context added, wired in. first attempt

cortex/reasoning/reasoning.py

@@ -1,5 +1,6 @@
 # reasoning.py
 import os
+import json
 from llm.llm_router import call_llm
 
 
@@ -14,11 +15,19 @@ async def reason_check(
     user_prompt: str,
     identity_block: dict | None,
     rag_block: dict | None,
-    reflection_notes: list[str]
+    reflection_notes: list[str],
+    context: dict | None = None
 ) -> str:
     """
     Build the *draft answer* for Lyra Cortex.
     This is the first-pass reasoning stage (no refinement yet).
+
+    Args:
+        user_prompt: Current user message
+        identity_block: Lyra's identity/persona configuration
+        rag_block: Relevant long-term memories from NeoMem
+        reflection_notes: Meta-awareness notes from reflection stage
+        context: Unified context state from context.py (session state, intake, rag, etc.)
     """
 
     # --------------------------------------------------------
@@ -47,21 +56,92 @@ async def reason_check(
     rag_txt = ""
     if rag_block:
         try:
-            rag_txt = f"Relevant Info (RAG):\n{rag_block}\n\n"
+            # Format NeoMem results with full structure
+            if isinstance(rag_block, list) and rag_block:
+                rag_txt = "Relevant Long-Term Memories (NeoMem):\n"
+                for idx, mem in enumerate(rag_block, 1):
+                    score = mem.get("score", 0.0)
+                    payload = mem.get("payload", {})
+                    data = payload.get("data", "")
+                    metadata = payload.get("metadata", {})
+
+                    rag_txt += f"\n[Memory {idx}] (relevance: {score:.2f})\n"
+                    rag_txt += f"Content: {data}\n"
+                    if metadata:
+                        rag_txt += f"Metadata: {json.dumps(metadata, indent=2)}\n"
+                rag_txt += "\n"
+            else:
+                rag_txt = f"Relevant Info (RAG):\n{str(rag_block)}\n\n"
         except Exception:
             rag_txt = f"Relevant Info (RAG):\n{str(rag_block)}\n\n"
 
+    # --------------------------------------------------------
+    # Context State (session continuity, timing, mode/mood)
+    # --------------------------------------------------------
+    context_txt = ""
+    if context:
+        try:
+            # Build human-readable context summary
+            context_txt = "=== CONTEXT STATE ===\n"
+            context_txt += f"Session: {context.get('session_id', 'unknown')}\n"
+            context_txt += f"Time since last message: {context.get('minutes_since_last_msg', 0):.1f} minutes\n"
+            context_txt += f"Message count: {context.get('message_count', 0)}\n"
+            context_txt += f"Mode: {context.get('mode', 'default')}\n"
+            context_txt += f"Mood: {context.get('mood', 'neutral')}\n"
+
+            if context.get('active_project'):
+                context_txt += f"Active project: {context['active_project']}\n"
+
+            # Include Intake multilevel summaries
+            intake = context.get('intake', {})
+            if intake:
+                context_txt += "\nShort-Term Memory (Intake):\n"
+
+                # L1 - Recent exchanges
+                if intake.get('L1'):
+                    l1_data = intake['L1']
+                    if isinstance(l1_data, list):
+                        context_txt += f"  L1 (recent): {len(l1_data)} exchanges\n"
+                    elif isinstance(l1_data, str):
+                        context_txt += f"  L1: {l1_data[:200]}...\n"
+
+                # L20 - Session overview (most important for continuity)
+                if intake.get('L20'):
+                    l20_data = intake['L20']
+                    if isinstance(l20_data, dict):
+                        summary = l20_data.get('summary', '')
+                        context_txt += f"  L20 (session overview): {summary}\n"
+                    elif isinstance(l20_data, str):
+                        context_txt += f"  L20: {l20_data}\n"
+
+                # L30 - Continuity report
+                if intake.get('L30'):
+                    l30_data = intake['L30']
+                    if isinstance(l30_data, dict):
+                        summary = l30_data.get('summary', '')
+                        context_txt += f"  L30 (continuity): {summary}\n"
+                    elif isinstance(l30_data, str):
+                        context_txt += f"  L30: {l30_data}\n"
+
+            context_txt += "\n"
+
+        except Exception as e:
+            # Fallback to JSON dump if formatting fails
+            context_txt = f"=== CONTEXT STATE ===\n{json.dumps(context, indent=2)}\n\n"
+
     # --------------------------------------------------------
     # Final assembled prompt
     # --------------------------------------------------------
     prompt = (
         f"{notes_section}"
         f"{identity_txt}"
+        f"{context_txt}"  # Context BEFORE RAG for better coherence
         f"{rag_txt}"
         f"User message:\n{user_prompt}\n\n"
         "Write the best possible *internal draft answer*.\n"
         "This draft is NOT shown to the user.\n"
         "Be factual, concise, and focused.\n"
+        "Use the context state to maintain continuity and reference past interactions naturally.\n"
     )
 
     # --------------------------------------------------------