Use intent model from archfc to pick prompt gateway (#328)

docs/source/build_with_arch/includes/rag/intent_detection.py

@@ -1,162 +0,0 @@
-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)