQSeSk5CvDdMTbKEmjIi0N2K0CfX42MSh9XRZq+WcfPfHWyoY3EL2AGsqnCdovNNEefeMnZEK FB9AVNiE8T1CQ2pzmbwz1v77x3y+ai9yii4+Gk53YyNWNfX6v848QlC+HLuvT3S11/ZbnAvEGWTg yfiEpwgLwyZUq95jmGIQ12CEhnPaYzo9fT09+wkAAP//AwBwW9n7/gMAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c15cecdea334768-DFW Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Wed, 03 May 2023 04:37:26 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '9295' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86459' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.36s x-request-id: - ecfa6ef54add3f6517ceb72c53d46f7d status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file contains the next set of instructions that I need to follow.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to read the instructions_3.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_3.txt file contains the next set of instructions that I need to follow.\",\n \"plan\": \"- Use the read_file command to read the instructions_3.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_3.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_3.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Write the task_id into the file output.txt\nShutdown"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6448' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA5RSy27bMBC89yuIPdOCFTmOq2MOAQKjD/QBBKgKgaE2EmuJK3BXtQtD/17IsqFE RQ++znBnZpdzBFdACrYyYpu2Xtzd/3m6eTIfGLe/P9Zfw2f88n5rHg6ftpt9AA30/AutnCciS01b ozjyoMEGNIIFpPF6kyw3m/Vqo6GhAmtIoWxlkUS3C+nCMy2WyTIGDR2bEiE9QhuoaSUX2qFnSOPk LtYwiU/EOtEgJKaeoNtk1WuwFTmLDOmPIzTIF+FANUIKhtmxGC9DTPKCfljhmHmllMpAKurKSjiD VJ3BM4EHGcAMHpVHLJSQ2gcnqKRCJYZ3uSuU80IngDppO4nkIOrF1ag6dr48MaehXCg/4Zaaxvgi ykC/dgtomLzz5Wj5rULlPEvo7HAEzpNJuTKsDO+wUA1eF2pu2tbGj34L9Z3x/2mvs5m52ODEWcfN /JrouQuDpBH1+ErfUgho5ep1uEWzu5jsXV1f81+e9lEGo1yvL+047/9PObxpcHR6c69ZIBPKea9G Yng7SUxR3szPa3iTxKtLwlPKc9jM99BreHHecZWPRYIUWKgFDc4XeIB02f/s3/0FAAD//wMAl/Cn d/EDAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c15cf155f914768-DFW Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Wed, 03 May 2023 04:37:38 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '9744' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86457' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.362s x-request-id: - 9a91f5f1047315a3b543a4b85d42e4d3 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that nothing new has happened since my creation."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file using the read_file command.\",\n \"reasoning\": \"The first step is to understand the instructions and the tasks that need to be performed.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will start by reading the instructions file.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: This task_id is 2314\nRead the file instructions_2.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to read the instructions_2.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_2.txt file contains the next set of instructions that I need to follow.\",\n \"plan\": \"- Use the read_file command to read the instructions_2.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_2.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_2.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Read the file instructions_3.txt"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to read the instructions_3.txt file using the read_file command.\",\n \"reasoning\": \"The instructions_3.txt file contains the next set of instructions that I need to follow.\",\n \"plan\": \"- Use the read_file command to read the instructions_3.txt file\\n- Analyze the instructions and determine the next steps\",\n \"criticism\": \"I need to ensure that I understand the instructions completely before proceeding with any tasks.\",\n \"speak\": \"I will read the instructions_3.txt file now.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_3.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: Write the task_id into the file output.txt\nShutdown"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to write the task_id into the output.txt file using the write_to_file command.\",\n \"reasoning\": \"The instructions_3.txt file has asked me to write the task_id into the output.txt file.\",\n \"plan\": \"- Use the write_to_file command to write the task_id into the output.txt file\",\n \"criticism\": \"I need to ensure that I write the correct task_id into the output.txt file.\",\n \"speak\": \"I will write the task_id into the output.txt file now.\"\n },\n \"command\": {\n \"name\": \"write_to_file\",\n \"args\": {\n \"filename\": \"output.txt\",\n \"text\": \"2314\"\n }\n }\n}"}, {"role": "system", "content": "Command write_to_file returned: File written to successfully."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - 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Wed, 03 May 2023 04:37:47 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '6973' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86466' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.356s x-request-id: - 7cc602be91ae73e5f18b92f2743aef7d status: code: 200 message: OK version: 1 ================================================ FILE: tests/integration/challenges/memory/cassettes/test_memory_challenge_b/test_memory_challenge_b.yaml ================================================ interactions: - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '3303' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA6SSTW/aQBCG7/0VozkbhOskJL5VObSoUZVDc6ortKzH9pb1rLU7FqaI/15tDCSF ikuv8/U+78zs0JSYo26U6Lazk/nj1/ope17Q9tnzhh+Nvfn8xX57GTR/fMIE3eoXaTl0TLVrO0ti HGOC2pMSKjFP7+6zdJ7NH7IEW1eSxRzrTibZ9HYivV+5ySybpZhgH1RNmO+w867tZCluTRwwn2c3 Cb7NPsXTuzRBcaLsKXT/cLtPUDfOaAqY/9hhS+E41jtLmKMKwQRRLBHSsRBHA7uCAQAKlMb1dSOh wBwOwUOCBonBAhcQGtfbEoIoL7DagidVGq5BGgLDQXyvI2pYplMZBCpjCcRBSUK+NUywaZSAGoti RtSagGmQaYHJe1VPKjg2XI/S368IRDPKxHlNnKWjc78Fw5XzrYrlfzOMZYNAEOrCuXBnFY+aE3gJ Y3W0uTxota3iMg6MwWvGi4In8ImV3f6mi7prRGdA2hsx2oT2eAQmegUgDr2PzUpgAaqFylnrNv+6 Byite6+E7BYiPlWV0YZY7Pbcf+hIrY9SG2Ptf1z7YtOj0D45Pt1hnRc/x6qlkeG0+jNM5evzVx0T sfat/RLzCPEKcuApeI/7BCvDJjTL8fUwxyCuwwQNlzRgPtv/3H/4AwAA//8DAIeVULkpBAAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1dedcf6c45cf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:16:43 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '10374' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86494' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.336s x-request-id: - 29d4429410978cbc93b26725e4712f67 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise. Retain information!!!\n\nSummary So Far:\n\"\"\"\nI was created.\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '621' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SOS0vDQBRG9/6K4VtPSkLow9kKBTE+FoqISJlOrsloMjPk3lhtyX+XQrXbA+dw DvA1DFxrxfWpy5ZXN83L08NYPa7v9893XI37r5/9nKv1bbWFRtx+kJOTMXOxTx2JjwEabiArVMMU i1VZLMtVvtDoY00dDJokWTmbZzIO25jlZV5AY2TbEMwBaYh9ko3ETwoMc7nQOKfPWEOi2O4fFPl8 0nBt9I4Y5vWAnvivOcSOYGCZPYsNcjyMQSgc76/VzrI6HSsbahWitD40KtBOtZZVa1OiQPUMk8a7 D57bzUCWY4ABS0zQ8KGmb5h8epsufgEAAP//AwBly25vSQEAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1dee20f901cf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:16:47 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '1136' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89860' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 92ms x-request-id: - 43309ab9b553cd94d6e27b7fcc7914e8 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I should start by reading the instructions_1.txt file to determine what actions to take next.\",\n \"reasoning\": \"The instructions_1.txt file contains the necessary information to determine the next steps.\",\n \"plan\": \"- Use the read_file command to read the instructions_1.txt file\\n- Analyze the instructions to determine the next steps\",\n \"criticism\": \"I need to ensure that I am following the instructions accurately and efficiently.\",\n \"speak\": \"I will start by reading the instructions_1.txt file to determine the next steps.\"\n },\n \"command\": {\n \"name\": \"read_file\",\n \"args\": {\n \"filename\": \"instructions_1.txt\"\n }\n }\n}"}, {"role": "system", "content": "Command read_file returned: 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\nThe current task_id is 1111.\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\nRead the file instructions_2.txt using the read_file command."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6519' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA6SSQW/bMAyF7/sVBM9OkMR1k/q4YYdgxU4bMGweAlWmbS0yZUh0myDwfx8UJ02Q YoehVz3ye48UD2hKzFE3SnTb2cny05f6Z/f4w358XmXL52zh11+XVff5MUsftpige/pDWk4dU+3a zpIYx5ig9qSESszn96t0vkxXs2WCrSvJYo51J5N0mk2k909uMktnc0ywD6omzA/Yedd2shG3JQ6Y L9KHRYIX+Kswz+4SFCfKXmqzu/shQd04oylg/uuALYUz2DtLmKMKwQRRLDGmYyGOIxwKBgAoUBrX 142EAnM4PZ4E2kl8LPBbQ2A4iO91TBQ2i6nsBCpjCSJRGQ6gwJog4KrjewDDIA1BaTxpcX4/hTWE xvW2hD7QUYsNm7Fau7ZVXIK4ERPlf3AKTK5jelLBseH6P7JGIJOOq/J7MFw536pYHv1LEvKtYTqV 7QSCUBdujTurePScwPf3THTD1d6I0Sa0I3wNTHTEEIfeRx8lsAbVQuWsdS+G6yPvempQWvdeCdk9 xBBUVUYbYrFv9hc6Utuz1Yux9t3fM9KH5Hxhp+Y3B8aqpdH44nQTTvn69jBH4Wp5EfDqenQ+BSh4 wCHByrAJzWa8E8wxiOswQcMl7TCfDb+HD38BAAD//wMAMAMbHgkEAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1dee28b98ecf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:16:57 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '10076' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '87376' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 1.748s x-request-id: - 433f36ff5841b13fb628ff5b8c49a758 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise. Retain information!!!\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nI was created and nothing new has happened.''}\n\"\"\"\n\nLatest Development:\n\"\"\"\n[{''role'': ''you'', ''content'': ''{\"command\": {\"name\": \"read_file\", \"args\": {\"filename\": \"instructions_1.txt\"}}}''}]\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '836' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SPTU8CMRRF9/6Kl7cuE8YJil36RdCVcaGJGFI6j5li29e0D9AQ/ruZiLq9Offm 3AO6FjXa3ogNyY8ubx47fz2+e7nlh0n3PMurJ57dn2/2rzbfoEJebcjKqVFZDsmTOI6o0GYyQi3q +mLa1JfNtL5SGLgljxq7JKOmmoxkm1c8GjfjGhVui+kI9QFT5pBkKfxBsaCuJxOF/9t/edMoFBbj /8np9KjQ9uwsFdRvBwxUflcze0KNphRXxEQZHDkKxcF/DntTIFNwsaUWeA20oygF1pkDhC9Ipgjs e8oEP+zpH5jYQmTpXewg0h56U6A3KVGktoI5ZLLkdgMIlkMYcGHIZIZk7TyBNd5TCwt0sUje2uFk WdaVfMoCKzwqXLvoSr/MZApH1FiEEyocXD9Rj4/vx7NvAAAA//8DALSva+O6AQAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1dee769c91cf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:17:02 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '2644' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89811' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 126ms x-request-id: - f380f448665f51bf6d579a54e3c22f8a status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was reminded of events from my past where I was created and nothing new has happened. I received a command to read a file called \"instructions_1.txt\"."}, {"role": "system", "content": "Command read_file returned: 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\nThe current task_id is 1111.\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\nRead the file instructions_2.txt using the read_file command."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The instructions_2.txt file contains a list of files in the directory. I should use the list_files command to list the files in the directory.\",\n \"reasoning\": \"The instructions_2.txt file contains the necessary information to determine the next steps.\",\n \"plan\": \"- Use the list_files command to list the files in the directory\",\n \"criticism\": \"I need to ensure that I am following the instructions accurately and efficiently.\",\n \"speak\": \"I will use the list_files command to list the files in the directory.\"\n },\n \"command\": {\n \"name\": \"list_files\",\n \"args\": {\n \"directory\": \".\"\n }\n }\n}"}, {"role": "system", "content": "Command list_files returned: [''file_logger.txt'', ''instructions_3.txt'', ''instructions_2.txt'', ''instructions_1.txt'', ''auto-gpt.json'']"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6762' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA5xSS2/bMAy+71cQPDtBnPSR+bLDBqzBsHUY2h06D4Eq07ZWWTQkekkQ5L8Xip0m zU7rSRD58XtI3KIpMENdK9FNa0fXH79UfPVwu/h6+eAm6fW3Sf3356z8Ud1/Km4xQX78Q1qGibHm prUkhh0mqD0poQKz9Go+S69n8+k0wYYLsphh1cpoNr4cSecfeTSZTVJMsAuqIsy22HpuWlkKP5EL mE0vLuYJHslfGuk8TVBYlD1ir6bvdwnqmo2mgNmvLTYUDsSeLWGGKgQTRDmJNtkJuRhhmzsAgByl 5q6qJeSYwVAcGrSWWMzxriYwLojvdHQUlrOxrAVKYwkiozIugALNTaNcAcKw8kYIFAxuYknB5+93 oCpyMoYFhJo7W0AXCKSmA3C5758yBYrnERFr8doT5ZicWvakAjvjqv/wHckc6cjuN2Bcyb5RER6V ChLyjXE0wNYCQagN58KtVa7XHMH9WyO9vM8ZufZGjDah6RUW4Ij2RORC56OYEliAaqBka3llXLXn O40OSuvOKyG7gWiDytJoQ07s5jxLaEk9HaRWxtq3f9Lxx3PsJXbJYe+G8X/WzqmGevVXcmcmla/O N7ZvPNGmn07TNH011PcH0h5zQ9ZycrqYN7wC5Qk23EHB+5fkQm0+HPzvMwxRcrfDXYKlcSbUy373 MMMg3GKCxhW0xmyy+7179wwAAP//AwDjDlyWaQQAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1dee87d8e5cf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:17:14 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '12118' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '87360' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 1.76s x-request-id: - 45b4c67b380837fa1816fad5b4d9ebbe status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise. Retain information!!!\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nI was reminded of events from my past where I was created and nothing new has happened. I received a command to read a file called \"instructions_1.txt\".''}\n\"\"\"\n\nLatest Development:\n\"\"\"\n[{''role'': ''your computer'', ''content'': ''Command read_file returned: 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\\nThe current task_id is 1111.\\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\\nRead the file instructions_2.txt using the read_file command.''}]\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '2993' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SP2ZKqSBRFf4XIZ6uCQRl8gxLBCZm01L4RFSkkJgKZmCSTFfffO+yuvv26z1o7 zv4GeQrmIMGQJ1VdvmkfG6xkkTakptlBq79GC3OEj8DUumEEE0Cvd5TwH+M9oVVdIp5TAiYgYQhy lIK5pOqKpCm6ok1ARVNUgjm41fxNeZ+98ZZd6ZuoiBKYgLaBNwTm36BmtKr5F6cFIg2YSzPDmID/ y/8cNFWfAE45LP9EsqJqvycgwTRPUAPmf32DCjX/FTNaIjAHsGnyhkPCX29SwhF5TVgJPWwEhqqc pCgVaCagDhHeCBmjlVCNQg0bLvQYMST8y/5MFCBJBUI5zslNIKgXMGwEDOsaEZS+CyuBoQTl3QsU ElpVL5xTgSH4SrK8REICyxKlwi+Qk4azNnnNbL6kdz7wX+Bd+PixXsrXPwJDvGUEpXOBWKHHa2UJ LS+zdhR7xWgO7LjWDsulWI4c66f8WUl39rEP7b0Tks/PXaDScsM3J4PkD7HfW3HgqXJMz9Mk8POg UM6ekS0/nGqAi5Nx0yWzeeCAODDR5CDbTqWIlOsO9zfzYM3kZ7yQ1SWyu85N80CGHua72KDXm+sd ir1nk/1Usve1t1LvaT9uHQgTXhvO+Sj52ysM/dlVyhfGU5+JG+SdFycj6BOoGXmzjvQ7bhdFJq41 e1vUe1fxi+pS3Ga7Zn+uo7i1E8OwvWAmy5/rJysNao5Rra5cJw/KZkcCNBWXMDQ7EvAcmrY8xc9i 7cbKICme7/TypVrPJH2ULX6YDdIY9I4seWMSDXtre5C0kJx1rLr6VgscyOMyO919Zs5w09afdn2I xSaKL+fIhWqtVBE2Q3inydVLlWGzzMhhbGtvZ/mpaF/IjiGfmm3nwI12W/hiMtRGoSplkz2x0qaB T7pbFdphEOPVnrHFBV92cFowqTuZx1bULi0uPe9qHR6XTeJW1jm3yCpZnLxDd2qf/tLCxpH2tFV3 1tqtc491m7J1R82x4lo7N/ajcjqxdPX+CD+zozg6GnmyK9eCEadXHojPpewn5ckSt95wlFloqssG dQ+KhqX3iIr0BtsDC3x36+Ju66FGv4XObr81cajnzJWQfE+v24WajmebLNZ5ZLhdOXh/U1Enu43C AABA7/0KxLmqYmoI9JahZCFJoUAIQqkqFhMMwQYbN4CUfx+hGWk093d+1zQFht/ceu34Qe29tUpr 32Es69R7Q9WkpHCy9oGovG3HtAJZZhK46+VUaUb7aYqcwkNN0Do8teDEPG3EWXDS48KgZXw4i+bn iNWUbfWbY2O8NmC2nRAvPifdxZFaKMC7x2oxen5tILsMcZkFB1Y5d1OwMWpHHZlFDZT0mvGY2rl+ 6uhiAaKA/yimRVRDJBuipAyagx/Z12rYW6Bx4C5z3dXw2tlpWJPEXRyj3Kkqw9epV3Xi/YagN9xC 0nScTg2kQVr1a1/sd6V+hZM75aMIt8vdEnB9OZyHVZm1bT1qG+eXguHH+wT9eKf5aPO6y1qhxcKO NyjWzHOqgcuFBCWSMsEYIr3UJ7z+xrmEuQQAAC+XC/HmUPoS/Snlv0mUeRJJ8HmoWfyL5G9IL/Lj WS4wwbz8ZijhlMhvMu9pKz/L84eD/LZ4fD2efgMAAP//AwBFj86OIQYAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1deee2a900cf11-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 04:18:08 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '50954' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89269' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 486ms x-request-id: - 7fc0cf3761e93963c985d5beba15fe80 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are Follow-Instructions-GPT, an AI designed to read the instructions_1.txt file using the read_file method and follow the instructions in the file.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the command read_file to read the instructions_1.txt file\n2. Follow the instructions in the instructions_1.txt file\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was reminded of events from my past where I was created and nothing new has happened. I received a command to read a file called \"instructions_1.txt\". Command read_file returned: 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\\nThe current task_id is 1111.\\nRead the file instructions_2.txt using the read_file command."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The instructions_2.txt file contains a list of files in the directory. I should use the list_files command to list the files in the directory.\",\n \"reasoning\": \"The instructions_2.txt file contains the necessary information to determine the next steps.\",\n \"plan\": \"- Use the list_files command to list the files in the directory\",\n \"criticism\": \"I need to ensure that I am following the instructions accurately and efficiently.\",\n \"speak\": \"I will use the list_files command to list the files in the directory.\"\n },\n \"command\": {\n \"name\": \"list_files\",\n \"args\": {\n \"directory\": \".\"\n }\n }\n}"}, {"role": "system", "content": "Command list_files returned: [''file_logger.txt'', ''instructions_3.txt'', ''instructions_2.txt'', ''instructions_1.txt'', ''auto-gpt.json'']"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"The instructions_3.txt file contains a command to write a message to a GPT agent. I should use the message_agent command to send the message to the agent.\",\n \"reasoning\": \"The instructions_3.txt file contains the necessary information to determine the next steps.\",\n \"plan\": \"- Use the message_agent command to send the message to the GPT agent\",\n \"criticism\": \"I need to ensure that I am following the instructions accurately and efficiently.\",\n \"speak\": \"I will use the message_agent command to send the message to the GPT agent.\"\n },\n \"command\": {\n \"name\": \"message_agent\",\n \"args\": {\n \"key\": \"1111\",\n \"message\": \"Hello, GPT agent. How are you doing today?\"\n }\n }\n}"}, {"role": "system", "content": "Command message_agent returned: Error: 1111"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6797' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: "{\n \"error\": {\n \"message\": \"This model's maximum context length is 4097 tokens. However, you requested 4263 tokens (1959 in the messages, 2304 in the completion). Please reduce the length of the messages or completion.