Repository: kroll-software/babyagi4all
Branch: main
Commit: 899cfb0d87c7
Files: 6
Total size: 14.9 KB

Directory structure:
gitextract_4kue40tb/

├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── babyagi.py
└── requirements.txt

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FILE CONTENTS
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================================================
FILE: .gitattributes
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*.py text eol=lf

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FILE: .gitignore
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__pycache__/
*.py[cod]
*$py.class

.env
env/
.venv
*venv/

.vscode/
.idea/

models
llama/

# for node
chroma/
node_modules/
.DS_Store

================================================
FILE: LICENSE
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MIT License

Copyright (c) 2023 by Kroll Software-Entwicklung

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
================================================
# BabyAGI4All

A small autonomous AI agent based on [BabyAGI](https://github.com/yoheinakajima/babyagi) by Yohei Nakajima.
</br>

Runs on CPU with the [GPT4All](https://github.com/nomic-ai/gpt4all) model by Nomic AI.
</br>

100% open source, 100% local, no API-keys needed.
</br>

# Installation:

1. Clone this repository
2. Install the requirements: *pip install -r requirements.txt*
3. Download a model file (see below)
4. Copy the file *.env.example* to *.env*
4. Edit the model-path and other preferences in the file *.env*

## Model Downloads

The following model files have been tested successfully:

* *gpt4all-lora-quantized-ggml.bin*
* *ggml-wizardLM-7B.q4_2.bin*
* *ggml-vicuna-7b-1.1-q4_2.bin*

Some of these model files can be downloaded from [here](https://github.com/nomic-ai/gpt4all-chat#manual-download-of-models).
</br>
</br>

Then run *python babyagi.py*
</br>

Have fun!
</br>


================================================
FILE: babyagi.py
================================================
import os
import time
import logging
from collections import deque
from typing import Dict, List
import importlib
import chromadb
from dotenv import load_dotenv
from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
from chromadb.utils.embedding_functions import InstructorEmbeddingFunction
from llama_cpp import Llama

# Load default environment variables (.env)
load_dotenv()

# Engine configuration
LLM_MODEL = "GPT4All"

# Table config
RESULTS_STORE_NAME = os.getenv("RESULTS_STORE_NAME", os.getenv("TABLE_NAME", ""))
assert RESULTS_STORE_NAME, "\033[91m\033[1m" + "RESULTS_STORE_NAME environment variable is missing from .env" + "\033[0m\033[0m"

# Run configuration
INSTANCE_NAME = os.getenv("INSTANCE_NAME", os.getenv("BABY_NAME", "BabyAGI"))
COOPERATIVE_MODE = "none"
JOIN_EXISTING_OBJECTIVE = False

# Goal configuation
OBJECTIVE = os.getenv("OBJECTIVE", "")
INITIAL_TASK = os.getenv("INITIAL_TASK", os.getenv("FIRST_TASK", ""))

# Model configuration
TEMPERATURE = float(os.getenv("TEMPERATURE", 0.2))

VERBOSE = (os.getenv("VERBOSE", "false").lower() == "true")

# Extensions support begin

def can_import(module_name):
    try:
        importlib.import_module(module_name)
        return True
    except ImportError:
        return False

print("\033[95m\033[1m"+"\n*****CONFIGURATION*****\n"+"\033[0m\033[0m")
print(f"Name  : {INSTANCE_NAME}")
print(f"Mode  : {'alone' if COOPERATIVE_MODE in ['n', 'none'] else 'local' if COOPERATIVE_MODE in ['l', 'local'] else 'distributed' if COOPERATIVE_MODE in ['d', 'distributed'] else 'undefined'}")
print(f"LLM   : {LLM_MODEL}")

# Check if we know what we are doing
assert OBJECTIVE, "\033[91m\033[1m" + "OBJECTIVE environment variable is missing from .env" + "\033[0m\033[0m"
assert INITIAL_TASK, "\033[91m\033[1m" + "INITIAL_TASK environment variable is missing from .env" + "\033[0m\033[0m"

MODEL_PATH = os.getenv("MODEL_PATH", "models/gpt4all-lora-quantized-ggml.bin")
    
print(f"GPT4All : {MODEL_PATH}" + "\n")
assert os.path.exists(MODEL_PATH), "\033[91m\033[1m" + f"Model can't be found." + "\033[0m\033[0m"

#CTX_MAX = 2048
#CTX_MAX = 8192
CTX_MAX = 16384
#THREADS_NUM = 16
THREADS_NUM = 4

llm = Llama(
    model_path=MODEL_PATH,
    n_ctx=CTX_MAX, n_threads=THREADS_NUM,
    use_mlock=True,
    verbose=False,
)

print("\033[94m\033[1m" + "\n*****OBJECTIVE*****\n" + "\033[0m\033[0m")
print(f"{OBJECTIVE}")

if not JOIN_EXISTING_OBJECTIVE: print("\033[93m\033[1m" + "\nInitial task:" + "\033[0m\033[0m" + f" {INITIAL_TASK}")
else: print("\033[93m\033[1m" + f"\nJoining to help the objective" + "\033[0m\033[0m")

