---
id: S01
parent: M019
milestone: M019
provides:
  - LightRAG service running on ub01 port 9621
  - Entity extraction API at POST /documents/text
  - Query API at POST /query with mix/local/global/hybrid modes
  - Music production entity types configured (Creator, Technique, Plugin, Synthesizer, Effect, Genre, DAW, SamplePack, SignalChain, Concept, Frequency, SoundDesignElement)
requires:
  []
affects:
  - S04
key_files:
  - docker-compose.yml
  - .env.lightrag
  - .gsd/KNOWLEDGE.md
key_decisions:
  - Used Python urllib for container healthcheck since LightRAG image lacks curl
  - Used 127.0.0.1 instead of localhost in container healthcheck for reliable resolution
  - Set env_file required:true since LightRAG cannot start without config
  - Used NetworkX graph storage + Qdrant vector storage for initial deployment
  - Used DGX Sparks as LLM backend with Ollama for embeddings only
patterns_established:
  - Python urllib healthcheck pattern for Docker images lacking curl
  - LightRAG service integration pattern: depends_on Qdrant + Ollama healthy, env_file for config, bind mount for persistent data
observability_surfaces:
  - GET /health on port 9621 returns full config and status JSON
  - Docker healthcheck via Python urllib reports healthy/unhealthy
  - Container logs show entity extraction details and LLM/embedding request outcomes
drill_down_paths:
  - .gsd/milestones/M019/slices/S01/tasks/T01-SUMMARY.md
  - .gsd/milestones/M019/slices/S01/tasks/T02-SUMMARY.md
duration: ""
verification_result: passed
completed_at: 2026-04-03T21:36:50.606Z
blocker_discovered: false
---

# S01: [B] LightRAG Deployment + Docker Integration

**LightRAG service deployed in Docker Compose on ub01 with Qdrant vector storage, Ollama embeddings, DGX Sparks LLM, and verified entity extraction producing music production entities.**

## What Happened

Two tasks: T01 added the chrysopedia-lightrag service definition to docker-compose.yml and created .env.lightrag with all configuration. T02 deployed on ub01, discovered the LightRAG Docker image lacks curl (healthcheck switched to Python urllib with 127.0.0.1), set the real API key, and verified end-to-end: entity extraction from music production text (10 entities, 9 relations including Creator, Plugin, Technique, Effect types) and query returning accurate results mentioning Serum, OTT, and Valhalla Room.

The service uses ghcr.io/hkuds/lightrag:latest on port 9621, depends on healthy Qdrant and Ollama services, stores data at /vmPool/r/services/chrysopedia_lightrag, and uses NetworkX for graph storage with QdrantVectorDBStorage for vectors. LLM requests go through DGX Sparks (fyn-llm-agent-chat model via OpenAI-compatible API), embeddings through the existing Ollama nomic-embed-text model.

## Verification

All slice-level checks pass:
- `ssh ub01 'curl -sf http://localhost:9621/health'` → 200 with full config JSON
- `ssh ub01 'docker ps --filter name=chrysopedia-lightrag'` → "Up N minutes (healthy)"
- `docker compose config | grep chrysopedia-lightrag` → PASS
- `docker compose config` exits 0
- `git check-ignore .env.lightrag` → IGNORED (via .env.* wildcard)
- All 10 existing Chrysopedia services remain healthy
- Entity extraction POST returns success with 10 entities + 9 relations
- Query POST returns response with correct music production entities (Serum, OTT, Valhalla Room)

## Requirements Advanced

- R010 — LightRAG service added to Docker Compose stack, all existing services remain healthy

## Requirements Validated

None.

## New Requirements Surfaced

None.

## Requirements Invalidated or Re-scoped

None.

## Deviations

Healthcheck changed from curl to Python urllib because LightRAG image lacks curl. Additional .env.lightrag settings added beyond plan (SUMMARY_LANGUAGE, MAX_ASYNC, RERANK_BINDING, OLLAMA_EMBEDDING_NUM_CTX) based on upstream env.example review. Files deployed via scp rather than git pull.

## Known Limitations

Qdrant client v1.15.1 in LightRAG image warns about server v1.13.2 mismatch — functions correctly but should be monitored. ub01 repo has uncommitted changes (files deployed via scp). NetworkX graph storage is file-based — adequate for initial deployment but may need upgrade for large corpus.

## Follow-ups

S04 (Reindex Existing Corpus Through LightRAG) depends on this slice. Consider upgrading Qdrant server to match client version. Consider switching to Neo4j graph storage if NetworkX becomes a bottleneck at scale.

## Files Created/Modified

- `docker-compose.yml` — Added chrysopedia-lightrag service with healthcheck, depends_on, volumes, port mapping
- `.env.lightrag` — Created with LLM, embedding, vector storage, graph storage, and entity type configuration
- `.gsd/KNOWLEDGE.md` — Added LightRAG healthcheck knowledge entry