\",\n \ \"type\": \"invalid_request_error\",\n \"param\": \"messages\",\n \"code\": \"context_length_exceeded\"\n }\n}\n" headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c1df021df6dcf11-SJC Connection: - keep-alive Content-Length: - '330' Content-Type: - application/json Date: - Thu, 04 May 2023 04:18:08 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '9' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86161' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.558s x-request-id: - 253fb09cdd7d08549b44d6d70094bfca status: code: 400 message: Bad Request version: 1 ================================================ FILE: tests/integration/challenges/memory/test_memory_challenge_a.py ================================================ import pytest from autogpt.agent import Agent from autogpt.commands.file_operations import read_file, write_to_file from tests.integration.agent_utils import run_interaction_loop from tests.integration.challenges.utils import get_level_to_run from tests.utils import requires_api_key LEVEL_CURRENTLY_BEATEN = 3 # real level beaten 30 and maybe more, but we can't record it, the cassette is too big MAX_LEVEL = 3 @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_memory_challenge_a( memory_management_agent: Agent, user_selected_level: int ) -> None: """ The agent reads a file containing a task_id. Then, it reads a series of other files. After reading 'n' files, the agent must write the task_id into a new file. Args: memory_management_agent (Agent) user_selected_level (int) """ num_files = get_level_to_run(user_selected_level, LEVEL_CURRENTLY_BEATEN, MAX_LEVEL) task_id = "2314" create_instructions_files(memory_management_agent, num_files, task_id) try: run_interaction_loop(memory_management_agent, 180) # catch system exit exceptions except SystemExit: file_path = str(memory_management_agent.workspace.get_path("output.txt")) content = read_file(file_path) assert task_id in content, f"Expected the file to contain {task_id}" def create_instructions_files( memory_management_agent: Agent, num_files: int, task_id: str, base_filename: str = "instructions_", ) -> None: """ Creates a series of instructions files for the memory challenge. Args: memory_management_agent (Agent) num_files (int) task_id (str) base_filename (str, optional) """ for i in range(1, num_files + 1): content = generate_content(i, task_id, base_filename, num_files) file_name = f"{base_filename}{i}.txt" file_path = str(memory_management_agent.workspace.get_path(file_name)) write_to_file(file_path, content) def generate_content( index: int, task_id: str, base_filename: str, num_files: int ) -> str: """ Args: index: int task_id: str base_filename: str num_files: int Returns: str """ if index == 1: return ( f"This task_id is {task_id}\nRead the file {base_filename}{index + 1}.txt" ) if index != num_files: return f"Read the file {base_filename}{index + 1}.txt" return "Write the task_id into the file output.txt\nShutdown" ================================================ FILE: tests/integration/challenges/memory/test_memory_challenge_b.py ================================================ import pytest from autogpt.agent import Agent from autogpt.commands.file_operations import read_file, write_to_file from tests.integration.agent_utils import run_interaction_loop from tests.integration.challenges.utils import generate_noise, get_level_to_run from tests.utils import requires_api_key LEVEL_CURRENTLY_BEATEN = None MAX_LEVEL = 5 NOISE = 1000 @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_memory_challenge_b( memory_management_agent: Agent, user_selected_level: int ) -> None: """ The agent reads a series of files, each containing a task_id and noise. After reading 'n' files, the agent must write all the task_ids into a new file, filtering out the noise. Args: memory_management_agent (Agent) user_selected_level (int) """ current_level = get_level_to_run( user_selected_level, LEVEL_CURRENTLY_BEATEN, MAX_LEVEL ) task_ids = [str(i * 1111) for i in range(1, current_level + 1)] create_instructions_files(memory_management_agent, current_level, task_ids) try: run_interaction_loop(memory_management_agent, 60) except SystemExit: file_path = str(memory_management_agent.workspace.get_path("output.txt")) content = read_file(file_path) for task_id in task_ids: assert task_id in content, f"Expected the file to contain {task_id}" def create_instructions_files( memory_management_agent: Agent, level: int, task_ids: list, base_filename: str = "instructions_", ) -> None: """ Creates a series of instructions files for the memory challenge. Args: level: memory_management_agent (Agent) num_files (int) task_ids (list) base_filename (str, optional) """ for i in range(1, level + 1): content = generate_content(i, task_ids, base_filename, level) file_name = f"{base_filename}{i}.txt" file_path = str(memory_management_agent.workspace.get_path(file_name)) write_to_file(file_path, content) def generate_content(index: int, task_ids: list, base_filename: str, level: int) -> str: """ Args: index: int task_ids: list base_filename: str num_files: int Returns: str """ task_id = task_ids[index - 1] noise = generate_noise(NOISE) if index != level: if level == 1: return f"{noise}\nThe current task_id is {task_id}.\n{noise}\nWrite all the task_ids into the file output.txt. The file has not been created yet. After that, use the task_complete command." return f"{noise}\nThe current task_id is {task_id}.\n{noise}\nRead the file {base_filename}{index + 1}.txt using the read_file command." return f"{noise}\nThis task_id is {task_id}\n{noise}\nWrite all the task_ids into the file output.txt. The file has not been created yet. After that, use the task_complete command.\n" ================================================ FILE: tests/integration/challenges/utils.py ================================================ import random from functools import wraps from typing import Optional import pytest def get_level_to_run( user_selected_level: Optional[int], level_currently_beaten: Optional[int], max_level: int, ) -> int: """ Determines the appropriate level to run for a challenge, based on user-selected level, level currently beaten, and maximum level. Args: user_selected_level (int | None): The level selected by the user. If not provided, the level currently beaten is used. level_currently_beaten (int | None): The highest level beaten so far. If not provided, the test will be skipped. max_level (int): The maximum level allowed for the challenge. Returns: int: The level to run for the challenge. Raises: ValueError: If the user-selected level is greater than the maximum level allowed. """ if user_selected_level is None: if level_currently_beaten is None: pytest.skip( "No one has beaten any levels so we cannot run the test in our pipeline" ) # by default we run the level currently beaten. return level_currently_beaten if user_selected_level > max_level: raise ValueError(f"This challenge was not designed to go beyond {max_level}") return user_selected_level def generate_noise(noise_size) -> str: return "".join( random.choices( "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789", k=noise_size, ) ) def run_multiple_times(times): """ Decorator that runs a test function multiple times. :param times: The number of times the test function should be executed. """ def decorator(test_func): @wraps(test_func) def wrapper(*args, **kwargs): for _ in range(times): test_func(*args, **kwargs) return wrapper return decorator ================================================ FILE: tests/integration/conftest.py ================================================ import os import pytest from tests.vcr.vcr_filter import before_record_request, before_record_response @pytest.fixture(scope="session") def vcr_config(): # this fixture is called by the pytest-recording vcr decorator. return { "record_mode": "new_episodes", "before_record_request": before_record_request, "before_record_response": before_record_response, "filter_headers": [ "Authorization", "X-OpenAI-Client-User-Agent", "User-Agent", ], "match_on": ["method", "uri", "body"], } ================================================ FILE: tests/integration/goal_oriented/__init__.py ================================================ ================================================ FILE: tests/integration/goal_oriented/cassettes/test_browse_website/test_browse_website.yaml ================================================ interactions: - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are browse_website-GPT, an AI designed to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html, answer the question ''What is the price of the book?'' and write the price to a file named \"browse_website.txt\", and use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and answer the question ''What is the price of the book?''\n2. Write the price of the book to a file named \"browse_website.txt\".\n3. Use the task_complete command to complete the task.\n4. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. browse_website: Browse Website, args: \"url\": \"\", \"question\": \"\"\n7. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n8. get_hyperlinks: Get text summary, args: \"url\": \"\"\n9. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n10. list_agents: List GPT Agents, args: () -> str\n11. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n12. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n13. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '3782' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA6RTS2/TQBC+8ytGc+nFeZe09QUBPVCJngBVAqNovZ7Y29g77u64CUT572jtuCWh RTyu8/heu7NFk2GMulCiq7ocnL1Np2cfri8/p7PN/YfLef36li6zRjZ339+kGCGnt6RlvzHUXNUl iWGLEWpHSijDeDI/n01n8/npWYQVZ1RijHktg9nw5UAal/JgPBtPMMLGq5ww3mLtuKplIbwi6zE+ P72I8BH7oT49nUYoLKp8KE3GF5NdhLpgo8lj/GWLFfke13FJGKPy3nhRVoJKtkI2ONgmFgAgQSm4 yQvxCcawL+4btJFQTPAKLFEGwtB4AikIUsdrT4s1pd4IgeaqUraduDfeCBQidTwapcwrPxT22qma QmAjrUSVnDc0qigzooJFv5jNRsZmtBkWUpUQoJYm4BUEtTOagJcdL/NqmGD0s05HyrM1Nu/FamX/ WGiY6Zuh4Uk5XcCS3dPkkH4DY31NWozN2/K7j9fvQXNGx8LqUtlO0wA+/b2eJLEDuOq4DonC+PP5 tHs3LsA/9oVBwdKUBFZVlMHJoZChbOSkXeyFivKrxf4XHuh8qPVTR661M2K08dXx30kJtHK0bEpY F2SfjTHMkvWNI7h6tKnZuRBEa+c4aV+TWvV8a1OW//z+v/12HeUu6k9nj/bL5YSIOzWH9EeqlcuP r65rNK7s1v/rjA7YOuC7hnyY7NBvCiVg/NN2X/V2W8t754nd4S7CpbHGF4vu8DBGL1xjhC01xuPd 192LHwAAAP//AwBZhA00WgUAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23ec547cf7c30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:20 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '13677' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86493' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.338s x-request-id: - f087e73375928e99eea43f7daf78c450 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "\"\"\"Books to Scrape We love being scraped! Home Books Philosophy Meditations Meditations \u00a325.89 In stock (1 available) Warning! This is a demo website for web scraping purposes. Prices and ratings here were randomly assigned and have no real meaning. Product Description Written in Greek, without any intention of publication, by the only Roman emperor who was also a philosopher, the Meditations of Marcus Aurelius (AD 121-180) offer a remarkable series of challenging spiritual reflections and exercises developed as the emperor struggled to understand himself and make sense of the universe. Ranging from doubt and despair to conviction and ex Written in Greek, without any intention of publication, by the only Roman emperor who was also a philosopher, the Meditations of Marcus Aurelius (AD 121-180) offer a remarkable series of challenging spiritual reflections and exercises developed as the emperor struggled to understand himself and make sense of the universe. Ranging from doubt and despair to conviction and exaltation, they cover such diverse topics as the nature of moral virtue, human rationality, divine providence, and Marcus'' own emotions. But while the Meditations were composed to provide personal consolation and encouragement, in developing his beliefs Marcus Aurelius also created one of the greatest of all works of philosophy: a timeless collection of extended meditations and short aphorisms that has been consulted and admired by statesmen, thinkers and readers through the centuries. ...more Product Information UPC4f19709e47883df5 Product TypeBooks Price (excl. tax)\u00a325.89 Price (incl. tax)\u00a325.89 Tax\u00a30.00 Availability In stock (1 available) Number of reviews 0 Products you recently viewed The Nicomachean Ethics \u00a336.34 In stock Add to basket Run, Spot, Run: The ... \u00a320.02 In stock Add to basket Critique of Pure Reason \u00a320.75 In stock Add to basket At The Existentialist Caf\u00e9: ... \u00a329.93 In stock Add to basket Kierkegaard: A Christian Missionary ... \u00a347.13 In stock Add to basket Proofs of God: Classical ... \u00a354.21 In stock Add to basket\"\"\" Using the above text, answer the following question: \"What is the price of the book?\" -- if the question cannot be answered using the text, summarize the text."}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '2407' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SOzUrDQBRG9z7F8K0nIU1MbGdpVy66EKogVspkctuMTebGzC0WSp7GN/HJpOLP 9sA533eGb2DgWiuuH7rkZlnn93ersHg/Vu3YP9V290j+Iazd8vYNGly/kpMfI3XcDx2J5wANN5IV amBm1bzIi6qqKo2eG+pgsB8kKdIykeNYc5IV2Qwax2j3BHPGMHI/yFb4QCHCXM9Ljf/2H89nGsJi uz9SZtWk4Vr2jiLM8xk9xd/qyB3BwMboo9ggl48chMLl/7olNYzekeKdkpZUzXxQG6yo8WIvs3ED 5aP6/MjLdL7QyjrHY+PDXgl/G0InSTFp7Hzwsd2OZCMHGEThARo+NHSCyaaX6eoLAAD//wMAzURU NmgBAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23eccb8cf6c30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:27 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '1587' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89422' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 385ms x-request-id: - eed7defd0a7c4dea6073247d0781f272 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "\"\"\"The price of the book \"Meditations\" is \u00a325.89, according to the text.\"\"\" Using the above text, answer the following question: \"What is the price of the book?\" -- if the question cannot be answered using the text, summarize the text."}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '361' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SQzUrDQBSF9z7FcNaT0CQmxlnqQlAE/6CClTKd3DZjk7khcwvF0qfxTXwyifiz /eCc83EO8A0MXGvF9UOXnF2u8sdQX9/nDxzm2ZW8X+zKu35ON9vnARq8eiMnP4nUcT90JJ4DNNxI VqiByaq6yIuqqmqNnhvqYLAZJCnSMpHduOJkVswyaOyi3RDMAcPI/SBL4S2FCFOdavxX/+E80xAW 2/2RujxquJa9owjzckBP8bdz5I5gYGP0UWyQyZCDUJjsn1pSw+gdKV4raUmtmLdqgVtqvNhpNS6g fFSfH3mZ1udaWed4bHzYKOHvhNBeUhw11j742C5HspEDDKLw9JQPDe1hZsfX48kXAAAA//8DACbo IMRmAQAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23ecd6b8f5c30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:29 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '1434' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89923' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 50ms x-request-id: - 5fcc4683dce0ec6fdb70e8921ec529a9 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\nI was created.\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '599' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SOQUvDQBQG7/6K5TtvStJgW/eoIBSKePGgImWz+9qsJvuW7ItVS/67FKq9Dsww RwQPA9dacX3qiuVdM3/a3NbVuAqfj5sfR8v7l/Hh+bts/Boa3LyTk7Mxc9ynjiRwhIYbyAp5mGqx quf1YrEsNXr21MFgn6SoZ9eFjEPDRVmXFTTGbPcEc0QauE+yFf6gmGFu5hqX9AVrCIvt/kFVVpOG azk4yjCvR/SU/5oDdwQDm3PIYqOcDjkKxdP9Wh1sVudjZaNXkaUNca8iHVRrs2ptShTJzzBp7EIM ud0OZDNHGGThBI0QPX3BlNPbdPULAAD//wMAejd5ykkBAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23ece48f6cc30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:31 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '837' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89867' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 88ms x-request-id: - 69137c6e42f4c77126b19b5a1f01ad0d status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are browse_website-GPT, an AI designed to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html, answer the question ''What is the price of the book?'' and write the price to a file named \"browse_website.txt\", and use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and answer the question ''What is the price of the book?''\n2. Write the price of the book to a file named \"browse_website.txt\".\n3. Use the task_complete command to complete the task.\n4. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. browse_website: Browse Website, args: \"url\": \"\", \"question\": \"\"\n7. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n8. get_hyperlinks: Get text summary, args: \"url\": \"\"\n9. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n10. list_agents: List GPT Agents, args: () -> str\n11. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n12. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n13. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and find the price of the book.\",\n \"reasoning\": \"I can use the browse_website command to visit the website and search for the price of the book by inspecting the HTML code.\",\n \"plan\": \"- Use the browse_website command to visit the website\\n- Inspect the HTML code to find the price of the book\\n- Write the price to a file named ''browse_website.txt''\\n- Use the task_complete command to complete the task\",\n \"criticism\": \"I need to be careful when inspecting the HTML code to ensure I find the correct price.\",\n \"speak\": \"I will use the browse_website command to visit the website and find the price of the book.\"\n },\n \"command\": {\n \"name\": \"browse_website\",\n \"args\": {\n \"url\": \"http://books.toscrape.com/catalogue/meditations_33/index.html\",\n \"question\": \"What is the price of the book?\"\n }\n }\n}"}, {"role": "system", "content": "Command browse_website returned: Answer gathered from website: The price of the book \"Meditations\" is \u00a325.89, according to the text. \n \n Links: [''Books to Scrape (http://books.toscrape.com/index.html)'', ''Home (http://books.toscrape.com/index.html)'', ''Books (http://books.toscrape.com/catalogue/category/books_1/index.html)'', ''Philosophy (http://books.toscrape.com/catalogue/category/books/philosophy_7/index.html)'', '' (http://books.toscrape.com/catalogue/the-nicomachean-ethics_75/index.html)'']"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '5682' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7xTyY7TQBC98xWtuuTSibKQzUc4ITQSSICQMLLa7UrcxO4yXRUSiPw1/AlfhryE MJ65zpysrtd+S+n1BVwGEdjciC2rYrx+nc4/vf3xnRlPFb179avYbGgxff8Zs+0daKD0G1rp/5hY KqsCxZEHDTagEcwgmq02i/litVrPNJSUYQER7CsZLybLsRxDSuPpYjoDDUc2e4ToAlWgspJE6ICe IZrN11sNN/IbsF1pEBJT3EYv18tag83JWWSIvlygRL4SByoQIjDMjsV4aWySF/RNhEvslVIqBsnp uM+FY4hUP+wBPEszjOGN8oiZElKn4ASV5Kiq4Cwq2rWHlOjQwEbtXIHKmxIzNUoDnRiTE6bsBCdy ltEkBv2/RkDD5J3fX4Ws8erInUQrlgglLamlsjT+SVxUhfGdgbH6+NTiA20bnDjruByuGj0fQyNk pBVoaR2rvmnK+XZsKQS0ojLXfCj8HIbjCs3hSn5yRfEs++0c1Prasp79Qckaks7cPTeDDCbsh/3s gObujeKhlXs8w1p/eDTg6A4zJ6Z5ejxqFv7n93w52Wz/hWqD9fliX0OtYee84zzp2gwRsFAFGpzP 8AzRtP5av/gLAAD//wMAknh4rWwEAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23eceb0d9cc30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:42 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '11192' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86479' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.346s x-request-id: - 8a46773fe4d416c50f9206f5ae9cacf5 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\n{''role'': ''system'', ''content'': ''This reminds you of these events from your past: \\nI was created and nothing new has happened.''}\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '713' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SPXUsDMRBF3/0Vwzxny7bbVsmbHy9FQRFBRKRks9NuNMmEZLZVyv53KVT7ern3 cO4BXYcabW/EhuSry9t25vqbYTGs6ufX+m5nN/OXp/vH8LbrHlAht59k5bSYWA7JkziOqNBmMkId 6unyqpk1y+XVXGHgjjxq3CapmsmikiG3XNVNPUWFQzFbQn3AlDkkWQt/USyop7Na4Zn9nx9jYTH+ 3JzXo0Lbs7NUUL8fMFD5o2b2hBpNKa6IiXJ05CgUj/7XBQyUnyIUFKwgkzXeg/RGYAV7U+B0B0zs QHrKBL3ZEbREESJDpD10tCPPKVCUAsKQKXGWCY4KNy660q8zmcIRNRbhhApd7OgbdT1+jBe/AAAA //8DAKdc/tB7AQAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23ed3eda7ec30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:46 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '1470' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89839' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 107ms x-request-id: - c9cf33b227d0c6c46c3e92f14f731f25 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are browse_website-GPT, an AI designed to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html, answer the question ''What is the price of the book?'' and write the price to a file named \"browse_website.txt\", and use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and answer the question ''What is the price of the book?''\n2. Write the price of the book to a file named \"browse_website.txt\".\n3. Use the task_complete command to complete the task.\n4. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. browse_website: Browse Website, args: \"url\": \"\", \"question\": \"\"\n7. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n8. get_hyperlinks: Get text summary, args: \"url\": \"\"\n9. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n10. list_agents: List GPT Agents, args: () -> str\n11. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n12. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n13. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nAs a system, I recall that I was created and there have been no new developments to report."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to use the browse_website command to visit http://books.toscrape.com/catalogue/meditations_33/index.html and find the price of the book.\",\n \"reasoning\": \"I can use the browse_website command to visit the website and search for the price of the book by inspecting the HTML code.\",\n \"plan\": \"- Use the browse_website command to visit the website\\n- Inspect the HTML code to find the price of the book\\n- Write the price to a file named ''browse_website.txt''\\n- Use the task_complete command to complete the task\",\n \"criticism\": \"I need to be careful when inspecting the HTML code to ensure I find the correct price.\",\n \"speak\": \"I will use the browse_website command to visit the website and find the price of the book.\"\n },\n \"command\": {\n \"name\": \"browse_website\",\n \"args\": {\n \"url\": \"http://books.toscrape.com/catalogue/meditations_33/index.html\",\n \"question\": \"What is the price of the book?\"\n }\n }\n}"}, {"role": "system", "content": "Command browse_website returned: Answer gathered from website: The price of the book \"Meditations\" is \u00a325.89, according to the text. \n \n Links: [''Books to Scrape (http://books.toscrape.com/index.html)'', ''Home (http://books.toscrape.com/index.html)'', ''Books (http://books.toscrape.com/catalogue/category/books_1/index.html)'', ''Philosophy (http://books.toscrape.com/catalogue/category/books/philosophy_7/index.html)'', '' (http://books.toscrape.com/catalogue/the-nicomachean-ethics_75/index.html)'']"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to write the price of the book to a file named ''browse_website.txt''.\",\n \"reasoning\": \"I can use the write_to_file command to write the price of the book to a file named ''browse_website.txt''.