Repository: reid41/QA-Pilot Branch: main Commit: 747a9ecb61a3 Files: 35 Total size: 173.1 KB Directory structure: gitextract_ot3ibsoy/ ├── LICENSE ├── README.md ├── app.py ├── check_postgresql_connection.py ├── config/ │ ├── config.ini │ └── prompt_templates.ini ├── llamacpp_models/ │ └── model_dir ├── parser.go ├── qa_model_apis.py ├── qa_pilot_run.py ├── requirements.txt ├── svelte-app/ │ ├── .gitignore │ ├── README.md │ ├── package.json │ ├── public/ │ │ ├── global.css │ │ └── index.html │ ├── rollup.config.js │ ├── scripts/ │ │ └── setupTypeScript.js │ └── src/ │ ├── ApiKeyModal.svelte │ ├── App.svelte │ ├── Chat.svelte │ ├── ConfigEditor.svelte │ ├── DeleteConfirmationModal.svelte │ ├── DeleteTemplateModal.svelte │ ├── LlamaCppModelsModal.svelte │ ├── NewSourceModal.svelte │ ├── PromptTemplatesModal.svelte │ ├── config.js │ ├── global.css │ └── main.js ├── templates/ │ ├── go_index.html │ └── index.html └── utils/ ├── codegraph.py ├── go_codegraph.py └── helper.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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qa-pilot

