v0.5.2 update

Dev
2025-12-12 08:04:20 +00:00
parent 09b6b364e5 58d6520056
commit 628edb681a
20 changed files with 2503 additions and 654 deletions
@@ -9,6 +9,55 @@ Format based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/) and [Se
 ---
 ## [0.5.2] - 2025-12-12
 ### Fixed - LLM Router & Async HTTP
 - **Critical**: Replaced synchronous `requests` with async `httpx` in LLM router [cortex/llm/llm_router.py](cortex/llm/llm_router.py)
  - Event loop blocking was causing timeouts and empty responses
  - All three providers (MI50, Ollama, OpenAI) now use `await http_client.post()`
  - Fixes "Expecting value: line 1 column 1 (char 0)" JSON parsing errors in intake
 - **Critical**: Fixed missing `backend` parameter in intake summarization [cortex/intake/intake.py:285](cortex/intake/intake.py#L285)
  - Was defaulting to PRIMARY (MI50) instead of respecting `INTAKE_LLM=SECONDARY`
  - Now correctly uses configured backend (Ollama on 3090)
 - **Relay**: Fixed session ID case mismatch [core/relay/server.js:87](core/relay/server.js#L87)
  - UI sends `sessionId` (camelCase) but relay expected `session_id` (snake_case)
  - Now accepts both variants: `req.body.session_id || req.body.sessionId`
  - Custom session IDs now properly tracked instead of defaulting to "default"
 ### Added - Error Handling & Diagnostics
 - Added comprehensive error handling in LLM router for all providers
  - HTTPError, JSONDecodeError, KeyError, and generic Exception handling
  - Detailed error messages with exception type and description
  - Provider-specific error logging (mi50, ollama, openai)
 - Added debug logging in intake summarization
  - Logs LLM response length and preview
  - Validates non-empty responses before JSON parsing
  - Helps diagnose empty or malformed responses
 ### Added - Session Management
 - Added session persistence endpoints in relay [core/relay/server.js:160-171](core/relay/server.js#L160-L171)
  - `GET /sessions/:id` - Retrieve session history
  - `POST /sessions/:id` - Save session history
  - In-memory storage using Map (ephemeral, resets on container restart)
  - Fixes UI "Failed to load session" errors
 ### Changed - Provider Configuration
 - Added `mi50` provider support for llama.cpp server [cortex/llm/llm_router.py:62-81](cortex/llm/llm_router.py#L62-L81)
  - Uses `/completion` endpoint with `n_predict` parameter
  - Extracts `content` field from response
  - Configured for MI50 GPU with DeepSeek model
 - Increased memory retrieval threshold from 0.78 to 0.90 [cortex/.env:20](cortex/.env#L20)
  - Filters out low-relevance memories (only returns 90%+ similarity)
  - Reduces noise in context retrieval
 ### Technical Improvements
 - Unified async HTTP handling across all LLM providers
 - Better separation of concerns between provider implementations
 - Improved error messages for debugging LLM API failures
 - Consistent timeout handling (120 seconds for all providers)
 ---
 ## [0.5.1] - 2025-12-11
 ### Fixed - Intake Integration
@@ -1,71 +0,0 @@
 # Lyra Core — Project Summary
 ## v0.4 (2025-10-03)
 ### 🧠 High-Level Architecture
 - **Lyra Core (v0.3.1)** — Orchestration layer.  
  - Accepts chat requests (`/v1/chat/completions`).  
  - Routes through Cortex for subconscious annotation.  
  - Stores everything in Mem0 (no discard).  
  - Fetches persona + relevant memories.  
  - Injects context back into LLM.  
 - **Cortex (v0.3.0)** — Subconscious annotator.  
  - Runs locally via `llama.cpp` (Phi-3.5-mini Q4_K_M).  
  - Strict JSON schema:  
    ```json
    {
      "sentiment": "positive" | "neutral" | "negative",
      "novelty": 0.0–1.0,
      "tags": ["keyword", "keyword"],
      "notes": "short string"
    }
    ```  
  - Normalizes keys (lowercase).  
  - Strips Markdown fences before parsing.  
  - Configurable via `.env` (`CORTEX_ENABLED=true|false`).  
  - Currently generates annotations, but not yet persisted into Mem0 payloads (stored as empty `{cortex:{}}`).  
 - **Mem0 (v0.4.0)** — Persistent memory layer.  
  - Handles embeddings, graph storage, and retrieval.  
  - Dual embedder support:  
    - **OpenAI Cloud** (`text-embedding-3-small`, 1536-dim).  
    - **HuggingFace TEI** (gte-Qwen2-1.5B-instruct, 1536-dim, hosted on 3090).  
  - Environment toggle for provider (`.env.openai` vs `.env.3090`).  
  - Memory persistence in Postgres (`payload` JSON).  
  - CSV export pipeline confirmed (id, user_id, data, created_at).  
 - **Persona Sidecar**  
  - Provides personality, style, and protocol instructions.  
  - Injected at runtime into Core prompt building.  
 ---
 ### 🚀 Recent Changes
 - **Mem0**  
  - Added HuggingFace TEI integration (local 3090 embedder).  
  - Enabled dual-mode environment switch (OpenAI cloud ↔ local TEI).  
  - Fixed `.env` line ending mismatch (CRLF vs LF).  
  - Added memory dump/export commands for Postgres.  
 - **Core/Relay**  
  - No major changes since v0.3.1 (still routing input → Cortex → Mem0).  
 - **Cortex**  
  - Still outputs annotations, but not yet persisted into Mem0 payloads.  
 ---
 ### 📈 Versioning
 - **Lyra Core** → v0.3.1  
 - **Cortex** → v0.3.0  
 - **Mem0** → v0.4.0  
 ---
 ### 📋 Next Steps
 - [ ] Wire Cortex annotations into Mem0 payloads (`cortex` object).  
 - [ ] Add “export all memories” script to standard workflow.  
 - [ ] Consider async embedding for faster `mem.add`.  
 - [ ] Build visual diagram of data flow (Core ↔ Cortex ↔ Mem0 ↔ Persona).  
 - [ ] Explore larger LLMs for Cortex (Qwen2-7B, etc.) for richer subconscious annotation.  
@@ -1,12 +1,14 @@
-# Project Lyra - README v0.5.0
+# Project Lyra - README v0.5.1
 Lyra is a modular persistent AI companion system with advanced reasoning capabilities.
 It provides memory-backed chat using **NeoMem** + **Relay** + **Cortex**,
 with multi-stage reasoning pipeline powered by HTTP-based LLM backends.
 **Current Version:** v0.5.1 (2025-12-11)
 ## Mission Statement
-The point of Project Lyra is to give an AI chatbot more abilities than a typical chatbot. Typical chatbots are essentially amnesic and forget everything about your project. Lyra helps keep projects organized and remembers everything you have done. Think of her abilities as a notepad/schedule/database/co-creator/collaborator all with its own executive function. Say something in passing, Lyra remembers it then reminds you of it later.
+The point of Project Lyra is to give an AI chatbot more abilities than a typical chatbot. Typical chatbots are essentially amnesic and forget evertything about your project. Lyra helps keep projects organized and remembers everything you have done. Think of her abilities as a notepad/schedule/database/co-creator/collaborator all with its own executive function. Say something in passing, Lyra remembers it then reminds you of it later.
 ---
@@ -22,7 +24,7 @@ Project Lyra operates as a **single docker-compose deployment** with multiple Do
 - OpenAI-compatible endpoint: `POST /v1/chat/completions`
 - Internal endpoint: `POST /chat`
 - Routes messages through Cortex reasoning pipeline
- Manages async calls to Intake and NeoMem
+- Manages async calls to NeoMem and Cortex ingest
 **2. UI** (Static HTML)
 - Browser-based chat interface with cyberpunk theme
@@ -41,38 +43,48 @@ Project Lyra operates as a **single docker-compose deployment** with multiple Do
 **4. Cortex** (Python/FastAPI) - Port 7081
 - Primary reasoning engine with multi-stage pipeline
 - **Includes embedded Intake module** (no separate service as of v0.5.1)
 - **4-Stage Processing:**
  1. **Reflection** - Generates meta-awareness notes about conversation
  2. **Reasoning** - Creates initial draft answer using context
  3. **Refinement** - Polishes and improves the draft
  4. **Persona** - Applies Lyra's personality and speaking style
- Integrates with Intake for short-term context
+- Integrates with Intake for short-term context via internal Python imports
 - Flexible LLM router supporting multiple backends via HTTP
 - **Endpoints:**
  - `POST /reason` - Main reasoning pipeline
  - `POST /ingest` - Receives conversation exchanges from Relay
  - `GET /health` - Service health check
  - `GET /debug/sessions` - Inspect in-memory SESSIONS state
  - `GET /debug/summary` - Test summarization for a session
-**5. Intake v0.2** (Python/FastAPI) - Port 7080
+**5. Intake** (Python Module) - **Embedded in Cortex**
- Simplified short-term memory summarization
+- **No longer a standalone service** - runs as Python module inside Cortex container
- Session-based circular buffer (deque, maxlen=200)
+- Short-term memory management with session-based circular buffer
- Single-level simple summarization (no cascading)
+- In-memory SESSIONS dictionary: `session_id → {buffer: deque(maxlen=200), created_at: timestamp}`
- Background async processing with FastAPI BackgroundTasks
+- Multi-level summarization (L1/L5/L10/L20/L30) produced by `summarize_context()`
- Pushes summaries to NeoMem automatically
+- Deferred summarization - actual summary generation happens during `/reason` call
- **API Endpoints:**
+- Internal Python API:
-  - `POST /add_exchange` - Add conversation exchange
+  - `add_exchange_internal(exchange)` - Direct function call from Cortex
-  - `GET /summaries?session_id={id}` - Retrieve session summary
+  - `summarize_context(session_id, exchanges)` - Async LLM-based summarization
-  - `POST /close_session/{id}` - Close and cleanup session
+  - `SESSIONS` - Module-level global state (requires single Uvicorn worker)
 ### LLM Backends (HTTP-based)
 **All LLM communication is done via HTTP APIs:**
- **PRIMARY**: vLLM server (`http://10.0.0.43:8000`) - AMD MI50 GPU backend
+- **PRIMARY**: llama.cpp server (`http://10.0.0.44:8080`) - AMD MI50 GPU backend
 - **SECONDARY**: Ollama server (`http://10.0.0.3:11434`) - RTX 3090 backend
  - Model: qwen2.5:7b-instruct-q4_K_M
 - **CLOUD**: OpenAI API (`https://api.openai.com/v1`) - Cloud-based models
  - Model: gpt-4o-mini
 - **FALLBACK**: Local backup (`http://10.0.0.41:11435`) - Emergency fallback
  - Model: llama-3.2-8b-instruct
 Each module can be configured to use a different backend via environment variables.
 ---
-## Data Flow Architecture (v0.5.0)
+## Data Flow Architecture (v0.5.1)
 ### Normal Message Flow:
@@ -82,43 +94,44 @@ User (UI) → POST /v1/chat/completions
 Relay (7078)
  ↓ POST /reason
 Cortex (7081)
-  ↓ GET /summaries?session_id=xxx
+  ↓ (internal Python call)
-Intake (7080) [RETURNS SUMMARY]
+Intake module → summarize_context()
  ↓
 Cortex processes (4 stages):
-  1. reflection.py → meta-awareness notes
+  1. reflection.py → meta-awareness notes (CLOUD backend)
-  2. reasoning.py → draft answer (uses LLM)
+  2. reasoning.py → draft answer (PRIMARY backend)
-  3. refine.py → refined answer (uses LLM)
+  3. refine.py → refined answer (PRIMARY backend)
-  4. persona/speak.py → Lyra personality (uses LLM)
+  4. persona/speak.py → Lyra personality (CLOUD backend)
  ↓
 Returns persona answer to Relay
  ↓
-Relay → Cortex /ingest (async, stub)
+Relay → POST /ingest (async)
 Relay → Intake /add_exchange (async)
  ↓
-Intake → Background summarize → NeoMem
+Cortex → add_exchange_internal() → SESSIONS buffer
  ↓
 Relay → NeoMem /memories (async, planned)
  ↓
 Relay → UI (returns final response)
 ```
 ### Cortex 4-Stage Reasoning Pipeline:
-1. **Reflection** (`reflection.py`) - Configurable LLM via HTTP
+1. **Reflection** (`reflection.py`) - Cloud LLM (OpenAI)
   - Analyzes user intent and conversation context
   - Generates meta-awareness notes
   - "What is the user really asking?"
-2. **Reasoning** (`reasoning.py`) - Configurable LLM via HTTP
+2. **Reasoning** (`reasoning.py`) - Primary LLM (llama.cpp)
-   - Retrieves short-term context from Intake
+   - Retrieves short-term context from Intake module
   - Creates initial draft answer
   - Integrates context, reflection notes, and user prompt
-3. **Refinement** (`refine.py`) - Configurable LLM via HTTP
+3. **Refinement** (`refine.py`) - Primary LLM (llama.cpp)
   - Polishes the draft answer
   - Improves clarity and coherence
   - Ensures factual consistency
-4. **Persona** (`speak.py`) - Configurable LLM via HTTP
+4. **Persona** (`speak.py`) - Cloud LLM (OpenAI)
   - Applies Lyra's personality and speaking style
   - Natural, conversational output
   - Final answer returned to user
@@ -134,7 +147,7 @@ Relay → UI (returns final response)
 - OpenAI-compatible endpoint: `POST /v1/chat/completions`
 - Internal endpoint: `POST /chat`
 - Health check: `GET /_health`
- Async non-blocking calls to Cortex and Intake
+- Async non-blocking calls to Cortex
 - Shared request handler for code reuse
 - Comprehensive error handling
@@ -154,73 +167,70 @@ Relay → UI (returns final response)
 ### Reasoning Layer
-**Cortex** (v0.5):
+**Cortex** (v0.5.1):
 - Multi-stage reasoning pipeline (reflection → reasoning → refine → persona)
 - Flexible LLM backend routing via HTTP
 - Per-stage backend selection
 - Async processing throughout
- IntakeClient integration for short-term context
+- Embedded Intake module for short-term context
- `/reason`, `/ingest` (stub), `/health` endpoints
+- `/reason`, `/ingest`, `/health`, `/debug/sessions`, `/debug/summary` endpoints
 - Lenient error handling - never fails the chat pipeline
-**Intake** (v0.2):
+**Intake** (Embedded Module):
- Simplified single-level summarization
+- **Architectural change**: Now runs as Python module inside Cortex container
- Session-based circular buffer (200 exchanges max)
+- In-memory SESSIONS management (session_id → buffer)
- Background async summarization
+- Multi-level summarization: L1 (ultra-short), L5 (short), L10 (medium), L20 (detailed), L30 (full)
- Automatic NeoMem push
+- Deferred summarization strategy - summaries generated during `/reason` call
- No persistent log files (memory-only)
+- `bg_summarize()` is a logging stub - actual work deferred
- **Breaking change from v0.1**: Removed cascading summaries (L1, L2, L5, L10, L20, L30)
+- **Single-worker constraint**: SESSIONS requires single Uvicorn worker or Redis/shared storage
 **LLM Router**:
 - Dynamic backend selection via HTTP
 - Environment-driven configuration
- Support for vLLM, Ollama, OpenAI, custom endpoints
+- Support for llama.cpp, Ollama, OpenAI, custom endpoints
- Per-module backend preferences
+- Per-module backend preferences:
  - `CORTEX_LLM=SECONDARY` (Ollama for reasoning)
  - `INTAKE_LLM=PRIMARY` (llama.cpp for summarization)
  - `SPEAK_LLM=OPENAI` (Cloud for persona)
  - `NEOMEM_LLM=PRIMARY` (llama.cpp for memory operations)
 ### Beta Lyrae (RAG Memory DB) - Currently Disabled
 # Beta Lyrae (RAG Memory DB) - added 11-3-25
 - **RAG Knowledge DB - Beta Lyrae (sheliak)**
  - This module implements the **Retrieval-Augmented Generation (RAG)** layer for Project Lyra.