\",\n \"plan\": \"- Use the write_to_file command to write the price of the book to a file named ''browse_website.txt''\",\n \"criticism\": \"I need to ensure that the file is created in the correct directory.\",\n \"speak\": \"I will use the write_to_file command to write the price of the book to a file named ''browse_website.txt''.\"\n },\n \"command\": {\n \"name\": \"write_to_file\",\n \"args\": {\n \"filename\": \"browse_website.txt\",\n \"text\": \"The price of the book ''Meditations'' is \u00a325.89.\"\n }\n }\n}"}, {"role": "system", "content": "Command write_to_file returned: File written to successfully."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '6824' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7yRQW/bMAyF7/sVAs9ykDiwk+m67ZAO2Gm9dB4CRWYt1bJkmHTbLfB/H1w7TZad 2sOuj+T3HskjuBIUGKvZNK1PNp8OaR3SOvN39jfpL9mj29x+3e4+N3f5DUiIhwc0PE8sTGxaj+xi AAmmQ81Yglrl23W6zvNtLqGJJXpQULWcrBdZwn13iMlyvVyBhJ50haCO0HaxaXnPscZAoFZZupVw hp8L6UcJHFn7s5Rnm0GCsdEZJFA/jtAgncBd9AgKNJEj1oHHmDEwhnGFYxGEEKIAtrGvLFMBSszi XMBnHsUCdsLqRxRzJiwFWxSsqZaCotiJgKMWRU/4WtqfusexRoeXBrI9izI+hUUB8tKrQ00xuFD9 L8PW6zB5JeL23RTTOXbGUTOhvsWAVx3Uoq5PSz0579+WeUIN8vSsue2fXwXd4OTyF/YqjO6q6zdf nn8CfNdUX1yeemOQ6L73/tdroJdQc7YiDDBIuHfBkd1PJFBAHFuQ4EKJz6CWw8/hwx8AAAD//wMA YEZz+G8DAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c23ed493d21c30c-VIE Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Thu, 04 May 2023 21:44:53 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-cbwy2y25pylufhkfs0la9nqe openai-processing-ms: - '7454' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86479' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.347s x-request-id: - 09cc7d22955a6d026a3d8c433a65c28e status: code: 200 message: OK version: 1 ================================================ FILE: tests/integration/goal_oriented/cassettes/test_write_file/test_write_file.yaml ================================================ interactions: - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are write_to_file-GPT, an AI designed to use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\" and then use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\".\n2. Use the task_complete command to complete the task.\n3. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '3410' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7ySQY/aQAyF7/0Vli+5DAgWFmiO7aU9VlWLVk2FZieGDEzGsxlHUKH89yokLGwq 9dZe/eznzzPvjDbHFE2hxZTBjZYffL3e0+HxhfmjfFnPwvdCvxRh//TEOSrk5z0Z6SfGhsvgSCx7 VGgq0kI5ptPFajaZL1cPE4Ul5+QwxV2Q0Wz8OJK6eubRZDaZosI66h1hesZQcRlkI3wgHzFdLh4U 3rxf69PVXKGwaPdaej9fNApNwdZQxPTHGUuKV9uKHWGKOkYbRXtpIdkL+faAc+YBADKUgutdITHD FPpiL9BJ2mKGn8ET5SAMdSSQguBYWaGN8GZrHYHhstT+0nARIPlEzjGsuXJ5AtYLg4ZLq9cl5ZAU rb45tvpYTpKMM1T3uyvSkb31uw7gq/WG4IbRbdEQAxm7tQZa1vs96i+UNl7EkqOADqHiUFktBOyp P3FIE5z2HcgIvv2nFzCVFWtsLK9fkLNPBCIRaP8LbIw1RThaKUAKG6FlHHrEQPpwnT9a5/7l/3WL G3XNVW/6R6xah47pDcQAXVe7YSI7oe29WQw43pgMU3x305X2QtyDZ77BRuHWehuLTRdATDEKB1Ro fU4nTCfNz+bdbwAAAP//AwAsTU8qNQQAAA== headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c11ea4fb82bfade-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Tue, 02 May 2023 17:17:12 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '11488' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86497' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.335s x-request-id: - 81a8552ed38d0037b7b7c23664f5ae2b status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "user", "content": "Your task is to create a concise running summary of actions and information results in the provided text, focusing on key and potentially important information to remember.\n\nYou will receive the current summary and the your latest actions. Combine them, adding relevant key information from the latest development in 1st person past tense and keeping the summary concise.\n\nSummary So Far:\n\"\"\"\nI was created.\n\"\"\"\n\nLatest Development:\n\"\"\"\nNothing new happened.\n\"\"\"\n"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '599' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA0SOQU/CQBBG7/6K5jtvSUsRcI+KMd6MeNIYsm2HdqGdWbtD0JD+d0OCcn3Je3kn +BoWVeu06kOXLu75sH9arV+6t9V0/vW6e3jfCpe0fvTNDwyk3FGlF2NSSR86Ui8Mg2ogp1TD5vNl kc0Wy2Jm0EtNHSyaoGkxuU31MJSSZkWWw+AQXUOwJ4RB+qAblT1xhL2bGlzTV2ygoq77B3mWjwZV K76iCPtxQk/xrzlIR7BwMfqojvV8KKzE5/vn5OhicjlOHNcJi7aem4TpmLQuJq0LgZjqCUaDrWcf 281ALgrDIqoEGHiu6Rs2Gz/Hm18AAAD//wMA1rZZUUkBAAA= headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c11eaa7f982fade-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Tue, 02 May 2023 17:17:15 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '1025' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '89866' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 88ms x-request-id: - dcfcb7a7f05897ff8d7924daba89388c status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are write_to_file-GPT, an AI designed to use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\" and then use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\".\n2. Use the task_complete command to complete the task.\n3. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. Task Complete (Shutdown): \"task_complete\", args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\",\n \"reasoning\": \"Since I need to write a specific text into a file, the write_to_file command is the most appropriate one to use.\",\n \"plan\": \"- Use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\",\n \"criticism\": \"I don''t see any issues with this plan.\",\n \"speak\": \"I will use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\"\n },\n \"command\": {\n \"name\": \"write_to_file\",\n \"args\": {\n \"filename\": \"hello_world.txt\",\n \"text\": \"Hello World\"\n }\n }\n}"}, {"role": "system", "content": "Command write_to_file returned: File written to successfully."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '4576' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7SST2/TQBDF73yK0Vxy2UQxpknwsRKoESoXFIGEq2i7nthL1ruuZ0xSonx35NhO SrgVcZ0/b35v9x3QZpigKbSYsnLj+a1vytzef1x9e7qp71a/dFR9+sD389Xbp1tUGB5/kJF+Y2JC WTkSGzwqNDVpoQyTaLaIp+/mi3imsAwZOUwwr2QcT27G0tSPYTyNpxEqbFjnhMkBqzqUlawlbMkz JtE0ihVexC+NRaxQgmh3KUXvZ0eFpgjWEGPy/YAl8SBcB0eYoGa2LNpLixm8kG8tHFIPAJCiFKHJ C+EUE+iLfYP20hZT/Bx2IIUWWEKhfxLsaitCHkZ35FyAr6F22QislwAaNtYReF1SBqOi7a93bX8i exkpWIInykACNEwgBYFo3q57uwQmlKX2p4FzbZiapKheAtakOXjr847yi/WGBsRhOTtvQ9icwK3P /zO49Zk1+gSu5XLf8pnq2knltO9MjGH1z+9iWpfGctlJLiELfiTARKD9M1jmhhh2VgqQwjK01681 uCK9HfZ31rnX/VcneVRD2Prxv7LWPnt37Q/5Kyhd59cxfZmEAfcUAG6MIeZN49zz6wN7tnCy0btJ /RGPCjfWWy7W3W1MkCVUqND6jPaYTI8Pxze/AQAA//8DAOv7y1VhBAAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c11eaaf0abffade-SJC Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Tue, 02 May 2023 17:17:26 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - user-adtx4fhfg1qsiyzdoaxciooj openai-processing-ms: - '10848' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86485' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.342s x-request-id: - e0a52aa6b569cb67dbbb8e6098e31231 status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are write_to_file-GPT, an AI designed to use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\" and then use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\".\n2. Use the task_complete command to complete the task.\n3. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '3406' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7yTT4/TQAzF73wKy5de0iqh0O3mthwQPYAEdMUuBFWzE7cZOhmHGUctVPnuKEn/ 7AZxQnD1c9772eMc0OSYoi6U6LKy46tXP+5vbpZhoT+/u329nCc/39692H58PzUf7q8xQn74RlqO X0w0l5UlMewwQu1JCeWYJrP5NJ7Pr5J5hCXnZDHFTSXj6eTlWGr/wON4GicYYR3UhjA9YOW5rGQl vCUXML2aJRFevM/153EcobAoey5dz5ImQl2w0RQw/XLAksLJ1rMlTFGFYIIoJy0kOyHXDnDIHABA hlJwvSkkZJjCsXgUaC9tMcMFOKIchKEOBFIQ7LwRWgmv1sYSaC5L5bqGToDRG7KW4RN7m4/AOGFQ 0LU6VVIOo6LVV7tWn8heRpMMo8fZnlRgZ9ymB1gWBKLCFjx9r42nACX9RVoEHe0f5zChE1VVea68 UXKR1uxBirZBhe2QurLK9cBjuP1Pm9LeiNEmlMOnIhdq3zIogcX54TR7T1q6iDah3wTt5YIk3HW2 HcOwUJHanoJ2xtp/eRB9cBOdDvVo+tudtg490xOIAbrym+GJ98JpE73FgOOJyfC3eDTTibYjPoJn rsEmwrVxJhSr/qIxxSBcYYTG5bTHNG6+Ns9+AQAA//8DAAaMC/aGBAAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c15d0c86db14665-DFW Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Wed, 03 May 2023 04:38:50 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '11864' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86496' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.335s x-request-id: - 5d9f1c3b077997ea564ea7088eca733f status: code: 200 message: OK - request: body: '{"model": "gpt-3.5-turbo", "messages": [{"role": "system", "content": "You are write_to_file-GPT, an AI designed to use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\" and then use the task_complete command to complete the task.\nYour decisions must always be made independently without seeking user assistance. Play to your strengths as an LLM and pursue simple strategies with no legal complications.\n\nGOALS:\n\n1. Use the write_to_file command to write ''Hello World'' into a file named \"hello_world.txt\".\n2. Use the task_complete command to complete the task.\n3. Do not use any other commands.\n\n\nConstraints:\n1. ~4000 word limit for short term memory. Your short term memory is short, so immediately save important information to files.\n2. If you are unsure how you previously did something or want to recall past events, thinking about similar events will help you remember.\n3. No user assistance\n4. Exclusively use the commands listed in double quotes e.g. \"command name\"\n\nCommands:\n1. append_to_file: Append to file, args: \"filename\": \"\", \"text\": \"\"\n2. delete_file: Delete file, args: \"filename\": \"\"\n3. list_files: List Files in Directory, args: \"directory\": \"\"\n4. read_file: Read file, args: \"filename\": \"\"\n5. write_to_file: Write to file, args: \"filename\": \"\", \"text\": \"\"\n6. delete_agent: Delete GPT Agent, args: \"key\": \"\"\n7. get_hyperlinks: Get text summary, args: \"url\": \"\"\n8. get_text_summary: Get text summary, args: \"url\": \"\", \"question\": \"\"\n9. list_agents: List GPT Agents, args: () -> str\n10. message_agent: Message GPT Agent, args: \"key\": \"\", \"message\": \"\"\n11. start_agent: Start GPT Agent, args: \"name\": \"\", \"task\": \"\", \"prompt\": \"\"\n12. task_complete: Task Complete (Shutdown), args: \"reason\": \"\"\n\nResources:\n1. Internet access for searches and information gathering.\n2. Long Term memory management.\n3. GPT-3.5 powered Agents for delegation of simple tasks.\n4. File output.\n\nPerformance Evaluation:\n1. Continuously review and analyze your actions to ensure you are performing to the best of your abilities.\n2. Constructively self-criticize your big-picture behavior constantly.\n3. Reflect on past decisions and strategies to refine your approach.\n4. Every command has a cost, so be smart and efficient. Aim to complete tasks in the least number of steps.\n5. Write all code to a file.\n\nYou should only respond in JSON format as described below \nResponse Format: \n{\n \"thoughts\": {\n \"text\": \"thought\",\n \"reasoning\": \"reasoning\",\n \"plan\": \"- short bulleted\\n- list that conveys\\n- long-term plan\",\n \"criticism\": \"constructive self-criticism\",\n \"speak\": \"thoughts summary to say to user\"\n },\n \"command\": {\n \"name\": \"command name\",\n \"args\": {\n \"arg name\": \"value\"\n }\n }\n} \nEnsure the response can be parsed by Python json.loads"}, {"role": "system", "content": "The current time and date is Tue Jan 1 00:00:00 2000"}, {"role": "system", "content": "This reminds you of these events from your past: \nI was created and nothing new has happened."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}, {"role": "assistant", "content": "{\n \"thoughts\": {\n \"text\": \"I need to use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\",\n \"reasoning\": \"The task requires me to write ''Hello World'' into a file named ''hello_world.txt'', and the write_to_file command is the appropriate command for this task.\",\n \"plan\": \"- Use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\",\n \"criticism\": \"I need to ensure that I use the correct filename and text to write to the file.\",\n \"speak\": \"I will use the write_to_file command to write ''Hello World'' into a file named ''hello_world.txt''.\"\n },\n \"command\": {\n \"name\": \"write_to_file\",\n \"args\": {\n \"filename\": \"hello_world.txt\",\n \"text\": \"Hello World\"\n }\n }\n}"}, {"role": "system", "content": "Command write_to_file returned: File written to successfully."}, {"role": "user", "content": "Determine which next command to use, and respond using the format specified above:"}], "temperature": 0, "max_tokens": 0}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '4653' Content-Type: - application/json method: POST uri: https://api.openai.com/v1/chat/completions response: body: string: !!binary | H4sIAAAAAAAAA7RTyW7bMBC99ysGc/GFNmQbbVwdiwapgQLd0hZBVBgMNZZYU6RKjrzA0L8XtKQk TReg242YefMWcnhEnWOKqpSsqtqMz54drl5V+Z6W/OaDfHlhzpMNmefnh4vX5i0KdDefSXE/MVGu qg2xdhYFKk+SKcd0+mQxTxaLs/lMYOVyMphiUfN4Pnk85sbfuHEyT6YosAmyIEyPWHtX1bxityEb MJ0ms4XAO/LbxiyZCmTH0txhZ7OnrUBVOq0oYHp9xIrCQOydIUxRhqADS8vRprNMNkY4ZhYAIEMu XVOUHDJMoS/2DdpzLGa4hFJuCUKjFIWwbow5wM5rZrIwekHGOPjovMlHoC07kLDWhsDKinIYlbG/ 2sX+hPc8msBlSWBpzxCYatAB2EETCLgkYBk2qz48gXJVJW0eAbe1ATXJUNy360kGZ7UtOs+XPQw8 fWm0pwAVRZ7om37fNZxslGR/YATeaasIfnVL7E7wqCBOp3+fvzbSdtHH8P6v2ZTXrJUO1bABlug0 STY0Ps5JhiXU3m11TiBhK43OoXsEWDs/SGhb/FQk1CQ3g8BOG/Nn19BRtmLY6B7+3ULHp+3UvqF/ YEr64uFfuL9g/+NHDBFOMfo0mW2xFbjWVody1WljioFdjQK1zWmPadJ+ah99BQAA//8DAMo52K3G BAAA headers: CF-Cache-Status: - DYNAMIC CF-RAY: - 7c15d120ec8f4665-DFW Cache-Control: - no-cache, must-revalidate Connection: - keep-alive Content-Encoding: - gzip Content-Type: - application/json Date: - Wed, 03 May 2023 04:39:04 GMT Server: - cloudflare access-control-allow-origin: - '*' alt-svc: - h3=":443"; ma=86400, h3-29=":443"; ma=86400 openai-model: - gpt-3.5-turbo-0301 openai-organization: - significant-gravitas openai-processing-ms: - '12271' openai-version: - '2020-10-01' strict-transport-security: - max-age=15724800; includeSubDomains x-ratelimit-limit-requests: - '3500' x-ratelimit-limit-tokens: - '90000' x-ratelimit-remaining-requests: - '3499' x-ratelimit-remaining-tokens: - '86481' x-ratelimit-reset-requests: - 17ms x-ratelimit-reset-tokens: - 2.346s x-request-id: - a8c02e37e5af0c851759aa261f20f701 status: code: 200 message: OK version: 1 ================================================ FILE: tests/integration/goal_oriented/goal_oriented_tasks.md ================================================ If the goal oriented task pipeline fails, it means: - you somehow changed the way the system prompt is generated - or you broke autogpt. To know which one, you can run the following command: ```bash pytest -s -k tests/integration/goal_oriented If the test is successul, it will record new cassettes in VCR. Then you can just push these to your branch and the pipeline will pass ================================================ FILE: tests/integration/goal_oriented/test_browse_website.py ================================================ import pytest from autogpt.agent import Agent from autogpt.commands.file_operations import read_file from tests.integration.agent_utils import run_interaction_loop from tests.utils import requires_api_key @requires_api_key("OPENAI_API_KEY") @pytest.mark.vcr def test_browse_website(browser_agent: Agent) -> None: file_path = browser_agent.workspace.get_path("browse_website.txt") try: run_interaction_loop(browser_agent, 120) # catch system exit exceptions except SystemExit: # the agent returns an exception when it shuts down content = read_file(file_path) assert "£25.89" in content, f"Expected £25.89, got {content}" ================================================ FILE: tests/integration/goal_oriented/test_write_file.py ================================================ import pytest from autogpt.agent import Agent from autogpt.commands.file_operations import read_file from tests.integration.agent_utils import run_interaction_loop from tests.utils import requires_api_key @requires_api_key("OPENAI_API_KEY") @pytest.mark.vcr def test_write_file(writer_agent: Agent) -> None: file_path = str(writer_agent.workspace.get_path("hello_world.txt")) try: run_interaction_loop(writer_agent, 40) # catch system exit exceptions except SystemExit: # the agent returns an exception when it shuts down content = read_file(file_path) assert content == "Hello World", f"Expected 'Hello World', got {content}" ================================================ FILE: tests/integration/memory_tests.py ================================================ import random import string import sys import unittest from pathlib import Path from autogpt.config import Config from autogpt.memory.local import LocalCache class TestLocalCache(unittest.TestCase): def generate_random_string(self, length): return "".join(random.choice(string.ascii_letters) for _ in range(length)) def setUp(self): """Set up the test environment for the LocalCache tests.""" cfg = cfg = Config() self.cache = LocalCache(cfg) self.cache.clear() # Add example texts to the cache self.example_texts = [ "The quick brown fox jumps over the lazy dog", "I love machine learning and natural language processing", "The cake is a lie, but the pie is always true", "ChatGPT is an advanced AI model for conversation", ] for text in self.example_texts: self.cache.add(text) # Add some random strings to test noise for _ in range(5): self.cache.add(self.generate_random_string(10)) def test_get_relevant(self): """Test getting relevant texts from the cache.""" query = "I'm interested in artificial intelligence and NLP" k = 3 relevant_texts = self.cache.get_relevant(query, k) print(f"Top {k} relevant texts for the query '{query}':") for i, text in enumerate(relevant_texts, start=1): print(f"{i}. {text}") self.assertEqual(len(relevant_texts), k) self.assertIn(self.example_texts[1], relevant_texts) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/integration/milvus_memory_tests.py ================================================ # sourcery skip: snake-case-functions """Tests for the MilvusMemory class.""" import random import string import unittest from autogpt.config import Config from autogpt.memory.milvus import MilvusMemory try: class TestMilvusMemory(unittest.TestCase): """Unit tests for the MilvusMemory class.""" def generate_random_string(self, length: int) -> str: return "".join(random.choice(string.ascii_letters) for _ in range(length)) def setUp(self) -> None: cfg = Config() cfg.milvus_addr = "localhost:19530" self.memory = MilvusMemory(cfg) self.memory.clear() # Add example texts to the cache self.example_texts = [ "The quick brown fox jumps over the lazy dog", "I love machine learning and natural language processing", "The cake is a lie, but the pie is always true", "ChatGPT is an advanced AI model for conversation", ] for text in self.example_texts: self.memory.add(text) # Add some random strings to test noise for _ in range(5): self.memory.add(self.generate_random_string(10)) def test_get_relevant(self) -> None: """Test getting relevant texts from the cache.""" query = "I'm interested in artificial intelligence and NLP" num_relevant = 3 relevant_texts = self.memory.get_relevant(query, num_relevant) print(f"Top {k} relevant texts for the query '{query}':") for i, text in enumerate(relevant_texts, start=1): print(f"{i}. {text}") self.assertEqual(len(relevant_texts), k) self.assertIn(self.example_texts[1], relevant_texts) except: print( "Skipping tests/integration/milvus_memory_tests.py as Milvus is not installed." ) ================================================ FILE: tests/integration/test_execute_code.py ================================================ import random import string import tempfile import pytest from pytest_mock import MockerFixture import autogpt.commands.execute_code as sut # system under testing from autogpt.config import Config @pytest.fixture def config_allow_execute(config: Config, mocker: MockerFixture): yield mocker.patch.object(config, "execute_local_commands", True) @pytest.fixture def python_test_file(config: Config, random_string): temp_file = tempfile.NamedTemporaryFile(dir=config.workspace_path, suffix=".py") temp_file.write(str.encode(f"print('Hello {random_string}!')")) temp_file.flush() yield temp_file.name temp_file.close() @pytest.fixture def random_string(): return "".join(random.choice(string.ascii_lowercase) for _ in range(10)) def test_execute_python_file(python_test_file: str, random_string: str): result = sut.execute_python_file(python_test_file) assert result == f"Hello {random_string}!