llama-stack-mirror/docs/source/providers/vector_io/inline_qdrant.md
Varsha 1f0766308d
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feat: Add openAI compatible APIs to Qdrant (#2465)
# What does this PR do?
Adds support to Vector store Open AI APIs in Qdrant.

<!-- If resolving an issue, uncomment and update the line below -->
 Closes #2463 


## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->

Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
Co-authored-by: ehhuang <ehhuang@users.noreply.github.com>
Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com>
2025-08-01 00:41:34 -04:00

2.2 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

Usage

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

  1. Install the necessary dependencies.
  2. Configure your Llama Stack project to use Qdrant.
  3. 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
kvstore utils.kvstore.config.RedisKVStoreConfig | utils.kvstore.config.SqliteKVStoreConfig | utils.kvstore.config.PostgresKVStoreConfig | utils.kvstore.config.MongoDBKVStoreConfig No sqlite

Sample Configuration

path: ${env.QDRANT_PATH:=~/.llama/~/.llama/dummy}/qdrant.db
kvstore:
  type: sqlite
  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/qdrant_registry.db