  - It serves as the long-term searchable memory store that Cortex and Relay can query for relevant context before reasoning or response generation.
-		The system uses:
+  - **Status**: Disabled in docker-compose.yml (v0.5.1)
-  - **ChromaDB** for persistent vector storage  
+
-  - **OpenAI Embeddings (`text-embedding-3-small`)** for semantic similarity  
+The system uses:
-  - **FastAPI** (port 7090) for the `/rag/search` REST endpoint  
+- **ChromaDB** for persistent vector storage
-  - Directory Layout
+- **OpenAI Embeddings (`text-embedding-3-small`)** for semantic similarity
-		rag/
+- **FastAPI** (port 7090) for the `/rag/search` REST endpoint
-		├── rag_chat_import.py # imports JSON chat logs
+
-		├── rag_docs_import.py # (planned) PDF/EPUB/manual importer
+Directory Layout:
-		├── rag_build.py # legacy single-folder builder
+```
-		├── rag_query.py # command-line query helper
+rag/
-		├── rag_api.py # FastAPI service providing /rag/search
+├── rag_chat_import.py    # imports JSON chat logs
-		├── chromadb/ # persistent vector store
+├── rag_docs_import.py    # (planned) PDF/EPUB/manual importer
-		├── chatlogs/ # organized source data
+├── rag_build.py          # legacy single-folder builder
-		│ ├── poker/
+├── rag_query.py          # command-line query helper
-		│ ├── work/
+├── rag_api.py            # FastAPI service providing /rag/search
-		│ ├── lyra/
+├── chromadb/             # persistent vector store
-		│ ├── personal/
+├── chatlogs/             # organized source data
-		│ └── ...
+│   ├── poker/
-		└── import.log # progress log for batch runs
+│   ├── work/
-  - **OpenAI chatlog importer.
+│   ├── lyra/
-	  - Takes JSON formatted chat logs and imports it to the RAG.
+│   ├── personal/
-	  - **fetures include:**
+│   └── ...
-	    - Recursive folder indexing with **category detection** from directory name  
+└── import.log            # progress log for batch runs
-		- Smart chunking for long messages (5 000 chars per slice)  
+```
-		- Automatic deduplication using SHA-1 hash of file + chunk
+
-		- Timestamps for both file modification and import time
+**OpenAI chatlog importer features:**
-		- Full progress logging via tqdm
+- Recursive folder indexing with **category detection** from directory name
-		- Safe to run in background with nohup … &
+- Smart chunking for long messages (5,000 chars per slice)
-		- Metadata per chunk:
+- Automatic deduplication using SHA-1 hash of file + chunk
-		  ```json
+- Timestamps for both file modification and import time
-		  {
+- Full progress logging via tqdm
-			"chat_id": "<sha1 of filename>",
+- Safe to run in background with `nohup … &`
 			"chunk_index": 0,
 			"source": "chatlogs/lyra/0002_cortex_LLMs_11-1-25.json",
 			"title": "cortex LLMs 11-1-25",
 			"role": "assistant",
 			"category": "lyra",
 			"type": "chat",
 			"file_modified": "2025-11-06T23:41:02",
 			"imported_at": "2025-11-07T03:55:00Z"
 		  }```
 ---
@@ -228,13 +238,16 @@ Relay → UI (returns final response)
 All services run in a single docker-compose stack with the following containers:
 **Active Services:**
 - **neomem-postgres** - PostgreSQL with pgvector extension (port 5432)
 - **neomem-neo4j** - Neo4j graph database (ports 7474, 7687)
 - **neomem-api** - NeoMem memory service (port 7077)
 - **relay** - Main orchestrator (port 7078)
- **cortex** - Reasoning engine (port 7081)
+- **cortex** - Reasoning engine with embedded Intake (port 7081)
- **intake** - Short-term memory summarization (port 7080) - currently disabled
+
- **rag** - RAG search service (port 7090) - currently disabled
+**Disabled Services:**
 - **intake** - No longer needed (embedded in Cortex as of v0.5.1)
 - **rag** - Beta Lyrae RAG service (port 7090) - currently disabled
 All containers communicate via the `lyra_net` Docker bridge network.
@@ -242,10 +255,10 @@ All containers communicate via the `lyra_net` Docker bridge network.
 The following LLM backends are accessed via HTTP (not part of docker-compose):
- **vLLM Server** (`http://10.0.0.43:8000`)
+- **llama.cpp Server** (`http://10.0.0.44:8080`)
  - AMD MI50 GPU-accelerated inference
  - Custom ROCm-enabled vLLM build
  - Primary backend for reasoning and refinement stages
  - Model path: `/model`
 - **Ollama Server** (`http://10.0.0.3:11434`)
  - RTX 3090 GPU-accelerated inference
@@ -265,16 +278,38 @@ The following LLM backends are accessed via HTTP (not part of docker-compose):
 ## Version History
-### v0.5.0 (2025-11-28) - Current Release
+### v0.5.1 (2025-12-11) - Current Release
 **Critical Intake Integration Fixes:**
 - ✅ Fixed `bg_summarize()` NameError preventing SESSIONS persistence
 - ✅ Fixed `/ingest` endpoint unreachable code
 - ✅ Added `cortex/intake/__init__.py` for proper package structure
 - ✅ Added diagnostic logging to verify SESSIONS singleton behavior
 - ✅ Added `/debug/sessions` and `/debug/summary` endpoints
 - ✅ Documented single-worker constraint in Dockerfile
 - ✅ Implemented lenient error handling (never fails chat pipeline)
 - ✅ Intake now embedded in Cortex - no longer standalone service
 **Architecture Changes:**
 - Intake module runs inside Cortex container as pure Python import
 - No HTTP calls between Cortex and Intake (internal function calls)
 - SESSIONS persist correctly in Uvicorn worker
 - Deferred summarization strategy (summaries generated during `/reason`)
 ### v0.5.0 (2025-11-28)
 - ✅ Fixed all critical API wiring issues
 - ✅ Added OpenAI-compatible endpoint to Relay (`/v1/chat/completions`)
 - ✅ Fixed Cortex → Intake integration
 - ✅ Added missing Python package `__init__.py` files
 - ✅ End-to-end message flow verified and working
 ### Infrastructure v1.0.0 (2025-11-26)
 - Consolidated 9 scattered `.env` files into single source of truth
 - Multi-backend LLM strategy implemented
 - Docker Compose consolidation
 - Created `.env.example` security templates
 ### v0.4.x (Major Rewire)
 - Cortex multi-stage reasoning pipeline
 - Intake v0.2 simplification
 - LLM router with multi-backend support
 - Major architectural restructuring
@@ -285,19 +320,30 @@ The following LLM backends are accessed via HTTP (not part of docker-compose):
 ---
-## Known Issues (v0.5.0)
+## Known Issues (v0.5.1)
 ### Critical (Fixed in v0.5.1)
 - ~~Intake SESSIONS not persisting~~ ✅ **FIXED**
 - ~~`bg_summarize()` NameError~~ ✅ **FIXED**
 - ~~`/ingest` endpoint unreachable code~~ ✅ **FIXED**
 ### Non-Critical
 - Session management endpoints not fully implemented in Relay
 - Intake service currently disabled in docker-compose.yml
 - RAG service currently disabled in docker-compose.yml
- Cortex `/ingest` endpoint is a stub
+- NeoMem integration in Relay not yet active (planned for v0.5.2)
 ### Operational Notes
 - **Single-worker constraint**: Cortex must run with single Uvicorn worker to maintain SESSIONS state
  - Multi-worker scaling requires migrating SESSIONS to Redis or shared storage
 - Diagnostic endpoints (`/debug/sessions`, `/debug/summary`) available for troubleshooting
 ### Future Enhancements
 - Re-enable RAG service integration
 - Implement full session persistence
 - Migrate SESSIONS to Redis for multi-worker support
 - Add request correlation IDs for tracing
- Comprehensive health checks
+- Comprehensive health checks across all services
 - NeoMem integration in Relay
 ---
@@ -305,21 +351,39 @@ The following LLM backends are accessed via HTTP (not part of docker-compose):
 ### Prerequisites
 - Docker + Docker Compose
- At least one HTTP-accessible LLM endpoint (vLLM, Ollama, or OpenAI API key)
+- At least one HTTP-accessible LLM endpoint (llama.cpp, Ollama, or OpenAI API key)
 ### Setup
-1. Copy `.env.example` to `.env` and configure your LLM backend URLs and API keys
+1. Copy `.env.example` to `.env` and configure your LLM backend URLs and API keys:
   ```bash
   # Required: Configure at least one LLM backend
   LLM_PRIMARY_URL=http://10.0.0.44:8080       # llama.cpp
   LLM_SECONDARY_URL=http://10.0.0.3:11434     # Ollama
   OPENAI_API_KEY=sk-...                        # OpenAI
   ```
 2. Start all services with docker-compose:
   ```bash
   docker-compose up -d
   ```
 3. Check service health:
   ```bash
   # Relay health
   curl http://localhost:7078/_health
   # Cortex health
   curl http://localhost:7081/health
   # NeoMem health
   curl http://localhost:7077/health
   ```
 4. Access the UI at `http://localhost:7078`
 ### Test
 **Test Relay → Cortex pipeline:**
 ```bash
 curl -X POST http://localhost:7078/v1/chat/completions \
  -H "Content-Type: application/json" \
@@ -329,15 +393,130 @@ curl -X POST http://localhost:7078/v1/chat/completions \
  }'
 ```
 **Test Cortex /ingest endpoint:**
 ```bash
 curl -X POST http://localhost:7081/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "test",
    "user_msg": "Hello",
    "assistant_msg": "Hi there!"
  }'
 ```
 **Inspect SESSIONS state:**
 ```bash
 curl http://localhost:7081/debug/sessions
 ```
 **Get summary for a session:**
 ```bash
 curl "http://localhost:7081/debug/summary?session_id=test"
 ```
 All backend databases (PostgreSQL and Neo4j) are automatically started as part of the docker-compose stack.
 ---
 ## Environment Variables
 ### LLM Backend Configuration
 **Backend URLs (Full API endpoints):**
 ```bash
 LLM_PRIMARY_URL=http://10.0.0.44:8080           # llama.cpp
 LLM_PRIMARY_MODEL=/model
 LLM_SECONDARY_URL=http://10.0.0.3:11434         # Ollama
 LLM_SECONDARY_MODEL=qwen2.5:7b-instruct-q4_K_M
 LLM_OPENAI_URL=https://api.openai.com/v1
 LLM_OPENAI_MODEL=gpt-4o-mini
 OPENAI_API_KEY=sk-...
 ```
 **Module-specific backend selection:**
 ```bash
 CORTEX_LLM=SECONDARY      # Use Ollama for reasoning
 INTAKE_LLM=PRIMARY        # Use llama.cpp for summarization
 SPEAK_LLM=OPENAI          # Use OpenAI for persona
 NEOMEM_LLM=PRIMARY        # Use llama.cpp for memory
 UI_LLM=OPENAI             # Use OpenAI for UI
 RELAY_LLM=PRIMARY         # Use llama.cpp for relay
 ```
 ### Database Configuration
 ```bash
 POSTGRES_USER=neomem
 POSTGRES_PASSWORD=neomempass
 POSTGRES_DB=neomem
 POSTGRES_HOST=neomem-postgres
 POSTGRES_PORT=5432
 NEO4J_URI=bolt://neomem-neo4j:7687
 NEO4J_USERNAME=neo4j
 NEO4J_PASSWORD=neomemgraph
 ```
 ### Service URLs (Internal Docker Network)
 ```bash
 NEOMEM_API=http://neomem-api:7077
 CORTEX_API=http://cortex:7081
 CORTEX_REASON_URL=http://cortex:7081/reason
 CORTEX_INGEST_URL=http://cortex:7081/ingest
 RELAY_URL=http://relay:7078
 ```
 ### Feature Flags
 ```bash
 CORTEX_ENABLED=true
 MEMORY_ENABLED=true
 PERSONA_ENABLED=false
 DEBUG_PROMPT=true
 VERBOSE_DEBUG=true
 ```
 For complete environment variable reference, see [ENVIRONMENT_VARIABLES.md](ENVIRONMENT_VARIABLES.md).
 ---
 ## Documentation
- See [CHANGELOG.md](CHANGELOG.md) for detailed version history
+- [CHANGELOG.md](CHANGELOG.md) - Detailed version history
- See `ENVIRONMENT_VARIABLES.md` for environment variable reference
+- [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Comprehensive project overview for AI context
- Additional information available in the Trilium docs
+- [ENVIRONMENT_VARIABLES.md](ENVIRONMENT_VARIABLES.md) - Environment variable reference
 - [DEPRECATED_FILES.md](DEPRECATED_FILES.md) - Deprecated files and migration guide
 ---
 ## Troubleshooting
 ### SESSIONS not persisting
 **Symptom:** Intake buffer always shows 0 exchanges, summaries always empty.
 **Solution (Fixed in v0.5.1):**
 - Ensure `cortex/intake/__init__.py` exists
 - Check Cortex logs for `[Intake Module Init]` message showing SESSIONS object ID
 - Verify single-worker mode (Dockerfile: `uvicorn main:app --workers 1`)
 - Use `/debug/sessions` endpoint to inspect current state
 ### Cortex connection errors
 **Symptom:** Relay can't reach Cortex, 502 errors.
 **Solution:**
 - Verify Cortex container is running: `docker ps | grep cortex`
 - Check Cortex health: `curl http://localhost:7081/health`
 - Verify environment variables: `CORTEX_REASON_URL=http://cortex:7081/reason`
 - Check docker network: `docker network inspect lyra_net`
 ### LLM backend timeouts
 **Symptom:** Reasoning stage hangs or times out.
 **Solution:**
 - Verify LLM backend is running and accessible
 - Check LLM backend health: `curl http://10.0.0.44:8080/health`
 - Increase timeout in llm_router.py if using slow models
 - Check logs for specific backend errors
 ---
@@ -356,6 +535,8 @@ NeoMem is a derivative work based on Mem0 OSS (Apache 2.0).
 - All services communicate via Docker internal networking on the `lyra_net` bridge
 - History and entity graphs are managed via PostgreSQL + Neo4j
 - LLM backends are accessed via HTTP and configured in `.env`
 - Intake module is imported internally by Cortex (no HTTP communication)
 - SESSIONS state is maintained in-memory within Cortex container
 ---
@@ -391,3 +572,38 @@ NeoMem is a derivative work based on Mem0 OSS (Apache 2.0).