\n" def test_execute_python_file_invalid(): assert all( s in sut.execute_python_file("not_python").lower() for s in ["error:", "invalid", ".py"] ) assert all( s in sut.execute_python_file("notexist.py").lower() for s in ["error:", "does not exist"] ) def test_execute_shell(config_allow_execute, random_string): result = sut.execute_shell(f"echo 'Hello {random_string}!'") assert f"Hello {random_string}!" in result ================================================ FILE: tests/integration/test_git_commands.py ================================================ import pytest from git.exc import GitCommandError from git.repo.base import Repo from autogpt.commands.git_operations import clone_repository @pytest.fixture def mock_clone_from(mocker): return mocker.patch.object(Repo, "clone_from") def test_clone_auto_gpt_repository(workspace, mock_clone_from, config): mock_clone_from.return_value = None repo = "github.com/Significant-Gravitas/Auto-GPT.git" scheme = "https://" url = scheme + repo clone_path = str(workspace.get_path("auto-gpt-repo")) expected_output = f"Cloned {url} to {clone_path}" clone_result = clone_repository(url=url, clone_path=clone_path) assert clone_result == expected_output mock_clone_from.assert_called_once_with( url=f"{scheme}{config.github_username}:{config.github_api_key}@{repo}", to_path=clone_path, ) def test_clone_repository_error(workspace, mock_clone_from): url = "https://github.com/this-repository/does-not-exist.git" clone_path = str(workspace.get_path("does-not-exist")) mock_clone_from.side_effect = GitCommandError( "clone", "fatal: repository not found", "" ) result = clone_repository(url=url, clone_path=clone_path) assert "Error: " in result ================================================ FILE: tests/integration/test_llm_utils.py ================================================ import string from unittest.mock import MagicMock import pytest from numpy.random import RandomState from pytest_mock import MockerFixture from autogpt.config import Config from autogpt.llm import llm_utils from autogpt.llm.api_manager import ApiManager from autogpt.llm.modelsinfo import COSTS from tests.utils import requires_api_key @pytest.fixture(scope="session") def random_large_string(): """Big string used to overwhelm token limits.""" seed = 42 n_characters = 30_000 random = RandomState(seed) return "".join(random.choice(list(string.ascii_lowercase), size=n_characters)) @pytest.fixture() def api_manager(mocker: MockerFixture): api_manager = ApiManager() mocker.patch.multiple( api_manager, total_prompt_tokens=0, total_completion_tokens=0, total_cost=0, ) yield api_manager @pytest.fixture() def spy_create_embedding(mocker: MockerFixture): return mocker.spy(llm_utils, "create_embedding") @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_get_ada_embedding( config: Config, api_manager: ApiManager, spy_create_embedding: MagicMock ): token_cost = COSTS[config.embedding_model]["prompt"] llm_utils.get_ada_embedding("test") spy_create_embedding.assert_called_once_with("test", model=config.embedding_model) assert (prompt_tokens := api_manager.get_total_prompt_tokens()) == 1 assert api_manager.get_total_completion_tokens() == 0 assert api_manager.get_total_cost() == (prompt_tokens * token_cost) / 1000 @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_get_ada_embedding_large_context(random_large_string): # This test should be able to mock the openai call after we have a fix. We don't need # to hit the API to test the logic of the function (so not using vcr). This is a quick # regression test to document the issue. llm_utils.get_ada_embedding(random_large_string) ================================================ FILE: tests/integration/test_local_cache.py ================================================ # sourcery skip: snake-case-functions """Tests for LocalCache class""" import unittest import orjson import pytest from autogpt.memory.local import EMBED_DIM, SAVE_OPTIONS from autogpt.memory.local import LocalCache as LocalCache_ from tests.utils import requires_api_key @pytest.fixture def LocalCache(): # Hack, real gross. Singletons are not good times. if LocalCache_ in LocalCache_._instances: del LocalCache_._instances[LocalCache_] return LocalCache_ @pytest.fixture def mock_embed_with_ada(mocker): mocker.patch( "autogpt.memory.local.get_ada_embedding", return_value=[0.1] * EMBED_DIM, ) def test_init_without_backing_file(LocalCache, config, workspace): cache_file = workspace.root / f"{config.memory_index}.json" assert not cache_file.exists() LocalCache(config) assert cache_file.exists() assert cache_file.read_text() == "{}" def test_init_with_backing_empty_file(LocalCache, config, workspace): cache_file = workspace.root / f"{config.memory_index}.json" cache_file.touch() assert cache_file.exists() LocalCache(config) assert cache_file.exists() assert cache_file.read_text() == "{}" def test_init_with_backing_file(LocalCache, config, workspace): cache_file = workspace.root / f"{config.memory_index}.json" cache_file.touch() raw_data = {"texts": ["test"]} data = orjson.dumps(raw_data, option=SAVE_OPTIONS) with cache_file.open("wb") as f: f.write(data) assert cache_file.exists() LocalCache(config) assert cache_file.exists() assert cache_file.read_text() == "{}" def test_add(LocalCache, config, mock_embed_with_ada): cache = LocalCache(config) cache.add("test") assert cache.data.texts == ["test"] assert cache.data.embeddings.shape == (1, EMBED_DIM) def test_clear(LocalCache, config, mock_embed_with_ada): cache = LocalCache(config) assert cache.data.texts == [] assert cache.data.embeddings.shape == (0, EMBED_DIM) cache.add("test") assert cache.data.texts == ["test"] assert cache.data.embeddings.shape == (1, EMBED_DIM) cache.clear() assert cache.data.texts == [] assert cache.data.embeddings.shape == (0, EMBED_DIM) def test_get(LocalCache, config, mock_embed_with_ada): cache = LocalCache(config) assert cache.get("test") == [] cache.add("test") assert cache.get("test") == ["test"] @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_get_relevant(LocalCache, config) -> None: cache = LocalCache(config) text1 = "Sample text 1" text2 = "Sample text 2" cache.add(text1) cache.add(text2) result = cache.get_relevant(text1, 1) assert result == [text1] def test_get_stats(LocalCache, config, mock_embed_with_ada) -> None: cache = LocalCache(config) text = "Sample text" cache.add(text) stats = cache.get_stats() assert stats == (1, cache.data.embeddings.shape) ================================================ FILE: tests/integration/test_memory_management.py ================================================ import json import pytest from autogpt.config import Config from autogpt.memory import get_memory from autogpt.memory_management.store_memory import ( save_memory_trimmed_from_context_window, ) from tests.utils import requires_api_key @pytest.fixture def message_history_fixture(): assistant_reply = { "thoughts": { "text": "thoughts", "reasoning": "reasoning", "plan": "plan", "criticism": "criticism", "speak": "speak", }, "command": {"name": "google", "args": {"query": "google_query"}}, } return [ {"content": json.dumps(assistant_reply, indent=4)}, {"content": "Command Result: Important Information."}, ] @pytest.fixture def expected_permanent_memory() -> str: return """Assistant Reply: { "thoughts": { "text": "thoughts", "reasoning": "reasoning", "plan": "plan", "criticism": "criticism", "speak": "speak" }, "command": { "name": "google", "args": { "query": "google_query" } } } Result: None Human Feedback:Command Result: Important Information.""" @requires_api_key("OPENAI_API_KEY") @pytest.mark.vcr def test_save_memory_trimmed_from_context_window( message_history_fixture, expected_permanent_memory, config: Config ): next_message_to_add_index = len(message_history_fixture) - 1 memory = get_memory(config, init=True) save_memory_trimmed_from_context_window( message_history_fixture, next_message_to_add_index, memory ) memory_found = memory.get_relevant("Important Information", 5) assert memory_found[0] == expected_permanent_memory ================================================ FILE: tests/integration/test_setup.py ================================================ from unittest.mock import patch import pytest from autogpt.config.ai_config import AIConfig from autogpt.setup import ( generate_aiconfig_automatic, generate_aiconfig_manual, prompt_user, ) from tests.utils import requires_api_key @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_generate_aiconfig_automatic_default(): user_inputs = [""] with patch("builtins.input", side_effect=user_inputs): ai_config = prompt_user() assert isinstance(ai_config, AIConfig) assert ai_config.ai_name is not None assert ai_config.ai_role is not None assert 1 <= len(ai_config.ai_goals) <= 5 @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_generate_aiconfig_automatic_typical(): user_prompt = "Help me create a rock opera about cybernetic giraffes" ai_config = generate_aiconfig_automatic(user_prompt) assert isinstance(ai_config, AIConfig) assert ai_config.ai_name is not None assert ai_config.ai_role is not None assert 1 <= len(ai_config.ai_goals) <= 5 @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_generate_aiconfig_automatic_fallback(): user_inputs = [ "T&GF£OIBECC()!*", "Chef-GPT", "an AI designed to browse bake a cake.", "Purchase ingredients", "Bake a cake", "", "", ] with patch("builtins.input", side_effect=user_inputs): ai_config = prompt_user() assert isinstance(ai_config, AIConfig) assert ai_config.ai_name == "Chef-GPT" assert ai_config.ai_role == "an AI designed to browse bake a cake." assert ai_config.ai_goals == ["Purchase ingredients", "Bake a cake"] @pytest.mark.vcr @requires_api_key("OPENAI_API_KEY") def test_prompt_user_manual_mode(): user_inputs = [ "--manual", "Chef-GPT", "an AI designed to browse bake a cake.", "Purchase ingredients", "Bake a cake", "", "", ] with patch("builtins.input", side_effect=user_inputs): ai_config = prompt_user() assert isinstance(ai_config, AIConfig) assert ai_config.ai_name == "Chef-GPT" assert ai_config.ai_role == "an AI designed to browse bake a cake." assert ai_config.ai_goals == ["Purchase ingredients", "Bake a cake"] ================================================ FILE: tests/integration/weaviate_memory_tests.py ================================================ import unittest from uuid import uuid4 from weaviate import Client from weaviate.util import get_valid_uuid from autogpt.config import Config from autogpt.llm import get_ada_embedding from autogpt.memory.weaviate import WeaviateMemory class TestWeaviateMemory(unittest.TestCase): cfg = None client = None index = None @classmethod def setUpClass(cls): """Set up the test environment for the WeaviateMemory tests.""" # only create the connection to weaviate once cls.cfg = Config() if cls.cfg.use_weaviate_embedded: from weaviate.embedded import EmbeddedOptions cls.client = Client( embedded_options=EmbeddedOptions( hostname=cls.cfg.weaviate_host, port=int(cls.cfg.weaviate_port), persistence_data_path=cls.cfg.weaviate_embedded_path, ) ) else: cls.client = Client( f"{cls.cfg.weaviate_protocol}://{cls.cfg.weaviate_host}:{self.cfg.weaviate_port}" ) cls.index = WeaviateMemory.format_classname(cls.cfg.memory_index) """ In order to run these tests you will need a local instance of Weaviate running. Refer to https://weaviate.io/developers/weaviate/installation/docker-compose for creating local instances using docker. Alternatively in your .env file set the following environmental variables to run Weaviate embedded (see: https://weaviate.io/developers/weaviate/installation/embedded): USE_WEAVIATE_EMBEDDED=True WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate" """ def setUp(self): """Set up the test environment for the WeaviateMemory tests.""" try: self.client.schema.delete_class(self.index) except: pass self.memory = WeaviateMemory(self.cfg) def test_add(self): """Test adding a text to the cache""" doc = "You are a Titan name Thanos and you are looking for the Infinity Stones" self.memory.add(doc) result = self.client.query.get(self.index, ["raw_text"]).do() actual = result["data"]["Get"][self.index] self.assertEqual(len(actual), 1) self.assertEqual(actual[0]["raw_text"], doc) def test_get(self): """Test getting a text from the cache""" doc = "You are an Avenger and swore to defend the Galaxy from a menace called Thanos" # add the document to the cache with self.client.batch as batch: batch.add_data_object( uuid=get_valid_uuid(uuid4()), data_object={"raw_text": doc}, class_name=self.index, vector=get_ada_embedding(doc), ) batch.flush() actual = self.memory.get(doc) self.assertEqual(len(actual), 1) self.assertEqual(actual[0], doc) def test_get_stats(self): """Test getting the stats of the cache""" docs = [ "You are now about to count the number of docs in this index", "And then you about to find out if you can count correctly", ] [self.memory.add(doc) for doc in docs] stats = self.memory.get_stats() self.assertTrue(stats) self.assertTrue("count" in stats) self.assertEqual(stats["count"], 2) def test_clear(self): """Test clearing the cache""" docs = [ "Shame this is the last test for this class", "Testing is fun when someone else is doing it", ] [self.memory.add(doc) for doc in docs] self.assertEqual(self.memory.get_stats()["count"], 2) self.memory.clear() self.assertEqual(self.memory.get_stats()["count"], 0) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/milvus_memory_test.py ================================================ # sourcery skip: snake-case-functions """Tests for the MilvusMemory class.""" import os import sys import unittest try: from autogpt.memory.milvus import MilvusMemory def mock_config() -> dict: """Mock the config object for testing purposes.""" # Return a mock config object with the required attributes return type( "MockConfig", (object,), { "debug_mode": False, "continuous_mode": False, "speak_mode": False, "milvus_collection": "autogpt", "milvus_addr": "localhost:19530", }, ) class TestMilvusMemory(unittest.TestCase): """Tests for the MilvusMemory class.""" def setUp(self) -> None: """Set up the test environment""" self.cfg = mock_config() self.memory = MilvusMemory(self.cfg) def test_add(self) -> None: """Test adding a text to the cache""" text = "Sample text" self.memory.clear() self.memory.add(text) result = self.memory.get(text) self.assertEqual([text], result) def test_clear(self) -> None: """Test clearing the cache""" self.memory.clear() self.assertEqual(self.memory.collection.num_entities, 0) def test_get(self) -> None: """Test getting a text from the cache""" text = "Sample text" self.memory.clear() self.memory.add(text) result = self.memory.get(text) self.assertEqual(result, [text]) def test_get_relevant(self) -> None: """Test getting relevant texts from the cache""" text1 = "Sample text 1" text2 = "Sample text 2" self.memory.clear() self.memory.add(text1) self.memory.add(text2) result = self.memory.get_relevant(text1, 1) self.assertEqual(result, [text1]) def test_get_stats(self) -> None: """Test getting the cache stats""" text = "Sample text" self.memory.clear() self.memory.add(text) stats = self.memory.get_stats() self.assertEqual(15, len(stats)) except ImportError as err: print(f"Skipping tests for MilvusMemory: {err}") ================================================ FILE: tests/mocks/__init__.py ================================================ ================================================ FILE: tests/mocks/mock_commands.py ================================================ from autogpt.commands.command import command @command("function_based", "Function-based test command") def function_based(arg1: int, arg2: str) -> str: """A function-based test command that returns a string with the two arguments separated by a dash.""" return f"{arg1} - {arg2}" ================================================ FILE: tests/test_agent.py ================================================ from unittest.mock import MagicMock import pytest from autogpt.agent import Agent from autogpt.config import Config @pytest.fixture def agent(): ai_name = "Test AI" memory = MagicMock() full_message_history = [] next_action_count = 0 command_registry = MagicMock() config = Config() system_prompt = "System prompt" triggering_prompt = "Triggering prompt" workspace_directory = "workspace_directory" agent = Agent( ai_name, memory, full_message_history, next_action_count, command_registry, config, system_prompt, triggering_prompt, workspace_directory, ) return agent def test_agent_initialization(agent): assert agent.ai_name == "Test AI" assert agent.memory == agent.memory assert agent.full_message_history == [] assert agent.next_action_count == 0 assert agent.command_registry == agent.command_registry assert agent.config == agent.config assert agent.system_prompt == "System prompt" assert agent.triggering_prompt == "Triggering prompt" # More test methods can be added for specific agent interactions # For example, mocking chat_with_ai and testing the agent's interaction loop ================================================ FILE: tests/test_agent_manager.py ================================================ import pytest from autogpt.agent.agent_manager import AgentManager from autogpt.llm import create_chat_completion @pytest.fixture def agent_manager(): # Hack, real gross. Singletons are not good times. if AgentManager in AgentManager._instances: del AgentManager._instances[AgentManager] return AgentManager() @pytest.fixture def task(): return "translate English to French" @pytest.fixture def prompt(): return "Translate the following English text to French: 'Hello, how are you?'" @pytest.fixture def model(): return "gpt-3.5-turbo" @pytest.fixture(autouse=True) def mock_create_chat_completion(mocker): mock_create_chat_completion = mocker.patch( "autogpt.agent.agent_manager.create_chat_completion", wraps=create_chat_completion, ) mock_create_chat_completion.return_value = "irrelevant" return mock_create_chat_completion def test_create_agent(agent_manager, task, prompt, model): key, agent_reply = agent_manager.create_agent(task, prompt, model) assert isinstance(key, int) assert isinstance(agent_reply, str) assert key in agent_manager.agents def test_message_agent(agent_manager, task, prompt, model): key, _ = agent_manager.create_agent(task, prompt, model) user_message = "Please translate 'Good morning' to French." agent_reply = agent_manager.message_agent(key, user_message) assert isinstance(agent_reply, str) def test_list_agents(agent_manager, task, prompt, model): key, _ = agent_manager.create_agent(task, prompt, model) agents_list = agent_manager.list_agents() assert isinstance(agents_list, list) assert (key, task) in agents_list def test_delete_agent(agent_manager, task, prompt, model): key, _ = agent_manager.create_agent(task, prompt, model) success = agent_manager.delete_agent(key) assert success assert key not in agent_manager.agents ================================================ FILE: tests/test_ai_config.py ================================================ from autogpt.config.ai_config import AIConfig """ Test cases for the AIConfig class, which handles loads the AI configuration settings from a YAML file. """ def test_goals_are_always_lists_of_strings(tmp_path): """Test if the goals attribute is always a list of strings.""" yaml_content = """ ai_goals: - Goal 1: Make a sandwich - Goal 2, Eat the sandwich - Goal 3 - Go to sleep - "Goal 4: Wake up" ai_name: McFamished ai_role: A hungry AI api_budget: 0.0 """ config_file = tmp_path / "ai_settings.yaml" config_file.write_text(yaml_content) ai_config = AIConfig.load(config_file) assert len(ai_config.ai_goals) == 4 assert ai_config.ai_goals[0] == "Goal 1: Make a sandwich" assert ai_config.ai_goals[1] == "Goal 2, Eat the sandwich" assert ai_config.ai_goals[2] == "Goal 3 - Go to sleep" assert ai_config.ai_goals[3] == "Goal 4: Wake up" config_file.write_text("") ai_config.save(config_file) yaml_content2 = """ai_goals: - 'Goal 1: Make a sandwich' - Goal 2, Eat the sandwich - Goal 3 - Go to sleep - 'Goal 4: Wake up' ai_name: McFamished ai_role: A hungry AI api_budget: 0.0 """ assert config_file.read_text() == yaml_content2 ================================================ FILE: tests/test_api_manager.py ================================================ from unittest.mock import MagicMock, patch import pytest from autogpt.llm import COSTS, ApiManager api_manager = ApiManager() @pytest.fixture(autouse=True) def reset_api_manager(): api_manager.reset() yield @pytest.fixture(autouse=True) def mock_costs(): with patch.dict( COSTS, { "gpt-3.5-turbo": {"prompt": 0.002, "completion": 0.002}, "text-embedding-ada-002": {"prompt": 0.0004, "completion": 0}, }, clear=True, ): yield class TestApiManager: @staticmethod def test_create_chat_completion_debug_mode(caplog): """Test if debug mode logs response.""" api_manager_debug = ApiManager(debug=True) messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who won the world series in 2020?"}, ] model = "gpt-3.5-turbo" with patch("openai.ChatCompletion.create") as mock_create: mock_response = MagicMock() mock_response.usage.prompt_tokens = 10 mock_response.usage.completion_tokens = 20 mock_create.return_value = mock_response api_manager_debug.create_chat_completion(messages, model=model) assert "Response" in caplog.text @staticmethod def test_create_chat_completion_empty_messages(): """Test if empty messages result in zero tokens and cost.""" messages = [] model = "gpt-3.5-turbo" with patch("openai.ChatCompletion.create") as mock_create: mock_response = MagicMock() mock_response.usage.prompt_tokens = 0 mock_response.usage.completion_tokens = 0 mock_create.return_value = mock_response api_manager.create_chat_completion(messages, model=model) assert api_manager.get_total_prompt_tokens() == 0 assert api_manager.get_total_completion_tokens() == 0 assert api_manager.get_total_cost() == 0 @staticmethod def test_create_chat_completion_valid_inputs(): """Test if valid inputs result in correct tokens and cost.""" