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Do Le Long An edited this page Jun 4, 2024 · 1 revision

To implement and ensure a smooth conversation with the LLM, you'll need to establish a mechanism for maintaining conversational context and state. Here's how you can approach this:

  1. Load and Parse JSON Data: Initially, load your JSON files and parse them to create a knowledge base that the LLM can use. This should be done once when the chatbot session starts.
import json

def load_json_data(json_files):
    knowledge_base = {}
    for file_name in json_files:
        with open(file_name, 'r') as file:
            data = json.load(file)
            knowledge_base.update(data)
    return knowledge_base
  1. Initialize LLM with Knowledge Base: Create a function that initializes the LLM with the knowledge base. This function will be called once at the beginning of the conversation.
def initialize_llm_with_knowledge(knowledge_base):
    # Pseudo-code: Replace with actual LLM initialization code
    llama_3_model = LLM('llama-3', knowledge_base=knowledge_base)
    return llama_3_model
  1. Generate Response: Implement the generate_response function that takes the user's input and the current state of the conversation to generate a response. This function will be called with each user interaction.
def generate_response(llama_3_model, user_input, conversation_state):
    # Pseudo-code: Replace with actual LLM interaction logic
    response, updated_state = llama_3_model.respond_to(user_input, state=conversation_state)
    return response, updated_state
  1. Maintain Conversation State: To maintain the conversation state, you can store the state in a database or in-memory storage after each interaction. This state should include the conversation history and any other relevant information that the LLM needs to remember.
def update_conversation_state(conversation_id, updated_state):
    # Pseudo-code: Replace with actual state update logic
    database.update_conversation_state(conversation_id, updated_state)
  1. FastAPI Endpoint: Your FastAPI endpoint will handle the initialization of the LLM, maintain the conversation state, and generate responses.
from fastapi import FastAPI, HTTPException

app = FastAPI()
knowledge_base = load_json_data(['path/to/json1', 'path/to/json2'])
llama_3_model = initialize_llm_with_knowledge(knowledge_base)

@app.post("/chatbot/{conversation_id}")
async def chatbot_endpoint(conversation_id: str, user_input: str):
    try:
        conversation_state = database.get_conversation_state(conversation_id)
        response, updated_state = generate_response(llama_3_model, user_input, conversation_state)
        update_conversation_state(conversation_id, updated_state)
        return {"response": response}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

In this setup, the LLM is initialized with the knowledge base only once, and the conversation state is updated and maintained across the user's interactions. This allows the user to have a smooth conversation with the LLM without needing to re-feed the JSON data each time.

Please note that the above code is a high-level example and will need to be adapted to fit the specifics of your LLM and its API. Additionally, you'll need to implement the actual database logic for storing and retrieving the conversation state. Ensure that you handle errors and edge cases to provide a seamless user experience. 🚀

Source: Conversation with Copilot, 6/4/2024

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