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# What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> - Updates provider and distro codegen to handle the new format - Migrates provider and distro files to the new format ## Test Plan - Manual testing <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
106 lines
3.2 KiB
Text
106 lines
3.2 KiB
Text
---
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description: |
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[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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That means you'll get fast and efficient vector retrieval.
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## Features
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- Lightweight and easy to use
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- Fully integrated with Llama Stack
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- GPU support
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- **Vector search** - FAISS supports pure vector similarity search using embeddings
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## Search Modes
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**Supported:**
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- **Vector Search** (`mode="vector"`): Performs vector similarity search using embeddings
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**Not Supported:**
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- **Keyword Search** (`mode="keyword"`): Not supported by FAISS
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- **Hybrid Search** (`mode="hybrid"`): Not supported by FAISS
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> **Note**: FAISS is designed as a pure vector similarity search library. See the [FAISS GitHub repository](https://github.com/facebookresearch/faiss) for more details about FAISS's core functionality.
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## Usage
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To use Faiss in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Faiss.
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3. Start storing and querying vectors.
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## Installation
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You can install Faiss using pip:
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```bash
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pip install faiss-cpu
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```
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## Documentation
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See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for
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more details about Faiss in general.
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sidebar_label: Faiss
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title: inline::faiss
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---
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# inline::faiss
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## Description
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[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It
|
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allows you to store and query vectors directly in memory.
|
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That means you'll get fast and efficient vector retrieval.
|
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|
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## Features
|
|
|
|
- Lightweight and easy to use
|
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- Fully integrated with Llama Stack
|
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- GPU support
|
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- **Vector search** - FAISS supports pure vector similarity search using embeddings
|
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|
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## Search Modes
|
|
|
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**Supported:**
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- **Vector Search** (`mode="vector"`): Performs vector similarity search using embeddings
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**Not Supported:**
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- **Keyword Search** (`mode="keyword"`): Not supported by FAISS
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- **Hybrid Search** (`mode="hybrid"`): Not supported by FAISS
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> **Note**: FAISS is designed as a pure vector similarity search library. See the [FAISS GitHub repository](https://github.com/facebookresearch/faiss) for more details about FAISS's core functionality.
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## Usage
|
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To use Faiss in your Llama Stack project, follow these steps:
|
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Faiss.
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3. Start storing and querying vectors.
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## Installation
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You can install Faiss using pip:
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```bash
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pip install faiss-cpu
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```
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## Documentation
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See [Faiss' documentation](https://faiss.ai/) or the [Faiss Wiki](https://github.com/facebookresearch/faiss/wiki) for
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more details about Faiss in general.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | |
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## Sample Configuration
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```yaml
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/faiss_store.db
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```
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