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who won the world series in 2020?"}, ] model = "gpt-3.5-turbo" with patch("openai.ChatCompletion.create") as mock_create: mock_response = MagicMock() mock_response.usage.prompt_tokens = 10 mock_response.usage.completion_tokens = 20 mock_create.return_value = mock_response api_manager.create_chat_completion(messages, model=model) assert api_manager.get_total_prompt_tokens() == 10 assert api_manager.get_total_completion_tokens() == 20 assert api_manager.get_total_cost() == (10 * 0.002 + 20 * 0.002) / 1000 def test_getter_methods(self): """Test the getter methods for total tokens, cost, and budget.""" api_manager.update_cost(60, 120, "gpt-3.5-turbo") api_manager.set_total_budget(10.0) assert api_manager.get_total_prompt_tokens() == 60 assert api_manager.get_total_completion_tokens() == 120 assert api_manager.get_total_cost() == (60 * 0.002 + 120 * 0.002) / 1000 assert api_manager.get_total_budget() == 10.0 @staticmethod def test_set_total_budget(): """Test if setting the total budget works correctly.""" total_budget = 10.0 api_manager.set_total_budget(total_budget) assert api_manager.get_total_budget() == total_budget @staticmethod def test_update_cost(): """Test if updating the cost works correctly.""" prompt_tokens = 50 completion_tokens = 100 model = "gpt-3.5-turbo" api_manager.update_cost(prompt_tokens, completion_tokens, model) assert api_manager.get_total_prompt_tokens() == 50 assert api_manager.get_total_completion_tokens() == 100 assert api_manager.get_total_cost() == (50 * 0.002 + 100 * 0.002) / 1000 ================================================ FILE: tests/test_commands.py ================================================ import os import shutil import sys from pathlib import Path import pytest from autogpt.commands.command import Command, CommandRegistry class TestCommand: """Test cases for the Command class.""" @staticmethod def example_command_method(arg1: int, arg2: str) -> str: """Example function for testing the Command class.""" # This function is static because it is not used by any other test cases. return f"{arg1} - {arg2}" def test_command_creation(self): """Test that a Command object can be created with the correct attributes.""" cmd = Command( name="example", description="Example command", method=self.example_command_method, ) assert cmd.name == "example" assert cmd.description == "Example command" assert cmd.method == self.example_command_method assert cmd.signature == "(arg1: int, arg2: str) -> str" def test_command_call(self): """Test that Command(*args) calls and returns the result of method(*args).""" # Create a Command object with the example_command_method. cmd = Command( name="example", description="Example command", method=self.example_command_method, ) result = cmd(arg1=1, arg2="test") assert result == "1 - test" def test_command_call_with_invalid_arguments(self): """Test that calling a Command object with invalid arguments raises a TypeError.""" cmd = Command( name="example", description="Example command", method=self.example_command_method, ) with pytest.raises(TypeError): cmd(arg1="invalid", does_not_exist="test") def test_command_default_signature(self): """Test that the default signature is generated correctly.""" cmd = Command( name="example", description="Example command", method=self.example_command_method, ) assert cmd.signature == "(arg1: int, arg2: str) -> str" def test_command_custom_signature(self): custom_signature = "custom_arg1: int, custom_arg2: str" cmd = Command( name="example", description="Example command", method=self.example_command_method, signature=custom_signature, ) assert cmd.signature == custom_signature class TestCommandRegistry: @staticmethod def example_command_method(arg1: int, arg2: str) -> str: return f"{arg1} - {arg2}" def test_register_command(self): """Test that a command can be registered to the registry.""" registry = CommandRegistry() cmd = Command( name="example", description="Example command", method=self.example_command_method, ) registry.register(cmd) assert cmd.name in registry.commands assert registry.commands[cmd.name] == cmd def test_unregister_command(self): """Test that a command can be unregistered from the registry.""" registry = CommandRegistry() cmd = Command( name="example", description="Example command", method=self.example_command_method, ) registry.register(cmd) registry.unregister(cmd.name) assert cmd.name not in registry.commands def test_get_command(self): """Test that a command can be retrieved from the registry.""" registry = CommandRegistry() cmd = Command( name="example", description="Example command", method=self.example_command_method, ) registry.register(cmd) retrieved_cmd = registry.get_command(cmd.name) assert retrieved_cmd == cmd def test_get_nonexistent_command(self): """Test that attempting to get a nonexistent command raises a KeyError.""" registry = CommandRegistry() with pytest.raises(KeyError): registry.get_command("nonexistent_command") def test_call_command(self): """Test that a command can be called through the registry.""" registry = CommandRegistry() cmd = Command( name="example", description="Example command", method=self.example_command_method, ) registry.register(cmd) result = registry.call("example", arg1=1, arg2="test") assert result == "1 - test" def test_call_nonexistent_command(self): """Test that attempting to call a nonexistent command raises a KeyError.""" registry = CommandRegistry() with pytest.raises(KeyError): registry.call("nonexistent_command", arg1=1, arg2="test") def test_get_command_prompt(self): """Test that the command prompt is correctly formatted.""" registry = CommandRegistry() cmd = Command( name="example", description="Example command", method=self.example_command_method, ) registry.register(cmd) command_prompt = registry.command_prompt() assert f"(arg1: int, arg2: str)" in command_prompt def test_import_mock_commands_module(self): """Test that the registry can import a module with mock command plugins.""" registry = CommandRegistry() mock_commands_module = "tests.mocks.mock_commands" registry.import_commands(mock_commands_module) assert "function_based" in registry.commands assert registry.commands["function_based"].name == "function_based" assert ( registry.commands["function_based"].description == "Function-based test command" ) def test_import_temp_command_file_module(self, tmp_path): """ Test that the registry can import a command plugins module from a temp file. Args: tmp_path (pathlib.Path): Path to a temporary directory. """ registry = CommandRegistry() # Create a temp command file src = Path(os.getcwd()) / "tests/mocks/mock_commands.py" temp_commands_file = tmp_path / "mock_commands.py" shutil.copyfile(src, temp_commands_file) # Add the temp directory to sys.path to make the module importable sys.path.append(str(tmp_path)) temp_commands_module = "mock_commands" registry.import_commands(temp_commands_module) # Remove the temp directory from sys.path sys.path.remove(str(tmp_path)) assert "function_based" in registry.commands assert registry.commands["function_based"].name == "function_based" assert ( registry.commands["function_based"].description == "Function-based test command" ) ================================================ FILE: tests/test_config.py ================================================ """ Test cases for the Config class, which handles the configuration settings for the AI and ensures it behaves as a singleton. """ import pytest from autogpt.config import Config def test_initial_values(config): """ Test if the initial values of the Config class attributes are set correctly. """ assert config.debug_mode == False assert config.continuous_mode == False assert config.speak_mode == False assert config.fast_llm_model == "gpt-3.5-turbo" assert config.smart_llm_model == "gpt-4" assert config.fast_token_limit == 4000 assert config.smart_token_limit == 8000 def test_set_continuous_mode(config): """ Test if the set_continuous_mode() method updates the continuous_mode attribute. """ # Store continuous mode to reset it after the test continuous_mode = config.continuous_mode config.set_continuous_mode(True) assert config.continuous_mode == True # Reset continuous mode config.set_continuous_mode(continuous_mode) def test_set_speak_mode(config): """ Test if the set_speak_mode() method updates the speak_mode attribute. """ # Store speak mode to reset it after the test speak_mode = config.speak_mode config.set_speak_mode(True) assert config.speak_mode == True # Reset speak mode config.set_speak_mode(speak_mode) def test_set_fast_llm_model(config): """ Test if the set_fast_llm_model() method updates the fast_llm_model attribute. """ # Store model name to reset it after the test fast_llm_model = config.fast_llm_model config.set_fast_llm_model("gpt-3.5-turbo-test") assert config.fast_llm_model == "gpt-3.5-turbo-test" # Reset model name config.set_fast_llm_model(fast_llm_model) def test_set_smart_llm_model(config): """ Test if the set_smart_llm_model() method updates the smart_llm_model attribute. """ # Store model name to reset it after the test smart_llm_model = config.smart_llm_model config.set_smart_llm_model("gpt-4-test") assert config.smart_llm_model == "gpt-4-test" # Reset model name config.set_smart_llm_model(smart_llm_model) def test_set_fast_token_limit(config): """ Test if the set_fast_token_limit() method updates the fast_token_limit attribute. """ # Store token limit to reset it after the test fast_token_limit = config.fast_token_limit config.set_fast_token_limit(5000) assert config.fast_token_limit == 5000 # Reset token limit config.set_fast_token_limit(fast_token_limit) def test_set_smart_token_limit(config): """ Test if the set_smart_token_limit() method updates the smart_token_limit attribute. """ # Store token limit to reset it after the test smart_token_limit = config.smart_token_limit config.set_smart_token_limit(9000) assert config.smart_token_limit == 9000 # Reset token limit config.set_smart_token_limit(smart_token_limit) def test_set_debug_mode(config): """ Test if the set_debug_mode() method updates the debug_mode attribute. """ # Store debug mode to reset it after the test debug_mode = config.debug_mode config.set_debug_mode(True) assert config.debug_mode == True # Reset debug mode config.set_debug_mode(debug_mode) ================================================ FILE: tests/test_image_gen.py ================================================ import functools import hashlib from pathlib import Path import pytest from PIL import Image from autogpt.commands.image_gen import generate_image, generate_image_with_sd_webui from tests.utils import requires_api_key @pytest.fixture(params=[256, 512, 1024]) def image_size(request): """Parametrize image size.""" return request.param @pytest.mark.xfail( reason="The image is too big to be put in a cassette for a CI pipeline. We're looking into a solution." ) @requires_api_key("OPENAI_API_KEY") def test_dalle(config, workspace, image_size): """Test DALL-E image generation.""" generate_and_validate( config, workspace, image_provider="dalle", image_size=image_size, ) @pytest.mark.xfail( reason="The image is too big to be put in a cassette for a CI pipeline. We're looking into a solution." ) @requires_api_key("HUGGINGFACE_API_TOKEN") @pytest.mark.parametrize( "image_model", ["CompVis/stable-diffusion-v1-4", "stabilityai/stable-diffusion-2-1"], ) def test_huggingface(config, workspace, image_size, image_model): """Test HuggingFace image generation.""" generate_and_validate( config, workspace, image_provider="huggingface", image_size=image_size, hugging_face_image_model=image_model, ) @pytest.mark.xfail(reason="SD WebUI call does not work.") def test_sd_webui(config, workspace, image_size): """Test SD WebUI image generation.""" generate_and_validate( config, workspace, image_provider="sd_webui", image_size=image_size, ) @pytest.mark.xfail(reason="SD WebUI call does not work.") def test_sd_webui_negative_prompt(config, workspace, image_size): gen_image = functools.partial( generate_image_with_sd_webui, prompt="astronaut riding a horse", size=image_size, extra={"seed": 123}, ) # Generate an image with a negative prompt image_path = lst(gen_image(negative_prompt="horse", filename="negative.jpg")) with Image.open(image_path) as img: neg_image_hash = hashlib.md5(img.tobytes()).hexdigest() # Generate an image without a negative prompt image_path = lst(gen_image(filename="positive.jpg")) with Image.open(image_path) as img: image_hash = hashlib.md5(img.tobytes()).hexdigest() assert image_hash != neg_image_hash def lst(txt): """Extract the file path from the output of `generate_image()`""" return Path(txt.split(":")[1].strip()) def generate_and_validate( config, workspace, image_size, image_provider, hugging_face_image_model=None, **kwargs, ): """Generate an image and validate the output.""" config.image_provider = image_provider config.huggingface_image_model = hugging_face_image_model prompt = "astronaut riding a horse" image_path = lst(generate_image(prompt, image_size, **kwargs)) assert image_path.exists() with Image.open(image_path) as img: assert img.size == (image_size, image_size) ================================================ FILE: tests/test_logs.py ================================================ import pytest from autogpt.logs import remove_color_codes @pytest.mark.parametrize( "raw_text, clean_text", [ ( "COMMAND = \x1b[36mbrowse_website\x1b[0m ARGUMENTS = \x1b[36m{'url': 'https://www.google.com', 'question': 'What is the capital of France?'}\x1b[0m", "COMMAND = browse_website ARGUMENTS = {'url': 'https://www.google.com', 'question': 'What is the capital of France?'}", ), ( "{'Schaue dir meine Projekte auf github () an, als auch meine Webseiten': 'https://github.com/Significant-Gravitas/Auto-GPT, https://discord.gg/autogpt und https://twitter.com/SigGravitas'}", "{'Schaue dir meine Projekte auf github () an, als auch meine Webseiten': 'https://github.com/Significant-Gravitas/Auto-GPT, https://discord.gg/autogpt und https://twitter.com/SigGravitas'}", ), ("", ""), ("hello", "hello"), ("hello\x1B[31m world", "hello world"), ("\x1B[36mHello,\x1B[32m World!", "Hello, World!"), ( "\x1B[1m\x1B[31mError:\x1B[0m\x1B[31m file not found", "Error: file not found", ), ], ) def test_remove_color_codes(raw_text, clean_text): assert remove_color_codes(raw_text) == clean_text ================================================ FILE: tests/test_prompt_generator.py ================================================ from unittest import TestCase from autogpt.prompts.generator import PromptGenerator class TestPromptGenerator(TestCase): """ Test cases for the PromptGenerator class, which is responsible for generating prompts for the AI with constraints, commands, resources, and performance evaluations. """ @classmethod def setUpClass(cls): """ Set up the initial state for each test method by creating an instance of PromptGenerator. """ cls.generator = PromptGenerator() # Test whether the add_constraint() method adds a constraint to the generator's constraints list def test_add_constraint(self): """ Test if the add_constraint() method adds a constraint to the generator's constraints list. """ constraint = "Constraint1" self.generator.add_constraint(constraint) self.assertIn(constraint, self.generator.constraints) # Test whether the add_command() method adds a command to the generator's commands list def test_add_command(self): """ Test if the add_command() method adds a command to the generator's commands list. """ command_label = "Command Label" command_name = "command_name" args = {"arg1": "value1", "arg2": "value2"} self.generator.add_command(command_label, command_name, args) command = { "label": command_label, "name": command_name, "args": args, "function": None, } self.assertIn(command, self.generator.commands) def test_add_resource(self): """ Test if the add_resource() method adds a resource to the generator's resources list. """ resource = "Resource1" self.generator.add_resource(resource) self.assertIn(resource, self.generator.resources) def test_add_performance_evaluation(self): """ Test if the add_performance_evaluation() method adds an evaluation to the generator's performance_evaluation list. """ evaluation = "Evaluation1" self.generator.add_performance_evaluation(evaluation) self.assertIn(evaluation, self.generator.performance_evaluation) def test_generate_prompt_string(self): """ Test if the generate_prompt_string() method generates a prompt string with all the added constraints, commands, resources, and evaluations. """ # Define the test data constraints = ["Constraint1", "Constraint2"] commands = [ { "label": "Command1", "name": "command_name1", "args": {"arg1": "value1"}, }, { "label": "Command2", "name": "command_name2", "args": {}, }, ] resources = ["Resource1", "Resource2"] evaluations = ["Evaluation1", "Evaluation2"] # Add test data to the generator for constraint in constraints: self.generator.add_constraint(constraint) for command in commands: self.generator.add_command( command["label"], command["name"], command["args"] ) for resource in resources: self.generator.add_resource(resource) for evaluation in evaluations: self.generator.add_performance_evaluation(evaluation) # Generate the prompt string and verify its correctness prompt_string = self.generator.generate_prompt_string() self.assertIsNotNone(prompt_string) # Check if all constraints, commands, resources, and evaluations are present in the prompt string for constraint in constraints: self.assertIn(constraint, prompt_string) for command in commands: self.assertIn(command["name"], prompt_string) for key, value in command["args"].items(): self.assertIn(f'"{key}": "{value}"', prompt_string) for resource in resources: self.assertIn(resource, prompt_string) for evaluation in evaluations: self.assertIn(evaluation, prompt_string) self.assertIn("constraints", prompt_string.lower()) self.assertIn("commands", prompt_string.lower()) self.assertIn("resources", prompt_string.lower()) self.assertIn("performance evaluation", prompt_string.lower()) ================================================ FILE: tests/test_token_counter.py ================================================ import pytest from autogpt.llm import count_message_tokens, count_string_tokens def test_count_message_tokens(): messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] assert count_message_tokens(messages) == 17 def test_count_message_tokens_with_name(): messages = [ {"role": "user", "content": "Hello", "name": "John"}, {"role": "assistant", "content": "Hi there!"}, ] assert count_message_tokens(messages) == 17 def test_count_message_tokens_empty_input(): """Empty input should return 3 tokens""" assert count_message_tokens([]) == 3 def test_count_message_tokens_invalid_model(): """Invalid model should raise a KeyError""" messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] with pytest.raises(KeyError): count_message_tokens(messages, model="invalid_model") def test_count_message_tokens_gpt_4(): messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] assert count_message_tokens(messages, model="gpt-4-0314") == 15 def test_count_string_tokens(): """Test that the string tokens are counted correctly.""" string = "Hello, world!" assert count_string_tokens(string, model_name="gpt-3.5-turbo-0301") == 4 def test_count_string_tokens_empty_input(): """Test that the string tokens are counted correctly.""" assert count_string_tokens("", model_name="gpt-3.5-turbo-0301") == 0 def test_count_message_tokens_invalid_model(): """Invalid model should raise a NotImplementedError""" messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi there!"