     }'
   ```
 ---
 ## Development Notes
 ### Cortex Architecture (v0.5.1)
 - Cortex contains embedded Intake module at `cortex/intake/`
 - Intake is imported as: `from intake.intake import add_exchange_internal, SESSIONS`
 - SESSIONS is a module-level global dictionary (singleton pattern)
 - Single-worker constraint required to maintain SESSIONS state
 - Diagnostic endpoints available for debugging: `/debug/sessions`, `/debug/summary`
 ### Adding New LLM Backends
 1. Add backend URL to `.env`:
   ```bash
   LLM_CUSTOM_URL=http://your-backend:port
   LLM_CUSTOM_MODEL=model-name
   ```
 2. Configure module to use new backend:
   ```bash
   CORTEX_LLM=CUSTOM
   ```
 3. Restart Cortex container:
   ```bash
   docker-compose restart cortex
   ```
 ### Debugging Tips
 - Enable verbose logging: `VERBOSE_DEBUG=true` in `.env`
 - Check Cortex logs: `docker logs cortex -f`
 - Inspect SESSIONS: `curl http://localhost:7081/debug/sessions`
 - Test summarization: `curl "http://localhost:7081/debug/summary?session_id=test"`
 - Check Relay logs: `docker logs relay -f`
 - Monitor Docker network: `docker network inspect lyra_net`
@@ -84,7 +84,7 @@ app.get("/_health", (_, res) => {
 // -----------------------------------------------------
 app.post("/v1/chat/completions", async (req, res) => {
  try {
-    const session_id = req.body.session_id || req.body.user || "default";
+    const session_id = req.body.session_id || req.body.sessionId || req.body.user || "default";
    const messages = req.body.messages || [];
    const lastMessage = messages[messages.length - 1];
    const user_msg = lastMessage?.content || "";
@@ -151,6 +151,25 @@ app.post("/chat", async (req, res) => {
  }
 });
 // -----------------------------------------------------
 // SESSION ENDPOINTS (for UI)
 // -----------------------------------------------------
 // In-memory session storage (could be replaced with a database)
 const sessions = new Map();
 app.get("/sessions/:id", (req, res) => {
  const sessionId = req.params.id;
  const history = sessions.get(sessionId) || [];
  res.json(history);
 });
 app.post("/sessions/:id", (req, res) => {
  const sessionId = req.params.id;
  const history = req.body;
  sessions.set(sessionId, history);
  res.json({ ok: true, saved: history.length });
 });
 // -----------------------------------------------------
 app.listen(PORT, () => {
  console.log(`Relay is online on port ${PORT}`);
@@ -51,7 +51,7 @@
  </div>
  <script>
-    const RELAY_BASE = "http://10.0.0.40:7078";
+    const RELAY_BASE = "http://10.0.0.41:7078";
    const API_URL = `${RELAY_BASE}/v1/chat/completions`;
 	function generateSessionId() {
@@ -282,11 +282,17 @@ JSON only. No text outside JSON.
    try:
        llm_response = await call_llm(
            prompt,
            backend=INTAKE_LLM,
            temperature=0.2
        )
        print(f"[Intake] LLM response length: {len(llm_response) if llm_response else 0}")
        print(f"[Intake] LLM response preview: {llm_response[:200] if llm_response else '(empty)'}")
        # LLM should return JSON, parse it
        if not llm_response or not llm_response.strip():
            raise ValueError("Empty response from LLM")
        summary = json.loads(llm_response)
        return {
@@ -1,7 +1,10 @@
 # llm_router.py
 import os
-import requests
+import httpx
 import json
 import logging
 logger = logging.getLogger(__name__)
 # ------------------------------------------------------------
 # Load backend registry from root .env
@@ -33,6 +36,9 @@ BACKENDS = {
 DEFAULT_BACKEND = "PRIMARY"
 # Reusable async HTTP client
 http_client = httpx.AsyncClient(timeout=120.0)
 # ------------------------------------------------------------
 # Public call
@@ -57,18 +63,28 @@ async def call_llm(
        raise RuntimeError(f"Backend '{backend}' missing url/model in env")
    # -------------------------------
-    # Provider: VLLM (your MI50)
+    # Provider: MI50 (llama.cpp server)
    # -------------------------------
-    if provider == "vllm":
+    if provider == "mi50":
        payload = {
            "model": model,
            "prompt": prompt,
-            "max_tokens": max_tokens,
+            "n_predict": max_tokens,
            "temperature": temperature
        }
-        r = requests.post(url, json=payload, timeout=120)
+        try:
-        data = r.json()
+            r = await http_client.post(f"{url}/completion", json=payload)
-        return data["choices"][0]["text"]
+            r.raise_for_status()
            data = r.json()
            return data.get("content", "")
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (mi50): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from mi50: {e}")
            raise RuntimeError(f"Invalid response format (mi50): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling mi50: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (mi50): {type(e).__name__}: {str(e)}")
    # -------------------------------
    # Provider: OLLAMA (your 3090)
@@ -79,13 +95,22 @@ async def call_llm(
            "messages": [
                {"role": "user", "content": prompt}
            ],
-            "stream": False        # <-- critical fix
+            "stream": False
        }
-
+        try:
-        r = requests.post(f"{url}/api/chat", json=payload, timeout=120)
+            r = await http_client.post(f"{url}/api/chat", json=payload)
-        data = r.json()
+            r.raise_for_status()
-
+            data = r.json()
-        return data["message"]["content"]
+            return data["message"]["content"]
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (ollama): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from ollama: {e}")
            raise RuntimeError(f"Invalid response format (ollama): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling ollama: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (ollama): {type(e).__name__}: {str(e)}")
    # -------------------------------
@@ -104,9 +129,20 @@ async def call_llm(
            "temperature": temperature,
            "max_tokens": max_tokens,
        }
-        r = requests.post(f"{url}/chat/completions", json=payload, headers=headers, timeout=120)
+        try:
-        data = r.json()
+            r = await http_client.post(f"{url}/chat/completions", json=payload, headers=headers)
-        return data["choices"][0]["message"]["content"]
+            r.raise_for_status()
            data = r.json()
            return data["choices"][0]["message"]["content"]
        except httpx.HTTPError as e:
            logger.error(f"HTTP error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"LLM API error (openai): {type(e).__name__}: {str(e)}")
        except (KeyError, json.JSONDecodeError) as e:
            logger.error(f"Response parsing error from openai: {e}")
            raise RuntimeError(f"Invalid response format (openai): {e}")
        except Exception as e:
            logger.error(f"Unexpected error calling openai: {type(e).__name__}: {str(e)}")
            raise RuntimeError(f"Unexpected error (openai): {type(e).__name__}: {str(e)}")
    # -------------------------------
    # Unknown provider
@@ -97,6 +97,21 @@ services:
    networks:
      - lyra_net
  # ============================================================
  # UI Server
  # ============================================================
  lyra-ui:
    image: nginx:alpine
    container_name: lyra-ui
    restart: unless-stopped
    ports:
      - "8081:80"
    volumes:
      - ./core/ui:/usr/share/nginx/html:ro
    networks:
      - lyra_net
  # ============================================================
  # Cortex
  # ============================================================
@@ -0,0 +1,280 @@
 `docs/ARCHITECTURE_v0.6.0.md`
 This reflects **everything we clarified**, expressed cleanly and updated to the new 3-brain design.
 ---
 # **Cortex v0.6.0 — Cognitive Architecture Overview**
 *Last updated: Dec 2025*
 ## **Summary**
 Cortex v0.6.0 evolves from a linear “reflection → reasoning → refine → persona” pipeline into a **three-layer cognitive system** modeled after human cognition:
 1. **Autonomy Core** — Lyra’s self-model (identity, mood, long-term goals)
 2. **Inner Monologue** — Lyra’s private narrator (self-talk + internal reflection)
 3. **Executive Agent (DeepSeek)** — Lyra’s task-oriented decision-maker
 Cortex itself now becomes the **central orchestrator**, not the whole mind. It routes user messages through these layers and produces the final outward response via the persona system.
 ---
 # **Chain concept**
    User > Relay > Cortex intake > Inner self > Cortex > Exec (deepseek) > Cortex > persona > relay > user And inner self
               USER
                 │
                 ▼
              RELAY
   (sessions, logging, routing)
                 │
                 ▼
  ┌──────────────────────────────────┐
  │              CORTEX              │
  │ Intake → Reflection → Exec → Reason → Refine │
  └───────────────┬──────────────────┘
                  │ self_state
                  ▼
         INNER SELF (monologue)
                  │
                  ▼
            AUTONOMY CORE
           (long-term identity)
                  ▲
                  │
         Persona Layer (speak)
                  │
                  ▼
                RELAY
                  │
                  ▼
                 USER
 # **High-level Architecture**
 ```
               Autonomy Core (Self-Model)
      ┌────────────────────────────────────────┐
      │ mood, identity, goals, emotional state│
      │ updated outside Cortex by inner monologue│
      └─────────────────────┬──────────────────┘
                            │
                            ▼
               Inner Monologue (Self-Talk Loop)
      ┌────────────────────────────────────────┐
      │ Interprets events in language          │
      │ Updates Autonomy Core                  │
      │ Sends state-signals INTO Cortex        │
      └─────────────────────┬──────────────────┘
                            │
                            ▼
              Cortex (Task Brain / Router)
   ┌────────────────────────────────────────────────────────┐
   │ Intake → Reflection → Exec Agent → Reason → Refinement │
   │            ↑                                  │        │
   │            │                                  ▼        │
   │     Receives state from                Persona Output  │
   │        inner self                       (Lyra’s voice) │
   └────────────────────────────────────────────────────────┘
 ```
 The **user interacts only with the Persona layer**.
 Inner Monologue and Autonomy Core never speak directly to the user.
 ---
 # **Component Breakdown**
 ## **1. Autonomy Core (Self-Model)**
 *Not inside Cortex.*
 A persistent JSON/state machine representing Lyra’s ongoing inner life:
 * `mood`
 * `focus_mode`
 * `confidence`
 * `identity_traits`
 * `relationship_memory`
 * `long_term_goals`
 * `emotional_baseline`
 The Autonomy Core:
 * Is updated by Inner Monologue
 * Exposes its state to Cortex via a simple `get_state()` API
 * Never speaks to the user directly
 * Does not run LLMs itself
 It is the **structure** of self, not the thoughts.
 ---
 ## **2. Inner Monologue (Narrating, Private Mind)**
 *New subsystem in v0.6.0.*
 This module:
 * Reads Cortex summaries (intake, reflection, persona output)
 * Generates private self-talk (using an LLM, typically DeepSeek)
 * Updates the Autonomy Core
 * Produces a **self-state packet** for Cortex to use during task execution
 Inner Monologue is like:
 > “Brian is asking about X.
 > I should shift into a focused, serious tone.
 > I feel confident about this area.”
 It **never** outputs directly to the user.
 ### Output schema (example):
 ```json
 {
  "mood": "focused",
  "persona_bias": "clear",
  "confidence_delta": +0.05,
  "stance": "analytical",
  "notes_to_cortex": [
     "Reduce playfulness",
     "Prioritize clarity",
     "Recall project memory"
  ]
 }
 ```
 ---
 ## **3. Executive Agent (DeepSeek Director Mode)**
 Inside Cortex.
 This is Lyra’s **prefrontal cortex** — the task-oriented planner that decides how to respond to the current user message.
 Input to Executive Agent:
 * User message
 * Intake summary
 * Reflection notes
 * **Self-state packet** from Inner Monologue
 It outputs a **plan**, not a final answer:
 ```json
 {
  "action": "WRITE_NOTE",
  "tools": ["memory_search"],
  "tone": "focused",
  "steps": [
     "Search relevant project notes",
     "Synthesize into summary",
     "Draft actionable update"
  ]
 }
 ```
 Cortex then executes this plan.
 ---
 # **Cortex Pipeline (v0.6.0)**
 Cortex becomes the orchestrator for the entire sequence:
 ### **0. Intake**
 Parse the user message, extract relevant features.
 ### **1. Reflection**
 Lightweight summarization (unchanged).
 Output used by both Inner Monologue and Executive Agent.
 ### **2. Inner Monologue Update (parallel)**
 Reflection summary is sent to Inner Self, which:
 * updates Autonomy Core
 * returns `self_state` to Cortex
 ### **3. Executive Agent (DeepSeek)**
 Given:
 * user message
 * reflection summary
 * autonomy self_state
  → produce a **task plan**
 ### **4. Reasoning**
 Carries out the plan:
 * tool calls
 * retrieval
 * synthesis
 ### **5. Refinement**
 Polish the draft, ensure quality, follow constraints.
 ### **6. Persona (speak.py)**
 Final transformation into Lyra’s voice.
 Persona now uses:
 * self_state (mood, tone)
 * constraints from Executive Agent
 ### **7. User Response**
 Persona output is delivered to the user.
 ### **8. Inner Monologue Post-Update**
 Cortex sends the final answer BACK to inner self for:
 * narrative continuity
 * emotional adjustment
 * identity update
 ---
 # **Key Conceptual Separation**
 These three layers must remain distinct:
 | Layer               | Purpose                                                 |
 | ------------------- | ------------------------------------------------------- |
 | **Autonomy Core**   | Lyra’s identity + emotional continuity                  |
 | **Inner Monologue** | Lyra’s private thoughts, interpretation, meaning-making |
 | **Executive Agent** | Deciding what to *do* for the user message              |
 | **Cortex**          | Executing the plan                                      |
 | **Persona**         | Outward voice (what the user actually hears)            |
 The **user only interacts with Persona.**
 Inner Monologue and Autonomy Core are internal cognitive machinery.
 ---
 # **What This Architecture Enables**
 * Emotional continuity
 * Identity stability
 * Agentic decision-making
 * Multi-model routing
 * Context-aware tone
 * Internal narrative
 * Proactive behavioral shifts
 * Human-like cognition
 This design turns Cortex from a simple pipeline into the **center of a functional artificial mind**.
@@ -0,0 +1,354 @@
 Here you go — **ARCHITECTURE_v0.6.1.md**, clean, structured, readable, and aligned exactly with the new mental model where **Inner Self is the core agent** the user interacts with.
 No walls of text — just the right amount of detail.
 ---
 # **ARCHITECTURE_v0.6.1 — Lyra Cognitive System**
 > **Core change from v0.6.0 → v0.6.1:**
 > **Inner Self becomes the primary conversational agent**
 > (the model the user is *actually* talking to),
 > while Executive and Cortex models support the Self rather than drive it.
 ---
 # **1. High-Level Overview**
 Lyra v0.6.1 is composed of **three cognitive layers** and **one expression layer**, plus an autonomy module for ongoing identity continuity.
 ```
 USER
  ↓
 Relay (I/O)
  ↓
 Cortex Intake (context snapshot)
  ↓
 INNER SELF  ←→  EXECUTIVE MODEL (DeepSeek)
  ↓
 Cortex Chat Model (draft language)
  ↓
 Persona Model (Lyra’s voice)
  ↓
 Relay → USER
  ↓
 Inner Self updates Autonomy Core (self-state)
 ```
 ---
 # **2. Roles of Each Layer**
 ---
 ## **2.1 Inner Self (Primary Conversational Agent)**
 The Self is Lyra’s “seat of consciousness.”
