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# What does this PR do? Simple approach to get some provider pages in the docs. Add or update description fields in the provider configuration class using Pydantic’s Field, ensuring these descriptions are clear and complete, as they will be used to auto-generate provider documentation via ./scripts/distro_codegen.py instead of editing the docs manually. Signed-off-by: Sébastien Han <seb@redhat.com>
1.9 KiB
1.9 KiB
inline::qdrant
Description
Qdrant is an inline and remote vector database provider for Llama Stack. It allows you to store and query vectors directly in memory. That means you'll get fast and efficient vector retrieval.
By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)
Features
- Lightweight and easy to use
- Fully integrated with Llama Stack
- Apache 2.0 license terms
- Store embeddings and their metadata
- Supports search by Keyword and Hybrid search
- Multilingual and Multimodal retrieval
- Medatata filtering
- GPU support
Usage
To use Qdrant in your Llama Stack project, follow these steps:
- Install the necessary dependencies.
- Configure your Llama Stack project to use Qdrant.
- Start storing and querying vectors.
Installation
You can install Qdrant using docker:
docker pull qdrant/qdrant
Documentation
See the Qdrant documentation for more details about Qdrant in general.
Configuration
| Field | Type | Required | Default | Description |
|---|---|---|---|---|
path |
<class 'str'> |
No | PydanticUndefined |
Sample Configuration
path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db