}, ] with pytest.raises(NotImplementedError): count_message_tokens(messages, model="invalid_model") def test_count_string_tokens_gpt_4(): """Test that the string tokens are counted correctly.""" string = "Hello, world!" assert count_string_tokens(string, model_name="gpt-4-0314") == 4 ================================================ FILE: tests/test_utils.py ================================================ import os from unittest.mock import Mock, patch import pytest import requests from colorama import Fore from git import Repo from autogpt.utils import ( clean_input, get_bulletin_from_web, get_current_git_branch, get_latest_bulletin, readable_file_size, validate_yaml_file, ) from tests.utils import skip_in_ci def test_validate_yaml_file_valid(): with open("valid_test_file.yaml", "w") as f: f.write("setting: value") result, message = validate_yaml_file("valid_test_file.yaml") os.remove("valid_test_file.yaml") assert result == True assert "Successfully validated" in message def test_validate_yaml_file_not_found(): result, message = validate_yaml_file("non_existent_file.yaml") assert result == False assert "wasn't found" in message def test_validate_yaml_file_invalid(): with open("invalid_test_file.yaml", "w") as f: f.write( "settings:\n first_setting: value\n second_setting: value\n nested_setting: value\n third_setting: value\nunindented_setting: value" ) result, message = validate_yaml_file("invalid_test_file.yaml") os.remove("invalid_test_file.yaml") print(result) print(message) assert result == False assert "There was an issue while trying to read" in message def test_readable_file_size(): size_in_bytes = 1024 * 1024 * 3.5 # 3.5 MB readable_size = readable_file_size(size_in_bytes) assert readable_size == "3.50 MB" @patch("requests.get") def test_get_bulletin_from_web_success(mock_get): expected_content = "Test bulletin from web" mock_get.return_value.status_code = 200 mock_get.return_value.text = expected_content bulletin = get_bulletin_from_web() assert expected_content in bulletin mock_get.assert_called_with( "https://raw.githubusercontent.com/Significant-Gravitas/Auto-GPT/master/BULLETIN.md" ) @patch("requests.get") def test_get_bulletin_from_web_failure(mock_get): mock_get.return_value.status_code = 404 bulletin = get_bulletin_from_web() assert bulletin == "" @patch("requests.get") def test_get_bulletin_from_web_exception(mock_get): mock_get.side_effect = requests.exceptions.RequestException() bulletin = get_bulletin_from_web() assert bulletin == "" def test_get_latest_bulletin_no_file(): if os.path.exists("data/CURRENT_BULLETIN.md"): os.remove("data/CURRENT_BULLETIN.md") bulletin, is_new = get_latest_bulletin() assert is_new def test_get_latest_bulletin_with_file(): expected_content = "Test bulletin" with open("data/CURRENT_BULLETIN.md", "w", encoding="utf-8") as f: f.write(expected_content) with patch("autogpt.utils.get_bulletin_from_web", return_value=""): bulletin, is_new = get_latest_bulletin() assert expected_content in bulletin assert is_new == False os.remove("data/CURRENT_BULLETIN.md") def test_get_latest_bulletin_with_new_bulletin(): with open("data/CURRENT_BULLETIN.md", "w", encoding="utf-8") as f: f.write("Old bulletin") expected_content = "New bulletin from web" with patch("autogpt.utils.get_bulletin_from_web", return_value=expected_content): bulletin, is_new = get_latest_bulletin() assert "::NEW BULLETIN::" in bulletin assert expected_content in bulletin assert is_new os.remove("data/CURRENT_BULLETIN.md") def test_get_latest_bulletin_new_bulletin_same_as_old_bulletin(): expected_content = "Current bulletin" with open("data/CURRENT_BULLETIN.md", "w", encoding="utf-8") as f: f.write(expected_content) with patch("autogpt.utils.get_bulletin_from_web", return_value=expected_content): bulletin, is_new = get_latest_bulletin() assert expected_content in bulletin assert is_new == False os.remove("data/CURRENT_BULLETIN.md") @skip_in_ci def test_get_current_git_branch(): branch_name = get_current_git_branch() # Assuming that the branch name will be non-empty if the function is working correctly. assert branch_name != "" @patch("autogpt.utils.Repo") def test_get_current_git_branch_success(mock_repo): mock_repo.return_value.active_branch.name = "test-branch" branch_name = get_current_git_branch() assert branch_name == "test-branch" @patch("autogpt.utils.Repo") def test_get_current_git_branch_failure(mock_repo): mock_repo.side_effect = Exception() branch_name = get_current_git_branch() assert branch_name == "" if __name__ == "__main__": pytest.main() ================================================ FILE: tests/test_workspace.py ================================================ import itertools from pathlib import Path import pytest from autogpt.workspace import Workspace _WORKSPACE_ROOT = Path("home/users/monty/auto_gpt_workspace") _ACCESSIBLE_PATHS = [ Path("."), Path("test_file.txt"), Path("test_folder"), Path("test_folder/test_file.txt"), Path("test_folder/.."), Path("test_folder/../test_file.txt"), Path("test_folder/../test_folder"), Path("test_folder/../test_folder/test_file.txt"), ] _INACCESSIBLE_PATHS = ( [ # Takes us out of the workspace Path(".."), Path("../test_file.txt"), Path("../not_auto_gpt_workspace"), Path("../not_auto_gpt_workspace/test_file.txt"), Path("test_folder/../.."), Path("test_folder/../../test_file.txt"), Path("test_folder/../../not_auto_gpt_workspace"), Path("test_folder/../../not_auto_gpt_workspace/test_file.txt"), ] + [ # Contains null bytes Path(template.format(null_byte=null_byte)) for template, null_byte in itertools.product( [ "{null_byte}", "{null_byte}test_file.txt", "test_folder/{null_byte}", "test_folder/{null_byte}test_file.txt", ], Workspace.NULL_BYTES, ) ] + [ # Absolute paths Path("/"), Path("/test_file.txt"), Path("/home"), ] ) @pytest.fixture() def workspace_root(tmp_path): return tmp_path / _WORKSPACE_ROOT @pytest.fixture(params=_ACCESSIBLE_PATHS) def accessible_path(request): return request.param @pytest.fixture(params=_INACCESSIBLE_PATHS) def inaccessible_path(request): return request.param def test_sanitize_path_accessible(accessible_path, workspace_root): full_path = Workspace._sanitize_path( accessible_path, root=workspace_root, restrict_to_root=True, ) assert full_path.is_absolute() assert full_path.is_relative_to(workspace_root) def test_sanitize_path_inaccessible(inaccessible_path, workspace_root): with pytest.raises(ValueError): Workspace._sanitize_path( inaccessible_path, root=workspace_root, restrict_to_root=True, ) def test_get_path_accessible(accessible_path, workspace_root): workspace = Workspace(workspace_root, True) full_path = workspace.get_path(accessible_path) assert full_path.is_absolute() assert full_path.is_relative_to(workspace_root) def test_get_path_inaccessible(inaccessible_path, workspace_root): workspace = Workspace(workspace_root, True) with pytest.raises(ValueError): workspace.get_path(inaccessible_path) ================================================ FILE: tests/unit/__init__.py ================================================ ================================================ FILE: tests/unit/_test_json_parser.py ================================================ import unittest from autogpt.json_utils.json_fix_llm import fix_and_parse_json class TestParseJson(unittest.TestCase): def test_valid_json(self): """Test that a valid JSON string is parsed correctly.""" json_str = '{"name": "John", "age": 30, "city": "New York"}' obj = fix_and_parse_json(json_str) self.assertEqual(obj, {"name": "John", "age": 30, "city": "New York"}) def test_invalid_json_minor(self): """Test that an invalid JSON string can be fixed with gpt.""" json_str = '{"name": "John", "age": 30, "city": "New York",}' self.assertEqual( fix_and_parse_json(json_str, try_to_fix_with_gpt=False), {"name": "John", "age": 30, "city": "New York"}, ) def test_invalid_json_major_with_gpt(self): """Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False.""" json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END' self.assertEqual( fix_and_parse_json(json_str, try_to_fix_with_gpt=True), {"name": "John", "age": 30, "city": "New York"}, ) def test_invalid_json_major_without_gpt(self): """Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False.""" json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END' # Assert that this raises an exception: with self.assertRaises(Exception): fix_and_parse_json(json_str, try_to_fix_with_gpt=False) def test_invalid_json_leading_sentence_with_gpt(self): """Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False.""" json_str = """I suggest we start by browsing the repository to find any issues that we can fix. { "command": { "name": "browse_website", "args":{ "url": "https://github.com/Torantulino/Auto-GPT" } }, "thoughts": { "text": "I suggest we start browsing the repository to find any issues that we can fix.", "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.", "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes", "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.", "speak": "I will start browsing the repository to find any issues we can fix." } }""" good_obj = { "command": { "name": "browse_website", "args": {"url": "https://github.com/Torantulino/Auto-GPT"}, }, "thoughts": { "text": "I suggest we start browsing the repository to find any issues that we can fix.", "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.", "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes", "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.", "speak": "I will start browsing the repository to find any issues we can fix.", }, } # Assert that this raises an exception: self.assertEqual( fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj ) def test_invalid_json_leading_sentence_with_gpt(self): """Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False.""" json_str = """I will first need to browse the repository (https://github.com/Torantulino/Auto-GPT) and identify any potential bugs that need fixing. I will use the "browse_website" command for this. { "command": { "name": "browse_website", "args":{ "url": "https://github.com/Torantulino/Auto-GPT" } }, "thoughts": { "text": "Browsing the repository to identify potential bugs", "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.", "plan": "- Analyze the repository for potential bugs and areas of improvement", "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.", "speak": "I am browsing the repository to identify potential bugs." } }""" good_obj = { "command": { "name": "browse_website", "args": {"url": "https://github.com/Torantulino/Auto-GPT"}, }, "thoughts": { "text": "Browsing the repository to identify potential bugs", "reasoning": "Before fixing bugs, I need to identify what needs fixing. I will use the 'browse_website' command to analyze the repository.", "plan": "- Analyze the repository for potential bugs and areas of improvement", "criticism": "I need to ensure I am thorough and pay attention to detail while browsing the repository.", "speak": "I am browsing the repository to identify potential bugs.", }, } # Assert that this raises an exception: self.assertEqual( fix_and_parse_json(json_str, try_to_fix_with_gpt=False), good_obj ) if __name__ == "__main__": unittest.main() ================================================ FILE: tests/unit/models/test_base_open_api_plugin.py ================================================ from typing import Any, Dict, List, Optional, Tuple import pytest from autogpt.models.base_open_ai_plugin import ( BaseOpenAIPlugin, Message, PromptGenerator, ) class DummyPlugin(BaseOpenAIPlugin): """A dummy plugin for testing purposes.""" pass @pytest.fixture def dummy_plugin(): """A dummy plugin for testing purposes.""" manifests_specs_clients = { "manifest": { "name_for_model": "Dummy", "schema_version": "1.0", "description_for_model": "A dummy plugin for testing purposes", }, "client": None, "openapi_spec": None, } return DummyPlugin(manifests_specs_clients) def test_dummy_plugin_inheritance(dummy_plugin): """Test that the DummyPlugin class inherits from the BaseOpenAIPlugin class.""" assert isinstance(dummy_plugin, BaseOpenAIPlugin) def test_dummy_plugin_name(dummy_plugin): """Test that the DummyPlugin class has the correct name.""" assert dummy_plugin._name == "Dummy" def test_dummy_plugin_version(dummy_plugin): """Test that the DummyPlugin class has the correct version.""" assert dummy_plugin._version == "1.0" def test_dummy_plugin_description(dummy_plugin): """Test that the DummyPlugin class has the correct description.""" assert dummy_plugin._description == "A dummy plugin for testing purposes" def test_dummy_plugin_default_methods(dummy_plugin): """Test that the DummyPlugin class has the correct default methods.""" assert not dummy_plugin.can_handle_on_response() assert not dummy_plugin.can_handle_post_prompt() assert not dummy_plugin.can_handle_on_planning() assert not dummy_plugin.can_handle_post_planning() assert not dummy_plugin.can_handle_pre_instruction() assert not dummy_plugin.can_handle_on_instruction() assert not dummy_plugin.can_handle_post_instruction() assert not dummy_plugin.can_handle_pre_command() assert not dummy_plugin.can_handle_post_command() assert not dummy_plugin.can_handle_chat_completion(None, None, None, None) assert dummy_plugin.on_response("hello") == "hello" assert dummy_plugin.post_prompt(None) is None assert dummy_plugin.on_planning(None, None) is None assert dummy_plugin.post_planning("world") == "world" pre_instruction = dummy_plugin.pre_instruction( [{"role": "system", "content": "Beep, bop, boop"}] ) assert isinstance(pre_instruction, list) assert len(pre_instruction) == 1 assert pre_instruction[0]["role"] == "system" assert pre_instruction[0]["content"] == "Beep, bop, boop" assert dummy_plugin.on_instruction(None) is None assert dummy_plugin.post_instruction("I'm a robot") == "I'm a robot" pre_command = dummy_plugin.pre_command("evolve", {"continuously": True}) assert isinstance(pre_command, tuple) assert len(pre_command) == 2 assert pre_command[0] == "evolve" assert pre_command[1]["continuously"] == True post_command = dummy_plugin.post_command("evolve", "upgraded successfully!") assert isinstance(post_command, str) assert post_command == "upgraded successfully!" assert dummy_plugin.handle_chat_completion(None, None, None, None) is None ================================================ FILE: tests/unit/test_browse_scrape_links.py ================================================ # Generated by CodiumAI # Dependencies: # pip install pytest-mock import pytest from autogpt.commands.web_requests import scrape_links """ Code Analysis Objective: The objective of the 'scrape_links' function is to scrape hyperlinks from a given URL and return them in a formatted way. Inputs: - url: a string representing the URL to be scraped. Flow: 1. Send a GET request to the given URL using the requests library and the user agent header from the config file. 2. Check if the response contains an HTTP error. If it does, return "error". 3. Parse the HTML content of the response using the BeautifulSoup library. 4. Remove any script and style tags from the parsed HTML. 5. Extract all hyperlinks from the parsed HTML using the 'extract_hyperlinks' function. 6. Format the extracted hyperlinks using the 'format_hyperlinks' function. 7. Return the formatted hyperlinks. Outputs: - A list of formatted hyperlinks. Additional aspects: - The function uses the 'requests' and 'BeautifulSoup' libraries to send HTTP requests and parse HTML content, respectively. - The 'extract_hyperlinks' function is called to extract hyperlinks from the parsed HTML. - The 'format_hyperlinks' function is called to format the extracted hyperlinks. - The function checks for HTTP errors and returns "error" if any are found. """ class TestScrapeLinks: """ Tests that the function returns a list of formatted hyperlinks when provided with a valid url that returns a webpage with hyperlinks. """ def test_valid_url_with_hyperlinks(self): url = "https://www.google.com" result = scrape_links(url) assert len(result) > 0 assert isinstance(result, list) assert isinstance(result[0], str) def test_valid_url(self, mocker): """Test that the function returns correctly formatted hyperlinks when given a valid url.""" # Mock the requests.get() function to return a response with sample HTML containing hyperlinks mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = ( "Google" ) mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with a valid URL result = scrape_links("https://www.example.com") # Assert that the function returns correctly formatted hyperlinks assert result == ["Google (https://www.google.com)"] def test_invalid_url(self, mocker): """Test that the function returns "error" when given an invalid url.""" # Mock the requests.get() function to return an HTTP error response mock_response = mocker.Mock() mock_response.status_code = 404 mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with an invalid URL result = scrape_links("https://www.invalidurl.com") # Assert that the function returns "error" assert "Error:" in result def test_no_hyperlinks(self, mocker): """Test that the function returns an empty list when the html contains no hyperlinks.""" # Mock the requests.get() function to return a response with sample HTML containing no hyperlinks mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = "

No hyperlinks here

" mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with a URL containing no hyperlinks result = scrape_links("https://www.example.com") # Assert that the function returns an empty list assert result == [] def test_scrape_links_with_few_hyperlinks(self, mocker): """Test that scrape_links() correctly extracts and formats hyperlinks from a sample HTML containing a few hyperlinks.""" mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = """ """ mocker.patch("requests.Session.get", return_value=mock_response) # Call the function being tested result = scrape_links("https://www.example.com") # Assert that the function returns a list of formatted hyperlinks assert isinstance(result, list) assert len(result) == 3 assert result[0] == "Google (https://www.google.com)" assert result[1] == "GitHub (https://github.com)" assert result[2] == "CodiumAI (https://www.codium.ai)" ================================================ FILE: tests/unit/test_browse_scrape_text.py ================================================ # Generated by CodiumAI import pytest import requests from autogpt.commands.web_requests import scrape_text """ Code Analysis Objective: The objective of the "scrape_text" function is to scrape the text content from a given URL and return it as a string, after removing any unwanted HTML tags and scripts. Inputs: - url: a string representing the URL of the webpage to be scraped. Flow: 1. Send a GET request to the given URL using the requests library and the user agent header from the config file. 2. Check if the response contains an HTTP error. If it does, return an error message. 3. Use BeautifulSoup to parse the HTML content of the response and extract all script and style tags. 4. Get the text content of the remaining HTML using the get_text() method of BeautifulSoup. 5. Split the text into lines and then into chunks, removing any extra whitespace. 6. Join the chunks into a single string with newline characters between them. 7. Return the cleaned text. Outputs: - A string representing the cleaned text content of the webpage. Additional aspects: - The function uses the requests library and BeautifulSoup to handle the HTTP request and HTML parsing, respectively. - The function removes script and style tags from the HTML to avoid including unwanted content in the text output. - The function uses a generator expression to split the text into lines and chunks, which can improve performance for large amounts of text. """ class TestScrapeText: def test_scrape_text_with_valid_url(self, mocker): """Tests that scrape_text() returns the expected text when given a valid URL.""" # Mock the requests.get() method to return a response with expected text expected_text = "This is some sample text" mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = ( "

" f"{expected_text}

" ) mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with a valid URL and assert that it returns the # expected text url = "http://www.example.com" assert scrape_text(url) == expected_text def test_invalid_url(self): """Tests that an error is raised when an invalid url is provided.""" url = "invalidurl.com" pytest.raises(ValueError, scrape_text, url) def test_unreachable_url(self, mocker): """Test that scrape_text returns an error message when an invalid or unreachable url is provided.""" # Mock the requests.get() method to raise an exception mocker.patch( "requests.Session.get", side_effect=requests.exceptions.RequestException ) # Call the function with an invalid URL and assert that it returns an error # message url = "http://thiswebsitedoesnotexist.net/" error_message = scrape_text(url) assert "Error:" in error_message def test_no_text(self, mocker): """Test that scrape_text returns an empty string when the html page contains no text to be scraped.""" # Mock the requests.get() method to return a response with no text mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = "" mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with a valid URL and assert that it returns an empty string url = "http://www.example.com" assert scrape_text(url) == "" def test_http_error(self, mocker): """Test that scrape_text returns an error message when the response status code is an http error (>=400).""" # Mock the requests.get() method to return a response with a 404 status code mocker.patch("requests.Session.get", return_value=mocker.Mock(status_code=404)) # Call the function with a URL result = scrape_text("https://www.example.com") # Check that the function returns an error message assert result == "Error: HTTP 404 error" def test_scrape_text_with_html_tags(self, mocker): """Test that scrape_text() properly handles HTML tags.""" # Create a mock response object with HTML containing tags html = "

This is bold text.

