# Library RAG - Weaviate + BGE-M3 Embeddings
# ===========================================
#
# This docker-compose runs Weaviate with BAAI/bge-m3 embedding model.
#
# BGE-M3 Advantages:
#   - 1024 dimensions (vs 384 for MiniLM-L6) - 2.7x richer representation
#   - 8192 token context (vs 512) - 16x longer sequences
#   - Superior multilingual support (Greek, Latin, French, English)
#   - Better trained on academic/philosophical texts
#
# GPU Configuration:
#   - ENABLE_CUDA="1" - Uses NVIDIA GPU for faster vectorization
#   - ENABLE_CUDA="0" - Uses CPU only (slower but functional)
#   - GPU device mapping included for CUDA acceleration
#
# Migration Note (2024-12):
#   Migrated from sentence-transformers-multi-qa-MiniLM-L6-cos-v1 (384-dim)
#   to BAAI/bge-m3 (1024-dim). All collections were deleted and recreated.
#   See MIGRATION_BGE_M3.md for details.

services:
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.34.4
    restart: on-failure:0
    ports:
      - "8080:8080"
      - "50051:50051"
    environment:
      QUERY_DEFAULTS_LIMIT: "25"
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: "true"   # ok pour dev/local
      PERSISTENCE_DATA_PATH: "/var/lib/weaviate"
      CLUSTER_HOSTNAME: "node1"
      CLUSTER_GOSSIP_BIND_PORT: "7946"
      CLUSTER_DATA_BIND_PORT: "7947"
      # Fix for "No private IP address found" error
      CLUSTER_JOIN: ""
      DEFAULT_VECTORIZER_MODULE: "text2vec-transformers"
      ENABLE_MODULES: "text2vec-transformers"
      TRANSFORMERS_INFERENCE_API: "http://text2vec-transformers:8080"
      # Limits to prevent OOM crashes
      GOMEMLIMIT: "6GiB"
      GOGC: "100"
    volumes:
      - weaviate_data:/var/lib/weaviate
    mem_limit: 8g
    memswap_limit: 10g
    cpus: 4
    # Ensure Weaviate waits for text2vec-transformers to be healthy before starting
    depends_on:
      text2vec-transformers:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/v1/.well-known/ready"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 60s

  text2vec-transformers:
    # BAAI/bge-m3: Multilingual embedding model (1024 dimensions)
    # Superior for philosophical texts (Greek, Latin, French, English)
    # 8192 token context window (16x longer than MiniLM-L6)
    # Using ONNX version (only available format in Weaviate registry)
    #
    # GPU LIMITATION (Dec 2024):
    #   - Weaviate only provides ONNX version of BGE-M3 (no PyTorch)
    #   - ONNX runtime is CPU-optimized (no native CUDA support)
    #   - GPU acceleration would require NVIDIA NIM (different architecture)
    #   - Current setup: CPU-only with AVX2 optimization (functional but slower)
    image: cr.weaviate.io/semitechnologies/transformers-inference:baai-bge-m3-onnx-latest
    restart: on-failure:0
    ports:
      - "8090:8080"  # Expose vectorizer API for manual vectorization
    environment:
      # ONNX runtime - CPU only (CUDA not supported in ONNX version)
      ENABLE_CUDA: "0"
      # Increased timeouts for very long chunks (e.g., Peirce CP 3.403, CP 8.388, Menon chunk 10)
      # Default is 60s, increased to 600s (10 minutes) for exceptionally large texts (e.g., CP 8.388: 218k chars)
      WORKER_TIMEOUT: "600"
    mem_limit: 10g
    memswap_limit: 12g
    cpus: 3
    # Healthcheck ensures service is fully loaded before Weaviate starts
    # BGE-M3 model takes ~60-120s to load into memory
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/.well-known/ready"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 120s  # BGE-M3 model loading can take up to 2 minutes

volumes:
  weaviate_data: