---
description: |
  [Milvus](https://milvus.io/) 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:

  ```bash
  pip install pymilvus[milvus-lite]
  ```

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

  ```bash
  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:

  ```yaml
  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

  ```yaml
  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:

  ```yaml
  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:

  ```yaml
  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.

  ```python
  # 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.

  ```python
  # 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
  ```python
  # 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"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (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.

  ```python
  # 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.

  ```python
  # 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](https://milvus.io/docs/reranking.md).

  ## Documentation
  See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.

  For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).
sidebar_label: Remote - Milvus
title: remote::milvus
---

# remote::milvus

## Description


[Milvus](https://milvus.io/) 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:

```bash
pip install pymilvus[milvus-lite]
```

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

```bash
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:

```yaml
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

```yaml
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:

```yaml
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:

```yaml
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.

```python
# 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.

```python
# 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
```python
# 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"](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) (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.

```python
# 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.

```python
# 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](https://milvus.io/docs/reranking.md).

## Documentation
See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.

For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).


## 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 |
| `persistence` | `<class 'llama_stack.core.storage.datatypes.KVStoreReference'>` | No |  | 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](https://milvus.io/docs/install-overview.md) 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

```yaml
uri: ${env.MILVUS_ENDPOINT}
token: ${env.MILVUS_TOKEN}
persistence:
  namespace: vector_io::milvus_remote
  backend: kv_default
```
