CONTEXT_MEMORY_FIX.md

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 → 5 interactions)

# OLD: 
recent_interactions = context.get('interactions', [])[:3]

# NEW:
recent_interactions = context.get('interactions', [])[:5]  # Last 5 interactions

2. Added Response Storage

# 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 ✓
}

3. Enhanced Conversation History in Prompts

# 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

# 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: ❌ Lost context, gives generic answer

Q4: "Name a cricket journalist"
A4: ❌ Switches to gaming journalist (wrong sport!)

After Fix:

Q1: "Who is Sachin?"
A1: (Cricket info) ✓ Saved to context

Q2: "Is he the greatest?"
A2: (Compares Sachin to Bradman) ✓ Saved to context
   Context includes: Q1+A1, Q2+A2

Q3: "Define greatness parameters"
A3: ✓ Knows we're talking about CRICKET greatness
   Context includes: Q1+A1, Q2+A2, Q3+A3

Q4: "Name a cricket journalist"
A4: ✓ Suggests cricket journalists (Harsha Bhogle, etc.)
   Context includes: Q1+A1, Q2+A2, Q3+A3, Q4+A4

Technical Details

Context Structure Now:

{
  "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

• ✅ Better context retention (5 vs 3 interactions)
• ✅ Complete conversation history (Q&A pairs)
• ✅ Reduced factual errors due to context loss
• ✅ More coherent multi-turn conversations
• ✅ Sport/domain awareness maintained across turns