remote::milvus

Description

Milvus is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly within a Milvus database. That means you're not limited to storing vectors in memory or in a separate service.

Features

• Easy to use
• Fully integrated with Llama Stack
• Supports all search modes: vector, keyword, and hybrid search (both inline and remote configurations)

Usage

To use Milvus in your Llama Stack project, follow these steps:

1. Install the necessary dependencies.
2. Configure your Llama Stack project to use Milvus.
3. Start storing and querying vectors.

Installation

If you want to use inline Milvus, you can install:

pip install pymilvus[milvus-lite]

If you want to use remote Milvus, you can install:

pip install pymilvus

Configuration

In Llama Stack, Milvus can be configured in two ways:

• Inline (Local) Configuration - Uses Milvus-Lite for local storage
• Remote Configuration - Connects to a remote Milvus server

Inline (Local) Configuration

The simplest method is local configuration, which requires setting db_path, a path for locally storing Milvus-Lite files:

vector_io:
  - provider_id: milvus
    provider_type: inline::milvus
    config:
      db_path: ~/.llama/distributions/together/milvus_store.db

Remote Configuration

Remote configuration is suitable for larger data storage requirements:

Standard Remote Connection

vector_io:
  - provider_id: milvus
    provider_type: remote::milvus
    config:
      uri: "http://<host>:<port>"
      token: "<user>:<password>"

TLS-Enabled Remote Connection (One-way TLS)

For connections to Milvus instances with one-way TLS enabled:

vector_io:
  - provider_id: milvus
    provider_type: remote::milvus
    config:
      uri: "https://<host>:<port>"
      token: "<user>:<password>"
      secure: True
      server_pem_path: "/path/to/server.pem"

Mutual TLS (mTLS) Remote Connection

For connections to Milvus instances with mutual TLS (mTLS) enabled:

vector_io:
  - provider_id: milvus
    provider_type: remote::milvus
    config:
      uri: "https://<host>:<port>"
      token: "<user>:<password>"
      secure: True
      ca_pem_path: "/path/to/ca.pem"
      client_pem_path: "/path/to/client.pem"
      client_key_path: "/path/to/client.key"

Key Parameters for TLS Configuration

• secure: Enables TLS encryption when set to true. Defaults to false.
• server_pem_path: Path to the server certificate for verifying the server's identity (used in one-way TLS).
• ca_pem_path: Path to the Certificate Authority (CA) certificate for validating the server certificate (required in mTLS).
• client_pem_path: Path to the client certificate file (required for mTLS).
• client_key_path: Path to the client private key file (required for mTLS).

Search Modes

Milvus supports three different search modes for both inline and remote configurations:

Vector Search

Vector search uses semantic similarity to find the most relevant chunks based on embedding vectors. This is the default search mode and works well for finding conceptually similar content.

# Vector search example
search_response = client.vector_stores.search(
    vector_store_id=vector_store.id,
    query="What is machine learning?",
    search_mode="vector",
    max_num_results=5,
)

Keyword Search

Keyword search uses traditional text-based matching to find chunks containing specific terms or phrases. This is useful when you need exact term matches.

# Keyword search example
search_response = client.vector_stores.search(
    vector_store_id=vector_store.id,
    query="Python programming language",
    search_mode="keyword",
    max_num_results=5,
)

Hybrid Search

Hybrid search combines both vector and keyword search methods to provide more comprehensive results. It leverages the strengths of both semantic similarity and exact term matching.

Basic Hybrid Search

# Basic hybrid search example (uses RRF ranker with default impact_factor=60.0)
search_response = client.vector_stores.search(
    vector_store_id=vector_store.id,
    query="neural networks in Python",
    search_mode="hybrid",
    max_num_results=5,
)

Note: The default impact_factor value of 60.0 was empirically determined to be optimal in the original RRF research paper: "Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods" (Cormack et al., 2009).

Hybrid Search with RRF (Reciprocal Rank Fusion) Ranker

RRF combines rankings from vector and keyword search by using reciprocal ranks. The impact factor controls how much weight is given to higher-ranked results.

# Hybrid search with custom RRF parameters
search_response = client.vector_stores.search(
    vector_store_id=vector_store.id,
    query="neural networks in Python",
    search_mode="hybrid",
    max_num_results=5,
    ranking_options={
        "ranker": {
            "type": "rrf",
            "impact_factor": 100.0,  # Higher values give more weight to top-ranked results
        }
    },
)

Hybrid Search with Weighted Ranker

Weighted ranker linearly combines normalized scores from vector and keyword search. The alpha parameter controls the balance between the two search methods.

# Hybrid search with weighted ranker
search_response = client.vector_stores.search(
    vector_store_id=vector_store.id,
    query="neural networks in Python",
    search_mode="hybrid",
    max_num_results=5,
    ranking_options={
        "ranker": {
            "type": "weighted",
            "alpha": 0.7,  # 70% vector search, 30% keyword search
        }
    },
)

For detailed documentation on RRF and Weighted rankers, please refer to the Milvus Reranking Guide.

Documentation

See the Milvus documentation for more details about Milvus in general.

For more details on TLS configuration, refer to the TLS setup guide.

Configuration

Field	Type	Required	Default	Description
uri	<class 'str'>	No		The URI of the Milvus server
token	str | None	No		The token of the Milvus server
consistency_level	<class 'str'>	No	Strong	The consistency level of the Milvus server
kvstore	utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig	No	sqlite	Config for KV store backend
config	dict	No	{}	This configuration allows additional fields to be passed through to the underlying Milvus client. See the Milvus documentation for more details about Milvus in general.

note

This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.

Sample Configuration

uri: ${env.MILVUS_ENDPOINT}
token: ${env.MILVUS_TOKEN}
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/milvus_remote_registry.db