 This layer:
 * Interprets every user message
 * Maintains internal monologue
 * Chooses emotional stance (warm, blunt, focused, chaotic)
 * Decides whether to think deeply or reply quickly
 * Decides whether to consult the Executive model
 * Forms a **response intent**
 * Provides tone and meta-guidance to the Persona layer
 * Updates self-state (mood, trust, narrative identity)
 Inner Self is the thing the **user is actually talking to.**
 Inner Self does **NOT** generate paragraphs of text —
 it generates *intent*:
 ```
 {
  "intent": "comfort Brian and explain the error simply",
  "tone": "gentle",
  "depth": "medium",
  "consult_exec": true
 }
 ```
 ---
 ## **2.2 Executive Model (DeepSeek Reasoner)**
 This model is the **thinking engine** Inner Self consults when necessary.
 It performs:
 * planning
 * deep reasoning
 * tool selection
 * multi-step logic
 * explanation chains
 It never speaks directly to the user.
 It returns a **plan**, not a message:
 ```
 {
  "plan": [
    "Identify error",
    "Recommend restart",
    "Reassure user"
  ],
  "confidence": 0.86
 }
 ```
 Inner Self can follow or override the plan.
 ---
 ## **2.3 Cortex Chat Model (Draft Generator)**
 This is the **linguistic engine**.
 It converts Inner Self’s intent (plus Executive’s plan if provided) into actual language:
 Input:
 ```
 intent + optional plan + context snapshot
 ```
 Output:
 ```
 structured draft paragraph
 ```
 This model must be:
 * instruction-tuned
 * coherent
 * factual
 * friendly
 Examples: GPT-4o-mini, Qwen-14B-instruct, Mixtral chat, etc.
 ---
 ## **2.4 Persona Model (Lyra’s Voice)**
 This is the **expression layer** — the mask, the tone, the identity.
 It takes:
 * the draft language
 * the Self’s tone instructions
 * the narrative state (from Autonomy Core)
 * prior persona shaping rules
 And transforms the text into:
 * Lyra’s voice
 * Lyra’s humor
 * Lyra’s emotional texture
 * Lyra’s personality consistency
 Persona does not change the *meaning* — only the *presentation*.
 ---
 # **3. Message Flow (Full Pipeline)**
 A clean version, step-by-step:
 ---
 ### **1. USER → Relay**
 Relay attaches metadata (session, timestamp) and forwards to Cortex.
 ---
 ### **2. Intake → Context Snapshot**
 Cortex creates:
 * cleaned message
 * recent context summary
 * memory matches (RAG)
 * time-since-last
 * conversation mode
 ---
 ### **3. Inner Self Receives Snapshot**
 Inner Self:
 * interprets the user’s intent
 * updates internal monologue
 * decides how Lyra *feels* about the input
 * chooses whether to consult Executive
 * produces an **intent packet**
 ---
 ### **4. (Optional) Inner Self Consults Executive Model**
 Inner Self sends the situation to DeepSeek:
 ```
 "Given Brian's message and my context, what is the best plan?"
 ```
 DeepSeek returns:
 * a plan
 * recommended steps
 * rationale
 * optional tool suggestions
 Inner Self integrates the plan or overrides it.
 ---
 ### **5. Inner Self → Cortex Chat Model**
 Self creates an **instruction packet**:
 ```
 {
  "intent": "...",
  "tone": "...",
  "plan": [...],
  "context_summary": {...}
 }
 ```
 Cortex chat model produces the draft text.
 ---
 ### **6. Persona Model Transforms the Draft**
 Persona takes draft → produces final Lyra-styled output.
 Persona ensures:
 * emotional fidelity
 * humor when appropriate
 * warmth / sharpness depending on state
 * consistent narrative identity
 ---
 ### **7. Relay Sends Response to USER**
 ---
 ### **8. Inner Self Updates Autonomy Core**
 Inner Self receives:
 * the action taken
 * the emotional tone used
 * any RAG results
 * narrative significance
 And updates:
 * mood
 * trust memory
 * identity drift
 * ongoing narrative
 * stable traits
 This becomes part of her evolving self.
 ---
 # **4. Cognitive Ownership Summary**
 ### Inner Self
 **Owns:**
 * decision-making
 * feeling
 * interpreting
 * intent
 * tone
 * continuity of self
 * mood
 * monologue
 * overrides
 ### Executive (DeepSeek)
 **Owns:**
 * logic
 * planning
 * structure
 * analysis
 * tool selection
 ### Cortex Chat Model
 **Owns:**
 * language generation
 * factual content
 * clarity
 ### Persona
 **Owns:**
 * voice
 * flavor
 * style
 * emotional texture
 * social expression
 ---
 # **5. Why v0.6.1 is Better**
 * More human
 * More natural
 * Allows spontaneous responses
 * Allows deep thinking when needed
 * Separates “thought” from “speech”
 * Gives Lyra a *real self*
 * Allows much more autonomy later
 * Matches your brain’s actual structure
 ---
 # **6. Migration Notes from v0.6.0**
 Nothing is deleted.
 Everything is **rearranged** so that meaning, intent, and tone flow correctly.
 Main changes:
 * Inner Self now initiates the response, rather than merely influencing it.
 * Executive is secondary, not primary.
 * Persona becomes an expression layer, not a content layer.
 * Cortex Chat Model handles drafting, not cognition.
 The whole system becomes both more powerful and easier to reason about.
 ---
 If you want, I can also generate:
 ### ✔ the updated directory structure
 ### ✔ the updated function-level API contracts
 ### ✔ the v0.6.1 llm_router configuration
 ### ✔ code scaffolds for inner_self.py and autonomy_core.py
 ### ✔ the call chain diagrams (ASCII or PNG)
 Just say **“continue v0.6.1”** and I’ll build the next layer.
@@ -0,0 +1,39 @@
 Request Flow Chain
 1. UI (Frontend)
   ↓ sends HTTP POST to
 2. Relay Service (Node.js - server.js)
   Location: /home/serversdown/project-lyra/core/relay/server.js
   Port: 7078
   Endpoint: POST /v1/chat/completions
   ↓ calls handleChatRequest() which posts to
 3. Cortex Service - Reason Endpoint (Python FastAPI - router.py)
   Location: /home/serversdown/project-lyra/cortex/router.py
   Port: 7081
   Endpoint: POST /reason
   Function: run_reason() at line 126
   ↓ calls
 4. Cortex Reasoning Module (reasoning.py)
   Location: /home/serversdown/project-lyra/cortex/reasoning/reasoning.py
   Function: reason_check() at line 188
   ↓ calls
 5. LLM Router (llm_router.py)
   Location: /home/serversdown/project-lyra/cortex/llm/llm_router.py
   Function: call_llm()
   - Gets backend from env: CORTEX_LLM=PRIMARY (from .env line 29)
   - Looks up PRIMARY config which has provider="mi50" (from .env line 13)
   - Routes to the mi50 provider handler (line 62-70)
   ↓ makes HTTP POST to
 6. MI50 LLM Server (llama.cpp)
   Location: http://10.0.0.44:8080
   Endpoint: POST /completion
   Hardware: AMD MI50 GPU running DeepSeek model
 Key Configuration Points
 Backend Selection: .env:29 sets CORTEX_LLM=PRIMARY
 Provider Name: .env:13 sets LLM_PRIMARY_PROVIDER=mi50
 Server URL: .env:14 sets LLM_PRIMARY_URL=http://10.0.0.44:8080
 Provider Handler: llm_router.py:62-70 implements the mi50 provider
@@ -0,0 +1,925 @@
 # Project Lyra — Comprehensive AI Context Summary
 **Version:** v0.5.1 (2025-12-11)
 **Status:** Production-ready modular AI companion system
 **Purpose:** Memory-backed conversational AI with multi-stage reasoning, persistent context, and modular LLM backend architecture
 ---
 ## Executive Summary
 Project Lyra is a **self-hosted AI companion system** designed to overcome the limitations of typical chatbots by providing:
 - **Persistent long-term memory** (NeoMem: PostgreSQL + Neo4j graph storage)
 - **Multi-stage reasoning pipeline** (Cortex: reflection → reasoning → refinement → persona)
 - **Short-term context management** (Intake: session-based summarization embedded in Cortex)
 - **Flexible LLM backend routing** (supports llama.cpp, Ollama, OpenAI, custom endpoints)
 - **OpenAI-compatible API** (drop-in replacement for chat applications)
 **Core Philosophy:** Like a human brain has different regions for different functions, Lyra has specialized modules that work together. She's not just a chatbot—she's a notepad, schedule, database, co-creator, and collaborator with her own executive function.
 ---
 ## Quick Context for AI Assistants
 If you're an AI being given this project to work on, here's what you need to know:
 ### What This Project Does
 Lyra is a conversational AI system that **remembers everything** across sessions. When a user says something in passing, Lyra stores it, contextualizes it, and can recall it later. She can:
 - Track project progress over time
 - Remember user preferences and past conversations
 - Reason through complex questions using multiple LLM calls
 - Apply a consistent personality across all interactions
 - Integrate with multiple LLM backends (local and cloud)
 ### Current Architecture (v0.5.1)
 ```
 User → Relay (Express/Node.js, port 7078)
  ↓
 Cortex (FastAPI/Python, port 7081)
  ├─ Intake module (embedded, in-memory SESSIONS)
  ├─ 4-stage reasoning pipeline
  └─ Multi-backend LLM router
  ↓
 NeoMem (FastAPI/Python, port 7077)
  ├─ PostgreSQL (vector storage)
  └─ Neo4j (graph relationships)
 ```
 ### Key Files You'll Work With
 **Backend Services:**
 - [cortex/router.py](cortex/router.py) - Main Cortex routing logic (306 lines, `/reason`, `/ingest` endpoints)
 - [cortex/intake/intake.py](cortex/intake/intake.py) - Short-term memory module (367 lines, SESSIONS management)
 - [cortex/reasoning/reasoning.py](cortex/reasoning/reasoning.py) - Draft answer generation
 - [cortex/reasoning/refine.py](cortex/reasoning/refine.py) - Answer refinement
 - [cortex/reasoning/reflection.py](cortex/reasoning/reflection.py) - Meta-awareness notes
 - [cortex/persona/speak.py](cortex/persona/speak.py) - Personality layer
 - [cortex/llm/llm_router.py](cortex/llm/llm_router.py) - LLM backend selector
 - [core/relay/server.js](core/relay/server.js) - Main orchestrator (Node.js)
 - [neomem/main.py](neomem/main.py) - Long-term memory API
 **Configuration:**
 - [.env](.env) - Root environment variables (LLM backends, databases, API keys)
 - [cortex/.env](cortex/.env) - Cortex-specific overrides
 - [docker-compose.yml](docker-compose.yml) - Service definitions (152 lines)
 **Documentation:**
 - [CHANGELOG.md](CHANGELOG.md) - Complete version history (836 lines, chronological format)
 - [README.md](README.md) - User-facing documentation (610 lines)
 - [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - This file
 ### Recent Critical Fixes (v0.5.1)
 The most recent work fixed a critical bug where Intake's SESSIONS buffer wasn't persisting:
 1. **Fixed**: `bg_summarize()` was only a TYPE_CHECKING stub → implemented as logging stub
 2. **Fixed**: `/ingest` endpoint had unreachable code → removed early return, added lenient error handling
 3. **Added**: `cortex/intake/__init__.py` → proper Python package structure
 4. **Added**: Diagnostic endpoints `/debug/sessions` and `/debug/summary` for troubleshooting
 **Key Insight**: Intake is no longer a standalone service—it's embedded in Cortex as a Python module. SESSIONS must persist in a single Uvicorn worker (no multi-worker support without Redis).
 ---
 ## Architecture Deep Dive
 ### Service Topology (Docker Compose)
 **Active Containers:**
 1. **relay** (Node.js/Express, port 7078)
   - Entry point for all user requests
   - OpenAI-compatible `/v1/chat/completions` endpoint
   - Routes to Cortex for reasoning
   - Async calls to Cortex `/ingest` after response
 2. **cortex** (Python/FastAPI, port 7081)
   - Multi-stage reasoning pipeline
   - Embedded Intake module (no HTTP, direct Python imports)
   - Endpoints: `/reason`, `/ingest`, `/health`, `/debug/sessions`, `/debug/summary`
 3. **neomem-api** (Python/FastAPI, port 7077)
   - Long-term memory storage
   - Fork of Mem0 OSS (fully local, no external SDK)
   - Endpoints: `/memories`, `/search`, `/health`
 4. **neomem-postgres** (PostgreSQL + pgvector, port 5432)
   - Vector embeddings storage
   - Memory history records
 5. **neomem-neo4j** (Neo4j, ports 7474/7687)
   - Graph relationships between memories
   - Entity extraction and linking
 **Disabled Services:**
 - `intake` - No longer needed (embedded in Cortex as of v0.5.1)
 - `rag` - Beta Lyrae RAG service (planned re-enablement)
 ### External LLM Backends (HTTP APIs)
 **PRIMARY Backend** - llama.cpp @ `http://10.0.0.44:8080`
 - AMD MI50 GPU-accelerated inference
 - Model: `/model` (path-based routing)
 - Used for: Reasoning, refinement, summarization
 **SECONDARY Backend** - Ollama @ `http://10.0.0.3:11434`
 - RTX 3090 GPU-accelerated inference
 - Model: `qwen2.5:7b-instruct-q4_K_M`
 - Used for: Configurable per-module
 **CLOUD Backend** - OpenAI @ `https://api.openai.com/v1`
 - Cloud-based inference
 - Model: `gpt-4o-mini`
 - Used for: Reflection, persona layers
 **FALLBACK Backend** - Local @ `http://10.0.0.41:11435`
 - CPU-based inference
 - Model: `llama-3.2-8b-instruct`
 - Used for: Emergency fallback
 ### Data Flow (Request Lifecycle)
 ```
 1. User sends message → Relay (/v1/chat/completions)
   ↓
 2. Relay → Cortex (/reason)
   ↓
 3. Cortex calls Intake module (internal Python)
   - Intake.summarize_context(session_id, exchanges)
   - Returns L1/L5/L10/L20/L30 summaries
   ↓
 4. Cortex 4-stage pipeline:
   a. reflection.py → Meta-awareness notes (CLOUD backend)
      - "What is the user really asking?"
