# Sample Vector IO Configuration with Embedding Model Defaults
# This example demonstrates the new provider-level embedding configuration features

# Image and version info
version: 3
image_name: my-embedding-app

# APIs to serve
apis:
  - inference
  - vector_io

# Provider configurations
providers:
  # Inference provider for embedding models
  inference:
    - provider_id: local_inference
      provider_type: inline::ollama
      config:
        url: http://localhost:11434

  # Vector IO providers with embedding model defaults
  vector_io:
    # FAISS for fast local search with lightweight embeddings
    - provider_id: fast_local_search
      provider_type: inline::faiss
      config:
        kvstore:
          provider_type: sqlite
          config:
            db_path: ~/.llama/distributions/my-app/faiss_store.db
        # NEW: Default embedding model for this provider
        embedding_model: "all-MiniLM-L6-v2"
        # Dimension auto-lookup: 384 (from model registry)

    # SQLite Vec for lightweight vector storage with Matryoshka embeddings
    - provider_id: compact_storage
      provider_type: inline::sqlite_vec
      config:
        db_path: ~/.llama/distributions/my-app/sqlite_vec.db
        kvstore:
          provider_type: sqlite
          config:
            db_name: sqlite_vec_registry.db
        # Matryoshka embedding with custom dimension
        embedding_model: "nomic-embed-text"
        embedding_dimension: 256  # Reduced from default 768 for efficiency

    # Chroma for persistent local storage
    - provider_id: persistent_search
      provider_type: inline::chroma
      config:
        db_path: ~/.llama/distributions/my-app/chroma.db
        # High-quality embeddings for semantic search
        embedding_model: "sentence-transformers/all-mpnet-base-v2"
        # Auto-lookup dimension from model registry

    # Qdrant Cloud for production-scale search (when available)
    - provider_id: cloud_search
      provider_type: remote::qdrant
      config:
        api_key: "${env.QDRANT_API_KEY}"
        url: "${env.QDRANT_URL}"
        # Production-grade embedding model
        embedding_model: "text-embedding-3-small"
        embedding_dimension: 512  # Custom dimension for performance

# Model registry - ensure embedding models are properly configured
models:
  # Lightweight embedding model (384 dimensions)
  - model_id: all-MiniLM-L6-v2
    provider_id: local_inference
    provider_model_id: sentence-transformers/all-MiniLM-L6-v2
    model_type: embedding
    metadata:
      embedding_dimension: 384
      description: "Fast, lightweight embeddings for general use"

  # Matryoshka embedding model (variable dimensions)
  - model_id: nomic-embed-text
    provider_id: local_inference
    provider_model_id: nomic-embed-text
    model_type: embedding
    metadata:
      embedding_dimension: 768  # Default, can be overridden
      description: "Flexible Matryoshka embeddings supporting variable dimensions"

  # High-quality embedding model (768 dimensions)
  - model_id: sentence-transformers/all-mpnet-base-v2
    provider_id: local_inference
    provider_model_id: sentence-transformers/all-mpnet-base-v2
    model_type: embedding
    metadata:
      embedding_dimension: 768
      description: "High-quality embeddings for semantic search"

  # OpenAI embedding model (for cloud usage)
  - model_id: text-embedding-3-small
    provider_id: openai_inference  # Would need OpenAI provider configured
    provider_model_id: text-embedding-3-small
    model_type: embedding
    metadata:
      embedding_dimension: 1536  # Default OpenAI dimension
      description: "OpenAI's efficient embedding model"

# Optional: Configure specific vector databases (will use provider defaults)
vector_dbs:
  # Uses fast_local_search provider defaults (all-MiniLM-L6-v2, 384 dims)
  - vector_db_id: general_docs
    provider_id: fast_local_search

  # Uses compact_storage provider defaults (nomic-embed-text, 256 dims)
  - vector_db_id: compressed_knowledge
    provider_id: compact_storage

  # Uses persistent_search provider defaults (all-mpnet-base-v2, 768 dims)
  - vector_db_id: semantic_library
    provider_id: persistent_search

# Server configuration
server:
  host: 0.0.0.0
  port: 5000

# Logging configuration
logging:
  level: INFO