Free-Auto-GPT/BABYAGI.py

import os
import json
from dotenv import load_dotenv
from pathlib import Path
from json import JSONDecodeError
from collections import deque
from typing import Dict, List, Optional, Any
from langchain.vectorstores import FAISS
from langchain import HuggingFaceHub
from langchain.docstore import InMemoryDocstore
from langchain import LLMChain, PromptTemplate
from langchain.llms import BaseLLM
from FreeLLM import HuggingChatAPI  # FREE HUGGINGCHAT API
from FreeLLM import ChatGPTAPI  # FREE CHATGPT API
from FreeLLM import BingChatAPI  # FREE BINGCHAT API
from FreeLLM import BardChatAPI  # FREE GOOGLE BARD API
from langchain.vectorstores.base import VectorStore
from pydantic import BaseModel, Field
from langchain.chains.base import Chain
from langchain.experimental import BabyAGI
from BabyAgi import BabyAGIMod

import faiss

load_dotenv()

select_model = input(
    "Select the model you want to use (1, 2, 3 or 4) \n \
1) ChatGPT \n \
2) HuggingChat \n \
3) BingChat (NOT GOOD RESULT)\n \
4) BardChat \n \
>>> "
)

if select_model == "1":
    CG_TOKEN = os.getenv("CHATGPT_TOKEN", "your-chatgpt-token")

    if CG_TOKEN != "your-chatgpt-token":
        os.environ["CHATGPT_TOKEN"] = CG_TOKEN
    else:
        raise ValueError(
            "ChatGPT Token EMPTY. Edit the .env file and put your ChatGPT token"
        )

    start_chat = os.getenv("USE_EXISTING_CHAT", False)
    if os.getenv("USE_GPT4") == "True":
        model = "gpt-4"
    else:
        model = "default"

    llm = ChatGPTAPI.ChatGPT(token=os.environ["CHATGPT_TOKEN"], model=model)

elif select_model == "2":
    emailHF = os.getenv("emailHF", "your-emailHF")
    pswHF = os.getenv("pswHF", "your-pswHF")
    if emailHF != "your-emailHF" or pswHF != "your-pswHF":
        os.environ["emailHF"] = emailHF
        os.environ["pswHF"] = pswHF
    else:
        raise ValueError(
            "HuggingChat Token EMPTY. Edit the .env file and put your HuggingChat credentials"
        )
    
    llm = HuggingChatAPI.HuggingChat(email=os.environ["emailHF"], psw=os.environ["pswHF"])

elif select_model == "3":
    if not os.path.exists("cookiesBing.json"):
        raise ValueError(
            "File 'cookiesBing.json' not found! Create it and put your cookies in there in the JSON format."
        )
    cookie_path = Path() / "cookiesBing.json"
    with open("cookiesBing.json", "r") as file:
        try:
            file_json = json.loads(file.read())
        except JSONDecodeError:
            raise ValueError(
                "You did not put your cookies inside 'cookiesBing.json'! You can find the simple guide to get the cookie file here: https://github.com/acheong08/EdgeGPT/tree/master#getting-authentication-required."
            )
    llm = BingChatAPI.BingChat(
        cookiepath=str(cookie_path), conversation_style="creative"
    )

elif select_model == "4":
    GB_TOKEN = os.getenv("BARDCHAT_TOKEN", "your-googlebard-token")

    if GB_TOKEN != "your-googlebard-token":
        os.environ["BARDCHAT_TOKEN"] = GB_TOKEN
    else:
        raise ValueError(
            "GoogleBard Token EMPTY. Edit the .env file and put your GoogleBard token"
        )
    cookie_path = os.environ["BARDCHAT_TOKEN"]
    llm = BardChatAPI.BardChat(cookie=cookie_path)


    

HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN", "your-huggingface-token")

if HF_TOKEN != "your-huggingface-token":
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN
else:
    raise ValueError(
        "HuggingFace Token EMPTY. Edit the .env file and put your HuggingFace token"
    )


from Embedding import HuggingFaceEmbedding  # EMBEDDING FUNCTION

# Define your embedding model
embeddings_model = HuggingFaceEmbedding.newEmbeddingFunction

embedding_size = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model, index, InMemoryDocstore({}), {})

print(vectorstore)

# DEFINE TOOL
from langchain.agents import ZeroShotAgent, Tool, AgentExecutor
from langchain import OpenAI, LLMChain
from langchain.tools import BaseTool, DuckDuckGoSearchRun


todo_prompt = PromptTemplate.from_template(
    "I need to create a plan for complete me GOAl. Can you help me to create a TODO list? Create only the todo list for this objective: '{objective}'."
)
todo_chain = LLMChain(llm=llm, prompt=todo_prompt)
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Search",
        func=search.run,
        description="useful for when you need to answer questions about current events",
    ),
    Tool(
        name="TODO",
        func=todo_chain.run,
        description="useful for when you need to create a task list to complete a objective. You have to give an Input: a objective for which to create a to-do list. Output: just a list of tasks to do for that objective. It is important to give the target input 'objective' correctly!",
    ),
]


prefix = """Can you help me to performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}."""
suffix = """Question: {task}. 
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
    tools,
    prefix=prefix,
    suffix=suffix,
    input_variables=["objective", "task", "context", "agent_scratchpad"],
)

llm_chain = LLMChain(llm=llm, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names)
agent_executor = AgentExecutor.from_agent_and_tools(
    agent=agent, tools=tools, verbose=True
)

# START

# Logging of LLMChains
verbose = False

int_max_iterations = input(
    "Enter the maximum number of iterations: (Suggest from 3 and 5) "
)
max_iterations = int(int_max_iterations)

if input("Do you want to store the results? (y/n) ") == "y":
    store_results = True
else:
    store_results = False


# If None, will keep on going forever
max_iterations: Optional[int] = max_iterations
baby_agi = BabyAGIMod.BabyAGI.from_llm(
    llm=llm,
    vectorstore=vectorstore,
    task_execution_chain=agent_executor,
    verbose=verbose,
    max_iterations=max_iterations,
    store=store_results,
)


# DEFINE THE OBJECTIVE - MODIFY THIS
OBJECTIVE = input("Enter the objective of the AI system: (Be realistic!) ")


baby_agi({"objective": OBJECTIVE})