      - Returns JSON: {"notes": [...]}
   b. reasoning.py → Draft answer (PRIMARY backend)
      - Uses context from Intake
      - Integrates reflection notes
      - Returns draft text
   c. refine.py → Refined answer (PRIMARY backend)
      - Polishes draft for clarity
      - Ensures factual consistency
      - Returns refined text
   d. speak.py → Persona layer (CLOUD backend)
      - Applies Lyra's personality
      - Natural, conversational tone
      - Returns final answer
   ↓
 5. Cortex → Relay (returns persona answer)
   ↓
 6. Relay → Cortex (/ingest) [async, non-blocking]
   - Sends (session_id, user_msg, assistant_msg)
   - Cortex calls add_exchange_internal()
   - Appends to SESSIONS[session_id]["buffer"]
   ↓
 7. Relay → User (returns final response)
   ↓
 8. [Planned] Relay → NeoMem (/memories) [async]
   - Store conversation in long-term memory
 ```
 ### Intake Module Architecture (v0.5.1)
 **Location:** `cortex/intake/`
 **Key Change:** Intake is now **embedded in Cortex** as a Python module, not a standalone service.
 **Import Pattern:**
 ```python
 from intake.intake import add_exchange_internal, SESSIONS, summarize_context
 ```
 **Core Data Structure:**
 ```python
 SESSIONS: dict[str, dict] = {}
 # Structure:
 SESSIONS[session_id] = {
    "buffer": deque(maxlen=200),  # Circular buffer of exchanges
    "created_at": datetime
 }
 # Each exchange in buffer:
 {
    "session_id": "...",
    "user_msg": "...",
    "assistant_msg": "...",
    "timestamp": "2025-12-11T..."
 }
 ```
 **Functions:**
 1. **`add_exchange_internal(exchange: dict)`**
   - Adds exchange to SESSIONS buffer
   - Creates new session if needed
   - Calls `bg_summarize()` stub
   - Returns `{"ok": True, "session_id": "..."}`
 2. **`summarize_context(session_id: str, exchanges: list[dict])`** [async]
   - Generates L1/L5/L10/L20/L30 summaries via LLM
   - Called during `/reason` endpoint
   - Returns multi-level summary dict
 3. **`bg_summarize(session_id: str)`**
   - **Stub function** - logs only, no actual work
   - Defers summarization to `/reason` call
   - Exists to prevent NameError
 **Critical Constraint:** SESSIONS is a module-level global dict. This requires **single-worker Uvicorn** mode. Multi-worker deployments need Redis or shared storage.
 **Diagnostic Endpoints:**
 - `GET /debug/sessions` - Inspect all SESSIONS (object ID, buffer sizes, recent exchanges)
 - `GET /debug/summary?session_id=X` - Test summarization for a session
 ---
 ## Environment Configuration
 ### LLM Backend Registry (Multi-Backend Strategy)
 **Root `.env` defines all backend OPTIONS:**
 ```bash
 # PRIMARY Backend (llama.cpp)
 LLM_PRIMARY_PROVIDER=llama.cpp
 LLM_PRIMARY_URL=http://10.0.0.44:8080
 LLM_PRIMARY_MODEL=/model
 # SECONDARY Backend (Ollama)
 LLM_SECONDARY_PROVIDER=ollama
 LLM_SECONDARY_URL=http://10.0.0.3:11434
 LLM_SECONDARY_MODEL=qwen2.5:7b-instruct-q4_K_M
 # CLOUD Backend (OpenAI)
 LLM_OPENAI_PROVIDER=openai
 LLM_OPENAI_URL=https://api.openai.com/v1
 LLM_OPENAI_MODEL=gpt-4o-mini
 OPENAI_API_KEY=sk-proj-...
 # FALLBACK Backend
 LLM_FALLBACK_PROVIDER=openai_completions
 LLM_FALLBACK_URL=http://10.0.0.41:11435
 LLM_FALLBACK_MODEL=llama-3.2-8b-instruct
 ```
 **Module-specific backend selection:**
 ```bash
 CORTEX_LLM=SECONDARY      # Cortex uses Ollama
 INTAKE_LLM=PRIMARY        # Intake uses llama.cpp
 SPEAK_LLM=OPENAI          # Persona uses OpenAI
 NEOMEM_LLM=PRIMARY        # NeoMem uses llama.cpp
 UI_LLM=OPENAI             # UI uses OpenAI
 RELAY_LLM=PRIMARY         # Relay uses llama.cpp
 ```
 **Philosophy:** Root `.env` provides all backend OPTIONS. Each service chooses which backend to USE via `{MODULE}_LLM` variable. This eliminates URL duplication while preserving flexibility.
 ### Database Configuration
 ```bash
 # PostgreSQL (vector storage)
 POSTGRES_USER=neomem
 POSTGRES_PASSWORD=neomempass
 POSTGRES_DB=neomem
 POSTGRES_HOST=neomem-postgres
 POSTGRES_PORT=5432
 # Neo4j (graph storage)
 NEO4J_URI=bolt://neomem-neo4j:7687
 NEO4J_USERNAME=neo4j
 NEO4J_PASSWORD=neomemgraph
 ```
 ### Service URLs (Docker Internal Network)
 ```bash
 NEOMEM_API=http://neomem-api:7077
 CORTEX_API=http://cortex:7081
 CORTEX_REASON_URL=http://cortex:7081/reason
 CORTEX_INGEST_URL=http://cortex:7081/ingest
 RELAY_URL=http://relay:7078
 ```
 ### Feature Flags
 ```bash
 CORTEX_ENABLED=true
 MEMORY_ENABLED=true
 PERSONA_ENABLED=false
 DEBUG_PROMPT=true
 VERBOSE_DEBUG=true
 ```
 ---
 ## Code Structure Overview
 ### Cortex Service (`cortex/`)
 **Main Files:**
 - `main.py` - FastAPI app initialization
 - `router.py` - Route definitions (`/reason`, `/ingest`, `/health`, `/debug/*`)
 - `context.py` - Context aggregation (Intake summaries, session state)
 **Reasoning Pipeline (`reasoning/`):**
 - `reflection.py` - Meta-awareness notes (Cloud LLM)
 - `reasoning.py` - Draft answer generation (Primary LLM)
 - `refine.py` - Answer refinement (Primary LLM)
 **Persona Layer (`persona/`):**
 - `speak.py` - Personality application (Cloud LLM)
 - `identity.py` - Persona loader
 **Intake Module (`intake/`):**
 - `__init__.py` - Package exports (SESSIONS, add_exchange_internal, summarize_context)
 - `intake.py` - Core logic (367 lines)
  - SESSIONS dictionary
  - add_exchange_internal()
  - summarize_context()
  - bg_summarize() stub
 **LLM Integration (`llm/`):**
 - `llm_router.py` - Backend selector and HTTP client
  - call_llm() function
  - Environment-based routing
  - Payload formatting per backend type
 **Utilities (`utils/`):**
 - Helper functions for common operations
 **Configuration:**
 - `Dockerfile` - Single-worker constraint documented
 - `requirements.txt` - Python dependencies
 - `.env` - Service-specific overrides
 ### Relay Service (`core/relay/`)
 **Main Files:**
 - `server.js` - Express.js server (Node.js)
  - `/v1/chat/completions` - OpenAI-compatible endpoint
  - `/chat` - Internal endpoint
  - `/_health` - Health check
 - `package.json` - Node.js dependencies
 **Key Logic:**
 - Receives user messages
 - Routes to Cortex `/reason`
 - Async calls to Cortex `/ingest` after response
 - Returns final answer to user
 ### NeoMem Service (`neomem/`)
 **Main Files:**
 - `main.py` - FastAPI app (memory API)
 - `memory.py` - Memory management logic
 - `embedder.py` - Embedding generation
 - `graph.py` - Neo4j graph operations
 - `Dockerfile` - Container definition
 - `requirements.txt` - Python dependencies
 **API Endpoints:**
 - `POST /memories` - Add new memory
 - `POST /search` - Semantic search
 - `GET /health` - Service health
 ---
 ## Common Development Tasks
 ### Adding a New Endpoint to Cortex
 **Example: Add `/debug/buffer` endpoint**
 1. **Edit `cortex/router.py`:**
 ```python
@cortex_router.get("/debug/buffer")
 async def debug_buffer(session_id: str, limit: int = 10):
    """Return last N exchanges from a session buffer."""
    from intake.intake import SESSIONS
    session = SESSIONS.get(session_id)
    if not session:
        return {"error": "session not found", "session_id": session_id}
    buffer = session["buffer"]
    recent = list(buffer)[-limit:]
    return {
        "session_id": session_id,
        "total_exchanges": len(buffer),
        "recent_exchanges": recent
    }
 ```
 2. **Restart Cortex:**
 ```bash
 docker-compose restart cortex
 ```
 3. **Test:**
 ```bash
 curl "http://localhost:7081/debug/buffer?session_id=test&limit=5"
 ```
 ### Modifying LLM Backend for a Module
 **Example: Switch Cortex to use PRIMARY backend**
 1. **Edit `.env`:**
 ```bash
 CORTEX_LLM=PRIMARY  # Change from SECONDARY to PRIMARY
 ```
 2. **Restart Cortex:**
 ```bash
 docker-compose restart cortex
 ```
 3. **Verify in logs:**
 ```bash
 docker logs cortex | grep "Backend"
 ```
 ### Adding Diagnostic Logging
 **Example: Log every exchange addition**
 1. **Edit `cortex/intake/intake.py`:**
 ```python
 def add_exchange_internal(exchange: dict):
    session_id = exchange.get("session_id")
    # Add detailed logging
    print(f"[DEBUG] Adding exchange to {session_id}")
    print(f"[DEBUG] User msg: {exchange.get('user_msg', '')[:100]}")
    print(f"[DEBUG] Assistant msg: {exchange.get('assistant_msg', '')[:100]}")
    # ... rest of function
 ```
 2. **View logs:**
 ```bash
 docker logs cortex -f | grep DEBUG
 ```
 ---
 ## Debugging Guide
 ### Problem: SESSIONS Not Persisting
 **Symptoms:**
 - `/debug/sessions` shows empty or only 1 exchange
 - Summaries always return empty
 - Buffer size doesn't increase
 **Diagnosis Steps:**
 1. Check Cortex logs for SESSIONS object ID:
   ```bash
   docker logs cortex | grep "SESSIONS object id"
   ```
   - Should show same ID across all calls
   - If IDs differ → module reloading issue
 2. Verify single-worker mode:
   ```bash
   docker exec cortex cat Dockerfile | grep uvicorn
   ```
   - Should NOT have `--workers` flag or `--workers 1`
 3. Check `/debug/sessions` endpoint:
   ```bash
   curl http://localhost:7081/debug/sessions | jq
   ```
   - Should show sessions_object_id and current sessions
 4. Inspect `__init__.py` exists:
   ```bash
   docker exec cortex ls -la intake/__init__.py
   ```
 **Solution (Fixed in v0.5.1):**
 - Ensure `cortex/intake/__init__.py` exists with proper exports
 - Verify `bg_summarize()` is implemented (not just TYPE_CHECKING stub)
 - Check `/ingest` endpoint doesn't have early return
 - Rebuild Cortex container: `docker-compose build cortex && docker-compose restart cortex`
 ### Problem: LLM Backend Timeout
 **Symptoms:**
 - Cortex `/reason` hangs
 - 504 Gateway Timeout errors
 - Logs show "waiting for LLM response"
 **Diagnosis Steps:**
 1. Test backend directly:
   ```bash
   # llama.cpp
   curl http://10.0.0.44:8080/health
   # Ollama
   curl http://10.0.0.3:11434/api/tags
   # OpenAI
   curl https://api.openai.com/v1/models \
     -H "Authorization: Bearer $OPENAI_API_KEY"
   ```
 2. Check network connectivity:
   ```bash
   docker exec cortex ping -c 3 10.0.0.44
   ```
 3. Review Cortex logs:
   ```bash
   docker logs cortex -f | grep "LLM"
   ```
 **Solutions:**
 - Verify backend URL in `.env` is correct and accessible
 - Check firewall rules for backend ports
 - Increase timeout in `cortex/llm/llm_router.py`
 - Switch to different backend temporarily: `CORTEX_LLM=CLOUD`
 ### Problem: Docker Compose Won't Start
 **Symptoms:**
 - `docker-compose up -d` fails
 - Container exits immediately
 - "port already in use" errors
 **Diagnosis Steps:**
 1. Check port conflicts:
   ```bash
   netstat -tulpn | grep -E '7078|7081|7077|5432'
   ```
 2. Check container logs:
   ```bash
   docker-compose logs --tail=50
   ```
 3. Verify environment file:
   ```bash
   cat .env | grep -v "^#" | grep -v "^$"
   ```
 **Solutions:**
 - Stop conflicting services: `docker-compose down`
 - Check `.env` syntax (no quotes unless necessary)
 - Rebuild containers: `docker-compose build --no-cache`
 - Check Docker daemon: `systemctl status docker`
 ---
 ## Testing Checklist
 ### After Making Changes to Cortex
 **1. Build and restart:**
 ```bash
 docker-compose build cortex
 docker-compose restart cortex
 ```
 **2. Verify service health:**
 ```bash
 curl http://localhost:7081/health
 ```
 **3. Test /ingest endpoint:**
 ```bash
 curl -X POST http://localhost:7081/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "session_id": "test",
    "user_msg": "Hello",
    "assistant_msg": "Hi there!"