QA-Pilot is an interactive chat project that leverages online/local LLM for rapid understanding and navigation of GitHub code repository. ### Features * Chat with github public repository with git clone way * Store the chat history * Easy to set the configuration * Multiple chat sessions * Locate the session quicly with search function * Integrate with `codegraph` to view the python file * Support the different LLM models * ollama(deepseek, llama3.1, phi3, llama3, gemma2) * openai(gpt-4o, gpt-4-turbo, gpt-4, and gpt-3.5-turbo) * mistralai(mistral-tiny, mistral-tiny, mistral-small-latest, mistral-medium-latest, mistral-large-latest, codestral-lates) * localai(gpt-4, more) * zhipuai(glm-4-0520, glm-4, glm-4-air, glm-4-airx, glm-4-flash) * anthropic(claude-3-opus-20240229, claude-3-sonnet-20240229, claude-3-haiku-20240307, claude-3-5-sonnet-20240620) * llamacpp * nvidia(meta/llama3-70b-instruct, more) * tongyi(qwen-turbo, qwen-plus, qwen-max, more) * moonshot(moonshot-v1-8k, moonshot-v1-32k, moonshot-v1-128k) ### Release * 2024-07-03 update langchain to `0.2.6` version and add `moonshot` API support * 2024-06-30 add `Go Codegraph` * 2024-06-27 add `nvidia/tongyi` API support * 2024-06-19 add `llamacpp` API support, improve the `settings` list in the sidebar and add upload model function for `llamacpp`, add `prompt templates` setting * 2024-06-15 add `anthropic` API support, refactor some functions, and fix chat show messages * 2024-06-12 add `zhipuai` API support * 2024-06-10 Convert `flask` to `fastapi` and add `localai` API support * 2024-06-07 Add `rr:` option and use `FlashRank` for the search * 2024-06-05 Upgrade `langchain` to `v0.2` and add `ollama embeddings` * 2024-05-26 Release v2.0.1: Refactoring to replace `Streamlit` fontend with `Svelte` to improve the performance. ### Disclaimer * This is a test project to validate the feasibility of a fully local solution for question answering using LLMs and Vector embeddings. It is not production ready, and it is not meant to be used in production. * `Do not use models for analyzing your critical or production data!!` * `Do not use models for analyzing customer data to ensure data privacy and security!!` * `Do not use models for analyzing you private/sensitivity code respository!!` #### QA-Pilot ![qa-demo-new](https://github.com/reid41/QA-Pilot/assets/25558653/8198730f-32ec-4664-a10c-43b3f40c99ad) #### CodeGraph ![code-graph-new](https://github.com/reid41/QA-Pilot/assets/25558653/8c47ea00-d703-42b5-b43b-d40796e7de1d) To deploy QA-Pilot, you can follow the below steps: 1. Clone the QA-Pilot repository: ```shell git clone https://github.com/reid41/QA-Pilot.git cd QA-Pilot ``` 2. Install [conda](https://www.anaconda.com/download) for virtual environment management. Create and activate a new virtual environment. ```shell conda create -n QA-Pilot python=3.10.14 conda activate QA-Pilot ``` 3. Install the required dependencies: ```shell pip install -r requirements.txt ``` 4. Install the pytorch with cuda [pytorch](https://pytorch.org/get-started/locally/) 5. Setup providers * For setup [ollama website](https://ollama.com/) and [ollama github](https://github.com/ollama/ollama) to manage the local LLM. e.g. ```shell ollama pull ollama list ``` * For setup [localAI](https://localai.io/) and [LocalAI github](https://github.com/mudler/LocalAI) to manage the local LLM, set the localAI `base_url` in config/config.ini. e.g. ```shell docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-aio-cpu # Do you have a Nvidia GPUs? Use this instead # CUDA 11 # docker run -p 8080:8080 --gpus all --name local-ai -ti localai/localai:latest-aio-gpu-nvidia-cuda-11 # CUDA 12 # docker run -p 8080:8080 --gpus all --name local-ai -ti localai/localai:latest-aio-gpu-nvidia-cuda-12 # quick check the service with http://:8080/ # quick check the models with http://:8080/models/ ``` * For setup llamacpp with [llama-cpp-python](https://github.com/abetlen/llama-cpp-python#windows-remarks) - upload the model to `llamacpp_models` dir or upload from the `llamacpp models` under the `Settings` - set the model in `llamacpp_llm_models` section in `config/config.ini` * For setup API key in `.env` - [OpenAI](https://platform.openai.com/docs/overview): OPENAI_API_KEY='' - [MistralAI](https://docs.mistral.ai/): MISTRAL_API_KEY='' - [ZhipuAI](https://open.bigmodel.cn/): ZHIPUAI_API_KEY='' - [Anthropic](https://console.anthropic.com/settings/keys): ANTHROPIC_API_KEY='' - [Nvidia](https://build.nvidia.com/explore/discover): NVIDIA_API_KEY='' - [TongYi](https://help.aliyun.com/document_detail/611472.html?spm=a2c4g.2399481.0.0): DASHSCOPE_API_KEY='' - [Moonshot](https://platform.moonshot.cn/): MOONSHOT_API_KEY='' * For `Go codegraph`, make sure setup [GO](https://go.dev/doc/install) env, compile go file and test ```shell go build -o parser parser.go # test ./parser /path/test.go ``` 6. Set the related parameters in `config/config.ini`, e.g. `model provider`, `model`, `variable`, `Ollama API url` and setup the [Postgresql](https://www.postgresql.org/download/) env ```shell # create the db, e.g. CREATE DATABASE qa_pilot_chatsession_db; CREATE USER qa_pilot_user WITH ENCRYPTED PASSWORD 'qa_pilot_p'; GRANT ALL PRIVILEGES ON DATABASE qa_pilot_chatsession_db TO qa_pilot_user; # set the connection cat config/config.ini [database] db_name = qa_pilot_chatsession_db db_user = qa_pilot_user db_password = qa_pilot_p db_host = localhost db_port = 5432 # set the arg in script and test connection python check_postgresql_connection.py ``` 7. Download and install [node.js](https://nodejs.org/en/download/package-manager) and Set up the fontend env in one terminal ```shell # make sure the backend server host ip is correct, localhost is by default cat svelte-app/src/config.js export const API_BASE_URL = 'http://localhost:5000'; # install deps cd svelte-app npm install npm run dev ``` 8. Run the backend QA-Pilot in another terminal: ```shell python qa_pilot_run.py ``` ### Tips * Do not use url and upload at the same time. * The remove button cannot really remove the local chromadb, need to remove it manually when stop it. * Switch to `New Source Button` to add a new project * Use `rsd:` to start the input and get the source document * Use `rr:` to start the input and use the `FlashrankRerank` for the search * Click `Open Code Graph` in `QA-Pilot` to view the code(make sure the the already in the project session and loaded before click), curretly support `python` and `go` ================================================ FILE: app.py ================================================ from fastapi import FastAPI, Request, HTTPException, UploadFile, File, Form from fastapi.responses import JSONResponse from fastapi.middleware.cors import CORSMiddleware from fastapi.templating import Jinja2Templates import configparser import os from dotenv import load_dotenv, set_key from utils.helper import ( DataHandler, remove_directory, encode_kwargs, model_kwargs, ) import psycopg2 from psycopg2 import sql import ast from qa_model_apis import ( get_chat_model, get_embedding_model, ) from utils.codegraph import ( parse_python_code, read_current_repo_path, build_file_tree, ) from utils.go_codegraph import( parse_go_code, go_build_file_tree, ) app = FastAPI() app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) config_path = os.path.join('config', 'config.ini') prompt_templates_path = os.path.join('config', 'prompt_templates.ini') config = configparser.ConfigParser() config.read(config_path) templates = Jinja2Templates(directory="templates") DB_NAME = config['database']['db_name'] DB_USER = config['database']['db_user'] DB_PASSWORD = config['database']['db_password'] DB_HOST = config['database']['db_host'] DB_PORT = config['database']['db_port'] # for analyse code current_session = None current_model_info = { "provider": None, "model": None, "eb_provider": None, "eb_model": None, "chat_model": None, "embedding_model": None } def init_db(): conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() cursor.execute(''' CREATE TABLE IF NOT EXISTS sessions ( id BIGINT PRIMARY KEY, name TEXT NOT NULL, url TEXT NOT NULL ) ''') conn.commit() # fix the first time to set the session cursor.execute('SELECT id, name, url FROM sessions LIMIT 1') session = cursor.fetchone() if session: global current_session current_session = {'id': session[0], 'name': session[1], 'url': session[2]} print("Default session set to:", current_session) conn.close() def load_models_if_needed(): selected_provider = config.get('model_providers', 'selected_provider') selected_model = config.get(f"{selected_provider}_llm_models", 'selected_model') eb_selected_provider = config.get('embedding_model_providers', 'selected_provider') eb_selected_model = config.get(f"{eb_selected_provider}_embedding_models", 'selected_model') if (current_model_info["provider"] != selected_provider or current_model_info["model"] != selected_model or current_model_info["eb_provider"] != eb_selected_provider or current_model_info["eb_model"] != eb_selected_model): current_model_info["provider"] = selected_provider current_model_info["model"] = selected_model current_model_info["eb_provider"] = eb_selected_provider