fix: parse curated turns into proper user/assistant roles

- Added parse_curated_turn() function to correctly parse stored memories
- Fixed build_augmented_messages() to use proper message roles
- Layer 2 (semantic) and Layer 3 (context) now correctly parse
  User: X / Assistant: Y format into separate messages
- Resolves context corruption where turns were dumped as single user message

v2.0.2

app/utils.py

@@ -2,7 +2,7 @@
 from .config import config
 import tiktoken
 import os
-from typing import List, Dict
+from typing import List, Dict, Optional
 from datetime import datetime, timedelta
 from pathlib import Path
 
@@ -127,10 +127,70 @@ def load_system_prompt() -> str:
         return ""
 
 
+def parse_curated_turn(text: str) -> List[Dict]:
+    """Parse a curated turn into alternating user/assistant messages.
+    
+    Input format:
+        User: [question]
+        Assistant: [answer]
+        Timestamp: ISO datetime
+    
+    Returns list of message dicts with role and content.
+    Returns empty list if parsing fails.
+    """
+    if not text:
+        return []
+    
+    messages = []
+    lines = text.strip().split("\n")
+    
+    current_role = None
+    current_content = []
+    
+    for line in lines:
+        line = line.strip()
+        if line.startswith("User:"):
+            # Save previous content if exists
+            if current_role and current_content:
+                messages.append({
+                    "role": current_role,
+                    "content": "\n".join(current_content).strip()
+                })
+            current_role = "user"
+            current_content = [line[5:].strip()]  # Remove "User:" prefix
+        elif line.startswith("Assistant:"):
+            # Save previous content if exists
+            if current_role and current_content:
+                messages.append({
+                    "role": current_role,
+                    "content": "\n".join(current_content).strip()
+                })
+            current_role = "assistant"
+            current_content = [line[10:].strip()]  # Remove "Assistant:" prefix
+        elif line.startswith("Timestamp:"):
+            # Ignore timestamp line
+            continue
+        elif current_role:
+            # Continuation of current message
+            current_content.append(line)
+    
+    # Save last message
+    if current_role and current_content:
+        messages.append({
+            "role": current_role,
+            "content": "\n".join(current_content).strip()
+        })
+    
+    return messages
+
+
 async def build_augmented_messages(incoming_messages: List[Dict]) -> List[Dict]:
     """Build 4-layer augmented messages from incoming messages.
     
-    This is a standalone version that can be used by proxy_handler.py.
+    Layer 1: System prompt (preserved from incoming + vera context)
+    Layer 2: Semantic memories (curated, parsed into proper roles)
+    Layer 3: Recent context (raw turns, parsed into proper roles)
+    Layer 4: Current conversation (passed through)
     """
     import logging
     
@@ -153,6 +213,10 @@ async def build_augmented_messages(incoming_messages: List[Dict]) -> List[Dict]:
             search_context += msg.get("content", "") + " "
     
     messages = []
+    token_budget = {
+        "semantic": config.semantic_token_budget,
+        "context": config.context_token_budget
+    }
     
     # === LAYER 1: System Prompt ===
     system_content = ""
@@ -166,6 +230,7 @@ async def build_augmented_messages(incoming_messages: List[Dict]) -> List[Dict]:
     
     if system_content:
         messages.append({"role": "system", "content": system_content})
+        logger.info(f"Layer 1 (system): {count_tokens(system_content)} tokens")
     
     # === LAYER 2: Semantic (curated memories) ===
     qdrant = get_qdrant_service()
@@ -176,28 +241,71 @@ async def build_augmented_messages(incoming_messages: List[Dict]) -> List[Dict]:
         entry_type="curated"
     )
     
-    semantic_tokens = 0
+    semantic_messages = []
+    semantic_tokens_used = 0
+    
     for result in semantic_results:
         payload = result.get("payload", {})
         text = payload.get("text", "")
-        if text and semantic_tokens < config.semantic_token_budget:
+        if text:
-            messages.append({"role": "user", "content": text})  # Add as context
+            # Parse curated turn into proper user/assistant messages
-            semantic_tokens += count_tokens(text)
+            parsed = parse_curated_turn(text)
+            for msg in parsed:
+                msg_tokens = count_tokens(msg.get("content", ""))
+                if semantic_tokens_used + msg_tokens <= token_budget["semantic"]:
+                    semantic_messages.append(msg)
+                    semantic_tokens_used += msg_tokens
+                else:
+                    break
+        if semantic_tokens_used >= token_budget["semantic"]:
+            break
+    
+    # Add parsed messages to context
+    for msg in semantic_messages:
+        messages.append(msg)
+    
+    if semantic_messages:
+        logger.info(f"Layer 2 (semantic): {len(semantic_messages)} messages, ~{semantic_tokens_used} tokens")
     
     # === LAYER 3: Context (recent turns) ===
-    recent_turns = await qdrant.get_recent_turns(limit=20)
+    recent_turns = await qdrant.get_recent_turns(limit=50)
     
-    context_tokens = 0
+    context_messages = []
+    context_tokens_used = 0
+    
+    # Process oldest first for chronological order
     for turn in reversed(recent_turns):
         payload = turn.get("payload", {})
         text = payload.get("text", "")
-        if text and context_tokens < config.context_token_budget:
+        entry_type = payload.get("type", "raw")
-            messages.append({"role": "user", "content": text})  # Add as context
-            context_tokens += count_tokens(text)
         
-    # === LAYER 4: Current messages (passed through) ===
+        if text:
+            # Parse turn into messages
+            parsed = parse_curated_turn(text)
+            
+            for msg in parsed:
+                msg_tokens = count_tokens(msg.get("content", ""))
+                if context_tokens_used + msg_tokens <= token_budget["context"]:
+                    context_messages.append(msg)
+                    context_tokens_used += msg_tokens
+                else:
+                    break
+        
+        if context_tokens_used >= token_budget["context"]:
+            break
+    
+    # Add context messages (oldest first maintains conversation order)
+    for msg in context_messages:
+        messages.append(msg)
+    
+    if context_messages:
+        logger.info(f"Layer 3 (context): {len(context_messages)} messages, ~{context_tokens_used} tokens")
+    
+    # === LAYER 4: Current conversation ===
     for msg in incoming_messages:
-        if msg.get("role") != "system":  # Do not duplicate system
+        if msg.get("role") != "system":  # System already handled in Layer 1
             messages.append(msg)
     
+    logger.info(f"Layer 4 (current): {len([m for m in incoming_messages if m.get('role') != 'system'])} messages")
+    
     return messages