chatxbt-assistant.py

import os
from typing import Optional, Dict
from dotenv import load_dotenv
load_dotenv()

import chainlit as cl
from phi.assistant import Assistant
from phi.llm.openai import OpenAIChat
from phi.llm.groq import Groq
from phi.tools.duckduckgo import DuckDuckGo
from phi.tools.yfinance import YFinanceTools
from src.databases.postgres import sqlalchemy_engine
from src.tools.crypto_swap_toolkit import CryptoSwapTools
from src.tools.crypto_bridge_toolkit import CrossChainSwapTools
from src.tools.crypto_data_toolkit import CryptoDataTools
from src.tools.crypto_evm_wallet_toolkit import CryptoEVMWalletTools
from src.tools.user_profile_toolkit import UserProfileToolkit
from phi.storage.assistant.postgres import PgAssistantStorage
from src.knowledge_bases.combined import knowledge_base
from src.config.assistant import description, instruction

storage = PgAssistantStorage(
    # stores runs in the ai.assistant_runs table
    table_name="assistant_runs",
    db_engine=sqlalchemy_engine
)

@cl.oauth_callback
def oauth_callback(
  provider_id: str,
  token: str,
  raw_user_data: Dict[str, str],
  default_user: cl.User,
) -> Optional[cl.User]:
    # step-todo: will handle auth from v2 api
    return cl.User(
        identifier=raw_user_data.get('email'),
        email=raw_user_data.get('email'),
        name=raw_user_data.get('name'),
        metadata={**raw_user_data, **({'image':default_user.metadata.get('image')})}
    )


@cl.password_auth_callback
def auth_callback(username: str, password: str):
    # Fetch the user matching username from your database
    # and compare the hashed password with the value stored in the database
    if (username, password) == ("admin", "admin"):
        user = {
            "identifier": "admin", "email": username, "metadata": {"role": "admin", "provider": "credentials"}
        }
        cl.user_session.set("user_profile", user)
        return cl.User(
            identifier="admin", email=username, metadata={"role": "admin", "provider": "credentials"}
        )
    else:
        return None


@cl.set_starters
async def set_starters():
    return [
        cl.Starter(
            label="Create a crypto wallet for me",
            message="create a crypto wallet for me",
            icon="/public/wallet-svgrepo-com.svg",
        ),

        cl.Starter(
            label="Latest News on defi, crypto and solana",
            message="What news are currentlly trending on defi.",
            icon="/public/news-svgrepo-com.svg",
            ),
        cl.Starter(
            label="Get price of BTC, ETH and PEPE",
            message="Get me the Price of BTC, ETH and PEPE",
            icon="/public/coins-electronics-svgrepo-com.svg",
            ),
        cl.Starter(
            label="Get trending stocks",
            message="Get latest stock",
            icon="/public/stockchart-svgrepo-com.svg",
            )
        ]

@cl.on_chat_start
async def start():
    is_dev_mode = True if os.getenv("DEV_MODE") else False

    # Initialize the assistant
    cxbt_assistant = Assistant(
        description=description,
        instruction=instruction,
        # llm=OpenAIChat(model="gpt-4o"),
        llm=Groq(model="llama3-70b-8192"),
        tools=[
            UserProfileToolkit(),
            DuckDuckGo(),
            CryptoDataTools(),
            CryptoSwapTools(),
            CrossChainSwapTools(),
            CryptoEVMWalletTools(),
            YFinanceTools(stock_price=True)
        ],
        show_tool_calls=is_dev_mode,
        markdown=True,
        knowledge_base=knowledge_base,
        storage=storage,
        search_knowledge=True,
        read_chat_history=True,
        add_references_to_prompt=True,
        add_chat_history_to_prompt=True
    )
    cxbt_assistant.knowledge_base.load(recreate=False)

    # Set the assistant in the user session
    cl.user_session.set("agent", cxbt_assistant)

@cl.on_message
async def main(message: cl.Message):

    # Retrieve the assistant from the user session
    agent = cl.user_session.get("agent")

    # Process the user message using the assistant
    response = ""
    for delta in agent.run(message.content, stream=True):
        response += delta
    
    # Send the response back to the user
    await cl.Message(content=response).send()

# Run the Chainlit application
if __name__ == "__main__":
    cl.run()