deploy: c9b95c7c9f15441738c938bb27cca66eb86990e3

_downloads/76c29fa020a722e6be4b991f849978ce/intent_detection.py

@@ -0,0 +1,152 @@
+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)