Feature/parafraseo node

RAGManager/app/agents/nodes/parafraseo.py

@@ -1,9 +1,10 @@
 """Nodo 4: Parafraseo - Saves message, retrieves chat history, and paraphrases user input."""
 
+import json
 import logging
 
 from app.agents.state import AgentState
-from langchain_core.messages import SystemMessage
+from langchain_core.messages import HumanMessage, SystemMessage
 from langchain_openai import ChatOpenAI
 
 logger = logging.getLogger(__name__)
@@ -16,42 +17,198 @@ def parafraseo(state: AgentState) -> AgentState:
     Parafraseo node - Saves message to DB, retrieves chat history, and paraphrases user input.
 
     This node:
-    1. Saves the user's message to the chat session in PostgreSQL
-    2. Retrieves all chat messages for the session (including the newly saved message)
-    3. Paraphrases the user input using chat history to improve clarity
-    4. Prepares text for retrieval step
+    1. Receives a validated user message (after guard_inicial validation)
+    2. Saves the user's message to the chat session in PostgreSQL (endpoint 1 - placeholder)
+    3. Retrieves the last 10 messages of the conversation (endpoint 2 - placeholder)
+    4. Uses the last message to understand user's intentions and the remaining 9 (older) messages as context
+    5. Sends to LLM with instructions to return 3 differently phrased statements that encapsulate
+       the user's intentions according to the last message and chat history
 
     Args:
-        state: Agent state containing prompt, chat_session_id, and user_id
+        state: Agent state containing validated user message, chat_session_id, and user_id
 
     Returns:
-        Updated state with chat_messages, paraphrased_text set
+        Updated state with chat_messages, paraphrased_text, and paraphrased_statements set
     """
     updated_state = state.copy()
 
-    # TODO: Implement endpoint call to save message and retrieve chat history
+    # Get the validated user message from state
+    messages = state.get("messages", [])
+    if not messages:
+        logger.error("No messages found in state")
+        updated_state["error_message"] = "No user message found in state"
+        return updated_state
+
+    # Get the last message (the validated user message)
+    last_user_message = messages[-1]
+    user_message_content = last_user_message.content if hasattr(last_user_message, 'content') else str(last_user_message)
+
+    # TODO: Endpoint 1 - Save message to PostgreSQL database according to chat session
+    # This should:
+    # 1. Call an endpoint (not yet developed) that:
+    #    - Saves the current user message to the chat session in PostgreSQL
+    #    - Uses chat_session_id and user_id from state
+    #    - Returns success/failure status
+    # 2. Handle errors appropriately (session not found, permission denied, etc.)
+    logger.info("Endpoint 1 (save message to DB) not yet implemented - skipping")
+
+    # TODO: Endpoint 2 - Retrieve last 10 messages of the conversation
     # This should:
     # 1. Call an endpoint (not yet developed) that:
-    #    - Saves the current user message to the chat session
-    #    - Retrieves all chat messages for the session (including the newly saved message)
-    #    - Returns the updated chat_messages list
+    #    - Retrieves the last 10 messages for the chat session
+    #    - Returns a list of message dictionaries with structure: [{"sender": "user|assistant|system", "message": "...", "created_at": "..."}, ...]
+    #    - Messages should be ordered from oldest to newest (or newest to oldest, depending on API design)
     # 2. Update state with chat_messages from the endpoint response
     # 3. Handle errors appropriately (session not found, permission denied, etc.)
 
-    # Placeholder: For now, we'll use empty chat history
+    # Placeholder: For now, we'll simulate chat history with just the current message
     # Once the endpoint is implemented, replace this with the actual endpoint call
-    updated_state["chat_messages"] = None
-    logger.warning("Chat history retrieval endpoint not yet implemented - using empty history")
+    chat_messages = [
+        {"sender": "user", "message": user_message_content, "created_at": "2025-01-01T00:00:00"}
+    ]
+    updated_state["chat_messages"] = chat_messages
+    logger.warning("Endpoint 2 (retrieve chat history) not yet implemented - using current message only")
+
+    # Process chat history: last message (intentions) + 9 older messages (context)
+    # The last message is the most recent one (for understanding intentions)
+    # The remaining 9 messages are older (for context)
+    if len(chat_messages) >= 10:
+        # We have 10+ messages: use last one for intentions, previous 9 for context
+        context_messages = chat_messages[-10:-1]  # 9 older messages
+        intention_message = chat_messages[-1]  # Last message (most recent)
+    elif len(chat_messages) > 1:
+        # We have 2-9 messages: use last one for intentions, all previous for context
+        context_messages = chat_messages[:-1]  # All older messages
+        intention_message = chat_messages[-1]  # Last message
+    else:
+        # We have only 1 message: use it for intentions, no context
+        context_messages = []
+        intention_message = chat_messages[0] if chat_messages else {"sender": "user", "message": user_message_content}
+
+    # Format chat history for LLM prompt
+    # Each message should clearly indicate if it was sent by the user or the agent
+    def format_message_with_sender(msg: dict) -> str:
+        """Format a message with explicit sender label."""
+        sender = msg.get("sender", "unknown").lower()
+        message = msg.get("message", "")
+
+        # Normalize sender labels for clarity
+        if sender == "user":
+            sender_label = "User"
+        elif sender == "assistant":
+            sender_label = "Assistant"
+        elif sender == "system":
+            sender_label = "System"
+        else:
+            sender_label = sender.capitalize()
+
+        return f"{sender_label}: {message}"
+
+    context_text = ""
+    if context_messages:
+        context_lines = []
+        for msg in context_messages:
+            context_lines.append(format_message_with_sender(msg))
+        context_text = "\n".join(context_lines)
+
+    # Format the intention message with sender information
+    intention_sender = intention_message.get("sender", "user").lower()
+    intention_message_text = intention_message.get("message", user_message_content)
+
+    # Format intention message with sender label
+    if intention_sender == "user":
+        intention_label = "User"
+    elif intention_sender == "assistant":
+        intention_label = "Assistant"
+    elif intention_sender == "system":
+        intention_label = "System"
+    else:
+        intention_label = intention_sender.capitalize()
 