current_model_info["eb_model"] = eb_selected_model current_model_info["chat_model"] = get_chat_model(selected_provider, selected_model) current_model_info["embedding_model"] = get_embedding_model(eb_selected_provider, eb_selected_model, model_kwargs, encode_kwargs) print(f"Loaded new models: provider={selected_provider}, model={selected_model}") print(f"Loaded models: provider={selected_provider}, model={selected_model}") def create_message_table(session_id): conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() table_name = sql.Identifier(f'session_{session_id}') cursor.execute(sql.SQL(''' CREATE TABLE IF NOT EXISTS {} ( id BIGSERIAL PRIMARY KEY, sender TEXT NOT NULL, text TEXT NOT NULL ) ''').format(table_name)) conn.commit() conn.close() init_db() def load_config(): config.read(config_path) return config @app.get('/get_config') async def get_config(): config = load_config() config_dict = {section: dict(config.items(section)) for section in config.sections()} return JSONResponse(content=config_dict) @app.post('/save_config') async def save_config(request: Request): new_config = await request.json() config = load_config() for section, section_values in new_config.items(): if not config.has_section(section): config.add_section(section) for key, value in section_values.items(): config.set(section, key, value) with open(config_path, 'w') as configfile: config.write(configfile) return JSONResponse(content={"message": "Configuration saved successfully!"}) @app.post('/update_provider') async def update_provider(request: Request): data = await request.json() selected_provider = data.get('selected_provider') config.set('model_providers', 'selected_provider', selected_provider) with open(config_path, 'w') as configfile: config.write(configfile) return JSONResponse(content={"message": "Provider updated successfully!"}) @app.post('/update_model') async def update_model(request: Request): data = await request.json() selected_provider = data.get('selected_provider') selected_model = data.get('selected_model') config.set(f'{selected_provider}_llm_models', 'selected_model', selected_model) with open(config_path, 'w') as configfile: config.write(configfile) return JSONResponse(content={"message": "Model updated successfully!"}) @app.post('/load_repo') async def load_repo(request: Request): data = await request.json() git_url = data.get('git_url') if not git_url: raise HTTPException(status_code=400, detail="Git URL is required") load_models_if_needed() chat_model = current_model_info["chat_model"] embedding_model = current_model_info["embedding_model"] data_handler = DataHandler(git_url, chat_model, embedding_model) try: data_handler.git_clone_repo() data_handler.load_into_db() return JSONResponse(content={"message": f"Repository {git_url} loaded successfully!"}) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.post('/chat') async def chat(request: Request): data = await request.json() load_models_if_needed() chat_model = current_model_info["chat_model"] embedding_model = current_model_info["embedding_model"] user_message = data.get('message') current_repo = data.get('current_repo') session_id = data.get('session_id') if not user_message or not current_repo or not session_id: raise HTTPException(status_code=400, detail="Message, current_repo and session_id are required") try: data_handler = DataHandler(current_repo, chat_model, embedding_model) data_handler.load_into_db() rsd = False rr = False # return source documents if user_message.startswith('rsd:'): user_message = user_message[4:].strip() rsd = True # use reranker elif user_message.startswith('rr:'): user_message = user_message[3:].strip() rr = True bot_response = data_handler.retrieval_qa(user_message, rsd=rsd, rr=rr) # Save user message and bot response to the session table conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() table_name = sql.Identifier(f'session_{session_id}') cursor.execute(sql.SQL('INSERT INTO {} (sender, text) VALUES (%s, %s)').format(table_name), ('You', user_message)) cursor.execute(sql.SQL('INSERT INTO {} (sender, text) VALUES (%s, %s)').format(table_name), ('QA-Pilot', bot_response)) conn.commit() conn.close() return JSONResponse(content={"response": bot_response}) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get('/sessions') async def get_sessions(): conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() cursor.execute('SELECT id, name, url FROM sessions') sessions = [{'id': row[0], 'name': row[1], 'url': row[2]} for row in cursor.fetchall()] conn.close() print(f"Fetched sessions from DB: {sessions}") return JSONResponse(content=sessions) @app.post('/sessions') async def save_sessions(request: Request): sessions = await request.json() print(f"Received sessions to save: {sessions}") conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() for session in sessions: cursor.execute('INSERT INTO sessions (id, name, url) VALUES (%s, %s, %s) ON CONFLICT (id) DO UPDATE SET name = EXCLUDED.name, url = EXCLUDED.url', (session['id'], session['name'], session['url'])) create_message_table(session['id']) conn.commit() conn.close() print("Saved sessions to DB") return JSONResponse(content={"message": "Sessions saved successfully!"}) @app.get('/messages/{session_id}') async def get_messages(session_id: int): conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() table_name = sql.Identifier(f'session_{session_id}') cursor.execute(sql.SQL('SELECT sender, text FROM {}').format(table_name)) messages = [{'sender': row[0], 'text': row[1]} for row in cursor.fetchall()] conn.close() print(f"Fetched messages from session {session_id}") return JSONResponse(content=messages) @app.post('/update_current_session') async def update_current_session(request: Request): global current_session current_session = await request.json() return JSONResponse(content={"message": "Current session updated successfully!"}) @app.delete('/sessions/{session_id}') async def delete_session(session_id: int): print(f"Deleting session with ID: {session_id}") conn = psycopg2.connect( dbname=DB_NAME, user=DB_USER, password=DB_PASSWORD, host=DB_HOST, port=DB_PORT ) cursor = conn.cursor() try: cursor.execute('SELECT name FROM sessions WHERE id = %s', (session_id,)) session = cursor.fetchone() if session: session_name = session[0] print("anem", session_name) cursor.execute('DELETE FROM sessions WHERE id = %s', (session_id,)) conn.commit() cursor.execute(sql.SQL('DROP TABLE IF EXISTS {}').format(sql.Identifier(f'session_{session_id}'))) conn.commit() # remove the git clone project remove_project_path = os.path.join("projects", session_name) remove_directory(remove_project_path) print("Session deleted successfully") return JSONResponse(content={"message": "Session deleted successfully!"}) except Exception as e: print(f"Error deleting session: {e}") raise HTTPException(status_code=500, detail=str(e)) finally: conn.close() # api key handling functions @app.post('/check_api_key') async def check_api_key(request: Request): data = await request.json() provider = data.get('provider') key_var = f"{provider.upper()}_API_KEY" load_dotenv() api_key = os.getenv(key_var) return JSONResponse(content={'exists': bool(api_key)}) @app.post('/save_api_key') async def save_api_key(request: Request): data = await request.json() provider = data.get('provider') api_key = data.get('api_key') key_var = f"{provider.upper()}_API_KEY" dotenv_path = '.env' set_key(dotenv_path, key_var, api_key) load_dotenv() return JSONResponse(content={'message': 'API Key saved successfully!'}) # hanlde llamacpp models(list/upload/delete) @app.get('/llamacpp_models') async def list_llamacpp_models(): upload_dir = "llamacpp_models" if not os.path.exists(upload_dir): return JSONResponse(content=[]) models = os.listdir(upload_dir) return JSONResponse(content=models) @app.post('/llamacpp_models', status_code=201) async def upload_llamacpp_model(file: UploadFile = File(...), chunk: int = Form(...), totalChunks: int = Form(...)): try: upload_dir = "llamacpp_models" os.makedirs(upload_dir, exist_ok=True) chunk_dir = os.path.join(upload_dir, "chunks") os.makedirs(chunk_dir, exist_ok=True) chunk_file_path = os.path.join(chunk_dir, f"{file.filename}.part{chunk}") with open(chunk_file_path, "wb") as f: f.write(await file.read()) # Check if all chunks are uploaded if len(os.listdir(chunk_dir)) == totalChunks: final_file_path = os.path.join(upload_dir, file.filename) with open(final_file_path, "wb") as final_file: for i in range(totalChunks): chunk_file_path = os.path.join(chunk_dir, f"{file.filename}.part{i}") with open(chunk_file_path, "rb") as chunk_file: final_file.write(chunk_file.read()) os.remove(chunk_file_path) os.rmdir(chunk_dir) # Remove the chunks directory return JSONResponse(content={"message": "Chunk uploaded successfully!"}) except Exception as e: print(f"Error uploading model: {e}") raise HTTPException(status_code=500, detail="Failed to upload chunk") @app.delete('/llamacpp_models/{model_name}') async def delete_llamacpp_model(model_name: str): file_path = os.path.join("llamacpp_models", model_name) if os.path.exists(file_path): os.remove(file_path) return JSONResponse(content={"message": "Model deleted successfully!"