plano/docs/source/_include/intent_detection.py

from flask import Flask, request, jsonify
from datetime import datetime
import uuid
from langchain.memory import ConversationBufferMemory
from langchain.schema import AIMessage, HumanMessage
from langchain import OpenAI

app = Flask(__name__)

# Global dictionary to keep track of user memories
user_memories = {}

def get_user_conversation(user_id):
    """
    Retrieve the user's conversation memory using LangChain.
    If the user does not exist, initialize their conversation memory.
    """
    if user_id not in user_memories:
        user_memories[user_id] = ConversationBufferMemory(return_messages=True)
    return user_memories[user_id]

def update_user_conversation(user_id, client_messages, intent_changed):
    """
    Update the user's conversation memory with new messages using LangChain.
    Each message is augmented with a UUID, timestamp, and intent change marker.
    Only new messages are added to avoid duplication.
    """
    memory = get_user_conversation(user_id)
    stored_messages = memory.chat_memory.messages

    # Determine the number of stored messages
    num_stored_messages = len(stored_messages)
    new_messages = client_messages[num_stored_messages:]

    # Process each new message
    for index, message in enumerate(new_messages):
        role = message.get('role')
        content = message.get('content')
        metadata = {
            'uuid': str(uuid.uuid4()),
            'timestamp': datetime.utcnow().isoformat(),
            'intent_changed': False  # Default value
        }

        # Mark the intent change on the last message if detected
        if intent_changed and index == len(new_messages) - 1:
            metadata['intent_changed'] = True

        # Create a new message with metadata
        if role == 'user':
            memory.chat_memory.add_message(
                HumanMessage(content=content, additional_kwargs={'metadata': metadata})
            )
        elif role == 'assistant':
            memory.chat_memory.add_message(
                AIMessage(content=content, additional_kwargs={'metadata': metadata})
            )
        else:
            # Handle other roles if necessary
            pass

    return memory

def get_messages_since_last_intent(messages):
    """
    Retrieve messages from the last intent change onwards using LangChain.
    """
    messages_since_intent = []
    for message in reversed(messages):
        # Insert message at the beginning to maintain correct order
        messages_since_intent.insert(0, message)
        metadata = message.additional_kwargs.get('metadata', {})
        # Break if intent_changed is True
        if metadata.get('intent_changed', False) == True:
            break
    return messages_since_intent

def forward_to_llm(messages):
    """
    Forward messages to an upstream LLM using LangChain.
    """
    # Convert messages to a conversation string
    conversation = ""
    for message in messages:
        role = 'User' if isinstance(message, HumanMessage) else 'Assistant'
        content = message.content
        conversation += f"{role}: {content}\n"
    # Use LangChain's LLM to get a response. This call is proxied through Arch for end-to-end observability and traffic management
    llm = OpenAI()
    # Create a prompt that includes the conversation
    prompt = f"{conversation}Assistant:"
    response = llm(prompt)
    return response

@app.route('/process_rag', methods=['POST'])
def process_rag():
    # Extract JSON data from the request
    data = request.get_json()

    user_id = data.get('user_id')
    if not user_id:
        return jsonify({'error': 'User ID is required'}), 400

    client_messages = data.get('messages')
    if not client_messages or not isinstance(client_messages, list):
        return jsonify({'error': 'Messages array is required'}), 400

    # Extract the intent change marker from Arch's headers if present for the current prompt
    intent_changed_header = request.headers.get('x-arch-intent-marker', '').lower()
    if intent_changed_header in ['', 'false']:
        intent_changed = False
    elif intent_changed_header == 'true':
        intent_changed = True
    else:
        # Invalid value provided
        return jsonify({'error': 'Invalid value for x-arch-prompt-intent-change header'}), 400

    # Update user conversation based on intent change
    memory = update_user_conversation(user_id, client_messages, intent_changed)

    # Retrieve messages since last intent change for LLM
    messages_for_llm = get_messages_since_last_intent(memory.chat_memory.messages)

    # Forward messages to upstream LLM
    llm_response = forward_to_llm(messages_for_llm)

    # Prepare the messages to return
    messages_to_return = []
    for message in memory.chat_memory.messages:
        role = 'user' if isinstance(message, HumanMessage) else 'assistant'
        content = message.content
        metadata = message.additional_kwargs.get('metadata', {})
        message_entry = {
            'uuid': metadata.get('uuid'),
            'timestamp': metadata.get('timestamp'),
            'role': role,
            'content': content,
            'intent_changed': metadata.get('intent_changed', False)
        }
        messages_to_return.append(message_entry)

    # Prepare the response
    response = {
        'user_id': user_id,
        'messages': messages_to_return,
        'llm_response': llm_response
    }

    return jsonify(response), 200

if __name__ == '__main__':
    app.run(debug=True)