Type something to search...
to navigateto selectESC to close
Qdrant
Qdrant

Qdrant

 18.0k 1.2k
Vector Database
03 May, 2024
  Rust

What is Qdrant ?

Qdrant (read: quadrant) is a vector similarity search engine and vector database.

It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload

Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Getting Started

Python

Terminal window
pip install qdrant-client

The python client offers a convenient way to start with Qdrant locally:

Terminal window
from qdrant_client import QdrantClient


qdrant = QdrantClient(":memory:") # Create in-memory Qdrant instance, for testing, CI/CD


# OR


client = QdrantClient(path="path/to/db")  # Persists changes to disk, fast prototyping

Client-Server

This is the recommended method for production usage. To run the container, use the command:

Terminal window
docker run -p 6333:6333 qdrant/qdrant

Now you can connect to this with any client, including Python:

Terminal window
qdrant = QdrantClient("http://localhost:6333") # Connect to existing Qdrant instance, for production

Clients

Qdrant offers the following client libraries to help you integrate it into your application stack with ease:

Where do I go from here?

Demo Projects

Discover Semantic Text Search 🔍

Unlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!

Explore Similar Image Search - Food Discovery 🍕

There’s more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don’t know the dish’s name. Check it out!

Master Extreme Classification - E-commerce Product Categorization 📺

Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!

<tr>


    <td width="30%">


        <img src="https://qdrant.tech/content/images/text_search.png">


    </td>


    <td width="30%">


        <img src="https://qdrant.tech/content/images/image_search.png">


    </td>


    <td width="30%">


        <img src="https://qdrant.tech/content/images/recommendations.png">


    </td>


</tr>


<tr>


    <td>


        Semantic Text Search


    </td>


    <td>


        Similar Image Search


    </td>


    <td>


        Recommendations


    </td>


</tr>
<tr>


    <td>


        <img width="300px" src="https://qdrant.tech/content/images/chat_bots.png">


    </td>


    <td>


        <img width="300px" src="https://qdrant.tech/content/images/matching_engines.png">


    </td>


    <td>


        <img width="300px" src="https://qdrant.tech/content/images/anomalies_detection.png">


    </td>


</tr>


<tr>


    <td>


        Chat Bots


    </td>


    <td>


        Matching Engines


    </td>


    <td>


        Anomaly Detection


    </td>


</tr>

API

REST

Online OpenAPI 3.0 documentation is available here.

OpenAPI makes it easy to generate a client for virtually any framework or programming language.

You can also download raw OpenAPI definitions.

gRPC

For faster production-tier searches, Qdrant also provides a gRPC interface. You can find gRPC documentation here.

Features

Filtering and Payload

Qdrant enables JSON payloads to be associated with vectors, providing both storage and filtering based on payload values. It supports various combinations of should , must , and must_not conditions, ensuring retrieval of all relevant vectors unlike ElasticSearch post-filtering.

Rich Data Types

The vector payload accommodates diverse data types and query conditions, including string matching, numerical ranges, geo-locations, and more. These filtering conditions empower you to create custom business logic on top of similarity matching.

Query Planning and Payload Indexes

The query planner leverages stored payload information to optimize query execution. For instance, smaller search spaces limited by filters might benefit from full brute force over an index.

SIMD Hardware Acceleration

Utilizing modern CPU x86-x64 architectures, Qdrant delivers faster search performance on modern hardware.

Write-Ahead Logging

Qdrant ensures data persistence with update confirmation, even during power outages. The update journal stores all operations, enabling effortless reconstruction of the latest database state.

Distributed Deployment

As of v0.8.0, Qdrant supports distributed deployment. Multiple Qdrant machines form a cluster for horizontal scaling, coordinated through the Raft protocol.

Stand-alone

Qdrant operates independently, without reliance on external databases or orchestration controllers, simplifying configuration.

Qdrant Integrations

Examples and/or documentation of Qdrant integrations:

Tags
Related Tools