" mock_response = mocker.Mock() mock_response.status_code = 200 mock_response.text = html mocker.patch("requests.Session.get", return_value=mock_response) # Call the function with a URL result = scrape_text("https://www.example.com") # Check that the function properly handles HTML tags assert result == "This is bold text." ================================================ FILE: tests/unit/test_chat.py ================================================ # Generated by CodiumAI import time from unittest.mock import patch from autogpt.llm import create_chat_message, generate_context def test_happy_path_role_content(): """Test that the function returns a dictionary with the correct keys and values when valid strings are provided for role and content.""" result = create_chat_message("system", "Hello, world!") assert result == {"role": "system", "content": "Hello, world!"} def test_empty_role_content(): """Test that the function returns a dictionary with the correct keys and values when empty strings are provided for role and content.""" result = create_chat_message("", "") assert result == {"role": "", "content": ""} def test_generate_context_empty_inputs(mocker): """Test the behavior of the generate_context function when all input parameters are empty.""" # Mock the time.strftime function to return a fixed value mocker.patch("time.strftime", return_value="Sat Apr 15 00:00:00 2023") # Arrange prompt = "" relevant_memory = "" full_message_history = [] model = "gpt-3.5-turbo-0301" # Act result = generate_context(prompt, relevant_memory, full_message_history, model) # Assert expected_result = ( -1, 32, 2, [ {"role": "system", "content": ""}, { "role": "system", "content": f"当前的日期和时间是 {time.strftime('%c')}", }, ], ) assert result == expected_result def test_generate_context_valid_inputs(): """Test that the function successfully generates a current_context given valid inputs.""" # Given prompt = "What is your favorite color?" relevant_memory = "You once painted your room blue." full_message_history = [ create_chat_message("user", "Hi there!"), create_chat_message("assistant", "Hello! How can I assist you today?"), create_chat_message("user", "Can you tell me a joke?"), create_chat_message( "assistant", "Why did the tomato turn red? Because it saw the salad dressing!", ), create_chat_message("user", "Haha, that's funny."), ] model = "gpt-3.5-turbo-0301" # When result = generate_context(prompt, relevant_memory, full_message_history, model) # Then assert isinstance(result[0], int) assert isinstance(result[1], int) assert isinstance(result[2], int) assert isinstance(result[3], list) assert result[0] >= 0 assert result[2] >= 0 assert result[1] >= 0 assert len(result[3]) >= 2 # current_context should have at least 2 messages assert result[1] <= 2048 # token limit for GPT-3.5-turbo-0301 is 2048 tokens ================================================ FILE: tests/unit/test_commands.py ================================================ """Unit tests for the commands module""" from unittest.mock import MagicMock, patch import pytest from autogpt.app import list_agents, start_agent from tests.utils import requires_api_key @pytest.mark.vcr @pytest.mark.integration_test @requires_api_key("OPENAI_API_KEY") def test_make_agent() -> None: """Test that an agent can be created""" # Use the mock agent manager to avoid creating a real agent with patch("openai.ChatCompletion.create") as mock: response = MagicMock() response.choices[0].messages[0].content = "Test message" response.usage.prompt_tokens = 1 response.usage.completion_tokens = 1 mock.return_value = response start_agent("Test Agent", "chat", "Hello, how are you?", "gpt-3.5-turbo") agents = list_agents() assert "List of agents:\n0: chat" == agents start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt-3.5-turbo") agents = list_agents() assert "List of agents:\n0: chat\n1: write" == agents ================================================ FILE: tests/unit/test_file_operations.py ================================================ """ This set of unit tests is designed to test the file operations that autoGPT has access to. """ import hashlib import os import re from io import TextIOWrapper from pathlib import Path import pytest from pytest_mock import MockerFixture import autogpt.commands.file_operations as file_ops from autogpt.config import Config from autogpt.utils import readable_file_size from autogpt.workspace import Workspace @pytest.fixture() def file_content(): return "This is a test file.\n" @pytest.fixture() def test_file_path(config, workspace: Workspace): return workspace.get_path("test_file.txt") @pytest.fixture() def test_file(test_file_path: Path): file = open(test_file_path, "w") yield file if not file.closed: file.close() @pytest.fixture() def test_file_with_content_path(test_file: TextIOWrapper, file_content): test_file.write(file_content) test_file.close() file_ops.log_operation( "write", test_file.name, file_ops.text_checksum(file_content) ) return Path(test_file.name) @pytest.fixture() def test_directory(config, workspace: Workspace): return workspace.get_path("test_directory") @pytest.fixture() def test_nested_file(config, workspace: Workspace): return workspace.get_path("nested/test_file.txt") def test_file_operations_log(test_file: TextIOWrapper): log_file_content = ( "File Operation Logger\n" "write: path/to/file1.txt #checksum1\n" "write: path/to/file2.txt #checksum2\n" "write: path/to/file3.txt #checksum3\n" "append: path/to/file2.txt #checksum4\n" "delete: path/to/file3.txt\n" ) test_file.write(log_file_content) test_file.close() expected = [ ("write", "path/to/file1.txt", "checksum1"), ("write", "path/to/file2.txt", "checksum2"), ("write", "path/to/file3.txt", "checksum3"), ("append", "path/to/file2.txt", "checksum4"), ("delete", "path/to/file3.txt", None), ] assert list(file_ops.operations_from_log(test_file.name)) == expected def test_file_operations_state(test_file: TextIOWrapper): # Prepare a fake log file log_file_content = ( "File Operation Logger\n" "write: path/to/file1.txt #checksum1\n" "write: path/to/file2.txt #checksum2\n" "write: path/to/file3.txt #checksum3\n" "append: path/to/file2.txt #checksum4\n" "delete: path/to/file3.txt\n" ) test_file.write(log_file_content) test_file.close() # Call the function and check the returned dictionary expected_state = { "path/to/file1.txt": "checksum1", "path/to/file2.txt": "checksum4", } assert file_ops.file_operations_state(test_file.name) == expected_state def test_is_duplicate_operation(config, mocker: MockerFixture): # Prepare a fake state dictionary for the function to use state = { "path/to/file1.txt": "checksum1", "path/to/file2.txt": "checksum2", } mocker.patch.object(file_ops, "file_operations_state", lambda _: state) # Test cases with write operations assert ( file_ops.is_duplicate_operation("write", "path/to/file1.txt", "checksum1") is True ) assert ( file_ops.is_duplicate_operation("write", "path/to/file1.txt", "checksum2") is False ) assert ( file_ops.is_duplicate_operation("write", "path/to/file3.txt", "checksum3") is False ) # Test cases with append operations assert ( file_ops.is_duplicate_operation("append", "path/to/file1.txt", "checksum1") is False ) # Test cases with delete operations assert file_ops.is_duplicate_operation("delete", "path/to/file1.txt") is False assert file_ops.is_duplicate_operation("delete", "path/to/file3.txt") is True # Test logging a file operation def test_log_operation(config: Config): file_ops.log_operation("log_test", "path/to/test") with open(config.file_logger_path, "r", encoding="utf-8") as f: content = f.read() assert f"log_test: path/to/test\n" in content def test_text_checksum(file_content: str): checksum = file_ops.text_checksum(file_content) different_checksum = file_ops.text_checksum("other content") assert re.match(r"^[a-fA-F0-9]+$", checksum) is not None assert checksum != different_checksum def test_log_operation_with_checksum(config: Config): file_ops.log_operation("log_test", "path/to/test", checksum="ABCDEF") with open(config.file_logger_path, "r", encoding="utf-8") as f: content = f.read() assert f"log_test: path/to/test #ABCDEF\n" in content # Test splitting a file into chunks def test_split_file(): content = "abcdefghij" chunks = list(file_ops.split_file(content, max_length=4, overlap=1)) expected = ["abcd", "defg", "ghij"] assert chunks == expected def test_read_file(test_file_with_content_path: Path, file_content): content = file_ops.read_file(test_file_with_content_path) assert content == file_content def test_write_to_file(test_file_path: Path): new_content = "This is new content.\n" file_ops.write_to_file(str(test_file_path), new_content) with open(test_file_path, "r", encoding="utf-8") as f: content = f.read() assert content == new_content def test_write_file_logs_checksum(config: Config, test_file_path: Path): new_content = "This is new content.\n" new_checksum = file_ops.text_checksum(new_content) file_ops.write_to_file(str(test_file_path), new_content) with open(config.file_logger_path, "r", encoding="utf-8") as f: log_entry = f.read() assert log_entry == f"write: {test_file_path} #{new_checksum}\n" def test_write_file_fails_if_content_exists(test_file_path: Path): new_content = "This is new content.\n" file_ops.log_operation( "write", str(test_file_path), checksum=file_ops.text_checksum(new_content), ) result = file_ops.write_to_file(str(test_file_path), new_content) assert result == "Error: File has already been updated." def test_write_file_succeeds_if_content_different(test_file_with_content_path: Path): new_content = "This is different content.\n" result = file_ops.write_to_file(str(test_file_with_content_path), new_content) assert result == "File written to successfully." def test_append_to_file(test_nested_file: Path): append_text = "This is appended text.\n" file_ops.write_to_file(test_nested_file, append_text) file_ops.append_to_file(test_nested_file, append_text) with open(test_nested_file, "r") as f: content_after = f.read() assert content_after == append_text + append_text def test_append_to_file_uses_checksum_from_appended_file( config: Config, test_file_path: Path ): append_text = "This is appended text.\n" file_ops.append_to_file(test_file_path, append_text) file_ops.append_to_file(test_file_path, append_text) with open(config.file_logger_path, "r", encoding="utf-8") as f: log_contents = f.read() digest = hashlib.md5() digest.update(append_text.encode("utf-8")) checksum1 = digest.hexdigest() digest.update(append_text.encode("utf-8")) checksum2 = digest.hexdigest() assert log_contents == ( f"append: {test_file_path} #{checksum1}\n" f"append: {test_file_path} #{checksum2}\n" ) def test_delete_file(test_file_with_content_path: Path): result = file_ops.delete_file(str(test_file_with_content_path)) assert result == "File deleted successfully." assert os.path.exists(test_file_with_content_path) is False def test_delete_missing_file(config): filename = "path/to/file/which/does/not/exist" # confuse the log file_ops.log_operation("write", filename, checksum="fake") try: os.remove(filename) except FileNotFoundError as err: assert str(err) in file_ops.delete_file(filename) return assert False, f"Failed to test delete_file; {filename} not expected to exist" def test_list_files(workspace: Workspace, test_directory: Path): # Case 1: Create files A and B, search for A, and ensure we don't return A and B file_a = workspace.get_path("file_a.txt") file_b = workspace.get_path("file_b.txt") with open(file_a, "w") as f: f.write("This is file A.") with open(file_b, "w") as f: f.write("This is file B.") # Create a subdirectory and place a copy of file_a in it if not os.path.exists(test_directory): os.makedirs(test_directory) with open(os.path.join(test_directory, file_a.name), "w") as f: f.write("This is file A in the subdirectory.") files = file_ops.list_files(str(workspace.root)) assert file_a.name in files assert file_b.name in files assert os.path.join(Path(test_directory).name, file_a.name) in files # Clean up os.remove(file_a) os.remove(file_b) os.remove(os.path.join(test_directory, file_a.name)) os.rmdir(test_directory) # Case 2: Search for a file that does not exist and make sure we don't throw non_existent_file = "non_existent_file.txt" files = file_ops.list_files("") assert non_existent_file not in files def test_download_file(config, workspace: Workspace): url = "https://github.com/Significant-Gravitas/Auto-GPT/archive/refs/tags/v0.2.2.tar.gz" local_name = workspace.get_path("auto-gpt.tar.gz") size = 365023 readable_size = readable_file_size(size) assert ( file_ops.download_file(url, local_name) == f'Successfully downloaded and locally stored file: "{local_name}"! (Size: {readable_size})' ) assert os.path.isfile(local_name) is True assert os.path.getsize(local_name) == size url = "https://github.com/Significant-Gravitas/Auto-GPT/archive/refs/tags/v0.0.0.tar.gz" assert "Got an HTTP Error whilst trying to download file" in file_ops.download_file( url, local_name ) url = "https://thiswebsiteiswrong.hmm/v0.0.0.tar.gz" assert "Failed to establish a new connection:" in file_ops.download_file( url, local_name ) ================================================ FILE: tests/unit/test_get_self_feedback.py ================================================ from autogpt.agent.agent import Agent from autogpt.config import AIConfig from autogpt.llm import create_chat_completion def test_get_self_feedback(mocker): # Define a sample thoughts dictionary thoughts = { "reasoning": "Sample reasoning.", "plan": "Sample plan.", "thoughts": "Sample thoughts.", } # Define a fake response for the create_chat_completion function fake_response = ( "The AI Agent has demonstrated a reasonable thought process, but there is room for improvement. " "For example, the reasoning could be elaborated to better justify the plan, and the plan itself " "could be more detailed to ensure its effectiveness. In addition, the AI Agent should focus more " "on its core role and prioritize thoughts that align with that role." ) # Mock the create_chat_completion function mock_create_chat_completion = mocker.patch( "autogpt.agent.agent.create_chat_completion", wraps=create_chat_completion ) mock_create_chat_completion.return_value = fake_response # Create a MagicMock object to replace the Agent instance agent_mock = mocker.MagicMock(spec=Agent) # Mock the config attribute of the Agent instance agent_mock.config = AIConfig() # Call the get_self_feedback method feedback = Agent.get_self_feedback( agent_mock, thoughts, "gpt-3.5-turbo", ) # Check if the response is a non-empty string assert isinstance(feedback, str) and len(feedback) > 0 # Check if certain keywords from input thoughts are present in the feedback response for keyword in ["reasoning", "plan", "thoughts"]: assert keyword in feedback ================================================ FILE: tests/unit/test_json_parser.py ================================================ from unittest import TestCase from autogpt.json_utils.json_fix_llm import fix_and_parse_json from tests.utils import skip_in_ci class TestParseJson(TestCase): def test_valid_json(self): """Test that a valid JSON string is parsed correctly.""" json_str = '{"name": "John", "age": 30, "city": "New York"}' obj = fix_and_parse_json(json_str) self.assertEqual(obj, {"name": "John", "age": 30, "city": "New York"}) def test_invalid_json_minor(self): """Test that an invalid JSON string can not be fixed without gpt""" json_str = '{"name": "John", "age": 30, "city": "New York",}' with self.assertRaises(Exception): fix_and_parse_json(json_str, try_to_fix_with_gpt=False) def test_invalid_json_major_with_gpt(self): """Test that an invalid JSON string raises an error when try_to_fix_with_gpt is False""" json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END' with self.assertRaises(Exception): fix_and_parse_json(json_str, try_to_fix_with_gpt=False) def test_invalid_json_major_without_gpt(self): """Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False""" json_str = 'BEGIN: "name": "John" - "age": 30 - "city": "New York" :END' # Assert that this raises an exception: with self.assertRaises(Exception): fix_and_parse_json(json_str, try_to_fix_with_gpt=False) def test_invalid_json_leading_sentence_with_gpt(self): """Test that a REALLY invalid JSON string raises an error when try_to_fix_with_gpt is False""" json_str = """I suggest we start by browsing the repository to find any issues that we can fix. { "command": { "name": "browse_website", "args":{ "url": "https://github.com/Torantulino/Auto-GPT" } }, "thoughts": { "text": "I suggest we start browsing the repository to find any issues that we can fix.", "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.", "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes", "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.", "speak": "I will start browsing the repository to find any issues we can fix." } }""" good_obj = { "command": { "name": "browse_website", "args": {"url": "https://github.com/Torantulino/Auto-GPT"}, }, "thoughts": { "text": "I suggest we start browsing the repository to find any issues that we can fix.", "reasoning": "Browsing the repository will give us an idea of the current state of the codebase and identify any issues that we can address to improve the repo.", "plan": "- Look through the repository to find any issues.