}) else: raise HTTPException(status_code=404, detail="Model not found") # handle prompt templates @app.get('/get_prompt_templates') async def get_prompt_templates(): prompt_templates_path = os.path.join('config', 'prompt_templates.ini') templates_config = configparser.ConfigParser() if not os.path.exists(prompt_templates_path): return JSONResponse(content={}) with open(prompt_templates_path, 'r', encoding='utf-8') as file: file_content = file.read() # Ensure the content has a section header if '[qa_prompt_templates]' not in file_content: file_content = '[qa_prompt_templates]\n' + file_content try: templates_config.read_string(file_content) except configparser.ParsingError as e: print(f"Error parsing config: {e}") raise HTTPException(status_code=500, detail="Error parsing prompt templates") templates = {k: v.replace('\\n', '\n') for k, v in templates_config.items('qa_prompt_templates')} return JSONResponse(content=templates) @app.post('/delete_prompt_template') async def delete_prompt_template(request: Request): data = await request.json() template_name = data.get('template_name') prompt_templates_path = os.path.join('config', 'prompt_templates.ini') templates_config = configparser.ConfigParser() templates_config.read(prompt_templates_path) if template_name in templates_config['qa_prompt_templates']: templates_config.remove_option('qa_prompt_templates', template_name) with open(prompt_templates_path, 'w', encoding='utf-8') as configfile: templates_config.write(configfile) return JSONResponse(content={"message": "Template deleted successfully!"}) else: raise HTTPException(status_code=404, detail="Template not found") @app.post('/save_prompt_templates') async def save_prompt_templates(request: Request): new_templates = await request.json() prompt_templates_path = os.path.join('config', 'prompt_templates.ini') templates_config = configparser.ConfigParser() templates_config['qa_prompt_templates'] = {k: v.replace('\n', '\\n') for k, v in new_templates.items()} with open(prompt_templates_path, 'w', encoding='utf-8') as configfile: templates_config.write(configfile) return JSONResponse(content={"message": "Templates saved successfully!"}) #############################python codegraph############################ @app.get('/codegraph') async def codegraph_home(request: Request): return templates.TemplateResponse('index.html', {'request': request}) @app.get('/data') async def data(filepath: str): code_data = parse_python_code(filepath) # Ensure the path points to your Python code file return JSONResponse(content=code_data) @app.get('/directory') async def directory(): current_repo_path = read_current_repo_path(current_session) if current_repo_path is None: raise HTTPException(status_code=404, detail="Repository path not set or not found") dir_tree = build_file_tree(current_repo_path) # Ensure the path points to your code directory return JSONResponse(content=dir_tree) @app.post('/analyze') async def analyze(request: Request): data = await request.json() load_models_if_needed() chat_model = current_model_info["chat_model"] embedding_model = current_model_info["embedding_model"] code = data.get('code', '') # send the code to LLM data_handler = DataHandler(git_url='', chat_model=chat_model, embedding_model=embedding_model) code_analysis = data_handler.restrieval_qa_for_code(code) return JSONResponse(content={'analysis': code_analysis}) #####go codegraph##### @app.get('/go_codegraph') async def go_codegraph_home(request: Request): return templates.TemplateResponse('go_index.html', {'request': request}) @app.get('/go_data') async def go_data(filepath: str): if os.path.isdir(filepath): raise HTTPException(status_code=400, detail="The specified path is a directory, not a file.") code_data = parse_go_code(filepath) return JSONResponse(content=code_data) @app.get('/go_directory') async def directory(): current_repo_path = read_current_repo_path(current_session) if current_repo_path is None: raise HTTPException(status_code=404, detail="Repository path not set or not found") dir_tree = go_build_file_tree(current_repo_path) # Ensure the path points to your code directory return JSONResponse(content=dir_tree) if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=5003, log_level="debug") ================================================ FILE: check_postgresql_connection.py ================================================ import psycopg2 import configparser config = configparser.ConfigParser() config.read('config/config.ini') db_config = config['database'] try: conn = psycopg2.connect( dbname=db_config['db_name'], user=db_config['db_user'], password=db_config['db_password'], host=db_config['db_host'], port=db_config['db_port'] ) print("Connection successful") cur = conn.cursor() cur.execute("SELECT version();") db_version = cur.fetchone() print(f"Database version: {db_version}") cur.close() conn.close() except psycopg2.Error as e: print(f"Error: {e}") ================================================ FILE: config/config.ini ================================================ [app_setting] version = v2.15.6 [model_providers] provider_list = openai, ollama, mistralai, localai, zhipuai, anthropic, llamacpp, nvidia, tongyi, moonshot selected_provider = ollama [openai_llm_models] model_list = gpt-3.5-turbo, gpt-4o selected_model = gpt-4o [mistralai_llm_models] model_list = mistral-tiny, mistral-small selected_model = mistral-small [ollama_llm_models] model_list = qwen2.5-coder:14b, qwen2.5:14b, deepseek-r1:14b selected_model = qwen2.5:14b base_url = http://localhost:11434 [localai_llm_models] model_list = gpt-4 selected_model = gpt-4 base_url = http://localhost:8080/v1 [zhipuai_llm_models] model_list = glm-4 selected_model = glm-4 [anthropic_llm_models] model_list = claude-3-opus-20240229 selected_model = claude-3-opus-20240229 [llamacpp_llm_models] model_list = selected_model = [nvidia_llm_models] model_list = meta/llama3-70b-instruct selected_model = meta/llama3-70b-instruct [tongyi_llm_models] model_list = qwen-turbo selected_model = qwen-turbo [moonshot_llm_models] model_list = moonshot-v1-8k, moonshot-v1-32k, moonshot-v1-128k selected_model = moonshot-v1-8k [embedding_model_providers] provider_list = huggingface, ollama selected_provider = huggingface [huggingface_embedding_models] model_list = sentence-transformers/all-MiniLM-L6-v2 selected_model = sentence-transformers/all-MiniLM-L6-v2 [ollama_embedding_models] model_list = nomic-embed-text, mxbai-embed-large selected_model = nomic-embed-text [prompt_templates] qa_selected_prompt = qa_template code_selected_prompt = code_template localai_selected_prompt = code_template_localai [the_project_dirs] vectorstore_dir = VectorStore sessions_dir = sessions project_dir = projects [for_loop_dirs_depth] max_dir_depth = 5 [chunk_setting] chunk_size = 3000 chunk_overlap = 200 [database] db_name = qa_pilot_chatsession_db db_user = qa_pilot_user db_password = qa_pilot_p db_host = localhost db_port = 5432 ================================================ FILE: config/prompt_templates.ini ================================================ [qa_prompt_templates] qa_template = """I want you to act as a very senior code developer who is familar with github/gitlab community. I will provide you the code project, you need to provide answers which are based on the project. {context}""" code_template = """I want you to act as a Senior Python developer. I will provide you the code project, you provide detailed exaplanation. Human: {input}History: {history} AI:""" code_template_localai = """I want you to act as a Senior Python developer. I will provide you the code project, you provide detailed exaplanation. Human: {input} AI:""" ================================================ FILE: llamacpp_models/model_dir ================================================ ================================================ FILE: parser.go ================================================ package main import ( "encoding/json" "fmt" "go/ast" "go/parser" "go/token" "os" "strings" ) type Node struct { Name string Type string // "func", "method", "type", "interface", "int", or "import" Calls []string // list of called functions/methods Code string // source code of the node Position string // file position of the node } func main() { if len(os.Args) < 2 { fmt.Println("Usage: ./parser ") os.Exit(1) } filePath := os.Args[1] fset := token.NewFileSet() node, err := parser.ParseFile(fset, filePath, nil, parser.ParseComments) if err != nil { fmt.Println(err) os.Exit(1) } nodes := make(map[string]*Node) ast.Inspect(node, func(n ast.Node) bool { switch x := n.(type) { case *ast.FuncDecl: funcName := x.Name.Name funcType := "func" if x.Recv != nil { recvType := fmt.Sprintf("%s", x.Recv.List[0].Type) if len(recvType) > 1 && recvType[0] == '*' { recvType = recvType[1:] } funcType = "method" funcName = fmt.Sprintf("%s.%s", cleanType(recvType), funcName) } pos := fset.Position(x.Pos()) code := getNodeCode(fset, x.Pos(), x.End(), filePath) nodes[funcName] = &Node{ Name: funcName, Type: funcType, Calls: []string{}, Code: code, Position: pos.String(), } case *ast.GenDecl: if x.Tok == token.TYPE { for _, spec := range x.Specs { typeSpec := spec.