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# Context Window Increased to 20 Interactions for Stable UX
## Changes Made
### 1. Synthesis Agent Context Window: 5 → 20
**Files:**
- `src/agents/synthesis_agent.py`
- `Research_AI_Assistant/src/agents/synthesis_agent.py`
**Change:**
```python
# OLD:
recent_interactions = context.get('interactions', [])[:5] # Last 5 interactions
# NEW:
recent_interactions = context.get('interactions', [])[:20] # Last 20 interactions for stable UX
```
### 2. Context Manager Buffer: 10 → 40
**Files:**
- `context_manager.py`
- `Research_AI_Assistant/context_manager.py`
**Change:**
```python
# OLD:
# Keep only last 10 interactions in memory
context["interactions"] = [new_interaction] + context["interactions"][:9]
# NEW:
# Keep only last 40 interactions in memory (2x the context window for stability)
context["interactions"] = [new_interaction] + context["interactions"][:39]
```
## Rationale
### Moving Window Strategy
The system now maintains a **sliding window** of 20 interactions:
1. **Memory Buffer (40 interactions)**:
- Stores in-memory for fast retrieval
- Provides 2x the context window for stability
- Newest interaction is added, oldest is dropped beyond 40
2. **Context Window (20 interactions)**:
- Sent to LLM for each request
- Contains last 20 Q&A pairs
- Ensures deep conversation history
### Benefits
**Before (5 interactions):**
- Lost context after 3-4 questions
- Domain switching issues (cricket → gaming journalist)
- Inconsistent experience
**After (20 interactions):**
- ✅ Maintains context across 20+ questions
- ✅ Stable conversation flow
- ✅ No topic/domain switching
- ✅ Better UX for extended dialogues
## Technical Implementation
### Memory Management Flow
```
Initial:
Memory Buffer: [I1, I2, ..., I40] (40 slots)
Context Window: [I1, I2, ..., I20] (20 slots sent to LLM)
After 1 new interaction:
Memory Buffer: [I41, I1, I2, ..., I39] (I40 dropped)
Context Window: [I41, I1, I2, ..., I20] (I21 dropped from LLM context)
After 20 more interactions:
Memory Buffer: [I41, ..., I60, I1, ..., I20] (I21-40 dropped)
Context Window: [I41, ..., I60] (Still have 20 recent interactions)
```
### Database Storage
- Database stores **unlimited** interactions
- Memory buffer holds **40** for performance
- LLM gets **20** for context
- Moving window ensures recent context always available
## Performance Considerations
### Memory Usage
- **Per interaction**: ~1-2KB (text + metadata)
- **40 interactions buffer**: ~40-80KB per session
- **Negligible** impact on performance
### LLM Token Usage
- **20 Q&A pairs**: ~2000-4000 tokens (estimated)
- Well within Qwen model limits (8K tokens typically)
- Graceful handling if token limit exceeded
### Response Time
- **No impact** on response time
- Database queries unchanged
- In-memory buffer ensures fast retrieval
## Testing Recommendations
### Test Scenarios
1. **Short Conversation (5 interactions)**:
- All 5 interactions in context ✓
- Full conversation history available
2. **Medium Conversation (15 interactions)**:
- Last 15 interactions in context ✓
- Recent history maintained
3. **Long Conversation (30 interactions)**:
- Last 20 interactions in context ✓
- First 10 dropped (moving window)
- Still maintains recent context
4. **Extended Conversation (50+ interactions)**:
- Last 20 interactions in context ✓
- Memory buffer holds 40
- Database retains all for historical lookup
### Validation
- Verify context persistence across 20+ questions
- Check for domain/topic drift
- Ensure stable conversation flow
- Monitor memory usage
- Verify database persistence
## Migration Notes
### For Existing Sessions
- Existing sessions will upgrade on next interaction
- No data migration required
- Memory buffer automatically adjusted
- Database schema unchanged
### Backward Compatibility
- ✅ Compatible with existing sessions
- ✅ No breaking changes
- ✅ Graceful upgrade
## Summary
The context window has been increased from **5 to 20 interactions** with a **moving window** strategy:
- 📊 **Memory buffer**: 40 interactions (2x for stability)
- 🎯 **Context window**: 20 interactions (sent to LLM)
- 💾 **Database**: Unlimited (permanent storage)
- ✅ **Result**: Stable UX across extended conversations
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