workflow errors debugging v12

SESSION_CONTEXT_FIX.md

@@ -0,0 +1,133 @@
+# Session Context Integration Fix
+
+## Problem Identified
+
+User reported that session context is not working:
+- Second question "Tell me more about his career decorations" should have known "his" refers to "Sam Altman" from the first question
+- But the response was generic, suggesting no context retention
+
+## Root Cause Analysis
+
+From logs:
+```
+Session ID: d5e8171f  (SAME for both messages ✓)
+Context retrieved: 0 interactions  (BUG! ❌)
+```
+
+The session ID persists correctly, but **context is returning 0 interactions** even though the first interaction was saved to the database.
+
+## Fixes Applied
+
+### 1. Fixed Context Structure (`context_manager.py`)
+
+**Problem:** `_optimize_context()` was stripping the context and returning only a subset:
+```python
+return {
+    "essential_entities": self._extract_entities(context),
+    "conversation_summary": self._generate_summary(context),
+    "recent_interactions": context.get("interactions", [])[-3:],  # Only 3 items
+    "user_preferences": context.get("preferences", {}),
+    "active_tasks": context.get("active_tasks", [])
+}
+```
+
+**Solution:** Return full context structure including all interactions:
+```python
+return {
+    "session_id": context.get("session_id"),
+    "interactions": context.get("interactions", []),  # Full history
+    "preferences": context.get("preferences", {}),
+    "active_tasks": context.get("active_tasks", []),
+    "essential_entities": self._extract_entities(context),
+    "conversation_summary": self._generate_summary(context),
+    "last_activity": context.get("last_activity")
+}
+```
+
+### 2. Enhanced Synthesis Agent with Context (`src/agents/synthesis_agent.py`)
+
+**Problem:** Prompt didn't include conversation history.
+
+**Solution:** Modified `_build_synthesis_prompt()` to include conversation history:
+```python
+# Extract conversation history for context
+conversation_history = ""
+if context and context.get('interactions'):
+    recent_interactions = context.get('interactions', [])[:3]  # Last 3 interactions
+    if recent_interactions:
+        conversation_history = "\n\nPrevious conversation context:\n"
+        for i, interaction in enumerate(reversed(recent_interactions), 1):
+            user_msg = interaction.get('user_input', '')
+            if user_msg:
+                conversation_history += f"{i}. User asked: {user_msg}\n"
+```
+
+Now the prompt includes:
+```
+User Question: Tell me more about his career decorations
+
+Previous conversation context:
+1. User asked: Who is the CEO of OpenAI?
+
+Instructions: Provide a comprehensive, helpful response that directly addresses the question. If there's conversation context, use it to answer the current question appropriately. Be detailed and informative.
+
+Response:
+```
+
+### 3. Added Debugging (`src/agents/synthesis_agent.py`)
+
+Added logging to track context flow:
+```python
+# Log context for debugging
+if context:
+    logger.info(f"{self.agent_id} context has {len(context.get('interactions', []))} interactions")
+```
+
+## Expected Behavior After Fix
+
+### Example Conversation:
+
+**Q1:** "Who is the CEO of OpenAI?"
+- Session ID: `d5e8171f`
+- Context: `[]` (empty, first message)
+- Response: "Sam Altman"
+- Saved to DB: `interactions` table
+
+**Q2:** "Tell me more about his career decorations"
+- Session ID: `d5e8171f` (same)
+- Context: `[{"user_input": "Who is the CEO of OpenAI?", ...}]`
+- **LLM Prompt:**
+  ```
+  User Question: Tell me more about his career decorations
+  
+  Previous conversation context:
+  1. User asked: Who is the CEO of OpenAI?
+  
+  Instructions: ...use conversation context...
+  ```
+- Response: "Sam Altman's career decorations include..."
+- Uses "his" = "Sam Altman" from context ✓
+
+## Testing
+
+To verify the fix works:
+
+1. Ask: "Who is the CEO of OpenAI?"
+2. Check logs for "Context retrieved: 1 interactions"
+3. Ask follow-up: "Tell me more about him"
+4. Verify response mentions Sam Altman (not generic)
+
+## Files Modified
+
+- `context_manager.py` - Fixed context structure
+- `src/agents/synthesis_agent.py` - Added conversation history to prompt
+- Added debugging logs
+
+## Next Steps
+
+The fix is ready to test. The changes ensure:
+1. ✅ Full interaction history is preserved in context
+2. ✅ Conversation history is included in LLM prompts
+3. ✅ Follow-up questions can refer to previous messages
+4. ✅ Session persistence works end-to-end
+

context_manager.py

@@ -81,12 +81,15 @@ class EfficientContextManager:
         """
         Optimize context for LLM consumption
         """
+        # Keep the full context structure for LLM consumption
         return {
+            "session_id": context.get("session_id"),
+            "interactions": context.get("interactions", []),  # Keep full interaction history
+            "preferences": context.get("preferences", {}),
+            "active_tasks": context.get("active_tasks", []),
             "essential_entities": self._extract_entities(context),
             "conversation_summary": self._generate_summary(context),
-            "recent_interactions": context.get("interactions", [])[-3:],
-            "user_preferences": context.get("preferences", {}),
-            "active_tasks": context.get("active_tasks", [])
+            "last_activity": context.get("last_activity")
         }
     
     def _get_from_memory_cache(self, session_id: str) -> dict:

src/agents/synthesis_agent.py

@@ -48,6 +48,10 @@ class ResponseSynthesisAgent:
         try:
             logger.info(f"{self.agent_id} synthesizing {len(agent_outputs)} agent outputs")
             
+            # Log context for debugging
+            if context:
+                logger.info(f"{self.agent_id} context has {len(context.get('interactions', []))} interactions")
+            
             # Extract intent information
             intent_info = self._extract_intent_info(agent_outputs)
             primary_intent = intent_info.get('primary_intent', 'casual_conversation')
@@ -164,17 +168,28 @@ class ResponseSynthesisAgent:
     def _build_synthesis_prompt(self, agent_outputs: List[Dict[str, Any]], 
                               user_input: str, context: Dict[str, Any], 
                               primary_intent: str) -> str:
-        """Build prompt for LLM-based synthesis - optimized for Qwen instruct format"""
+        """Build prompt for LLM-based synthesis - optimized for Qwen instruct format with context"""
         
         # Build a comprehensive prompt for actual LLM generation
         agent_content = self._format_agent_outputs_for_synthesis(agent_outputs)
         
-        # Qwen instruct format - simpler, more direct
+        # Extract conversation history for context
+        conversation_history = ""
+        if context and context.get('interactions'):
+            recent_interactions = context.get('interactions', [])[:3]  # Last 3 interactions
+            if recent_interactions:
+                conversation_history = "\n\nPrevious conversation context:\n"
+                for i, interaction in enumerate(reversed(recent_interactions), 1):
+                    user_msg = interaction.get('user_input', '')
+                    if user_msg:
+                        conversation_history += f"{i}. User asked: {user_msg}\n"
+        
+        # Qwen instruct format with conversation history
         prompt = f"""User Question: {user_input}
-
+{conversation_history}
 {agent_content if agent_content else ""}
 
-Instructions: Provide a comprehensive, helpful response that directly addresses the question. Be detailed and informative.
+Instructions: Provide a comprehensive, helpful response that directly addresses the question. If there's conversation context, use it to answer the current question appropriately. Be detailed and informative.
 
 Response:"""