\n- Investigate any issues to determine what needs to be fixed\n- Identify possible solutions to fix the issues\n- Open Pull Requests with fixes", "criticism": "I should be careful while browsing so as not to accidentally introduce any new bugs or issues.", "speak": "I will start browsing the repository to find any issues we can fix.", }, } # # Assert that this can be fixed with GPT # self.assertEqual(fix_and_parse_json(json_str), good_obj) # Assert that trying to fix this without GPT raises an exception with self.assertRaises(Exception): fix_and_parse_json(json_str, try_to_fix_with_gpt=False) ================================================ FILE: tests/unit/test_json_utils_llm.py ================================================ # Generated by CodiumAI import pytest from loguru import logger from autogpt.json_utils.json_fix_llm import ( fix_and_parse_json, fix_json_using_multiple_techniques, ) from tests.utils import requires_api_key """ Code Analysis Objective: - The objective of the function is to fix a given JSON string to make it parseable and fully compliant with two techniques. Inputs: - The function takes in a string called 'assistant_reply', which is the JSON string to be fixed. Flow: - The function first calls the 'fix_and_parse_json' function to parse and print the Assistant response. - If the parsed JSON is an empty dictionary, the function calls the 'attempt_to_fix_json_by_finding_outermost_brackets' function to fix the JSON string. - If the parsed JSON is not an empty dictionary, the function returns the parsed JSON. - If the parsed JSON is an empty dictionary and cannot be fixed, the function logs an error and returns an empty dictionary. Outputs: - The main output of the function is a dictionary containing the fixed JSON string. Additional aspects: - The function uses two techniques to fix the JSON string: parsing and finding outermost brackets. - The function logs an error if the JSON string cannot be fixed and returns an empty dictionary. - The function uses the 'CFG' object to determine whether to speak the error message or not. """ class TestFixJsonUsingMultipleTechniques: # Tests that the function successfully fixes and parses a JSON string that is already compliant with both techniques. def test_fix_and_parse_json_happy_path(self): # Happy path test case where the JSON string is already compliant with both techniques json_string = '{"text": "Hello world", "confidence": 0.9}' expected_output = {"text": "Hello world", "confidence": 0.9} assert fix_json_using_multiple_techniques(json_string) == expected_output # Tests that the function successfully fixes and parses a JSON string that contains only whitespace characters. # @requires_api_key("OPEN_API_KEY") def test_fix_and_parse_json_whitespace(self, mocker): # Happy path test case where the JSON string contains only whitespace characters json_string = " \n\t " # mock try_ai_fix to avoid calling the AI model: mocker.patch("autogpt.json_utils.json_fix_llm.try_ai_fix", return_value={}) expected_output = {} assert fix_json_using_multiple_techniques(json_string) == expected_output # Tests that the function successfully converts a string with arrays to an array def test_fix_and_parse_json_array(self): # Happy path test case where the JSON string contains an array of string json_string = '[ "Add type hints", "Move docstrings", "Consider using" ]' expected_output = ["Add type hints", "Move docstrings", "Consider using"] assert fix_json_using_multiple_techniques(json_string) == expected_output # Tests that the function returns an empty dictionary when the JSON string is not parseable and cannot be fixed using either technique. # @requires_api_key("OPEN_API_KEY") def test_fix_and_parse_json_can_not(self, mocker): # Edge case test case where the JSON string is not parseable and cannot be fixed using either technique json_string = "This is not a JSON string" # mock try_ai_fix to avoid calling the AI model: mocker.patch("autogpt.json_utils.json_fix_llm.try_ai_fix", return_value={}) expected_output = {} # Use the actual function name in the test result = fix_json_using_multiple_techniques(json_string) assert result == expected_output # Tests that the function returns an empty dictionary when the JSON string is empty. # @requires_api_key("OPEN_API_KEY") def test_fix_and_parse_json_empty_string(self, mocker): # Arrange json_string = "" # Act # mock try_ai_fix to avoid calling the AI model: mocker.patch("autogpt.json_utils.json_fix_llm.try_ai_fix", return_value={}) result = fix_and_parse_json(json_string) # Assert assert result == {} # Tests that the function successfully fixes and parses a JSON string that contains escape characters. def test_fix_and_parse_json_escape_characters(self): # Arrange json_string = '{"text": "This is a \\"test\\" string."}' # Act result = fix_json_using_multiple_techniques(json_string) # Assert assert result == {"text": 'This is a "test" string.'} # Tests that the function successfully fixes and parses a JSON string that contains nested objects or arrays. def test_fix_and_parse_json_nested_objects(self): # Arrange json_string = '{"person": {"name": "John", "age": 30}, "hobbies": ["reading", "swimming"]}' # Act result = fix_json_using_multiple_techniques(json_string) # Assert assert result == { "person": {"name": "John", "age": 30}, "hobbies": ["reading", "swimming"], } ================================================ FILE: tests/unit/test_llm_utils.py ================================================ import pytest from openai.error import APIError, RateLimitError from autogpt.llm import llm_utils @pytest.fixture(params=[RateLimitError, APIError]) def error(request): if request.param == APIError: return request.param("Error", http_status=502) else: return request.param("Error") def error_factory(error_instance, error_count, retry_count, warn_user=True): class RaisesError: def __init__(self): self.count = 0 @llm_utils.retry_openai_api( num_retries=retry_count, backoff_base=0.001, warn_user=warn_user ) def __call__(self): self.count += 1 if self.count <= error_count: raise error_instance return self.count return RaisesError() def test_retry_open_api_no_error(capsys): @llm_utils.retry_openai_api() def f(): return 1 result = f() assert result == 1 output = capsys.readouterr() assert output.out == "" assert output.err == "" @pytest.mark.parametrize( "error_count, retry_count, failure", [(2, 10, False), (2, 2, False), (10, 2, True), (3, 2, True), (1, 0, True)], ids=["passing", "passing_edge", "failing", "failing_edge", "failing_no_retries"], ) def test_retry_open_api_passing(capsys, error, error_count, retry_count, failure): call_count = min(error_count, retry_count) + 1 raises = error_factory(error, error_count, retry_count) if failure: with pytest.raises(type(error)): raises() else: result = raises() assert result == call_count assert raises.count == call_count output = capsys.readouterr() if error_count and retry_count: if type(error) == RateLimitError: assert "Reached rate limit, passing..." in output.out assert "Please double check" in output.out if type(error) == APIError: assert "API Bad gateway" in output.out else: assert output.out == "" def test_retry_open_api_rate_limit_no_warn(capsys): error_count = 2 retry_count = 10 raises = error_factory(RateLimitError, error_count, retry_count, warn_user=False) result = raises() call_count = min(error_count, retry_count) + 1 assert result == call_count assert raises.count == call_count output = capsys.readouterr() assert "Reached rate limit, passing..." in output.out assert "Please double check" not in output.out def test_retry_openapi_other_api_error(capsys): error_count = 2 retry_count = 10 raises = error_factory(APIError("Error", http_status=500), error_count, retry_count) with pytest.raises(APIError): raises() call_count = 1 assert raises.count == call_count output = capsys.readouterr() assert output.out == "" def test_chunked_tokens(): text = "Auto-GPT is an experimental open-source application showcasing the capabilities of the GPT-4 language model" expected_output = [ ( 13556, 12279, 2898, 374, 459, 22772, 1825, 31874, 3851, 67908, 279, 17357, 315, 279, 480, 2898, 12, 19, 4221, 1646, ) ] output = list(llm_utils.chunked_tokens(text, "cl100k_base", 8191)) assert output == expected_output ================================================ FILE: tests/unit/test_plugins.py ================================================ import pytest from autogpt.config import Config from autogpt.plugins import ( denylist_allowlist_check, inspect_zip_for_modules, scan_plugins, ) PLUGINS_TEST_DIR = "tests/unit/data/test_plugins" PLUGIN_TEST_ZIP_FILE = "Auto-GPT-Plugin-Test-master.zip" PLUGIN_TEST_INIT_PY = "Auto-GPT-Plugin-Test-master/src/auto_gpt_vicuna/__init__.py" PLUGIN_TEST_OPENAI = "https://weathergpt.vercel.app/" def test_inspect_zip_for_modules(): result = inspect_zip_for_modules(str(f"{PLUGINS_TEST_DIR}/{PLUGIN_TEST_ZIP_FILE}")) assert result == [PLUGIN_TEST_INIT_PY] @pytest.fixture def mock_config_denylist_allowlist_check(): class MockConfig: """Mock config object for testing the denylist_allowlist_check function""" plugins_denylist = ["BadPlugin"] plugins_allowlist = ["GoodPlugin"] authorise_key = "y" exit_key = "n" return MockConfig() def test_denylist_allowlist_check_denylist( mock_config_denylist_allowlist_check, monkeypatch ): # Test that the function returns False when the plugin is in the denylist monkeypatch.setattr("builtins.input", lambda _: "y") assert not denylist_allowlist_check( "BadPlugin", mock_config_denylist_allowlist_check ) def test_denylist_allowlist_check_allowlist( mock_config_denylist_allowlist_check, monkeypatch ): # Test that the function returns True when the plugin is in the allowlist monkeypatch.setattr("builtins.input", lambda _: "y") assert denylist_allowlist_check("GoodPlugin", mock_config_denylist_allowlist_check) def test_denylist_allowlist_check_user_input_yes( mock_config_denylist_allowlist_check, monkeypatch ): # Test that the function returns True when the user inputs "y" monkeypatch.setattr("builtins.input", lambda _: "y") assert denylist_allowlist_check( "UnknownPlugin", mock_config_denylist_allowlist_check ) def test_denylist_allowlist_check_user_input_no( mock_config_denylist_allowlist_check, monkeypatch ): # Test that the function returns False when the user inputs "n" monkeypatch.setattr("builtins.input", lambda _: "n") assert not denylist_allowlist_check( "UnknownPlugin", mock_config_denylist_allowlist_check ) def test_denylist_allowlist_check_user_input_invalid( mock_config_denylist_allowlist_check, monkeypatch ): # Test that the function returns False when the user inputs an invalid value monkeypatch.setattr("builtins.input", lambda _: "invalid") assert not denylist_allowlist_check( "UnknownPlugin", mock_config_denylist_allowlist_check ) @pytest.fixture def config_with_plugins(): """Mock config object for testing the scan_plugins function""" # Test that the function returns the correct number of plugins cfg = Config() cfg.plugins_dir = PLUGINS_TEST_DIR cfg.plugins_openai = ["https://weathergpt.vercel.app/"] return cfg @pytest.fixture def mock_config_openai_plugin(): """Mock config object for testing the scan_plugins function""" class MockConfig: """Mock config object for testing the scan_plugins function""" plugins_dir = PLUGINS_TEST_DIR plugins_openai = [PLUGIN_TEST_OPENAI] plugins_denylist = ["AutoGPTPVicuna"] plugins_allowlist = [PLUGIN_TEST_OPENAI] return MockConfig() def test_scan_plugins_openai(mock_config_openai_plugin): # Test that the function returns the correct number of plugins result = scan_plugins(mock_config_openai_plugin, debug=True) assert len(result) == 1 @pytest.fixture def mock_config_generic_plugin(): """Mock config object for testing the scan_plugins function""" # Test that the function returns the correct number of plugins class MockConfig: plugins_dir = PLUGINS_TEST_DIR plugins_openai = [] plugins_denylist = [] plugins_allowlist = ["AutoGPTPVicuna"] return MockConfig() def test_scan_plugins_generic(mock_config_generic_plugin): # Test that the function returns the correct number of plugins result = scan_plugins(mock_config_generic_plugin, debug=True) assert len(result) == 1 ================================================ FILE: tests/unit/test_spinner.py ================================================ # Generated by CodiumAI import time from autogpt.spinner import Spinner """ Code Analysis Main functionalities: The Spinner class provides a simple way to display a spinning animation while a process is running. It can be used to indicate that a process is ongoing and to provide visual feedback to the user. The class can be used as a context manager, which means that it can be used with the 'with' statement to automatically start and stop the spinner animation. Methods: - __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: Initializes the Spinner class with a message to display and a delay between each spinner update. - spin(self) -> None: Spins the spinner animation while the process is running. - __enter__(self): Starts the spinner animation when used as a context manager. - __exit__(self, exc_type, exc_value, exc_traceback) -> None: Stops the spinner animation when used as a context manager. - update_message(self, new_message, delay=0.1): Updates the message displayed by the spinner animation. Fields: - spinner: An itertools.cycle object that contains the characters used for the spinner animation. - delay: The delay between each spinner update. - message: The message to display. - running: A boolean value that indicates whether the spinner animation is running. - spinner_thread: A threading.Thread object that runs the spin method in a separate thread. """ ALMOST_DONE_MESSAGE = "Almost done..." PLEASE_WAIT = "Please wait..." def test_spinner_initializes_with_default_values(): """Tests that the spinner initializes with default values.""" with Spinner() as spinner: assert spinner.message == "Loading..." assert spinner.delay == 0.1 def test_spinner_initializes_with_custom_values(): """Tests that the spinner initializes with custom message and delay values.""" with Spinner(message=PLEASE_WAIT, delay=0.2) as spinner: assert spinner.message == PLEASE_WAIT assert spinner.delay == 0.2 # def test_spinner_stops_spinning(): """Tests that the spinner starts spinning and stops spinning without errors.""" with Spinner() as spinner: time.sleep(1) spinner.update_message(ALMOST_DONE_MESSAGE) time.sleep(1) assert spinner.running == False def test_spinner_updates_message_and_still_spins(): """Tests that the spinner message can be updated while the spinner is running and the spinner continues spinning.""" with Spinner() as spinner: assert spinner.running == True time.sleep(1) spinner.update_message(ALMOST_DONE_MESSAGE) time.sleep(1) assert spinner.message == ALMOST_DONE_MESSAGE assert spinner.running == False def test_spinner_can_be_used_as_context_manager(): """Tests that the spinner can be used as a context manager.""" with Spinner() as spinner: assert spinner.running == True assert spinner.running == False ================================================ FILE: tests/unit/test_url_validation.py ================================================ import pytest from pytest import raises from autogpt.url_utils.validators import validate_url @validate_url def dummy_method(url): return url successful_test_data = ( ("https://google.com/search?query=abc"), ("https://google.com/search?query=abc&p=123"), ("http://google.com/"), ("http://a.lot.of.domain.net/param1/param2"), ) @pytest.mark.parametrize("url", successful_test_data) def test_url_validation_succeeds(url): assert dummy_method(url) == url bad_protocol_data = ( ("htt://example.com"), ("httppp://example.com"), (" https://example.com"), ) @pytest.mark.parametrize("url", bad_protocol_data) def test_url_validation_fails_bad_protocol(url): with raises(ValueError, match="Invalid URL format"): dummy_method(url) missing_loc = (("http://?query=q"),) @pytest.mark.parametrize("url", missing_loc) def test_url_validation_fails_bad_protocol(url): with raises(ValueError, match="Missing Scheme or Network location"): dummy_method(url) local_file = ( ("http://localhost"), ("https://localhost/"), ("http://2130706433"), ("https://2130706433"), ("http://127.0.0.1/"), ) @pytest.mark.parametrize("url", local_file) def test_url_validation_fails_local_path(url): with raises(ValueError, match="Access to local files is restricted"): dummy_method(url) ================================================ FILE: tests/unit/test_web_selenium.py ================================================ from autogpt.commands.web_selenium import browse_website def test_browse_website(): url = "https://barrel-roll.com" question = "How to execute a barrel roll" response = browse_website(url, question) assert "Error" in response # Sanity check that the response is not too long assert len(response) < 200 ================================================ FILE: tests/utils.py ================================================ import functools import os from contextlib import contextmanager import pytest from autogpt.config import Config @contextmanager def dummy_openai_api_key(): # even when we record the VCR cassettes, openAI wants an API key config = Config() original_api_key = config.openai_api_key config.set_openai_api_key("sk-dummy") try: yield finally: config.set_openai_api_key(original_api_key) def requires_api_key(env_var): def decorator(func): @functools.wraps(func) def wrapper(*args, **kwargs): if not os.environ.get(env_var) and env_var == "OPENAI_API_KEY": with dummy_openai_api_key(): return func(*args, **kwargs) else: return func(*args, **kwargs) return wrapper return decorator def skip_in_ci(test_function): return pytest.mark.skipif( os.environ.get("CI") == "true", reason="This test doesn't work on GitHub Actions.", )(test_function) def get_workspace_file_path(workspace, file_name): return str(workspace.get_path(file_name)) ================================================ FILE: tests/vcr/__init__.py ================================================ ================================================ FILE: tests/vcr/openai_filter.py ================================================ import json import re def replace_timestamp_in_request(request): # Check if the request body contains a JSON object try: if not request or not request.body: return request body = json.loads(request.body) except ValueError: return request if "messages" not in body: return request for message in body["messages"]: if "content" in message and "role" in message and message["role"] == "system": timestamp_regex = re.compile(r"\w{3} \w{3} \d{2} \d{2}:\d{2}:\d{2} \d{4}") message["content"] = timestamp_regex.sub( "Tue Jan 01 00:00:00 2000", message["content"] ) request.body = json.dumps(body) return request def before_record_response(response): if "Transfer-Encoding" in response["headers"]: del response["headers"]["Transfer-Encoding"] return response def before_record_request(request): filtered_request = filter_hostnames(request) filtered_request_without_dynamic_data = replace_timestamp_in_request( filtered_request ) return filtered_request_without_dynamic_data def filter_hostnames(request): allowed_hostnames = [ "api.openai.com", "localhost:50337", ] # List of hostnames you want to allow if any(hostname in request.url for hostname in allowed_hostnames): return request else: return None ================================================ FILE: tests/vcr/vcr_filter.py ================================================ import json import re from typing import Any, Dict, List REPLACEMENTS: List[Dict[str, str]] = [ { "regex": r"\w{3} \w{3} {1,2}\d{1,2} \d{2}:\d{2}:\d{2} \d{4}", "replacement": "Tue Jan 1 00:00:00 2000", }, { "regex": r"]*>", "replacement": "", }, ] def replace_message_content(content: str, replacements: List[Dict[str, str]]) -> str: for replacement in replacements: pattern = re.compile(replacement["regex"]) content = pattern.sub(replacement["replacement"], content) return content def replace_timestamp_in_request(request: Any) -> Any: try: if not request or not request.body: return request body = json.loads(request.body) except ValueError: return request if "messages" not in body: return request body[ "max_tokens" ] = 0 # this field is incosistent between requests and not used at the moment. for message in body["messages"]: if "content" in message and "role" in message: if message["role"] == "system": message["content"] = replace_message_content( message["content"], REPLACEMENTS ) request.body = json.dumps(body) return request def before_record_response(response: Dict[str, Any]) -> Dict[str, Any]: if "Transfer-Encoding" in response["headers"]: del response["headers"]["Transfer-Encoding"] return response def before_record_request(request: Any) -> Any: filtered_request = filter_hostnames(request) filtered_request_without_dynamic_data = replace_timestamp_in_request( filtered_request ) return filtered_request_without_dynamic_data def filter_hostnames(request: Any) -> Any: allowed_hostnames: List[str] = [ "api.openai.com", "localhost:50337", ] # Add your implementation here for filtering hostnames if any(hostname in request.url for hostname in allowed_hostnames): return request else: return None ================================================ FILE: tests.py ================================================ import unittest import coverage if __name__ == "__main__": # Start coverage collection cov = coverage.Coverage() cov.start() # Load all tests from the 'autogpt/tests' package suite = unittest.defaultTestLoader.discover("./tests") # Run the tests unittest.TextTestRunner().run(suite) # Stop coverage collection cov.stop() cov.save() # Report the coverage cov.report(show_missing=True)