Qdrant下一代矢量数据库：它提供了高效的矢量索引和检索功能，支持快速的相似度搜索和相关性计算，适用于各种AI应用领域

<p align="center"> <img height="100" src="https://github.com/qdrant/qdrant/raw/master/docs/logo.svg" alt="Qdrant"> </p> <p align="center"> <b>Vector Search Engine for the next generation of AI applications</b> </p> <p align=center> <a href="https://github.com/qdrant/qdrant/actions/workflows/rust.yml"><img src="https://img.shields.io/github/actions/workflow/status/qdrant/qdrant/rust.yml?style=flat-square" alt="Tests status"></a> <a href="https://qdrant.github.io/qdrant/redoc/index.html"><img src="https://img.shields.io/badge/Docs-OpenAPI%203.0-success?style=flat-square" alt="OpenAPI Docs"></a> <a href="https://github.com/qdrant/qdrant/blob/master/LICENSE"><img src="https://img.shields.io/github/license/qdrant/qdrant?style=flat-square" alt="Apache 2.0 License"></a> <a href="https://qdrant.to/discord"><img src="https://img.shields.io/discord/907569970500743200?logo=Discord&style=flat-square&color=7289da" alt="Discord"></a> <a href="https://qdrant.to/roadmap"><img src="https://img.shields.io/badge/Roadmap-2024-bc1439.svg?style=flat-square" alt="Roadmap 2024"></a> <a href="https://cloud.qdrant.io/"><img src="https://img.shields.io/badge/Qdrant-Cloud-24386C.svg?logo=cloud&style=flat-square" alt="Qdrant Cloud"></a> </p> **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](https://qdrant.tech/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](https://cloud.qdrant.io/)** ⛅ including a **free tier**. <p align="center"> <strong><a href="./QUICK_START.md">Quick Start</a> • <a href="#clients">Client Libraries</a> • <a href="#demo-projects">Demo Projects</a> • <a href="#integrations">Integrations</a> • <a href="#contacts">Contact</a> </strong> </p> ## Getting Started ### Python ``` pip install qdrant-client ``` The python client offers a convenient way to start with Qdrant locally: ```python 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: ```bash docker run -p 6333:6333 qdrant/qdrant ``` Now you can connect to this with any client, including Python: ```python 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: - Official: - [Go client](https://github.com/qdrant/go-client) - [Rust client](https://github.com/qdrant/rust-client) - [JavaScript/TypeScript client](https://github.com/qdrant/qdrant-js) - [Python client](https://github.com/qdrant/qdrant-client) - [.NET/C# client](https://github.com/qdrant/qdrant-dotnet) - [Java client](https://github.com/qdrant/java-client) - Community: - [Elixir](https://hexdocs.pm/qdrant/readme.html) - [PHP](https://github.com/hkulekci/qdrant-php) - [Ruby](https://github.com/andreibondarev/qdrant-ruby) - [Java](https://github.com/metaloom/qdrant-java-client) ### Where do I go from here? - [Quick Start Guide](https://github.com/qdrant/qdrant/blob/master/QUICK_START.md) - End to End [Colab Notebook](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing) demo with SentenceBERT and Qdrant - Detailed [Documentation](https://qdrant.tech/documentation/) are great starting points - [Step-by-Step Tutorial](https://qdrant.to/qdrant-tutorial) to create your first neural network project with Qdrant ## Demo Projects <a href="https://replit.com/@qdrant"><img align="right" src="https://replit.com/badge/github/qdrant/qdrant" alt="Run on Repl.it"></a> ### 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!](https://qdrant.to/semantic-search-demo) ### 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!](https://qdrant.to/food-discovery) ### 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!](https://qdrant.to/extreme-classification-demo) <details> <summary> More solutions </summary> <table> <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> </table> <table> <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> </table> </details> ## API ### REST Online OpenAPI 3.0 documentation is available [here](https://qdrant.github.io/qdrant/redoc/index.html). OpenAPI makes it easy to generate a client for virtually any framework or programming language. You can also download raw OpenAPI [definitions](https://github.com/qdrant/qdrant/blob/master/docs/redoc/master/openapi.json). ### gRPC For faster production-tier searches, Qdrant also provides a gRPC interface. You can find gRPC documentation [here](https://qdrant.tech/documentation/quick-start/#grpc). ## Features ### Filtering and Payload Qdrant can attach any JSON payloads to vectors, allowing for both the storage and filtering of data based on the values in these payloads. Payload supports a wide range of data types and query conditions, including keyword matching, full-text filtering, numerical ranges, geo-locations, and more. Filtering conditions can be combined in various ways, including `should`, `must`, and `must_not` clauses, ensuring that you can implement any desired business logic on top of similarity matching. ### Hybrid Search with Sparse Vectors To address the limitations of vector embeddings when searching for specific key