llama-stack-mirror/docs/source/providers/vector_io/inline_milvus.md
skamenan7 474b50b422 Add configurable embedding models for vector IO providers
This change lets users configure default embedding models at the provider level instead of always relying on system defaults. Each vector store provider can now specify an embedding_model and optional embedding_dimension in their config.

Key features:
- Auto-dimension lookup for standard models from the registry
- Support for Matryoshka embeddings with custom dimensions
- Three-tier priority: explicit params > provider config > system fallback
- Full backward compatibility - existing setups work unchanged
- Comprehensive test coverage with 20 test cases

Updated all vector IO providers (FAISS, Chroma, Milvus, Qdrant, etc.) with the new config fields and added detailed documentation with examples.

Fixes #2729
2025-07-15 16:46:40 -04:00

1.2 KiB

inline::milvus

Description

Please refer to the remote provider documentation.

Configuration

Field Type Required Default Description
db_path <class 'str'> No PydanticUndefined
kvstore utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig No sqlite Config for KV store backend (SQLite only for now)
consistency_level <class 'str'> No Strong The consistency level of the Milvus server
embedding_model str | None No Optional default embedding model for this provider. If not specified, will use system default.
embedding_dimension int | None No Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.

Sample Configuration

db_path: ${env.MILVUS_DB_PATH:=~/.llama/dummy}/milvus.db
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_registry.db