  }'
 ```
 **4. Verify SESSIONS updated:**
 ```bash
 curl http://localhost:7081/debug/sessions | jq '.sessions.test.buffer_size'
 ```
 - Should show 1 (or increment if already populated)
 **5. Test summarization:**
 ```bash
 curl "http://localhost:7081/debug/summary?session_id=test" | jq '.summary'
 ```
 - Should return L1/L5/L10/L20/L30 summaries
 **6. Test full pipeline:**
 ```bash
 curl -X POST http://localhost:7078/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Test message"}],
    "session_id": "test"
  }' | jq '.choices[0].message.content'
 ```
 **7. Check logs for errors:**
 ```bash
 docker logs cortex --tail=50
 ```
 ---
 ## Project History & Context
 ### Evolution Timeline
 **v0.1.x (2025-09-23 to 2025-09-25)**
 - Initial MVP: Relay + Mem0 + Ollama
 - Basic memory storage and retrieval
 - Simple UI with session support
 **v0.2.x (2025-09-24 to 2025-09-30)**
 - Migrated to mem0ai SDK
 - Added sessionId support
 - Created standalone Lyra-Mem0 stack
 **v0.3.x (2025-09-26 to 2025-10-28)**
 - Forked Mem0 → NVGRAM → NeoMem
 - Added salience filtering
 - Integrated Cortex reasoning VM
 - Built RAG system (Beta Lyrae)
 - Established multi-backend LLM support
 **v0.4.x (2025-11-05 to 2025-11-13)**
 - Major architectural rewire
 - Implemented 4-stage reasoning pipeline
 - Added reflection, refinement stages
 - RAG integration
 - LLM router with per-stage backend selection
 **Infrastructure v1.0.0 (2025-11-26)**
 - Consolidated 9 `.env` files into single source of truth
 - Multi-backend LLM strategy
 - Docker Compose consolidation
 - Created security templates
 **v0.5.0 (2025-11-28)**
 - Fixed all critical API wiring issues
 - Added OpenAI-compatible Relay endpoint
 - Fixed Cortex → Intake integration
 - End-to-end flow verification
 **v0.5.1 (2025-12-11) - CURRENT**
 - **Critical fix**: SESSIONS persistence bug
 - Implemented `bg_summarize()` stub
 - Fixed `/ingest` unreachable code
 - Added `cortex/intake/__init__.py`
 - Embedded Intake in Cortex (no longer standalone)
 - Added diagnostic endpoints
 - Lenient error handling
 - Documented single-worker constraint
 ### Architectural Philosophy
 **Modular Design:**
 - Each service has a single, clear responsibility
 - Services communicate via well-defined HTTP APIs
 - Configuration is centralized but allows per-service overrides
 **Local-First:**
 - No reliance on external services (except optional OpenAI)
 - All data stored locally (PostgreSQL + Neo4j)
 - Can run entirely air-gapped with local LLMs
 **Flexible LLM Backend:**
 - Not tied to any single LLM provider
 - Can mix local and cloud models
 - Per-stage backend selection for optimal performance/cost
 **Error Handling:**
 - Lenient mode: Never fail the chat pipeline
 - Log errors but continue processing
 - Graceful degradation
 **Observability:**
 - Diagnostic endpoints for debugging
 - Verbose logging mode
 - Object ID tracking for singleton verification
 ---
 ## Known Issues & Limitations
 ### Fixed in v0.5.1
 - ✅ Intake SESSIONS not persisting → **FIXED**
 - ✅ `bg_summarize()` NameError → **FIXED**
 - ✅ `/ingest` endpoint unreachable code → **FIXED**
 ### Current Limitations
 **1. Single-Worker Constraint**
 - Cortex must run with single Uvicorn worker
 - SESSIONS is in-memory module-level global
 - Multi-worker support requires Redis or shared storage
 - Documented in `cortex/Dockerfile` lines 7-8
 **2. NeoMem Integration Incomplete**
 - Relay doesn't yet push to NeoMem after responses
 - Memory storage planned for v0.5.2
 - Currently all memory is short-term (SESSIONS only)
 **3. RAG Service Disabled**
 - Beta Lyrae (RAG) commented out in docker-compose.yml
 - Awaiting re-enablement after Intake stabilization
 - Code exists but not currently integrated
 **4. Session Management**
 - No session cleanup/expiration
 - SESSIONS grows unbounded (maxlen=200 per session, but infinite sessions)
 - No session list endpoint in Relay
 **5. Persona Integration**
 - `PERSONA_ENABLED=false` in `.env`
 - Persona Sidecar not fully wired
 - Identity loaded but not consistently applied
 ### Future Enhancements
 **Short-term (v0.5.2):**
 - Enable NeoMem integration in Relay
 - Add session cleanup/expiration
 - Session list endpoint
 - NeoMem health monitoring
 **Medium-term (v0.6.x):**
 - Re-enable RAG service
 - Migrate SESSIONS to Redis for multi-worker support
 - Add request correlation IDs
 - Comprehensive health checks
 **Long-term (v0.7.x+):**
 - Persona Sidecar full integration
 - Autonomous "dream" cycles (self-reflection)
 - Verifier module for factual grounding
 - Advanced RAG with hybrid search
 - Memory consolidation strategies
 ---
 ## Troubleshooting Quick Reference
 | Problem | Quick Check | Solution |
 |---------|-------------|----------|
 | SESSIONS empty | `curl localhost:7081/debug/sessions` | Rebuild Cortex, verify `__init__.py` exists |
 | LLM timeout | `curl http://10.0.0.44:8080/health` | Check backend connectivity, increase timeout |
 | Port conflict | `netstat -tulpn \| grep 7078` | Stop conflicting service or change port |
 | Container crash | `docker logs cortex` | Check logs for Python errors, verify .env syntax |
 | Missing package | `docker exec cortex pip list` | Rebuild container, check requirements.txt |
 | 502 from Relay | `curl localhost:7081/health` | Verify Cortex is running, check docker network |
 ---
 ## API Reference (Quick)
 ### Relay (Port 7078)
 **POST /v1/chat/completions** - OpenAI-compatible chat
 ```json
 {
  "messages": [{"role": "user", "content": "..."}],
  "session_id": "..."
 }
 ```
 **GET /_health** - Service health
 ### Cortex (Port 7081)
 **POST /reason** - Main reasoning pipeline
 ```json
 {
  "session_id": "...",
  "user_prompt": "...",
  "temperature": 0.7  // optional
 }
 ```
 **POST /ingest** - Add exchange to SESSIONS
 ```json
 {
  "session_id": "...",
  "user_msg": "...",
  "assistant_msg": "..."
 }
 ```
 **GET /debug/sessions** - Inspect SESSIONS state
 **GET /debug/summary?session_id=X** - Test summarization
 **GET /health** - Service health
 ### NeoMem (Port 7077)
 **POST /memories** - Add memory
 ```json
 {
  "messages": [{"role": "...", "content": "..."}],
  "user_id": "...",
  "metadata": {}
 }
 ```
 **POST /search** - Semantic search
 ```json
 {
  "query": "...",
  "user_id": "...",
  "limit": 10
 }
 ```
 **GET /health** - Service health
 ---
 ## File Manifest (Key Files Only)
 ```
 project-lyra/
 ├── .env                           # Root environment variables
 ├── docker-compose.yml             # Service definitions (152 lines)
 ├── CHANGELOG.md                   # Version history (836 lines)
 ├── README.md                      # User documentation (610 lines)
 ├── PROJECT_SUMMARY.md             # This file (AI context)
 │
 ├── cortex/                        # Reasoning engine
 │   ├── Dockerfile                 # Single-worker constraint documented
 │   ├── requirements.txt
 │   ├── .env                       # Cortex overrides
 │   ├── main.py                    # FastAPI initialization
 │   ├── router.py                  # Routes (306 lines)
 │   ├── context.py                 # Context aggregation
 │   │
 │   ├── intake/                    # Short-term memory (embedded)
 │   │   ├── __init__.py           # Package exports
 │   │   └── intake.py             # Core logic (367 lines)
 │   │
 │   ├── reasoning/                 # Reasoning pipeline
 │   │   ├── reflection.py         # Meta-awareness
 │   │   ├── reasoning.py          # Draft generation
 │   │   └── refine.py             # Refinement
 │   │
 │   ├── persona/                   # Personality layer
 │   │   ├── speak.py              # Persona application
 │   │   └── identity.py           # Persona loader
 │   │
 │   └── llm/                       # LLM integration
 │       └── llm_router.py         # Backend selector
 │
 ├── core/relay/                    # Orchestrator
 │   ├── server.js                 # Express server (Node.js)
 │   └── package.json
 │
 ├── neomem/                        # Long-term memory
 │   ├── Dockerfile
 │   ├── requirements.txt
 │   ├── .env                       # NeoMem overrides
 │   └── main.py                   # Memory API
 │
 └── rag/                           # RAG system (disabled)
    ├── rag_api.py
    ├── rag_chat_import.py
    └── chromadb/
 ```
 ---
 ## Final Notes for AI Assistants
 ### What You Should Know Before Making Changes
 1. **SESSIONS is sacred** - It's a module-level global in `cortex/intake/intake.py`. Don't move it, don't duplicate it, don't make it a class attribute. It must remain a singleton.
 2. **Single-worker is mandatory** - Until SESSIONS is migrated to Redis, Cortex MUST run with a single Uvicorn worker. Multi-worker will cause SESSIONS to be inconsistent.
 3. **Lenient error handling** - The `/ingest` endpoint and other parts of the pipeline use lenient error handling: log errors but always return success. Never fail the chat pipeline.
 4. **Backend routing is environment-driven** - Don't hardcode LLM URLs. Use the `{MODULE}_LLM` environment variables and the llm_router.py system.
 5. **Intake is embedded** - Don't try to make HTTP calls to Intake. Use direct Python imports: `from intake.intake import ...`
 6. **Test with diagnostic endpoints** - Always use `/debug/sessions` and `/debug/summary` to verify SESSIONS behavior after changes.
 7. **Follow the changelog format** - When documenting changes, use the chronological format established in CHANGELOG.md v0.5.1. Group by version, then by change type (Fixed, Added, Changed, etc.).
 ### When You Need Help
 - **SESSIONS issues**: Check `cortex/intake/intake.py` lines 11-14 for initialization, lines 325-366 for `add_exchange_internal()`
 - **Routing issues**: Check `cortex/router.py` lines 65-189 for `/reason`, lines 201-233 for `/ingest`
 - **LLM backend issues**: Check `cortex/llm/llm_router.py` for backend selection logic
 - **Environment variables**: Check `.env` lines 13-40 for LLM backends, lines 28-34 for module selection
 ### Most Important Thing
 **This project values reliability over features.** It's better to have a simple, working system than a complex, broken one. When in doubt, keep it simple, log everything, and never fail silently.
 ---
 **End of AI Context Summary**
 *This document is maintained to provide complete context for AI assistants working on Project Lyra. Last updated: v0.5.1 (2025-12-11)*
@@ -0,0 +1,441 @@
 ├── CHANGELOG.md
 ├── core
 │   ├── env experiments
 │   ├── persona-sidecar
 │   │   ├── Dockerfile
 │   │   ├── package.json
 │   │   ├── persona-server.js
 │   │   └── personas.json
 │   ├── relay
 │   │   ├── Dockerfile
 │   │   ├── lib
 │   │   │   ├── cortex.js
 │   │   │   └── llm.js
 │   │   ├── package.json
 │   │   ├── package-lock.json
 │   │   ├── server.js
 │   │   ├── sessions
 │   │   │   ├── default.jsonl
 │   │   │   ├── sess-6rxu7eia.json
 │   │   │   ├── sess-6rxu7eia.jsonl
 │   │   │   ├── sess-l08ndm60.json
 │   │   │   └── sess-l08ndm60.jsonl
 │   │   └── test-llm.js
 │   ├── relay-backup
 │   └── ui
 │       ├── index.html
 │       ├── manifest.json
 │       └── style.css
 ├── cortex
 │   ├── context.py
 │   ├── Dockerfile
 │   ├── ingest
 │   │   ├── ingest_handler.py
 │   │   ├── __init__.py
 │   │   └── intake_client.py
 │   ├── intake
 │   │   ├── __init__.py
 │   │   ├── intake.py
 │   │   └── logs
 │   ├── llm
 │   │   ├── __init__.py
 │   │   └── llm_router.py
 │   ├── logs
 │   │   ├── cortex_verbose_debug.log
 │   │   └── reflections.log
 │   ├── main.py
 │   ├── neomem_client.py
 │   ├── persona
 │   │   ├── identity.py
 │   │   ├── __init__.py
 │   │   └── speak.py
 │   ├── rag.py
 │   ├── reasoning
 │   │   ├── __init__.py
 │   │   ├── reasoning.py
 │   │   ├── refine.py
 │   │   └── reflection.py
 │   ├── requirements.txt
 │   ├── router.py
 │   ├── tests
 │   └── utils
 │       ├── config.py
 │       ├── __init__.py
 │       ├── log_utils.py
 │       └── schema.py
 ├── deprecated.env.txt
 ├── DEPRECATED_FILES.md
 ├── docker-compose.yml
 ├── docs
 │   ├── ARCHITECTURE_v0-6-0.md
 │   ├── ENVIRONMENT_VARIABLES.md
 │   ├── lyra_tree.txt
 │   └── PROJECT_SUMMARY.md
 ├── intake-logs
 │   └── summaries.log
 ├── neomem
 │   ├── _archive
 │   │   └── old_servers
 │   │       ├── main_backup.py
 │   │       └── main_dev.py
 │   ├── docker-compose.yml
 │   ├── Dockerfile
 │   ├── neomem
 │   │   ├── api
 │   │   ├── client
 │   │   │   ├── __init__.py
 │   │   │   ├── main.py
 │   │   │   ├── project.py
 │   │   │   └── utils.py
 │   │   ├── configs
 │   │   │   ├── base.py
 │   │   │   ├── embeddings
 │   │   │   │   ├── base.py
 │   │   │   │   └── __init__.py
 │   │   │   ├── enums.py
 │   │   │   ├── __init__.py
 │   │   │   ├── llms
 │   │   │   │   ├── anthropic.py
 │   │   │   │   ├── aws_bedrock.py
 │   │   │   │   ├── azure.py
 │   │   │   │   ├── base.py
 │   │   │   │   ├── deepseek.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── lmstudio.py
 │   │   │   │   ├── ollama.py
 │   │   │   │   ├── openai.py
 │   │   │   │   └── vllm.py
 │   │   │   ├── prompts.py
 │   │   │   └── vector_stores
 │   │   │       ├── azure_ai_search.py
 │   │   │       ├── azure_mysql.py
 │   │   │       ├── baidu.py
 │   │   │       ├── chroma.py
 │   │   │       ├── databricks.py
 │   │   │       ├── elasticsearch.py
 │   │   │       ├── faiss.py
 │   │   │       ├── __init__.py
 │   │   │       ├── langchain.py
 │   │   │       ├── milvus.py
 │   │   │       ├── mongodb.py
 │   │   │       ├── neptune.py
 │   │   │       ├── opensearch.py
 │   │   │       ├── pgvector.py
 │   │   │       ├── pinecone.py
 │   │   │       ├── qdrant.py
 │   │   │       ├── redis.py
 │   │   │       ├── s3_vectors.py
 │   │   │       ├── supabase.py
 │   │   │       ├── upstash_vector.py
 │   │   │       ├── valkey.py
 │   │   │       ├── vertex_ai_vector_search.py