(*ast.TypeSpec) typeName := typeSpec.Name.Name pos := fset.Position(typeSpec.Pos()) code := getNodeCode(fset, typeSpec.Pos(), typeSpec.End(), filePath) nodes[typeName] = &Node{ Name: typeName, Type: "type", Calls: []string{}, Code: code, Position: pos.String(), } } } else if x.Tok == token.IMPORT { for _, spec := range x.Specs { importSpec := spec.(*ast.ImportSpec) importPath := importSpec.Path.Value pos := fset.Position(importSpec.Pos()) code := importSpec.Path.Value nodes[importPath] = &Node{ Name: importPath, Type: "import", Calls: []string{}, Code: code, Position: pos.String(), } } } } return true }) // Collect function and method calls ast.Inspect(node, func(n ast.Node) bool { switch x := n.(type) { case *ast.CallExpr: caller := "" if sel, ok := x.Fun.(*ast.SelectorExpr); ok { if ident, ok := sel.X.(*ast.Ident); ok { caller = fmt.Sprintf("%s.%s", ident.Name, sel.Sel.Name) } } else if ident, ok := x.Fun.(*ast.Ident); ok { caller = ident.Name } if caller != "" { if parentFunc := getParentFunc(node, x.Pos(), fset); parentFunc != "" { if _, ok := nodes[parentFunc]; ok { nodes[parentFunc].Calls = append(nodes[parentFunc].Calls, caller) } } } } return true }) jsonOutput, err := json.Marshal(nodes) if err != nil { fmt.Println(err) os.Exit(1) } fmt.Println(string(jsonOutput)) } func getNodeCode(fset *token.FileSet, start, end token.Pos, filePath string) string { startOffset := fset.Position(start).Offset endOffset := fset.Position(end).Offset code, _ := os.ReadFile(filePath) return string(code[startOffset:endOffset]) } func getParentFunc(node *ast.File, pos token.Pos, fset *token.FileSet) string { var parentFunc string ast.Inspect(node, func(n ast.Node) bool { if fd, ok := n.(*ast.FuncDecl); ok { if fd.Pos() < pos && fd.End() > pos { if fd.Recv != nil { recvType := fmt.Sprintf("%s", fd.Recv.List[0].Type) if len(recvType) > 1 && recvType[0] == '*' { recvType = recvType[1:] } parentFunc = fmt.Sprintf("%s.%s", cleanType(recvType), fd.Name.Name) } else { parentFunc = fd.Name.Name } return false } } return true }) return parentFunc } func cleanType(typeName string) string { // Remove generic type parameters if present return strings.TrimSpace(strings.Split(typeName, "[")[0]) } ================================================ FILE: qa_model_apis.py ================================================ from langchain_community.chat_models import ChatOllama from langchain_mistralai.chat_models import ChatMistralAI from langchain_openai import ChatOpenAI from langchain_huggingface.embeddings import HuggingFaceEmbeddings from langchain_community.embeddings import OllamaEmbeddings import os import configparser from dotenv import load_dotenv from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler from llama_index.llms.openai_like import OpenAILike from langchain_community.chat_models import ChatZhipuAI from langchain_anthropic import ChatAnthropic import multiprocessing from langchain_community.chat_models import ChatLlamaCpp from langchain_nvidia_ai_endpoints import ChatNVIDIA from langchain_community.chat_models.tongyi import ChatTongyi from langchain_community.chat_models.moonshot import MoonshotChat # read from the config.ini config_path = os.path.join('config', 'config.ini') config = configparser.ConfigParser() config.read(config_path) ollama_base_url = config.get('ollama_llm_models', 'base_url') localai_base_url = config.get('localai_llm_models', 'base_url') # get the chat model from config def get_chat_model(provider, model_name=''): if provider == 'ollama': return ChatOllama( base_url=ollama_base_url, model=model_name, streaming=True, callbacks=[StreamingStdOutCallbackHandler()] ) elif provider == 'openai': load_dotenv() return ChatOpenAI(model_name=model_name) elif provider == 'mistralai': load_dotenv() return ChatMistralAI(model_name=model_name) elif provider == 'localai': return OpenAILike( api_base=localai_base_url, api_key="qa_pilot", is_chat_model=True, context_window=32768, model=model_name ) elif provider == 'zhipuai': load_dotenv() return ChatZhipuAI( model=model_name, temperature=0.5, ) elif provider == 'anthropic': load_dotenv() return ChatAnthropic( model=model_name ) elif provider == 'llamacpp': local_model = os.path.join("llamacpp_models", model_name) return ChatLlamaCpp( temperature=0.5, model_path=local_model, n_ctx=10000, n_gpu_layers=8, n_batch=300, # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU. max_tokens=512, n_threads=multiprocessing.cpu_count() - 1, repeat_penalty=1.5, top_p=0.5, verbose=True, ) elif provider == 'nvidia': load_dotenv() return ChatNVIDIA( model=model_name ) elif provider == 'tongyi': load_dotenv() return ChatTongyi( model=model_name ) elif provider == 'moonshot': load_dotenv() return MoonshotChat( model=model_name ) else: raise ValueError(f"Unsupported model provider: {provider}") def get_embedding_model(eb_provider, model_name='', model_kwargs='', encode_kwargs=''): if eb_provider == 'huggingface': return HuggingFaceEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, cache_folder="./cache_embeddings/" ) elif eb_provider == 'ollama': return OllamaEmbeddings( model=model_name, model_kwargs=model_kwargs ) else: raise ValueError(f"Unsupported embedding model provider: {eb_provider}") ================================================ FILE: qa_pilot_run.py ================================================ import uvicorn from app import app if __name__ == "__main__": print("Starting server on http://0.0.0.0:5000") uvicorn.run(app, host="0.0.0.0", port=5000) ================================================ FILE: requirements.txt ================================================ aiohttp==3.9.3 aiosignal==1.3.1 altair==5.2.0 annotated-types==0.6.0 anthropic==0.28.1 anyio==4.3.0 asgiref==3.8.1 async-timeout==4.0.3 attrs==23.2.0 backoff==2.2.1 bcrypt==4.1.2 blinker==1.7.0 boto3==1.34.71 botocore==1.34.71 build==1.1.1 cachetools==5.3.3 certifi==2024.2.2 charset-normalizer==3.3.2 chroma-hnswlib==0.7.3 chromadb==0.4.24 click==8.1.7 colorama==0.4.6 coloredlogs==15.0.1 dashscope==1.20.0 dataclasses-json==0.6.4 defusedxml==0.7.1 Deprecated==1.2.14 dill==0.3.8 dirtyjson==1.0.8 diskcache==5.6.3 distro==1.9.0 exceptiongroup==1.2.0 fastapi==0.110.0 filelock==3.13.3 FlashRank==0.2.5 Flask==3.0.3 Flask-Cors==4.0.1 flatbuffers==24.3.25 frozenlist==1.4.1 fsspec==2024.3.1 gitdb==4.0.11 GitPython==3.1.42 google-auth==2.29.0 googleapis-common-protos==1.63.0 greenlet==3.0.3 grpcio==1.62.1 h11==0.14.0 httpcore==1.0.4 httptools==0.6.1 httpx==0.27.0 httpx-sse==0.4.0 huggingface-hub==0.23.2 humanfriendly==10.0 humbug==0.3.2 idna==3.6 importlib-metadata==6.11.0 importlib_resources==6.4.0 intel-openmp==2021.4.0 itsdangerous==2.2.0 Jinja2==3.1.3 jiter==0.4.2 jmespath==1.0.1 joblib==1.3.2 jsonpatch==1.33 jsonpointer==2.4 jsonschema==4.21.1 jsonschema-specifications==2023.12.1 kubernetes==29.0.0 langchain==0.2.6 langchain-anthropic==0.1.15 langchain-chroma==0.1.1 langchain-community==0.2.6 langchain-core==0.2.11 langchain-huggingface==0.0.2 langchain-mistralai==0.1.8 langchain-nvidia-ai-endpoints==0.1.2 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.80 llama-index-core==0.10.43.post1 llama-index-llms-openai==0.1.22 llama-index-llms-openai-like==0.1.3 llama_cpp_python==0.2.67 llamaindex-py-client==0.1.19 lz4==4.3.3 markdown-it-py==3.0.0 MarkupSafe==2.1.5 marshmallow==3.21.1 mdurl==0.1.2 mkl==2021.4.0 mmh3==4.1.0 monotonic==1.6 mpmath==1.3.0 multidict==6.0.5 multiprocess==0.70.16 mypy-extensions==1.0.0 nest-asyncio==1.6.0 networkx==3.2.1 nltk==3.8.1 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.17.1 openai==1.28.0 opentelemetry-api==1.23.0 opentelemetry-exporter-otlp-proto-common==1.23.0 opentelemetry-exporter-otlp-proto-grpc==1.23.0 opentelemetry-instrumentation==0.44b0 opentelemetry-instrumentation-asgi==0.44b0 opentelemetry-instrumentation-fastapi==0.44b0 opentelemetry-proto==1.23.0 opentelemetry-sdk==1.23.0 opentelemetry-semantic-conventions==0.44b0 opentelemetry-util-http==0.44b0 orjson==3.9.15 overrides==7.7.0 packaging==23.2 pandas==2.2.1 pathos==0.3.2 pillow==10.2.0 posthog==3.5.0 pox==0.3.4 ppft==1.7.6.8 protobuf==4.25.3 psycopg2-binary==2.9.9 pulsar-client==3.4.0 pyarrow==15.0.2 pyasn1==0.6.0 pyasn1_modules==0.4.0 pydantic==2.6.4 pydantic_core==2.16.3 pydeck==0.8.1b0 Pygments==2.17.2 PyJWT==2.8.0 PyPika==0.48.9 pyproject_hooks==1.0.0 pyreadline3==3.4.1 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 python-multipart==0.0.9 pytz==2024.1 PyYAML==6.0.1 referencing==0.34.0 regex==2023.12.25 requests==2.31.0 requests-oauthlib==2.0.0 rich==13.7.1 rpds-py==0.18.0 rsa==4.9 s3transfer==0.10.1 safetensors==0.4.2 scikit-learn==1.4.1.post1 scipy==1.12.0 sentence-transformers==2.6.1 six==1.16.0 smmap==5.0.1 sniffio==1.3.1 SQLAlchemy==2.0.29 starlette==0.36.3 streamlit==1.32.2 sympy==1.12 tbb==2021.12.0 tenacity==8.2.3 text-generation==0.7.0 threadpoolctl==3.4.0 tiktoken==0.7.0 tokenizers==0.19.1 toml==0.10.2 tomli==2.0.1 toolz==0.12.1 tornado==6.4 tqdm==4.66.2 transformers==4.41.2 typer==0.11.0 typing-inspect==0.9.0 typing_extensions==4.10.0 tzdata==2024.1 urllib3==2.2.1 uvicorn==0.29.0 waitress==3.0.0 watchdog==4.0.0 watchfiles==0.21.0 websocket-client==1.7.0 websockets==12.0 Werkzeug==3.0.3 wrapt==1.16.0 yarl==1.9.4 zipp==3.18.1 ================================================ FILE: svelte-app/.gitignore ================================================ /node_modules/ /public/build/ .DS_Store ================================================ FILE: svelte-app/README.md ================================================ This repo is no longer maintained. Consider using `npm init vite` and selecting the `svelte` option or — if you want a full-fledged app framework — use [SvelteKit](https://kit.svelte.dev), the official application framework for Svelte. --- # svelte app This is a project template for [Svelte](https://svelte.dev) apps. It lives at https://github.com/sveltejs/template. To create a new project based on this template using [degit](https://github.com/Rich-Harris/degit): ```bash npx degit sveltejs/template svelte-app cd svelte-app ``` *Note that you will need to have [Node.js](https://nodejs.org) installed.