-    # Paraphrase the last message using history
-    system_instruction = """You are an expert at paraphrasing user questions to be standalone and clear, given the conversation history.
-Reformulate the last user message to be a self-contained query that includes necessary context from previous messages.
-Do not answer the question, just rewrite it."""
+    intention_text = f"{intention_label}: {intention_message_text}"
+
+    # Create LLM prompt with instructions
+    system_instruction = """You are an expert at understanding user intentions and paraphrasing them in different ways.
+
+Given a user's last message and their conversation history, your task is to return exactly 3 differently phrased statements that encapsulate the user's intentions.
+
+The last message represents what the user wants to know or do right now. The conversation history provides context about what they've been discussing.
+
+Requirements:
+- Return exactly 3 different phrasings
+- Each phrasing should capture the user's core intention from their last message
+- Use the conversation history to understand context and references (like "it", "that", "the previous thing")
+- Each phrasing should be a complete, standalone statement that makes sense without the full conversation
+- The phrasings should be diverse - use different words, sentence structures, and perspectives
+- Format your response as a JSON array of exactly 3 strings: ["statement 1", "statement 2", "statement 3"]
+- Do not include any explanation, just the JSON array
+
+Example format:
+["What are the main features of the product?", "Can you explain the key characteristics of this product?", "I'd like to know what this product offers."]"""
+
+    # Build the prompt with context and intention
+    # Both context and intention messages now clearly show sender (User/Assistant/System)
+    if context_text:
+        user_prompt = f"""Conversation History (older messages for context):
+{context_text}
+
+Last Message (current intention):
+{intention_text}
+
+Return 3 differently phrased statements that encapsulate the user's intention from their last message, using the conversation history for context."""
+    else:
+        user_prompt = f"""Message (current intention):
+{intention_text}
+
+Return 3 differently phrased statements that encapsulate the user's intention from their message."""
 
-    # Use messages from state (will include chat history once endpoint is implemented)
-    messages = [SystemMessage(content=system_instruction)] + state.get("messages", [])
+    # Call LLM with the prompt
+    messages_for_llm = [
+        SystemMessage(content=system_instruction),
+        HumanMessage(content=user_prompt)
+    ]
 
-    response = llm.invoke(messages)
-    updated_state["paraphrased_text"] = response.content
+    try:
+        response = llm.invoke(messages_for_llm)
+        response_content = response.content.strip()
+
+        # Parse the JSON response to extract the 3 statements
+        try:
+            # Try to parse as JSON array
+            paraphrased_statements = json.loads(response_content)
+            if isinstance(paraphrased_statements, list) and len(paraphrased_statements) >= 3:
+                # Take the first 3 statements
+                paraphrased_statements = paraphrased_statements[:3]
+            elif isinstance(paraphrased_statements, list):
+                # If less than 3, pad with the last one or use as-is
+                while len(paraphrased_statements) < 3:
+                    paraphrased_statements.append(paraphrased_statements[-1] if paraphrased_statements else intention_text)
+            else:
+                # If not a list, create a list with the response
+                paraphrased_statements = [response_content, response_content, response_content]
+        except json.JSONDecodeError:
+            # If JSON parsing fails, try to extract statements from text
+            logger.warning("LLM response is not valid JSON, attempting to parse as text")
+            # Try to split by newlines or common separators
+            lines = [line.strip() for line in response_content.split('\n') if line.strip()]
+            if len(lines) >= 3:
+                paraphrased_statements = lines[:3]
+            else:
+                # Fallback: use the response as-is and duplicate if needed
+                paraphrased_statements = [response_content] * 3
+
+        # Store the results
+        updated_state["paraphrased_statements"] = paraphrased_statements
+        # Store the first statement in paraphrased_text for backward compatibility
+        updated_state["paraphrased_text"] = paraphrased_statements[0] if paraphrased_statements else intention_text
+
+        logger.info(f"Generated {len(paraphrased_statements)} paraphrased statements")
+
+    except Exception as e:
+        logger.error(f"Error calling LLM for paraphrasing: {e}")
+        # Fallback: use the original message
+        updated_state["paraphrased_statements"] = [intention_text, intention_text, intention_text]
+        updated_state["paraphrased_text"] = intention_text
+        updated_state["error_message"] = f"Error in paraphrasing: {str(e)}"
 
     return updated_state

RAGManager/app/agents/state.py

@@ -29,7 +29,8 @@ class AgentState(MessagesState):
     adjusted_text: str | None  # Text adjusted by initial fallback (if applicable)
 
     # Nodo 4: Parafraseo
-    paraphrased_text: str | None  # Paraphrased text from Parafraseo node
+    paraphrased_text: str | None  # Paraphrased text from Parafraseo node (first statement for backward compatibility)
+    paraphrased_statements: list[str] | None  # List of 3 differently phrased statements from Parafraseo node
 
     # Nodo 5: Retriever
     relevant_chunks: list[str] | None  # Chunks retrieved from vector DB