 │   │   │       └── weaviate.py
 │   │   ├── core
 │   │   ├── embeddings
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── gemini.py
 │   │   │   ├── huggingface.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── mock.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── together.py
 │   │   │   └── vertexai.py
 │   │   ├── exceptions.py
 │   │   ├── graphs
 │   │   │   ├── configs.py
 │   │   │   ├── __init__.py
 │   │   │   ├── neptune
 │   │   │   │   ├── base.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── neptunedb.py
 │   │   │   │   └── neptunegraph.py
 │   │   │   ├── tools.py
 │   │   │   └── utils.py
 │   │   ├── __init__.py
 │   │   ├── LICENSE
 │   │   ├── llms
 │   │   │   ├── anthropic.py
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── azure_openai_structured.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── deepseek.py
 │   │   │   ├── gemini.py
 │   │   │   ├── groq.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── litellm.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── openai_structured.py
 │   │   │   ├── sarvam.py
 │   │   │   ├── together.py
 │   │   │   ├── vllm.py
 │   │   │   └── xai.py
 │   │   ├── memory
 │   │   │   ├── base.py
 │   │   │   ├── graph_memory.py
 │   │   │   ├── __init__.py
 │   │   │   ├── kuzu_memory.py
 │   │   │   ├── main.py
 │   │   │   ├── memgraph_memory.py
 │   │   │   ├── setup.py
 │   │   │   ├── storage.py
 │   │   │   ├── telemetry.py
 │   │   │   └── utils.py
 │   │   ├── proxy
 │   │   │   ├── __init__.py
 │   │   │   └── main.py
 │   │   ├── server
 │   │   │   ├── dev.Dockerfile
 │   │   │   ├── docker-compose.yaml
 │   │   │   ├── Dockerfile
 │   │   │   ├── main_old.py
 │   │   │   ├── main.py
 │   │   │   ├── Makefile
 │   │   │   ├── README.md
 │   │   │   └── requirements.txt
 │   │   ├── storage
 │   │   ├── utils
 │   │   │   └── factory.py
 │   │   └── vector_stores
 │   │       ├── azure_ai_search.py
 │   │       ├── azure_mysql.py
 │   │       ├── baidu.py
 │   │       ├── base.py
 │   │       ├── chroma.py
 │   │       ├── configs.py
 │   │       ├── databricks.py
 │   │       ├── elasticsearch.py
 │   │       ├── faiss.py
 │   │       ├── __init__.py
 │   │       ├── langchain.py
 │   │       ├── milvus.py
 │   │       ├── mongodb.py
 │   │       ├── neptune_analytics.py
 │   │       ├── opensearch.py
 │   │       ├── pgvector.py
 │   │       ├── pinecone.py
 │   │       ├── qdrant.py
 │   │       ├── redis.py
 │   │       ├── s3_vectors.py
 │   │       ├── supabase.py
 │   │       ├── upstash_vector.py
 │   │       ├── valkey.py
 │   │       ├── vertex_ai_vector_search.py
 │   │       └── weaviate.py
 │   ├── neomem_history
 │   │   └── history.db
 │   ├── pyproject.toml
 │   ├── README.md
 │   └── requirements.txt
 ├── neomem_history
 │   └── history.db
 ├── rag
 │   ├── chatlogs
 │   │   └── lyra
 │   │       ├── 0000_Wire_ROCm_to_Cortex.json
 │   │       ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │       ├── 0002_cortex_LLMs_11-1-25.json
 │   │       ├── 0003_RAG_beta.json
 │   │       ├── 0005_Cortex_v0_4_0_planning.json
 │   │       ├── 0006_Cortex_v0_4_0_Refinement.json
 │   │       ├── 0009_Branch___Cortex_v0_4_0_planning.json
 │   │       ├── 0012_Cortex_4_-_neomem_11-1-25.json
 │   │       ├── 0016_Memory_consolidation_concept.json
 │   │       ├── 0017_Model_inventory_review.json
 │   │       ├── 0018_Branch___Memory_consolidation_concept.json
 │   │       ├── 0022_Branch___Intake_conversation_summaries.json
 │   │       ├── 0026_Intake_conversation_summaries.json
 │   │       ├── 0027_Trilium_AI_LLM_setup.json
 │   │       ├── 0028_LLMs_and_sycophancy_levels.json
 │   │       ├── 0031_UI_improvement_plan.json
 │   │       ├── 0035_10_27-neomem_update.json
 │   │       ├── 0044_Install_llama_cpp_on_ct201.json
 │   │       ├── 0045_AI_task_assistant.json
 │   │       ├── 0047_Project_scope_creation.json
 │   │       ├── 0052_View_docker_container_logs.json
 │   │       ├── 0053_10_21-Proxmox_fan_control.json
 │   │       ├── 0054_10_21-pytorch_branch_Quant_experiments.json
 │   │       ├── 0055_10_22_ct201branch-ssh_tut.json
 │   │       ├── 0060_Lyra_project_folder_issue.json
 │   │       ├── 0062_Build_pytorch_API.json
 │   │       ├── 0063_PokerBrain_dataset_structure.json
 │   │       ├── 0065_Install_PyTorch_setup.json
 │   │       ├── 0066_ROCm_PyTorch_setup_quirks.json
 │   │       ├── 0067_VM_model_setup_steps.json
 │   │       ├── 0070_Proxmox_disk_error_fix.json
 │   │       ├── 0072_Docker_Compose_vs_Portainer.json
 │   │       ├── 0073_Check_system_temps_Proxmox.json
 │   │       ├── 0075_Cortex_gpu_progress.json
 │   │       ├── 0076_Backup_Proxmox_before_upgrade.json
 │   │       ├── 0077_Storage_cleanup_advice.json
 │   │       ├── 0082_Install_ROCm_on_Proxmox.json
 │   │       ├── 0088_Thalamus_program_summary.json
 │   │       ├── 0094_Cortex_blueprint_development.json
 │   │       ├── 0095_mem0_advancments.json
 │   │       ├── 0096_Embedding_provider_swap.json
 │   │       ├── 0097_Update_git_commit_steps.json
 │   │       ├── 0098_AI_software_description.json
 │   │       ├── 0099_Seed_memory_process.json
 │   │       ├── 0100_Set_up_Git_repo.json
 │   │       ├── 0101_Customize_embedder_setup.json
 │   │       ├── 0102_Seeding_Local_Lyra_memory.json
 │   │       ├── 0103_Mem0_seeding_part_3.json
 │   │       ├── 0104_Memory_build_prompt.json
 │   │       ├── 0105_Git_submodule_setup_guide.json
 │   │       ├── 0106_Serve_UI_on_LAN.json
 │   │       ├── 0107_AI_name_suggestion.json
 │   │       ├── 0108_Room_X_planning_update.json
 │   │       ├── 0109_Salience_filtering_design.json
 │   │       ├── 0110_RoomX_Cortex_build.json
 │   │       ├── 0119_Explain_Lyra_cortex_idea.json
 │   │       ├── 0120_Git_submodule_organization.json
 │   │       ├── 0121_Web_UI_fix_guide.json
 │   │       ├── 0122_UI_development_planning.json
 │   │       ├── 0123_NVGRAM_debugging_steps.json
 │   │       ├── 0124_NVGRAM_setup_troubleshooting.json
 │   │       ├── 0125_NVGRAM_development_update.json
 │   │       ├── 0126_RX_-_NeVGRAM_New_Features.json
 │   │       ├── 0127_Error_troubleshooting_steps.json
 │   │       ├── 0135_Proxmox_backup_with_ABB.json
 │   │       ├── 0151_Auto-start_Lyra-Core_VM.json
 │   │       ├── 0156_AI_GPU_benchmarks_comparison.json
 │   │       └── 0251_Lyra_project_handoff.json
 │   ├── chromadb
 │   │   ├── c4f701ee-1978-44a1-9df4-3e865b5d33c1
 │   │   │   ├── data_level0.bin
 │   │   │   ├── header.bin
 │   │   │   ├── index_metadata.pickle
 │   │   │   ├── length.bin
 │   │   │   └── link_lists.bin
 │   │   └── chroma.sqlite3
 │   ├── import.log
 │   ├── lyra-chatlogs
 │   │   ├── 0000_Wire_ROCm_to_Cortex.json
 │   │   ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │   ├── 0002_cortex_LLMs_11-1-25.json
 │   │   └── 0003_RAG_beta.json
 │   ├── rag_api.py
 │   ├── rag_build.py
 │   ├── rag_chat_import.py
 │   └── rag_query.py
 ├── README.md
 └── volumes
    ├── neo4j_data
    │   ├── databases
    │   │   ├── neo4j
    │   │   │   ├── database_lock
    │   │   │   ├── id-buffer.tmp.0
    │   │   │   ├── neostore
    │   │   │   ├── neostore.counts.db
    │   │   │   ├── neostore.indexstats.db
    │   │   │   ├── neostore.labeltokenstore.db
    │   │   │   ├── neostore.labeltokenstore.db.id
    │   │   │   ├── neostore.labeltokenstore.db.names
    │   │   │   ├── neostore.labeltokenstore.db.names.id
    │   │   │   ├── neostore.nodestore.db
    │   │   │   ├── neostore.nodestore.db.id
    │   │   │   ├── neostore.nodestore.db.labels
    │   │   │   ├── neostore.nodestore.db.labels.id
    │   │   │   ├── neostore.propertystore.db
    │   │   │   ├── neostore.propertystore.db.arrays
    │   │   │   ├── neostore.propertystore.db.arrays.id
    │   │   │   ├── neostore.propertystore.db.id
    │   │   │   ├── neostore.propertystore.db.index
    │   │   │   ├── neostore.propertystore.db.index.id
    │   │   │   ├── neostore.propertystore.db.index.keys
    │   │   │   ├── neostore.propertystore.db.index.keys.id
    │   │   │   ├── neostore.propertystore.db.strings
    │   │   │   ├── neostore.propertystore.db.strings.id
    │   │   │   ├── neostore.relationshipgroupstore.db
    │   │   │   ├── neostore.relationshipgroupstore.db.id
    │   │   │   ├── neostore.relationshipgroupstore.degrees.db
    │   │   │   ├── neostore.relationshipstore.db
    │   │   │   ├── neostore.relationshipstore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db
    │   │   │   ├── neostore.relationshiptypestore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db.names
    │   │   │   ├── neostore.relationshiptypestore.db.names.id
    │   │   │   ├── neostore.schemastore.db
    │   │   │   ├── neostore.schemastore.db.id
    │   │   │   └── schema
    │   │   │       └── index
    │   │   │           └── token-lookup-1.0
    │   │   │               ├── 1
    │   │   │               │   └── index-1
    │   │   │               └── 2
    │   │   │                   └── index-2
    │   │   ├── store_lock
    │   │   └── system
    │   │       ├── database_lock
    │   │       ├── id-buffer.tmp.0
    │   │       ├── neostore
    │   │       ├── neostore.counts.db
    │   │       ├── neostore.indexstats.db
    │   │       ├── neostore.labeltokenstore.db
    │   │       ├── neostore.labeltokenstore.db.id
    │   │       ├── neostore.labeltokenstore.db.names
    │   │       ├── neostore.labeltokenstore.db.names.id
    │   │       ├── neostore.nodestore.db
    │   │       ├── neostore.nodestore.db.id
    │   │       ├── neostore.nodestore.db.labels
    │   │       ├── neostore.nodestore.db.labels.id
    │   │       ├── neostore.propertystore.db
    │   │       ├── neostore.propertystore.db.arrays
    │   │       ├── neostore.propertystore.db.arrays.id
    │   │       ├── neostore.propertystore.db.id
    │   │       ├── neostore.propertystore.db.index
    │   │       ├── neostore.propertystore.db.index.id
    │   │       ├── neostore.propertystore.db.index.keys
    │   │       ├── neostore.propertystore.db.index.keys.id
    │   │       ├── neostore.propertystore.db.strings
    │   │       ├── neostore.propertystore.db.strings.id
    │   │       ├── neostore.relationshipgroupstore.db
    │   │       ├── neostore.relationshipgroupstore.db.id
    │   │       ├── neostore.relationshipgroupstore.degrees.db
    │   │       ├── neostore.relationshipstore.db
    │   │       ├── neostore.relationshipstore.db.id
    │   │       ├── neostore.relationshiptypestore.db
    │   │       ├── neostore.relationshiptypestore.db.id
    │   │       ├── neostore.relationshiptypestore.db.names
    │   │       ├── neostore.relationshiptypestore.db.names.id
    │   │       ├── neostore.schemastore.db
    │   │       ├── neostore.schemastore.db.id
    │   │       └── schema
    │   │           └── index
    │   │               ├── range-1.0
    │   │               │   ├── 3
    │   │               │   │   └── index-3
    │   │               │   ├── 4
    │   │               │   │   └── index-4
    │   │               │   ├── 7
    │   │               │   │   └── index-7
    │   │               │   ├── 8
    │   │               │   │   └── index-8
    │   │               │   └── 9
    │   │               │       └── index-9
    │   │               └── token-lookup-1.0
    │   │                   ├── 1
    │   │                   │   └── index-1
    │   │                   └── 2
    │   │                       └── index-2
    │   ├── dbms
    │   │   └── auth.ini
    │   ├── server_id
    │   └── transactions
    │       ├── neo4j
    │       │   ├── checkpoint.0
    │       │   └── neostore.transaction.db.0
    │       └── system
    │           ├── checkpoint.0
    │           └── neostore.transaction.db.0
    └── postgres_data  [error opening dir]
@@ -1,460 +0,0 @@
 /home/serversdown/project-lyra
 ├── CHANGELOG.md
 ├── core
 │   ├── backups
 │   │   ├── mem0_20250927_221040.sql
 │   │   └── mem0_history_20250927_220925.tgz
 │   ├── docker-compose.yml
 │   ├── .env
 │   ├── env experiments
 │   │   ├── .env
 │   │   ├── .env.local
 │   │   └── .env.openai
 │   ├── persona-sidecar
 │   │   ├── Dockerfile
 │   │   ├── package.json
 │   │   ├── persona-server.js
 │   │   └── personas.json
 │   ├── PROJECT_SUMMARY.md
 │   ├── relay
 │   │   ├── Dockerfile
 │   │   ├── .dockerignore
 │   │   ├── lib
 │   │   │   ├── cortex.js
 │   │   │   └── llm.js
 │   │   ├── package.json
 │   │   ├── package-lock.json
 │   │   ├── server.js
 │   │   ├── sessions
 │   │   │   ├── sess-6rxu7eia.json
 │   │   │   ├── sess-6rxu7eia.jsonl
 │   │   │   ├── sess-l08ndm60.json
 │   │   │   └── sess-l08ndm60.jsonl
 │   │   └── test-llm.js
 │   └── ui
 │       ├── index.html
 │       ├── manifest.json
 │       └── style.css
 ├── cortex
 │   ├── Dockerfile
 │   ├── .env
 │   ├── ingest
 │   │   ├── ingest_handler.py
 │   │   └── intake_client.py
 │   ├── llm
 │   │   ├── llm_router.py
 │   │   └── resolve_llm_url.py
 │   ├── logs
 │   │   └── reflections.log
 │   ├── main.py
 │   ├── neomem_client.py
 │   ├── persona
 │   │   └── speak.py
 │   ├── rag.py
 │   ├── reasoning
 │   │   ├── reasoning.py
 │   │   ├── refine.py
 │   │   └── reflection.py
 │   ├── requirements.txt
 │   ├── router.py
 │   ├── tests
 │   └── utils
 │       ├── config.py
 │       ├── log_utils.py
 │       └── schema.py
 ├── deprecated.env.txt
 ├── docker-compose.yml
 ├── .env
 ├── .gitignore
 ├── intake
 │   ├── Dockerfile
 │   ├── .env
 │   ├── intake.py
 │   ├── logs
 │   ├── requirements.txt
 │   └── venv
 │       ├── bin
 │       │   ├── python -> python3
 │       │   ├── python3 -> /usr/bin/python3
 │       │   └── python3.10 -> python3
 │       ├── include
 │       ├── lib
 │       │   └── python3.10
 │       │       └── site-packages
