| # Long-Term Context Memory Fix | |
| ## Problem | |
| After 2-3 interactions, the system loses context and gives factually incorrect answers. In the user's example: | |
| - Discussed Sachin Tendulkar (cricket) | |
| - Lost context of sport and gave gaming journalist advice about Tom Bramwell | |
| ## Root Cause Analysis | |
| ### Issue 1: Limited Context Window | |
| - Only showing **last 3 interactions** in prompts | |
| - With longer conversations, early context gets lost | |
| ### Issue 2: Incomplete Context Storage | |
| - **OLD**: Only stored `user_input`, not the response | |
| - Context looked like this: | |
| ``` | |
| interactions: [ | |
| {"user_input": "Who is Sachin?", "timestamp": "..."}, | |
| {"user_input": "Is he the greatest?", "timestamp": "..."} | |
| ] | |
| ``` | |
| - **PROBLEM**: LLM doesn't know what was answered before! | |
| ### Issue 3: No Response Tracking | |
| - When retrieving context from DB, only user questions were available | |
| - Missing the actual conversation flow (Q&A pairs) | |
| ## Solution Implemented | |
| ### 1. Increased Context Window (3 β 5 interactions) | |
| ```python | |
| # OLD: | |
| recent_interactions = context.get('interactions', [])[:3] | |
| # NEW: | |
| recent_interactions = context.get('interactions', [])[:5] # Last 5 interactions | |
| ``` | |
| ### 2. Added Response Storage | |
| ```python | |
| # OLD: | |
| new_interaction = { | |
| "user_input": user_input, | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| # NEW: | |
| new_interaction = { | |
| "user_input": user_input, | |
| "timestamp": datetime.now().isoformat(), | |
| "response": response # Store the response text β | |
| } | |
| ``` | |
| ### 3. Enhanced Conversation History in Prompts | |
| ```python | |
| # OLD format: | |
| "1. User asked: Who is Sachin?\n" | |
| # NEW format: | |
| "Q1: Who is Sachin? | |
| A1: Sachin Ramesh Tendulkar is a legendary Indian cricketer... | |
| Q2: Is he the greatest? | |
| A2: The question of who is the greatest..." | |
| ``` | |
| ### 4. Updated Orchestrator to Save Responses | |
| ```python | |
| # After generating response, update context: | |
| response_text = str(result.get('response', '')) | |
| if response_text: | |
| self.context_manager._update_context(context, user_input, response_text) | |
| ``` | |
| ## Files Modified | |
| 1. **`src/agents/synthesis_agent.py`**: | |
| - Increased context window from 3 to 5 | |
| - Enhanced conversation history format to include Q&A pairs | |
| - Added support for displaying responses in prompts | |
| 2. **`context_manager.py`**: | |
| - Updated `_update_context()` to accept `response` parameter | |
| - Now stores full interaction (user_input + response) | |
| 3. **`orchestrator_engine.py`**: | |
| - Added call to update context with response after processing | |
| - Ensures responses are saved for future context retrieval | |
| 4. **Duplicates in `Research_AI_Assistant/`**: Applied same fixes | |
| ## Expected Behavior | |
| ### Before Fix: | |
| ``` | |
| Q1: "Who is Sachin?" | |
| A1: (Cricket info) | |
| Q2: "Is he the greatest?" | |
| A2: (Compares Sachin to Bradman) | |
| Q3: "Define greatness parameters" | |
| A3: β Lost context, gives generic answer | |
| Q4: "Name a cricket journalist" | |
| A4: β Switches to gaming journalist (wrong sport!) | |
| ``` | |
| ### After Fix: | |
| ``` | |
| Q1: "Who is Sachin?" | |
| A1: (Cricket info) β Saved to context | |
| Q2: "Is he the greatest?" | |
| A2: (Compares Sachin to Bradman) β Saved to context | |
| Context includes: Q1+A1, Q2+A2 | |
| Q3: "Define greatness parameters" | |
| A3: β Knows we're talking about CRICKET greatness | |
| Context includes: Q1+A1, Q2+A2, Q3+A3 | |
| Q4: "Name a cricket journalist" | |
| A4: β Suggests cricket journalists (Harsha Bhogle, etc.) | |
| Context includes: Q1+A1, Q2+A2, Q3+A3, Q4+A4 | |
| ``` | |
| ## Technical Details | |
| ### Context Structure Now: | |
| ```json | |
| { | |
| "session_id": "d5e8171f", | |
| "interactions": [ | |
| { | |
| "user_input": "Who is Sachin?", | |
| "timestamp": "2025-10-27T15:39:32", | |
| "response": "Sachin Ramesh Tendulkar is a legendary Indian cricketer..." | |
| }, | |
| { | |
| "user_input": "Is he the greatest?", | |
| "timestamp": "2025-10-27T15:40:04", | |
| "response": "The question of who is the greatest cricketer..." | |
| } | |
| ] | |
| } | |
| ``` | |
| ### Prompt Format: | |
| ``` | |
| User Question: Define greatness parameters | |
| Previous conversation: | |
| Q1: Who is Sachin? | |
| A1: Sachin Ramesh Tendulkar is a legendary Indian cricketer... | |
| Q2: Is he the greatest? What about Don Bradman? | |
| A2: The question of who is the greatest cricketer... | |
| Instructions: Provide a comprehensive, helpful response that directly addresses the question. If there's conversation context, use it to answer the current question appropriately. | |
| ``` | |
| ## Testing | |
| To verify the fix: | |
| 1. Ask about a specific topic: "Who is Sachin Tendulkar?" | |
| 2. Ask 3-4 follow-up questions without mentioning the sport | |
| 3. Verify the system still knows you're talking about cricket | |
| 4. Check logs for "context has X interactions" | |
| ## Impact | |
| - β Better context retention (5 vs 3 interactions) | |
| - β Complete conversation history (Q&A pairs) | |
| - β Reduced factual errors due to context loss | |
| - β More coherent multi-turn conversations | |
| - β Sport/domain awareness maintained across turns | |