simple context added to standard mode

cortex/intake/intake.py

@@ -326,11 +326,33 @@ def bg_summarize(session_id: str):
 # ─────────────────────────────
 # Internal entrypoint for Cortex
 # ─────────────────────────────
+def get_recent_messages(session_id: str, limit: int = 20) -> list:
+    """
+    Get recent raw messages from the session buffer.
+
+    Args:
+        session_id: Session identifier
+        limit: Maximum number of messages to return (default 20)
+
+    Returns:
+        List of message dicts with 'role' and 'content' fields
+    """
+    if session_id not in SESSIONS:
+        return []
+
+    buffer = SESSIONS[session_id]["buffer"]
+
+    # Convert buffer to list and get last N messages
+    messages = list(buffer)[-limit:]
+
+    return messages
+
+
 def add_exchange_internal(exchange: dict):
     """
     Direct internal call — bypasses FastAPI request handling.
     Cortex uses this to feed user/assistant turns directly
-    into Intake’s buffer and trigger full summarization.
+    into Intake's buffer and trigger full summarization.
     """
     session_id = exchange.get("session_id")
     if not session_id:

cortex/llm/llm_router.py

@@ -44,11 +44,22 @@ http_client = httpx.AsyncClient(timeout=120.0)
 # Public call
 # ------------------------------------------------------------
 async def call_llm(
-    prompt: str,
+    prompt: str = None,
+    messages: list = None,
     backend: str | None = None,
     temperature: float = 0.7,
     max_tokens: int = 512,
 ):
+    """
+    Call an LLM backend.
+
+    Args:
+        prompt: String prompt (for completion-style APIs like mi50)
+        messages: List of message dicts (for chat-style APIs like Ollama/OpenAI)
+        backend: Which backend to use (PRIMARY, SECONDARY, OPENAI, etc.)
+        temperature: Sampling temperature
+        max_tokens: Maximum tokens to generate
+    """
     backend = (backend or DEFAULT_BACKEND).upper()
 
     if backend not in BACKENDS:
@@ -69,7 +80,8 @@ async def call_llm(
         payload = {
             "prompt": prompt,
             "n_predict": max_tokens,
-            "temperature": temperature
+            "temperature": temperature,
+            "stop": ["User:", "\nUser:", "Assistant:", "\n\n\n"]
         }
         try:
             r = await http_client.post(f"{url}/completion", json=payload)
@@ -90,12 +102,20 @@ async def call_llm(
     # Provider: OLLAMA (your 3090)
     # -------------------------------
     if provider == "ollama":
+        # Use messages array if provided, otherwise convert prompt to single user message
+        if messages:
+            chat_messages = messages
+        else:
+            chat_messages = [{"role": "user", "content": prompt}]
+
         payload = {
             "model": model,
-            "messages": [
+            "messages": chat_messages,
-                {"role": "user", "content": prompt}
+            "stream": False,
-            ],
+            "options": {
-            "stream": False
+                "temperature": temperature,
+                "num_predict": max_tokens
+            }
         }
         try:
             r = await http_client.post(f"{url}/api/chat", json=payload)

cortex/router.py

@@ -351,17 +351,34 @@ async def run_simple(req: ReasonRequest):
     logger.info(f"📝 User: {req.user_prompt[:150]}...")
     logger.info(f"{'-'*100}\n")
 
-    # Get conversation history from context
+    # Get conversation history from context and intake buffer
     context_state = await collect_context(req.session_id, req.user_prompt)
 
-    # Build simple conversation history
+    # Get recent messages from Intake buffer
-    messages = []
+    from intake.intake import get_recent_messages
-    if context_state.get("recent_messages"):
+    recent_msgs = get_recent_messages(req.session_id, limit=20)
-        for msg in context_state["recent_messages"]:
+    logger.info(f"📋 Retrieved {len(recent_msgs)} recent messages from Intake buffer")
+
+    # Build simple conversation history with system message
+    system_message = {
+        "role": "system",
+        "content": (
+            "You are a helpful AI assistant. Provide direct, concise responses to the user's questions. "
+            "Maintain context from previous messages in the conversation."
+        )
+    }
+
+    messages = [system_message]
+
+    # Add conversation history
+
+    if recent_msgs:
+        for msg in recent_msgs:
             messages.append({
                 "role": msg.get("role", "user"),
                 "content": msg.get("content", "")
             })
+            logger.info(f"  - {msg.get('role')}: {msg.get('content', '')[:50]}...")
 
     # Add current user message
     messages.append({
@@ -369,30 +386,24 @@ async def run_simple(req: ReasonRequest):
         "content": req.user_prompt
     })
 
-    # Format messages into a simple prompt for the LLM
+    logger.info(f"📨 Total messages being sent to LLM: {len(messages)} (including system message)")
-    conversation = ""
-    for msg in messages:
-        role = msg["role"]
-        content = msg["content"]
-        if role == "user":
-            conversation += f"User: {content}\n\n"
-        elif role == "assistant":
-            conversation += f"Assistant: {content}\n\n"
-
-    conversation += "Assistant: "
 
     # Get backend from env (default to OPENAI for standard mode)
     backend = os.getenv("STANDARD_MODE_LLM", "OPENAI")
     temperature = req.temperature if req.temperature is not None else 0.7
 
-    # Direct LLM call
+    # Direct LLM call with messages (works for Ollama/OpenAI chat APIs)
     try:
-        response = await call_llm(
+        raw_response = await call_llm(
-            prompt=conversation,
+            messages=messages,
             backend=backend,
             temperature=temperature,
             max_tokens=2048
         )
+
+        # Clean response - just strip whitespace
+        response = raw_response.strip()
+
     except Exception as e:
         logger.error(f"❌ LLM call failed: {e}")
         response = f"Error: {str(e)}"