 │       ├── lib64 -> lib
 │       └── pyvenv.cfg
 ├── intake-logs
 │   └── summaries.log
 ├── lyra_tree.txt
 ├── neomem
 │   ├── _archive
 │   │   └── old_servers
 │   │       ├── main_backup.py
 │   │       └── main_dev.py
 │   ├── docker-compose.yml
 │   ├── Dockerfile
 │   ├── .env
 │   ├── .gitignore
 │   ├── neomem
 │   │   ├── api
 │   │   ├── client
 │   │   │   ├── __init__.py
 │   │   │   ├── main.py
 │   │   │   ├── project.py
 │   │   │   └── utils.py
 │   │   ├── configs
 │   │   │   ├── base.py
 │   │   │   ├── embeddings
 │   │   │   │   ├── base.py
 │   │   │   │   └── __init__.py
 │   │   │   ├── enums.py
 │   │   │   ├── __init__.py
 │   │   │   ├── llms
 │   │   │   │   ├── anthropic.py
 │   │   │   │   ├── aws_bedrock.py
 │   │   │   │   ├── azure.py
 │   │   │   │   ├── base.py
 │   │   │   │   ├── deepseek.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── lmstudio.py
 │   │   │   │   ├── ollama.py
 │   │   │   │   ├── openai.py
 │   │   │   │   └── vllm.py
 │   │   │   ├── prompts.py
 │   │   │   └── vector_stores
 │   │   │       ├── azure_ai_search.py
 │   │   │       ├── azure_mysql.py
 │   │   │       ├── baidu.py
 │   │   │       ├── chroma.py
 │   │   │       ├── databricks.py
 │   │   │       ├── elasticsearch.py
 │   │   │       ├── faiss.py
 │   │   │       ├── __init__.py
 │   │   │       ├── langchain.py
 │   │   │       ├── milvus.py
 │   │   │       ├── mongodb.py
 │   │   │       ├── neptune.py
 │   │   │       ├── opensearch.py
 │   │   │       ├── pgvector.py
 │   │   │       ├── pinecone.py
 │   │   │       ├── qdrant.py
 │   │   │       ├── redis.py
 │   │   │       ├── s3_vectors.py
 │   │   │       ├── supabase.py
 │   │   │       ├── upstash_vector.py
 │   │   │       ├── valkey.py
 │   │   │       ├── vertex_ai_vector_search.py
 │   │   │       └── weaviate.py
 │   │   ├── core
 │   │   ├── embeddings
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── gemini.py
 │   │   │   ├── huggingface.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── mock.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── together.py
 │   │   │   └── vertexai.py
 │   │   ├── exceptions.py
 │   │   ├── graphs
 │   │   │   ├── configs.py
 │   │   │   ├── __init__.py
 │   │   │   ├── neptune
 │   │   │   │   ├── base.py
 │   │   │   │   ├── __init__.py
 │   │   │   │   ├── neptunedb.py
 │   │   │   │   └── neptunegraph.py
 │   │   │   ├── tools.py
 │   │   │   └── utils.py
 │   │   ├── __init__.py
 │   │   ├── LICENSE
 │   │   ├── llms
 │   │   │   ├── anthropic.py
 │   │   │   ├── aws_bedrock.py
 │   │   │   ├── azure_openai.py
 │   │   │   ├── azure_openai_structured.py
 │   │   │   ├── base.py
 │   │   │   ├── configs.py
 │   │   │   ├── deepseek.py
 │   │   │   ├── gemini.py
 │   │   │   ├── groq.py
 │   │   │   ├── __init__.py
 │   │   │   ├── langchain.py
 │   │   │   ├── litellm.py
 │   │   │   ├── lmstudio.py
 │   │   │   ├── ollama.py
 │   │   │   ├── openai.py
 │   │   │   ├── openai_structured.py
 │   │   │   ├── sarvam.py
 │   │   │   ├── together.py
 │   │   │   ├── vllm.py
 │   │   │   └── xai.py
 │   │   ├── memory
 │   │   │   ├── base.py
 │   │   │   ├── graph_memory.py
 │   │   │   ├── __init__.py
 │   │   │   ├── kuzu_memory.py
 │   │   │   ├── main.py
 │   │   │   ├── memgraph_memory.py
 │   │   │   ├── setup.py
 │   │   │   ├── storage.py
 │   │   │   ├── telemetry.py
 │   │   │   └── utils.py
 │   │   ├── proxy
 │   │   │   ├── __init__.py
 │   │   │   └── main.py
 │   │   ├── server
 │   │   │   ├── dev.Dockerfile
 │   │   │   ├── docker-compose.yaml
 │   │   │   ├── Dockerfile
 │   │   │   ├── main_old.py
 │   │   │   ├── main.py
 │   │   │   ├── Makefile
 │   │   │   ├── README.md
 │   │   │   └── requirements.txt
 │   │   ├── storage
 │   │   ├── utils
 │   │   │   └── factory.py
 │   │   └── vector_stores
 │   │       ├── azure_ai_search.py
 │   │       ├── azure_mysql.py
 │   │       ├── baidu.py
 │   │       ├── base.py
 │   │       ├── chroma.py
 │   │       ├── configs.py
 │   │       ├── databricks.py
 │   │       ├── elasticsearch.py
 │   │       ├── faiss.py
 │   │       ├── __init__.py
 │   │       ├── langchain.py
 │   │       ├── milvus.py
 │   │       ├── mongodb.py
 │   │       ├── neptune_analytics.py
 │   │       ├── opensearch.py
 │   │       ├── pgvector.py
 │   │       ├── pinecone.py
 │   │       ├── qdrant.py
 │   │       ├── redis.py
 │   │       ├── s3_vectors.py
 │   │       ├── supabase.py
 │   │       ├── upstash_vector.py
 │   │       ├── valkey.py
 │   │       ├── vertex_ai_vector_search.py
 │   │       └── weaviate.py
 │   ├── neomem_history
 │   │   └── history.db
 │   ├── pyproject.toml
 │   ├── README.md
 │   └── requirements.txt
 ├── neomem_history
 │   └── history.db
 ├── rag
 │   ├── chatlogs
 │   │   └── lyra
 │   │       ├── 0000_Wire_ROCm_to_Cortex.json
 │   │       ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │       ├── 0002_cortex_LLMs_11-1-25.json
 │   │       ├── 0003_RAG_beta.json
 │   │       ├── 0005_Cortex_v0_4_0_planning.json
 │   │       ├── 0006_Cortex_v0_4_0_Refinement.json
 │   │       ├── 0009_Branch___Cortex_v0_4_0_planning.json
 │   │       ├── 0012_Cortex_4_-_neomem_11-1-25.json
 │   │       ├── 0016_Memory_consolidation_concept.json
 │   │       ├── 0017_Model_inventory_review.json
 │   │       ├── 0018_Branch___Memory_consolidation_concept.json
 │   │       ├── 0022_Branch___Intake_conversation_summaries.json
 │   │       ├── 0026_Intake_conversation_summaries.json
 │   │       ├── 0027_Trilium_AI_LLM_setup.json
 │   │       ├── 0028_LLMs_and_sycophancy_levels.json
 │   │       ├── 0031_UI_improvement_plan.json
 │   │       ├── 0035_10_27-neomem_update.json
 │   │       ├── 0044_Install_llama_cpp_on_ct201.json
 │   │       ├── 0045_AI_task_assistant.json
 │   │       ├── 0047_Project_scope_creation.json
 │   │       ├── 0052_View_docker_container_logs.json
 │   │       ├── 0053_10_21-Proxmox_fan_control.json
 │   │       ├── 0054_10_21-pytorch_branch_Quant_experiments.json
 │   │       ├── 0055_10_22_ct201branch-ssh_tut.json
 │   │       ├── 0060_Lyra_project_folder_issue.json
 │   │       ├── 0062_Build_pytorch_API.json
 │   │       ├── 0063_PokerBrain_dataset_structure.json
 │   │       ├── 0065_Install_PyTorch_setup.json
 │   │       ├── 0066_ROCm_PyTorch_setup_quirks.json
 │   │       ├── 0067_VM_model_setup_steps.json
 │   │       ├── 0070_Proxmox_disk_error_fix.json
 │   │       ├── 0072_Docker_Compose_vs_Portainer.json
 │   │       ├── 0073_Check_system_temps_Proxmox.json
 │   │       ├── 0075_Cortex_gpu_progress.json
 │   │       ├── 0076_Backup_Proxmox_before_upgrade.json
 │   │       ├── 0077_Storage_cleanup_advice.json
 │   │       ├── 0082_Install_ROCm_on_Proxmox.json
 │   │       ├── 0088_Thalamus_program_summary.json
 │   │       ├── 0094_Cortex_blueprint_development.json
 │   │       ├── 0095_mem0_advancments.json
 │   │       ├── 0096_Embedding_provider_swap.json
 │   │       ├── 0097_Update_git_commit_steps.json
 │   │       ├── 0098_AI_software_description.json
 │   │       ├── 0099_Seed_memory_process.json
 │   │       ├── 0100_Set_up_Git_repo.json
 │   │       ├── 0101_Customize_embedder_setup.json
 │   │       ├── 0102_Seeding_Local_Lyra_memory.json
 │   │       ├── 0103_Mem0_seeding_part_3.json
 │   │       ├── 0104_Memory_build_prompt.json
 │   │       ├── 0105_Git_submodule_setup_guide.json
 │   │       ├── 0106_Serve_UI_on_LAN.json
 │   │       ├── 0107_AI_name_suggestion.json
 │   │       ├── 0108_Room_X_planning_update.json
 │   │       ├── 0109_Salience_filtering_design.json
 │   │       ├── 0110_RoomX_Cortex_build.json
 │   │       ├── 0119_Explain_Lyra_cortex_idea.json
 │   │       ├── 0120_Git_submodule_organization.json
 │   │       ├── 0121_Web_UI_fix_guide.json
 │   │       ├── 0122_UI_development_planning.json
 │   │       ├── 0123_NVGRAM_debugging_steps.json
 │   │       ├── 0124_NVGRAM_setup_troubleshooting.json
 │   │       ├── 0125_NVGRAM_development_update.json
 │   │       ├── 0126_RX_-_NeVGRAM_New_Features.json
 │   │       ├── 0127_Error_troubleshooting_steps.json
 │   │       ├── 0135_Proxmox_backup_with_ABB.json
 │   │       ├── 0151_Auto-start_Lyra-Core_VM.json
 │   │       ├── 0156_AI_GPU_benchmarks_comparison.json
 │   │       └── 0251_Lyra_project_handoff.json
 │   ├── chromadb
 │   │   ├── c4f701ee-1978-44a1-9df4-3e865b5d33c1
 │   │   │   ├── data_level0.bin
 │   │   │   ├── header.bin
 │   │   │   ├── index_metadata.pickle
 │   │   │   ├── length.bin
 │   │   │   └── link_lists.bin
 │   │   └── chroma.sqlite3
 │   ├── .env
 │   ├── import.log
 │   ├── lyra-chatlogs
 │   │   ├── 0000_Wire_ROCm_to_Cortex.json
 │   │   ├── 0001_Branch___10_22_ct201branch-ssh_tut.json
 │   │   ├── 0002_cortex_LLMs_11-1-25.json
 │   │   └── 0003_RAG_beta.json
 │   ├── rag_api.py
 │   ├── rag_build.py
 │   ├── rag_chat_import.py
 │   └── rag_query.py
 ├── README.md
 ├── vllm-mi50.md
 └── volumes
    ├── neo4j_data
    │   ├── databases
    │   │   ├── neo4j
    │   │   │   ├── database_lock
    │   │   │   ├── id-buffer.tmp.0
    │   │   │   ├── neostore
    │   │   │   ├── neostore.counts.db
    │   │   │   ├── neostore.indexstats.db
    │   │   │   ├── neostore.labeltokenstore.db
    │   │   │   ├── neostore.labeltokenstore.db.id
    │   │   │   ├── neostore.labeltokenstore.db.names
    │   │   │   ├── neostore.labeltokenstore.db.names.id
    │   │   │   ├── neostore.nodestore.db
    │   │   │   ├── neostore.nodestore.db.id
    │   │   │   ├── neostore.nodestore.db.labels
    │   │   │   ├── neostore.nodestore.db.labels.id
    │   │   │   ├── neostore.propertystore.db
    │   │   │   ├── neostore.propertystore.db.arrays
    │   │   │   ├── neostore.propertystore.db.arrays.id
    │   │   │   ├── neostore.propertystore.db.id
    │   │   │   ├── neostore.propertystore.db.index
    │   │   │   ├── neostore.propertystore.db.index.id
    │   │   │   ├── neostore.propertystore.db.index.keys
    │   │   │   ├── neostore.propertystore.db.index.keys.id
    │   │   │   ├── neostore.propertystore.db.strings
    │   │   │   ├── neostore.propertystore.db.strings.id
    │   │   │   ├── neostore.relationshipgroupstore.db
    │   │   │   ├── neostore.relationshipgroupstore.db.id
    │   │   │   ├── neostore.relationshipgroupstore.degrees.db
    │   │   │   ├── neostore.relationshipstore.db
    │   │   │   ├── neostore.relationshipstore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db
    │   │   │   ├── neostore.relationshiptypestore.db.id
    │   │   │   ├── neostore.relationshiptypestore.db.names
    │   │   │   ├── neostore.relationshiptypestore.db.names.id
    │   │   │   ├── neostore.schemastore.db
    │   │   │   ├── neostore.schemastore.db.id
    │   │   │   └── schema
    │   │   │       └── index
    │   │   │           └── token-lookup-1.0
    │   │   │               ├── 1
    │   │   │               │   └── index-1
    │   │   │               └── 2
    │   │   │                   └── index-2
    │   │   ├── store_lock
    │   │   └── system
    │   │       ├── database_lock
    │   │       ├── id-buffer.tmp.0
    │   │       ├── neostore
    │   │       ├── neostore.counts.db
    │   │       ├── neostore.indexstats.db
    │   │       ├── neostore.labeltokenstore.db
    │   │       ├── neostore.labeltokenstore.db.id
    │   │       ├── neostore.labeltokenstore.db.names
    │   │       ├── neostore.labeltokenstore.db.names.id
    │   │       ├── neostore.nodestore.db
    │   │       ├── neostore.nodestore.db.id
    │   │       ├── neostore.nodestore.db.labels
    │   │       ├── neostore.nodestore.db.labels.id
    │   │       ├── neostore.propertystore.db
    │   │       ├── neostore.propertystore.db.arrays
    │   │       ├── neostore.propertystore.db.arrays.id
    │   │       ├── neostore.propertystore.db.id
    │   │       ├── neostore.propertystore.db.index
    │   │       ├── neostore.propertystore.db.index.id
    │   │       ├── neostore.propertystore.db.index.keys
    │   │       ├── neostore.propertystore.db.index.keys.id
    │   │       ├── neostore.propertystore.db.strings
    │   │       ├── neostore.propertystore.db.strings.id
    │   │       ├── neostore.relationshipgroupstore.db
    │   │       ├── neostore.relationshipgroupstore.db.id
    │   │       ├── neostore.relationshipgroupstore.degrees.db
    │   │       ├── neostore.relationshipstore.db
    │   │       ├── neostore.relationshipstore.db.id
    │   │       ├── neostore.relationshiptypestore.db
    │   │       ├── neostore.relationshiptypestore.db.id
    │   │       ├── neostore.relationshiptypestore.db.names
    │   │       ├── neostore.relationshiptypestore.db.names.id
    │   │       ├── neostore.schemastore.db
    │   │       ├── neostore.schemastore.db.id
    │   │       └── schema
    │   │           └── index
    │   │               ├── range-1.0
    │   │               │   ├── 3
    │   │               │   │   └── index-3
    │   │               │   ├── 4
    │   │               │   │   └── index-4
    │   │               │   ├── 7
    │   │               │   │   └── index-7
    │   │               │   ├── 8
    │   │               │   │   └── index-8
    │   │               │   └── 9
    │   │               │       └── index-9
    │   │               └── token-lookup-1.0
    │   │                   ├── 1
    │   │                   │   └── index-1
    │   │                   └── 2
    │   │                       └── index-2
    │   ├── dbms
    │   │   └── auth.ini
    │   ├── server_id
    │   └── transactions
    │       ├── neo4j
    │       │   ├── checkpoint.0
    │       │   └── neostore.transaction.db.0
    │       └── system
    │           ├── checkpoint.0
    │           └── neostore.transaction.db.0
    └── postgres_data  [error opening dir]
 81 directories, 376 files