project-lyra/README.md

# 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.

---

## Architecture Overview

Project Lyra operates as a **single docker-compose deployment** with multiple Docker containers networked together in a microservices architecture. Like how the brain has regions, Lyra has modules:

### Core Services

**1. Relay** (Node.js/Express) - Port 7078
- Main orchestrator and message router
- Coordinates all module interactions
- OpenAI-compatible endpoint: `POST /v1/chat/completions`
- Internal endpoint: `POST /chat`
- Routes messages through Cortex reasoning pipeline
- Manages async calls to NeoMem and Cortex ingest

**2. UI** (Static HTML)
- Browser-based chat interface with cyberpunk theme
- Connects to Relay
- Saves and loads sessions
- OpenAI-compatible message format

**3. NeoMem** (Python/FastAPI) - Port 7077
- Long-term memory database (fork of Mem0 OSS)
- Vector storage (PostgreSQL + pgvector) + Graph storage (Neo4j)
- RESTful API: `/memories`, `/search`
- Semantic memory updates and retrieval
- No external SDK dependencies - fully local

### Reasoning Layer

**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 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** (Python Module) - **Embedded in Cortex**
- **No longer a standalone service** - runs as Python module inside Cortex container
- Short-term memory management with session-based circular buffer
- In-memory SESSIONS dictionary: `session_id → {buffer: deque(maxlen=200), created_at: timestamp}`
- Multi-level summarization (L1/L5/L10/L20/L30) produced by `summarize_context()`
- Deferred summarization - actual summary generation happens during `/reason` call
- Internal Python API:
  - `add_exchange_internal(exchange)` - Direct function call from Cortex
  - `summarize_context(session_id, exchanges)` - Async LLM-based summarization
  - `SESSIONS` - Module-level global state (requires single Uvicorn worker)