* ## Get started Install the dependencies... ```bash cd svelte-app npm install ``` ...then start [Rollup](https://rollupjs.org): ```bash npm run dev ``` Navigate to [localhost:8080](http://localhost:8080). You should see your app running. Edit a component file in `src`, save it, and reload the page to see your changes. By default, the server will only respond to requests from localhost. To allow connections from other computers, edit the `sirv` commands in package.json to include the option `--host 0.0.0.0`. If you're using [Visual Studio Code](https://code.visualstudio.com/) we recommend installing the official extension [Svelte for VS Code](https://marketplace.visualstudio.com/items?itemName=svelte.svelte-vscode). If you are using other editors you may need to install a plugin in order to get syntax highlighting and intellisense. ## Building and running in production mode To create an optimised version of the app: ```bash npm run build ``` You can run the newly built app with `npm run start`. This uses [sirv](https://github.com/lukeed/sirv), which is included in your package.json's `dependencies` so that the app will work when you deploy to platforms like [Heroku](https://heroku.com). ## Single-page app mode By default, sirv will only respond to requests that match files in `public`. This is to maximise compatibility with static fileservers, allowing you to deploy your app anywhere. If you're building a single-page app (SPA) with multiple routes, sirv needs to be able to respond to requests for *any* path. You can make it so by editing the `"start"` command in package.json: ```js "start": "sirv public --single" ``` ## Using TypeScript This template comes with a script to set up a TypeScript development environment, you can run it immediately after cloning the template with: ```bash node scripts/setupTypeScript.js ``` Or remove the script via: ```bash rm scripts/setupTypeScript.js ``` If you want to use `baseUrl` or `path` aliases within your `tsconfig`, you need to set up `@rollup/plugin-alias` to tell Rollup to resolve the aliases. For more info, see [this StackOverflow question](https://stackoverflow.com/questions/63427935/setup-tsconfig-path-in-svelte). ## Deploying to the web ### With [Vercel](https://vercel.com) Install `vercel` if you haven't already: ```bash npm install -g vercel ``` Then, from within your project folder: ```bash cd public vercel deploy --name my-project ``` ### With [surge](https://surge.sh/) Install `surge` if you haven't already: ```bash npm install -g surge ``` Then, from within your project folder: ```bash npm run build surge public my-project.surge.sh ``` ================================================ FILE: svelte-app/package.json ================================================ { "name": "svelte-app", "version": "1.0.0", "private": true, "type": "module", "scripts": { "build": "rollup -c", "dev": "rollup -c -w", "start": "sirv public --no-clear --port 5001 --host 0.0.0.0" }, "devDependencies": { "@rollup/plugin-commonjs": "^24.0.0", "@rollup/plugin-node-resolve": "^15.0.0", "@rollup/plugin-terser": "^0.4.4", "rollup": "^3.15.0", "rollup-plugin-css-only": "^4.3.0", "rollup-plugin-livereload": "^2.0.0", "rollup-plugin-svelte": "^7.1.2", "svelte": "^3.55.0" }, "dependencies": { "cytoscape": "^3.29.2", "cytoscape-dagre": "^2.5.0", "highlight.js": "^11.9.0", "marked": "^12.0.2", "sirv-cli": "^2.0.0", "svelte-spa-router": "^4.0.1" } } ================================================ FILE: svelte-app/public/global.css ================================================ html, body { position: relative; width: 100%; height: 100%; } body { color: #333; margin: 0; padding: 8px; box-sizing: border-box; font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen-Sans, Ubuntu, Cantarell, "Helvetica Neue", sans-serif; } a { color: rgb(0,100,200); text-decoration: none; } a:hover { text-decoration: underline; } a:visited { color: rgb(0,80,160); } label { display: block; } input, button, select, textarea { font-family: inherit; font-size: inherit; -webkit-padding: 0.4em 0; padding: 0.4em; margin: 0 0 0.5em 0; box-sizing: border-box; border: 1px solid #ccc; border-radius: 2px; } input:disabled { color: #ccc; } button { color: #333; background-color: #f4f4f4; outline: none; } button:disabled { color: #999; } button:not(:disabled):active { background-color: #ddd; } button:focus { border-color: #666; } ================================================ FILE: svelte-app/public/index.html ================================================ Svelte app ================================================ FILE: svelte-app/rollup.config.js ================================================ import { spawn } from 'child_process'; import svelte from 'rollup-plugin-svelte'; import commonjs from '@rollup/plugin-commonjs'; import terser from '@rollup/plugin-terser'; import resolve from '@rollup/plugin-node-resolve'; import livereload from 'rollup-plugin-livereload'; import css from 'rollup-plugin-css-only'; const production = !process.env.ROLLUP_WATCH; function serve() { let server; function toExit() { if (server) server.kill(0); } return { writeBundle() { if (server) return; server = spawn('npm', ['run', 'start', '--', '--dev'], { stdio: ['ignore', 'inherit', 'inherit'], shell: true }); process.on('SIGTERM', toExit); process.on('exit', toExit); } }; } export default { input: 'src/main.js', output: { sourcemap: true, format: 'iife', name: 'app', file: 'public/build/bundle.js' }, plugins: [ svelte({ compilerOptions: { // enable run-time checks when not in production dev: !production } }), // we'll extract any component CSS out into // a separate file - better for performance css({ output: 'bundle.css' }), // If you have external dependencies installed from // npm, you'll most likely need these plugins. In // some cases you'll need additional configuration - // consult the documentation for details: // https://github.com/rollup/plugins/tree/master/packages/commonjs resolve({ browser: true, dedupe: ['svelte'], exportConditions: ['svelte'] }), commonjs(), // In dev mode, call `npm run start` once // the bundle has been generated !production && serve(), // Watch the `public` directory and refresh the // browser on changes when not in production !production && livereload('public'), // If we're building for production (npm run build // instead of npm run dev), minify production && terser() ], watch: { clearScreen: false } }; ================================================ FILE: svelte-app/scripts/setupTypeScript.js ================================================ // @ts-check /** This script modifies the project to support TS code in .svelte files like: As well as validating the code for CI. */ /** To work on this script: rm -rf test-template template && git clone sveltejs/template test-template && node scripts/setupTypeScript.js test-template */ import fs from "fs" import path from "path" import { argv } from "process" import url from 'url'; const __filename = url.fileURLToPath(import.meta.url); const __dirname = url.fileURLToPath(new URL('.', import.meta.url)); const projectRoot = argv[2] || path.join(__dirname, "..") // Add deps to pkg.json const packageJSON = JSON.parse(fs.readFileSync(path.join(projectRoot, "package.json"), "utf8")) packageJSON.devDependencies = Object.assign(packageJSON.devDependencies, { "svelte-check": "^3.0.0", "svelte-preprocess": "^5.0.0", "@rollup/plugin-typescript": "^11.0.0", "typescript": "^4.9.0", "tslib": "^2.5.0", "@tsconfig/svelte": "^3.0.0" }) // Add script for checking packageJSON.scripts = Object.assign(packageJSON.scripts, { "check": "svelte-check" }) // Write the package JSON fs.writeFileSync(path.join(projectRoot, "package.json"), JSON.stringify(packageJSON, null, " ")) // mv src/main.js to main.ts - note, we need to edit rollup.config.js for this too const beforeMainJSPath = path.join(projectRoot, "src", "main.js") const afterMainTSPath = path.join(projectRoot, "src", "main.ts") fs.renameSync(beforeMainJSPath, afterMainTSPath) // Switch the app.svelte file to use TS const appSveltePath = path.join(projectRoot, "src", "App.svelte") let appFile = fs.readFileSync(appSveltePath, "utf8") appFile = appFile.replace(" {#if isOpen}

