Qdrant - Vector Database for the next generation of AI applications. Also available in the cloud https://cloud.qdrant.io/
Vector Search Engine for the next generation of AI applications
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 pointsโvectors 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 ๐ฆ, 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!
Qdrant is also available as a fully managed Qdrant Cloud โ including a free tier.
Quick Start โข Client Libraries โข Demo Projects โข Integrations โข Contact
Getting Started Python
pip install qdrant-client
The python client offers a convenient way to start with Qdrant locally:
from qdrant_client import QdrantClient qdrant = QdrantClient(":memory:") # Create in-memory Qdrant instance, for testing, CI/CDClient-ServerOR
client = QdrantClient(path="path/to/db") # Persists changes to disk, fast prototyping
This is the recommended method for production usage. To run the container, use the command:
docker run -p 6333:6333 qdrant/qdrant
Now you can connect to this with any client, including Python:
qdrant = QdrantClient("http://localhost:6333") # Connect to existing Qdrant instance, for productionClients
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 ๐
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 ๐
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 ๐บ
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!
More solutions
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Semantic Text Search
Similar Image Search
Recommendations
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Chat Bots
Matching Engines
Anomaly Detection
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.
Integrations
Examples and/or documentation of Qdrant integrations:
Contacts
Contributors โจ
Thanks to the people who contributed to Qdrant:
License
Qdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.
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