# Results storage using local ChromaDB
class DefaultResultsStorage:
    def __init__(self):
        logging.getLogger('chromadb').setLevel(logging.ERROR)
        # Create Chroma collection
        chroma_persist_dir = "chroma"
        chroma_client = chromadb.Client(
            settings=chromadb.config.Settings(
                chroma_db_impl="duckdb+parquet",
                persist_directory=chroma_persist_dir,
            )
        )

        metric = "cosine"
        embedding_function = InstructorEmbeddingFunction()
        self.collection = chroma_client.get_or_create_collection(
            name=RESULTS_STORE_NAME,
            metadata={"hnsw:space": metric},
            embedding_function=embedding_function,
        )

    def add(self, task: Dict, result: Dict, result_id: str, vector: List):        
        embeddings = self.collection._embedding_function([vector])

        if (len(self.collection.get(ids=[result_id], include=[])["ids"]) > 0):  # Check if the result already exists
            self.collection.update(
                ids=result_id,
                embeddings=embeddings,
                documents=vector,
                metadatas={"task": task["task_name"], "result": result},
            )
        else:
            self.collection.add(
                ids=result_id,
                embeddings=embeddings,
                documents=vector,
                metadatas={"task": task["task_name"], "result": result},
            )

    def query(self, query: str, top_results_num: int) -> List[dict]:
        count: int = self.collection.count()
        if count == 0:
            return []
        results = self.collection.query(
            query_texts=query,
            n_results=min(top_results_num, count),
            include=["metadatas"]
        )        
        tasks = []
        count = len(results["ids"][0])
        for i in range(count):            
            resultidstr = results["ids"][0][i]            
            id = int(resultidstr[7:])
            item = results["metadatas"][0][i]            
            task = {'task_id': id, 'task_name': item["task"]}
            tasks.append(task)            
        return tasks
   

# Initialize results storage
results_storage = DefaultResultsStorage()

# Task storage supporting only a single instance of BabyAGI
class SingleTaskListStorage:
    def __init__(self):
        self.tasks = deque([])
        self.task_id_counter = 0

    def append(self, task: Dict):
        self.tasks.append(task)

    def replace(self, tasks: List[Dict]):
        self.tasks = deque(tasks)

    def popleft(self):
        return self.tasks.popleft()

    def is_empty(self):
        return False if self.tasks else True

    def next_task_id(self):
        self.task_id_counter += 1
        return self.task_id_counter

    def get_task_names(self):
        return [t["task_name"] for t in self.tasks]


# Initialize tasks storage
tasks_storage = SingleTaskListStorage()

def gpt_call(prompt: str, temperature: float = TEMPERATURE, max_tokens: int = 256):
    result = llm(prompt[:CTX_MAX], echo=True, temperature=temperature, max_tokens=max_tokens)
    return result['choices'][0]['text'][len(prompt):].strip()

def strip_numbered_list(nl: List[str]) -> List[str]:
    result_list = []
    filter_chars = ['#', '(', ')', '[', ']', '.', ':', ' ']

    for line in nl:
        line = line.strip()
        if len(line) > 0:
            parts = line.split(" ", 1)
            if len(parts) == 2:
                left_part = ''.join(x for x in parts[0] if not x in filter_chars)
                if left_part.isnumeric():
                    result_list.append(parts[1].strip())
                else:
                    result_list.append(line)
            else:
                result_list.append(line)

    # filter result_list
    result_list = [line for line in result_list if len(line) > 3]
    
    # remove duplicates
    result_list = list(set(result_list))
    return result_list

def fix_prompt(prompt: str) -> str:
    lines = prompt.split("\n") if "\n" in prompt else [prompt]    
    return "\n".join([line.strip() for line in lines])

def task_creation_agent(
    objective: str, result: Dict, task_description: str, task_list: List[str]
):    
    prompt = f"""
    Your objective: {objective}\n
    Take into account these previously completed tasks but don't repeat them: {task_list}.\n
    The last completed task has the result: {result["data"]}.\n
    Develop a task list based on the result.\n
    Response:"""

    prompt = fix_prompt(prompt)

    response = gpt_call(prompt)
    pos = response.find("1")
    if (pos > 0):
        response = response[pos - 1:]

    if response == '':
        print("\n*** Empty Response from task_creation_agent***")
        new_tasks_list = result["data"].split("\n") if len(result) > 0 else [response]
    else:
        new_tasks = response.split("\n") if "\n" in response else [response]
        new_tasks_list = strip_numbered_list(new_tasks)
        