### LLM Backends (HTTP-based)

**All LLM communication is done via HTTP APIs:**
- **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 (v0.5.1)

### Normal Message Flow:

```
User (UI) → POST /v1/chat/completions
  ↓
Relay (7078)
  ↓ POST /reason
Cortex (7081)
  ↓ (internal Python call)
Intake module → summarize_context()
  ↓
Cortex processes (4 stages):
  1. reflection.py → meta-awareness notes (CLOUD backend)
  2. reasoning.py → draft answer (PRIMARY backend)
  3. refine.py → refined answer (PRIMARY backend)
  4. persona/speak.py → Lyra personality (CLOUD backend)
  ↓
Returns persona answer to Relay
  ↓
Relay → POST /ingest (async)
  ↓
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`) - Cloud LLM (OpenAI)
   - Analyzes user intent and conversation context
   - Generates meta-awareness notes
   - "What is the user really asking?"

2. **Reasoning** (`reasoning.py`) - Primary LLM (llama.cpp)
   - Retrieves short-term context from Intake module
   - Creates initial draft answer
   - Integrates context, reflection notes, and user prompt

3. **Refinement** (`refine.py`) - Primary LLM (llama.cpp)
   - Polishes the draft answer
   - Improves clarity and coherence
   - Ensures factual consistency

4. **Persona** (`speak.py`) - Cloud LLM (OpenAI)
   - Applies Lyra's personality and speaking style
   - Natural, conversational output
   - Final answer returned to user

---

## Features

### Core Services

**Relay**:
- Main orchestrator and message router
- OpenAI-compatible endpoint: `POST /v1/chat/completions`
- Internal endpoint: `POST /chat`
- Health check: `GET /_health`
- Async non-blocking calls to Cortex
- Shared request handler for code reuse
- Comprehensive error handling

**NeoMem (Memory Engine)**:
- Forked from Mem0 OSS - fully independent
- Drop-in compatible API (`/memories`, `/search`)
- Local-first: runs on FastAPI with Postgres + Neo4j
- No external SDK dependencies
- Semantic memory updates - compares embeddings and performs in-place updates
- Default service: `neomem-api` (port 7077)

**UI**:
- Lightweight static HTML chat interface
- Cyberpunk theme
- Session save/load functionality
- OpenAI message format support

### Reasoning Layer

**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
- Embedded Intake module for short-term context
- `/reason`, `/ingest`, `/health`, `/debug/sessions`, `/debug/summary` endpoints
- Lenient error handling - never fails the chat pipeline

**Intake** (Embedded Module):
- **Architectural change**: Now runs as Python module inside Cortex container
- In-memory SESSIONS management (session_id → buffer)
- Multi-level summarization: L1 (ultra-short), L5 (short), L10 (medium), L20 (detailed), L30 (full)
- Deferred summarization strategy - summaries generated during `/reason` call
- `bg_summarize()` is a logging stub - actual work deferred
- **Single-worker constraint**: SESSIONS requires single Uvicorn worker or Redis/shared storage

**LLM Router**:
- Dynamic backend selection via HTTP
- Environment-driven configuration
- Support for llama.cpp, Ollama, OpenAI, custom endpoints
- 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

- **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.
  - **Status**: Disabled in docker-compose.yml (v0.5.1)

The system uses:
- **ChromaDB** for persistent vector storage
- **OpenAI Embeddings (`text-embedding-3-small`)** for semantic similarity
- **FastAPI** (port 7090) for the `/rag/search` REST endpoint

Directory Layout:
```
rag/
├── rag_chat_import.py    # imports JSON chat logs
├── rag_docs_import.py    # (planned) PDF/EPUB/manual importer
├── rag_build.py          # legacy single-folder builder
├── rag_query.py          # command-line query helper
├── rag_api.py            # FastAPI service providing /rag/search
├── chromadb/             # persistent vector store
├── chatlogs/             # organized source data
│   ├── poker/
│   ├── work/
│   ├── lyra/
│   ├── personal/
│   └── ...
└── import.log            # progress log for batch runs
```

**OpenAI chatlog importer features:**
- Recursive folder indexing with **category detection** from directory name
- 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
- Full progress logging via tqdm
- Safe to run in background with `nohup … &`

---

## Docker Deployment

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 with embedded Intake (port 7081)

**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.

## External LLM Services

The following LLM backends are accessed via HTTP (not part of docker-compose):

- **llama.cpp Server** (`http://10.0.0.44:8080`)
  - AMD MI50 GPU-accelerated inference
  - Primary backend for reasoning and refinement stages
  - Model path: `/model`

- **Ollama Server** (`http://10.0.0.3:11434`)
  - RTX 3090 GPU-accelerated inference
  - Secondary/configurable backend
  - Model: qwen2.5:7b-instruct-q4_K_M

- **OpenAI API** (`https://api.openai.com/v1`)
  - Cloud-based inference
  - Used for reflection and persona stages
  - Model: gpt-4o-mini

- **Fallback Server** (`http://10.0.0.41:11435`)
  - Emergency backup endpoint
  - Local llama-3.2-8b-instruct model

---

## Version History

### 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
- LLM router with multi-backend support
- Major architectural restructuring

### v0.3.x
- Beta Lyrae RAG system
- NeoMem integration
- Basic Cortex reasoning loop

---

## 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
- RAG service currently disabled in docker-compose.yml
- 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 across all services
- NeoMem integration in Relay

---

## Quick Start

### Prerequisites
- Docker + Docker Compose
- 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:
   ```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" \
  -d '{
    "messages": [{"role": "user", "content": "Hello Lyra!"}],
    "session_id": "test"
  }'
```

**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

- [CHANGELOG.md](CHANGELOG.md) - Detailed version history
- [PROJECT_SUMMARY.md](PROJECT_SUMMARY.md) - Comprehensive project overview for AI context
- [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

---

## License

NeoMem is a derivative work based on Mem0 OSS (Apache 2.0).
© 2025 Terra-Mechanics / ServersDown Labs. All modifications released under Apache 2.0.

**Built with Claude Code**

---

## Integration Notes

- NeoMem API is compatible with Mem0 OSS endpoints (`/memories`, `/search`)
- 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

---

## Beta Lyrae - RAG Memory System (Currently Disabled)

**Note:** The RAG service is currently disabled in docker-compose.yml

### Requirements
- Python 3.10+
- Dependencies: `chromadb openai tqdm python-dotenv fastapi uvicorn`
- Persistent storage: `./chromadb` or `/mnt/data/lyra_rag_db`

### Setup
1. Import chat logs (must be in OpenAI message format):
   ```bash
   python3 rag/rag_chat_import.py
   ```

2. Build and start the RAG API server:
   ```bash
   cd rag
   python3 rag_build.py
   uvicorn rag_api:app --host 0.0.0.0 --port 7090
   ```

3. Query the RAG system:
   ```bash
   curl -X POST http://127.0.0.1:7090/rag/search \
     -H "Content-Type: application/json" \
     -d '{
       "query": "What is the current state of Cortex?",
       "where": {"category": "lyra"}
     }'
   ```

---

## 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`