inline::faiss
Description
Faiss is an inline vector database provider for OGX. It allows you to store and query vectors directly in memory. That means you'll get fast and efficient vector retrieval.
Features
- Lightweight and easy to use
- Fully integrated with OGX
- GPU support
- Vector search - FAISS supports pure vector similarity search using embeddings
Search Modes
Supported:
- Vector Search (
mode="vector"): Performs vector similarity search using embeddings
Not Supported:
- Keyword Search (
mode="keyword"): Not supported by FAISS - Hybrid Search (
mode="hybrid"): Not supported by FAISS
Note: FAISS is designed as a pure vector similarity search library. See the FAISS GitHub repository for more details about FAISS's core functionality.
Usage
To use Faiss in your OGX project, follow these steps:
- Install the necessary dependencies.
- Configure your OGX project to use Faiss.
- Start storing and querying vectors.
Installation
You can install Faiss using pip:
pip install faiss-cpu
Documentation
See Faiss' documentation or the Faiss Wiki for more details about Faiss in general.
Configuration
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
persistence | KVStoreReference | No | ||
persistence.namespace | str | No | Key prefix for KVStore backends | |
persistence.backend | str | No | Name of backend from storage.backends | |
metadata_store | SqlStoreReference | None | No | SQL store reference for tenant-isolated vector store metadata | |
metadata_store.table_name | str | No | Name of the table to use for the SqlStore | |
metadata_store.backend | str | No | Name of backend from storage.backends |
Sample Configuration
persistence:
namespace: vector_io::faiss
backend: kv_default