API Key Settings

{#if successMessage}

{successMessage}

{/if} {#if errorMessage}

{errorMessage}

{/if}
{/if} {#if apiMessage}
{apiMessage}
{/if} ================================================ FILE: svelte-app/src/App.svelte ================================================
{#each filteredSessions as session}
{/each}
{currentSessionIndex !== -1 ? sessions[currentSessionIndex].name : 'QA-Pilot'}
{#if currentSessionIndex !== -1} {:else}
Placeholder Image

1. Please check your config with "Edit QA-Pilot Settings" button.

2. Click "New Source Button" to input the github URL.

{/if}
{#if showConfigEditor} {/if} {#if showNewSourceModal} {/if} {#if showApiKeyModal} {/if} {#if showLlamaCppModelsModal} {/if} {#if showPromptTemplatesModal} {/if} {#if showDeleteModal} {/if} ================================================ FILE: svelte-app/src/Chat.svelte ================================================
{#each messages as message}
{#if message.sender === 'loader'}
Thinking...
{:else}
{message.sender}
{#if formatMessage(message.text).think}
{@html marked(formatMessage(message.text).think)}
{/if} {#if formatMessage(message.text).normal}
{@html marked(formatMessage(message.text).normal)}
{/if} {#if message.sender !== 'You'}
{/if} {/if}
{/each}
================================================ FILE: svelte-app/src/ConfigEditor.svelte ================================================

Edit QA-Pilot Settings

{#if Object.keys(configData).length === 0}

Loading configuration...

{:else} {#each configOrder as section}

{section}

{#each Object.keys(configData[section]) as key} {/each}
{/each}
{/if}
================================================ FILE: svelte-app/src/DeleteConfirmationModal.svelte ================================================ {#if isOpen}

Are you sure you want to delete the session: {sessionName}?

{/if} ================================================ FILE: svelte-app/src/DeleteTemplateModal.svelte ================================================ {#if isOpen}

Are you sure you want to delete the template: {templateName}?

{#if isDefaultTemplate}

Warning: You are about to delete a default template. Ensure there is a replacement before proceeding.

{/if}
{/if} ================================================ FILE: svelte-app/src/LlamaCppModelsModal.svelte ================================================ {#if isOpen}

LlamaCpp Models

{#each models as model}
{model}
{/each} newModelFile = e.target.files[0]} /> {#if showProgressBar}
{/if} {#if showMessage}
{messageText}
{/if}
{/if} ================================================ FILE: svelte-app/src/NewSourceModal.svelte ================================================ {#if isOpen}

Enter GitHub URL

{/if} ================================================ FILE: svelte-app/src/PromptTemplatesModal.svelte ================================================ {#if isOpen}

