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Enhance assistant capabilities and configuration updates

004e7e3 over 1 year ago

3.21 kB

import os
import getpass
import sys
from pathlib import Path
from operator import itemgetter
from dotenv import load_dotenv
from lets_talk.config import (CREATE_VECTOR_DB,VECTOR_STORAGE_PATH)

# Load environment variables from .env file
load_dotenv()

if CREATE_VECTOR_DB:
    import pipeline
    #build vector store
    print("=== create vector db ===")
    # Use configuration from config rather than hardcoded values
    pipeline.create_vector_database()
    print("========================")
else:
    # Check if the vector store exists
    print("=== check vector db ===")
    if not Path(VECTOR_STORAGE_PATH).exists():
        print(f"Vector store not found at {VECTOR_STORAGE_PATH}. Please create it first.")
        sys.exit(1)

import chainlit as cl
from lets_talk.agent import build_agent,parse_output

tdg_agent = build_agent()

  
@cl.on_chat_start
async def setup_chain():
    # Check if API key is already set
    api_key = os.environ.get("OPENAI_API_KEY")
    if not api_key:
        # In a real app, you'd want to handle this more gracefully
        api_key = await cl.AskUserMessage(
            content="Please enter your OpenAI API Key:",
            timeout=60,
            raise_on_timeout=True
        ).send()
        os.environ["OPENAI_API_KEY"] = api_key.content

    # Set a loading message
    msg = cl.Message(content="Let's talk about [TheDataGuy](https://thedataguy.pro)'s blog posts, how can I help you?", author="System")
    await msg.send()
    
    # Store the chain in user session
    cl.user_session.set("agent", tdg_agent)
    
    

    

@cl.on_message
async def on_message(message: cl.Message):
    """
    Handler for user messages. Processes the query through the research agent
    and streams the response back to the user.
    
    Args:
        message: The user's message
    """
    agent_executor = cl.user_session.get("agent")
    
    # Create Chainlit message for streaming
    msg = cl.Message(content="")
    
    # Create a parent step for the research process
    with cl.Step(name="Agent") as step:
        # Run the agent executor with callbacks to stream the response
        result = await agent_executor.ainvoke(
            {"question": message.content},
            # config={
            #     "callbacks": [cl.AsyncLangchainCallbackHandler()],
            #     "configurable": {"session_id": message.id}  # Add session_id from message
            # }
        )
        
        # Add steps from agent's intermediate steps
        # for i, step_data in enumerate(result.get("intermediate_steps", [])):
        #     step_name = f"Using: {step_data[0].tool}"
        #     step_input = str(step_data[0].tool_input)
        #     step_output = str(step_data[1])
            
        #     # Create individual steps as children of the main step
        #     with cl.Step(name=step_name, type="tool") as substep:
        #         await cl.Message(
        #             content=f"**Input:** {step_input}\n\n**Output:** {step_output}",
        #         ).send()
   
    # Get the final answer
    final_answer = parse_output(result)
    
    # Stream tokens from the final_answer
    await msg.stream_token(final_answer)
    await msg.send()

  

	import os
	import getpass
	import sys
	from pathlib import Path
	from operator import itemgetter
	from dotenv import load_dotenv
	from lets_talk.config import (CREATE_VECTOR_DB,VECTOR_STORAGE_PATH)

	# Load environment variables from .env file
	load_dotenv()

	if CREATE_VECTOR_DB:
	import pipeline
	#build vector store
	print("=== create vector db ===")
	# Use configuration from config rather than hardcoded values
	pipeline.create_vector_database()
	print("========================")
	else:
	# Check if the vector store exists
	print("=== check vector db ===")
	if not Path(VECTOR_STORAGE_PATH).exists():
	print(f"Vector store not found at {VECTOR_STORAGE_PATH}. Please create it first.")
	sys.exit(1)

	import chainlit as cl
	from lets_talk.agent import build_agent,parse_output

	tdg_agent = build_agent()


	@cl.on_chat_start
	async def setup_chain():
	# Check if API key is already set
	api_key = os.environ.get("OPENAI_API_KEY")
	if not api_key:
	# In a real app, you'd want to handle this more gracefully
	api_key = await cl.AskUserMessage(
	content="Please enter your OpenAI API Key:",
	timeout=60,
	raise_on_timeout=True
	).send()
	os.environ["OPENAI_API_KEY"] = api_key.content

	# Set a loading message
	msg = cl.Message(content="Let's talk about [TheDataGuy](https://thedataguy.pro)'s blog posts, how can I help you?", author="System")
	await msg.send()

	# Store the chain in user session
	cl.user_session.set("agent", tdg_agent)





	@cl.on_message
	async def on_message(message: cl.Message):
	"""
	Handler for user messages. Processes the query through the research agent
	and streams the response back to the user.

	Args:
	message: The user's message
	"""
	agent_executor = cl.user_session.get("agent")

	# Create Chainlit message for streaming
	msg = cl.Message(content="")

	# Create a parent step for the research process
	with cl.Step(name="Agent") as step:
	# Run the agent executor with callbacks to stream the response
	result = await agent_executor.ainvoke(
	{"question": message.content},
	# config={
	# "callbacks": [cl.AsyncLangchainCallbackHandler()],
	# "configurable": {"session_id": message.id} # Add session_id from message
	# }
	)

	# Add steps from agent's intermediate steps
	# for i, step_data in enumerate(result.get("intermediate_steps", [])):
	# step_name = f"Using: {step_data[0].tool}"
	# step_input = str(step_data[0].tool_input)
	# step_output = str(step_data[1])

	# # Create individual steps as children of the main step
	# with cl.Step(name=step_name, type="tool") as substep:
	# await cl.Message(
	# content=f"Input: {step_input}\n\nOutput: {step_output}",
	# ).send()

	# Get the final answer
	final_answer = parse_output(result)

	# Stream tokens from the final_answer
	await msg.stream_token(final_answer)
	await msg.send()