    return [{"task_name": task_name} for task_name in (t for t in new_tasks_list if not t == '')]


def prioritization_agent():
    task_names = tasks_storage.get_task_names()
    next_task_id = tasks_storage.next_task_id()    

    prompt = f"""
    Please prioritize, summarize and consolidate the following tasks: {task_names}.\n
    Consider the ultimate objective: {OBJECTIVE}.\n
    Return the result as a numbered list.
    """

    prompt = fix_prompt(prompt)

    response = gpt_call(prompt)
    pos = response.find("1")
    if (pos > 0):
        response = response[pos - 1:]

    new_tasks = response.split("\n") if "\n" in response else [response]
    new_tasks = strip_numbered_list(new_tasks)
    new_tasks_list = []
    i = 0
    for task_string in new_tasks:        
        new_tasks_list.append({"task_id": i + next_task_id, "task_name": task_string})
        i += 1
    
    if len(new_tasks_list) > 0:
        tasks_storage.replace(new_tasks_list)


# Execute a task based on the objective and five previous tasks
def execution_agent(objective: str, task: str) -> str:
    """
    Executes a task based on the given objective and previous context.

    Args:
        objective (str): The objective or goal for the AI to perform the task.
        task (str): The task to be executed by the AI.

    Returns:
        str: The response generated by the AI for the given task.

    """
    
    context = context_agent(query=objective, top_results_num=5)

    context_list = [t['task_name'] for t in context if t['task_name'] != INITIAL_TASK]
    #context_list = [t['task_name'] for t in context]

    # remove duplicates
    context_list = list(set(context_list))    

    if VERBOSE and len(context_list) > 0:
        print("\n*******RELEVANT CONTEXT******\n")
        print(context_list)

    if task == INITIAL_TASK:
        prompt = f"""
        You are an AI who performs one task based on the following objective: {objective}.\n
        Your task: {task}\nResponse:"""
    else:
        prompt = f"""
        Your objective: {objective}.\n
        Take into account these previously completed tasks but don't repeat them: {context_list}.\n
        Your task: {task}\n
        Response:"""

    #Give an advice how to achieve your task!\n

    prompt = fix_prompt(prompt)

    result = gpt_call(prompt)
    pos = result.find("1")
    if (pos > 0):
        result = result[pos - 1:]
    return result


# Get the top n completed tasks for the objective
def context_agent(query: str, top_results_num: int):
    """
    Retrieves context for a given query from an index of tasks.

    Args:
        query (str): The query or objective for retrieving context.
        top_results_num (int): The number of top results to retrieve.

    Returns:
        list: A list of tasks as context for the given query, sorted by relevance.

    """
    results = results_storage.query(query=query, top_results_num=top_results_num)
    #print("\n***** RESULTS *****")
    #print(results)
    return results

# Add the initial task if starting new objective
if not JOIN_EXISTING_OBJECTIVE:
    initial_task = {
        "task_id": tasks_storage.next_task_id(),
        "task_name": INITIAL_TASK
    }
    tasks_storage.append(initial_task)

def main ():
    while True:
        # As long as there are tasks in the storage...
        if not tasks_storage.is_empty():
            # Print the task list
            print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m")
            for t in tasks_storage.get_task_names():
                print(" • "+t)

            # Step 1: Pull the first incomplete task
            task = tasks_storage.popleft()
            print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m")
            print(task['task_name'])

            # Send to execution function to complete the task based on the context
            result = execution_agent(OBJECTIVE, task["task_name"])            

            print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m")
            print(result)

            # Step 2: Enrich result and store in the results storage
            # This is where you should enrich the result if needed
            enriched_result = {
                "data": result
            }  
            # extract the actual result from the dictionary
            # since we don't do enrichment currently
            vector = enriched_result["data"]  

            result_id = f"result_{task['task_id']}"
            results_storage.add(task, result, result_id, vector)

            # Step 3: Create new tasks and reprioritize task list
            # only the main instance in cooperative mode does that
            new_tasks = task_creation_agent(
                OBJECTIVE,
                enriched_result,
                task["task_name"],
                tasks_storage.get_task_names(),
            )

            for new_task in new_tasks:
                if not new_task['task_name'] == '':
                    new_task.update({"task_id": tasks_storage.next_task_id()})
                    tasks_storage.append(new_task)

            if not JOIN_EXISTING_OBJECTIVE: prioritization_agent()

            # Sleep a bit before checking the task list again
            time.sleep(5) 

        else:
            print ("Ready, no more tasks.")

if __name__ == "__main__":
    main()


================================================
FILE: requirements.txt
================================================
argparse==1.4.0
chromadb==0.3.21
pre-commit>=3.2.0
python-dotenv==1.0.0
InstructorEmbedding>=1.0.0
llama-cpp-python==0.1.43