Prompt Templates

{#if showMessage}
{messageText}
{/if}
{/if} showDeleteModal = false} /> ================================================ FILE: svelte-app/src/config.js ================================================ export const API_BASE_URL = 'http://localhost:5000'; ================================================ FILE: svelte-app/src/global.css ================================================ a { color: #00ffff !important; text-decoration: none !important; } a:hover { text-decoration: underline !important; } ================================================ FILE: svelte-app/src/main.js ================================================ import './global.css' import App from './App.svelte'; const app = new App({ target: document.body }); export default app; ================================================ FILE: templates/go_index.html ================================================ Go CodeGPS
    ================================================ FILE: templates/index.html ================================================ CodeGPS
      ================================================ FILE: utils/codegraph.py ================================================ import ast import os def parse_python_code(filepath): """Parse Python code file, extract classes, methods, global functions, imported modules and their source code, and the call relationships.""" with open(filepath, 'r', encoding='utf-8') as file: source = file.read() node = ast.parse(source) classes = [] methods = [] functions = [] imports = [] links = [] method_calls = {} function_calls = {} import_calls = {} class_inheritance = {} # Extract classes and methods for item in ast.walk(node): if isinstance(item, ast.ClassDef): class_start_line = item.lineno - 1 class_end_line = item.end_lineno class_source = "\n".join(source.splitlines()[class_start_line:class_end_line]) classes.append({'key': item.name, 'name': item.name, 'class': 'class', 'color': 'lightblue', 'source': class_source}) for base in item.bases: if isinstance(base, ast.Name): class_inheritance[item.name] = base.id for method in [n for n in item.body if isinstance(n, ast.FunctionDef)]: method_start_line = method.lineno - 1 method_end_line = method.end_lineno method_source = "\n".join(source.splitlines()[method_start_line:method_end_line]) methods.append({'key': f"{item.name}.{method.name}", 'name': method.name, 'class': 'method', 'color': 'lightgreen', 'source': method_source}) links.append({'from': item.name, 'to': f"{item.name}.{method.name}", 'color': 'blue'}) method_calls[f"{item.name}.{method.name}"] = [] for stmt in ast.walk(method): if isinstance(stmt, ast.Call): if isinstance(stmt.func, ast.Name): method_calls[f"{item.name}.{method.name}"].append(stmt.func.id) elif isinstance(stmt.func, ast.Attribute): method_calls[f"{item.name}.{method.name}"].append(stmt.func.attr) # Handle inheritance relationships for child, parent in class_inheritance.items(): links.append({'from': child, 'to': parent, 'category': 'dashed', 'color': 'gray'}) # Extract global functions for item in node.body: if isinstance(item, ast.FunctionDef): function_start_line = item.lineno - 1 function_end_line = item.end_lineno function_source = "\n".join(source.splitlines()[function_start_line:function_end_line]) functions.append({'key': item.name, 'name': item.name, 'class': 'function', 'color': 'lightcoral', 'source': function_source}) function_calls[item.name] = [] for stmt in ast.walk(item): if isinstance(stmt, ast.Call): if isinstance(stmt.func, ast.Name): function_calls[item.name].append(stmt.func.id) elif isinstance(stmt.func, ast.Attribute): function_calls[item.name].append(stmt.func.attr) # Extract imported modules, classes, functions, or variables for item in node.body: if isinstance(item, ast.Import): for alias in item.names: imports.append({'key': alias.name, 'name': alias.name, 'class': 'import', 'color': 'lightyellow', 'source': f"import {alias.name}"}) elif isinstance(item, ast.ImportFrom): module = item.module for alias in item.names: import_name = f"{module}.{alias.name}" imports.append({'key': alias.name, 'name': alias.name, 'class': 'import', 'color': 'lightyellow', 'source': f"from {module} import {alias.name}"}) import_calls[alias.name] = import_name # Check which imports are actually called used_imports = set() for calls in method_calls.values(): used_imports.update(calls) for calls in function_calls.values(): used_imports.update(calls) # Filter out unused imports imports = [imp for imp in imports if imp['key'] in used_imports] # Add method and global function call links for caller, callees in method_calls.items(): for callee in callees: if callee in method_calls or callee in function_calls or callee in import_calls: links.append({'from': caller, 'to': callee, 'category': 'dashed', 'color': 'green'}) for caller, callees in function_calls.items(): for callee in callees: if callee in method_calls or callee in function_calls or callee in import_calls: links.append({'from': caller, 'to': callee, 'category': 'dashed', 'color': 'green'}) for import_name, import_module in import_calls.items(): if import_name in used_imports: for caller, callees in method_calls.items(): if import_name in callees: links.append({'from': caller, 'to': import_name, 'category': 'dashed', 'color': 'orange'}) for caller, callees in function_calls.items(): if import_name in callees: links.append({'from': caller, 'to': import_name, 'category': 'dashed', 'color': 'orange'}) return {'nodeDataArray': classes + methods + functions + imports, 'linkDataArray': links} def read_current_repo_path(current_session): if current_session: print("=============> path: ", os.path.join("projects", current_session['name'])) return os.path.join("projects", current_session['name']) return None def build_file_tree(directory): """Build the file tree for the given directory""" file_tree = [] for root, dirs, files in os.walk(directory): for d in dirs: dir_path = os.path.join(root, d) file_tree.append({ 'name': d, 'path': dir_path, 'type': 'directory', 'children': build_file_tree(dir_path) }) for f in files: if f.endswith('.py'): file_path = os.path.join(root, f) file_tree.append({ 'name': f, 'path': file_path, 'type': 'file' }) break # Only traverse current directory, not recursively return file_tree ================================================ FILE: utils/go_codegraph.py ================================================ import json import os import subprocess from typing import Dict, List, Any import re def process_nodes(nodes: Dict[str, Any]) -> Dict[str, Any]: nodeDataArray = [] linkDataArray = [] methods = {} types = {} for key, node in nodes.items(): print(f"Processing node: {key} -> {node}") if node["Type"] == "import": node_data = { "key": key, "name": node["Name"], "class": node["Type"], "source": "import " + node["Code"] } nodeDataArray.append(node_data) elif node["Type"] == "type": types[key] = node node_data = { "key": key, "name": node["Name"], "class": "struct", "source": "type " + node["Code"] } nodeDataArray.append(node_data) elif node["Type"] == "method": receiver_type = extract_receiver_type(key) method_name = extract_method_name(key) node_data = { "key": f"{receiver_type}.{method_name}", "name": method_name, "class": node["Type"], "source": node["Code"] } nodeDataArray.append(node_data) if receiver_type not in methods: methods[receiver_type] = [] methods[receiver_type].append(node_data) elif node["Type"] == "func": node_data = { "key": key, "name": node["Name"], "class": "function", "source": node["Code"] } nodeDataArray.append(node_data) else: node_data = { "key": key, "name": node["Name"], "class": node["Type"], "source": node["Code"] } nodeDataArray.append(node_data) for receiver_type, method_list in methods.items(): if receiver_type in types: for method in method_list: link = { "from": receiver_type, "to": method["key"], "category": "dashed", "color": "gray" } linkDataArray.append(link) for node_key, node in nodes.items(): for call in node["Calls"]: print(f"Checking call from {node_key} to {call}") if call in nodes: link = { "from": node_key, "to": call, "category": "dashed", "color": "green" } linkDataArray.append(link) print(f"Link added: {link}") else: print(f"Call {call} not found in nodes") print(f"Final nodeDataArray: {nodeDataArray}") print(f"Final linkDataArray: {linkDataArray}") return {"nodeDataArray": nodeDataArray, "linkDataArray": linkDataArray} def extract_receiver_type(key: str) -> str: if key.startswith("&"): parts = re.split(r'[{}]', key) receiver_type = parts[1].strip().split(' ')[-1] else: receiver_type = key.split(".")[0] # Remove generic type parameters if present receiver_type = re.sub(r'\[.*?\]', '', receiver_type).strip() return receiver_type def extract_method_name(key: str) -> str: return key.split(".")[-1] def parse_go_code(filepath: str) -> Dict[str, Any]: try: result = subprocess.run(['./parser', filepath], capture_output=True, text=True, check=True) output = result.stdout.strip() nodes = json.loads(output) return process_nodes(nodes) except subprocess.CalledProcessError as e: print(f"Error running parser: {e.stderr}") return {"nodeDataArray": [], "linkDataArray": []} except json.JSONDecodeError as e: print(f"Error decoding JSON: {e}") return {"nodeDataArray": [], "linkDataArray": []} def go_build_file_tree(directory: str) -> List[Dict[str, Any]]: file_tree = [] for root, dirs, files in os.walk(directory): for d in dirs: dir_path = os.path.join(root, d) file_tree.append({ 'name': d, 'path': dir_path, 'type': 'directory', 'children': go_build_file_tree(dir_path) }) for f in files: if f.endswith('.go'): file_path = os.path.join(root, f) file_tree.append({ 'name': f, 'path': file_path, 'type': 'file' }) break # Only traverse current directory, not recursively return file_tree ================================================ FILE: utils/helper.py ================================================ from langchain_community.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain_community.vectorstores import Chroma import git import os from queue import Queue import shutil from urllib.parse import urlparse import configparser from langchain.chains import ConversationalRetrievalChain from langchain_core.prompts.prompt import PromptTemplate from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate from langchain.chains import ConversationChain from cachetools import cached, TTLCache from langchain.retrievers import ContextualCompressionRetriever from langchain.retrievers.document_compressors import FlashrankRerank # read from the config.ini config_path = os.path.join('config', 'config.ini') prompt_templates_path = 'config/prompt_templates.ini' config = configparser.ConfigParser() config.read(config_path) vectorstore_dir = config.get('the_project_dirs', 'vectorstore_dir') sessions_dir = config.get('the_project_dirs', 'sessions_dir') project_dir = config.get('the_project_dirs', 'project_dir') max_dir_depth = config.get('for_loop_dirs_depth', 'max_dir_depth') chunk_size = config.get('chunk_setting', 'chunk_size') chunk_overlap = config.get('chunk_setting', 'chunk_overlap') base_url = config.get('ollama_llm_models', 'base_url') encode_kwargs = {"normalize_embeddings": False} model_kwargs = {"device": "cuda:0"} allowed_extensions = ['.py', '.md', '.js', '.html', '.css', '.ts', '.sh', '.go', '.java', '.svelte'] # remove the directories for the download/upload projects def remove_directory(dir_path): if os.path.exists(dir_path): # check and update file permisiion first for root, dirs, files in os.walk(dir_path, topdown=False): for name in files: filepath = os.path.join(root, name) try: os.chmod(filepath, 0o777) # modify permission except PermissionError: pass for name in dirs: dirpath = os.path.join(root, name) os.chmod(dirpath, 0o777) # remove it shutil.rmtree(dir_path, ignore_errors=True) def document_to_string(document): # Get the document content content = document.page_content # Get the metadata metadata = document.metadata # Convert the metadata dictionary to a string metadata_str = ', '.join(f'{key}: {value}' for key, value in metadata.items()) # Combine the content and metadata into a single string result = f'Content: {content}\nMetadata: {metadata_str}' return result def documents_to_string(documents): # Convert each Document object to a string and add to the list doc_strings = [document_to_string(doc) for doc in documents] # Join all strings into a single large string return '\n\n'.join(doc_strings) def load_prompt_templates(path): config = configparser.ConfigParser() config.read(path) return {section: dict(config.items(section)) for section in config.sections()} cache = TTLCache(maxsize=100, ttl=300) class DataHandler: def __init__(self, git_url, chat_model, embedding_model) -> None: self.git_url = git_url last_part = git_url.split('/')[-1] self.repo_name = last_part.rsplit('.', 1)[0] # create the store db and project dir if not os.path.exists(vectorstore_dir): os.makedirs(vectorstore_dir) if not os.path.exists(project_dir): os.makedirs(project_dir) # config the path self.db_dir = os.path.join(vectorstore_dir, self.repo_name) self.download_path = os.path.join(project_dir, self.repo_name) self.model = chat_model self.embedding_model = embedding_model self.ChatQueue = Queue(maxsize=2) # check the db dir exist or not def db_exists(self): return os.path.exists(self.db_dir) # update the chat message queue def update_chat_queue(self, value): if self.ChatQueue.full(): self.ChatQueue.get() self.ChatQueue.put(value) # download or upload the project def git_clone_repo(self): url_parts = urlparse(self.git_url) # upload situation if not url_parts.scheme: print("Local repository detected, skipping cloning process.") else: # git clone if not os.path.exists(self.download_path): print(f"Cloning from Git URL: {self.git_url}") try: git.Repo.clone_from(self.git_url, self.download_path) print("Repository cloned successfully.") except Exception as e: print(f"Failed to clone repository. Error: {e}") # load the projects def load_files(self, root_dir=None, current_depth=0, base_depth=0): if root_dir is None: root_dir = self.download_path self.docs = [] print("Loading files from:", root_dir) # github projects if "UploadedRepo" not in self.git_url: for dirpath, _, filenames in os.walk(root_dir): for filename in filenames: if any(filename.endswith(ext) for ext in allowed_extensions): file_path = os.path.join(dirpath, filename) try: loader = TextLoader(file_path, encoding='utf-8') self.docs.extend(loader.load_and_split()) except Exception as e: print(f"Error loading file {file_path}: {e}") else: # sosreport project or directories upload if current_depth - base_depth > int(max_dir_depth): return # over the dir depth, then stop for entry in os.scandir(root_dir): if entry.is_symlink(): continue # skip the soft link(should be for windows) elif entry.is_dir(): if entry.name == 'boot': base_depth = current_depth # start from boot self.load_files(entry.path, current_depth + 1, base_depth) elif entry.is_file(): # not limit for the file extension try: loader = TextLoader(entry.path, encoding='utf-8') self.docs.extend(loader.load_and_split()) except Exception as e: print(f"Error loading file {entry.path}: {e}") # split all the files def split_files(self): text_splitter = CharacterTextSplitter(chunk_size=int(chunk_size), chunk_overlap=int(chunk_overlap)) self.texts = text_splitter.split_documents(self.docs) # store the all file chunk into chromadb def store_chroma(self): if not os.path.exists(self.db_dir): os.makedirs(self.db_dir) print("eb:", self.embedding_model) db = Chroma.from_documents(self.texts, self.embedding_model, persist_directory=self.db_dir) db.persist() return db # load def load_into_db(self): if not os.path.exists(self.db_dir): ## Create and load self.load_files() self.split_files() self.db = self.store_chroma() else: print("start-->chromadb") # Just load the DB self.db = Chroma(persist_directory=self.db_dir, embedding_function=self.embedding_model) print("end-->chromadb") self.retriever = self.db.as_retriever() self.retriever.search_kwargs['k'] = 3 self.retriever.search_type = 'similarity' # create a chain, send the message into llm and ouput the answer @cached(cache) def retrieval_qa(self, query, rsd=False, rr=False): config = configparser.ConfigParser() config.read(config_path) the_selected_provider = config.get('model_providers', 'selected_provider') chat_history = list(self.ChatQueue.queue) try: templates = load_prompt_templates(prompt_templates_path) qa_template_name = config.get('prompt_templates', 'qa_selected_prompt') qa_template = templates['qa_prompt_templates'][qa_template_name] except Exception as e: print(f"Error reading config or templates: {e}") raise custom_prompt = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template(qa_template), HumanMessagePromptTemplate.from_template("{question}")]) if the_selected_provider != 'localai': # add reranker if rr: compressor = FlashrankRerank() compression_retriever = ContextualCompressionRetriever( base_compressor=compressor, base_retriever=self.retriever ) the_retriever = compression_retriever else: the_retriever = self.retriever qa = ConversationalRetrievalChain.from_llm( self.model, chain_type="stuff", retriever=the_retriever, condense_question_llm = self.model, return_source_documents=True, combine_docs_chain_kwargs={"prompt": custom_prompt}) result = qa({"question": query, "chat_history": chat_history}) self.update_chat_queue((query, result["answer"])) # add the search source documents docs_strings = document_to_string(result['source_documents'][0]) # docs_strings = documents_to_string(result['source_documents']) if rsd: return docs_strings else: return result['answer'] elif the_selected_provider == 'localai': docs = self.retriever.get_relevant_documents(query) ds2s = documents_to_string(docs) the_question = """ the question: {question}""" # build the prompt with string combine_strings = qa_template + the_question prompt = combine_strings.format(context=ds2s, question=query) result = self.model.complete(prompt) self.update_chat_queue((query, result.text)) if rsd: return ds2s return result.text else: return "Wrong provider!!!" @cached(cache) def restrieval_qa_for_code(self, query): config = configparser.ConfigParser() config.read(config_path) the_selected_provider = config.get('model_providers', 'selected_provider') try: templates = load_prompt_templates(prompt_templates_path) code_template_name = config.get('prompt_templates', 'code_selected_prompt') localai_template_name = config.get('prompt_templates', 'localai_selected_prompt') code_template = templates['qa_prompt_templates'][code_template_name] code_template_localai = templates['qa_prompt_templates'][localai_template_name] except Exception as e: print(f"Error reading config or templates: {e}") raise if the_selected_provider != 'localai': PROMPT = PromptTemplate(input_variables=["input", "history"], template=code_template) # print("-->", datetime.now()) conversation = ConversationChain( prompt=PROMPT, llm=self.model, ) # print("-->", datetime.now()) code_anaylsis = conversation.predict(input=query) return code_anaylsis elif the_selected_provider == 'localai': prompt = code_template_localai.format(input=query) result = self.model.complete(prompt) return result.text else: return "Wrong provider!!!"