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suggestions to update README (#3290)
* suggestions to update README * grammarly * Update README.md Co-authored-by: Nirant <NirantK@users.noreply.github.com> * Update README.md Co-authored-by: Nirant <NirantK@users.noreply.github.com> * Update README.md Co-authored-by: Nirant <NirantK@users.noreply.github.com> * Update README.md Co-authored-by: Nirant <NirantK@users.noreply.github.com> --------- Co-authored-by: Nirant <NirantK@users.noreply.github.com>
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Andrey Vasnetsov
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generall
generall
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README.md
54
README.md
@@ -66,15 +66,18 @@ qdrant = QdrantClient("http://localhost:6333") # Connect to existing Qdrant inst
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Qdrant offers the following client libraries to help you integrate it into your application stack with ease:
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- Official: [Go client](https://github.com/qdrant/go-client)
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- Official: [Rust client](https://github.com/qdrant/rust-client)
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- Official: [JavaScript/TypeScript client](https://github.com/qdrant/qdrant-js)
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- Official: [Python client](https://github.com/qdrant/qdrant-client)
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- Official: [.NET/C# client](https://github.com/qdrant/qdrant-dotnet)
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- Community: [Elixir](https://hexdocs.pm/qdrant/readme.html)
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- Community: [PHP](https://github.com/hkulekci/qdrant-php)
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- Community: [Ruby](https://github.com/andreibondarev/qdrant-ruby)
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- Community: [Java](https://github.com/metaloom/qdrant-java-client)
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- Official:
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- [Go client](https://github.com/qdrant/go-client)
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- [Rust client](https://github.com/qdrant/rust-client)
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- [JavaScript/TypeScript client](https://github.com/qdrant/qdrant-js)
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- [Python client](https://github.com/qdrant/qdrant-client)
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- [.NET/C# client](https://github.com/qdrant/qdrant-dotnet)
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- [Java client](https://github.com/qdrant/java-client)
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- Community:
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- [Elixir](https://hexdocs.pm/qdrant/readme.html)
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- [PHP](https://github.com/hkulekci/qdrant-php)
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- [Ruby](https://github.com/andreibondarev/qdrant-ruby)
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- [Java](https://github.com/metaloom/qdrant-java-client)
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### Where do I go from here?
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@@ -169,31 +172,40 @@ For faster production-tier searches, Qdrant also provides a gRPC interface. You
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### Filtering and Payload
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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.
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Qdrant can attach any JSON payloads to vectors, allowing for both the storage and filtering of data based on the values in these payloads.
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Payload supports a wide range of data types and query conditions, including keyword matching, full-text filtering, numerical ranges, geo-locations, and more.
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### Rich Data Types
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Filtering conditions can be combined in various ways, including `should`, `must`, and `must_not` clauses,
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ensuring that you can implement any desired business logic on top of similarity matching.
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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.
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### Query Planning and Payload Indexes
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### Hybrid Search with Sparse Vectors
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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.
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To address the limitations of vector embeddings when searching for specific keywords, Qdrant introduces support for sparse vectors in addition to the regular dense ones.
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### SIMD Hardware Acceleration
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Sparse vectors can be viewed as an generalisation of BM25 or TF-IDF ranking. They enable you to harness the capabilities of transformer-based neural networks to weigh individual tokens effectively.
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Utilizing modern CPU x86-x64 architectures, Qdrant delivers faster search performance on modern hardware.
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### Write-Ahead Logging
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### Vector Quantization and On-Disk Storage
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Qdrant provides multiple options to make vector search cheaper and more resource-efficient.
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Built-in vector quantization reduces RAM usage by up to 97% and dynamically manages the trade-off between search speed and precision.
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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.
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### Distributed Deployment
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As of [v0.8.0](https://github.com/qdrant/qdrant/releases/tag/v0.8.0), Qdrant supports distributed deployment. Multiple Qdrant machines form a cluster for horizontal scaling, coordinated through the [Raft](https://raft.github.io/) protocol.
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Qdrant offers comprehensive horizontal scaling support through two key mechanisms:
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1. Size expansion via sharding and throughput enhancement via replication
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2. Zero-downtime rolling updates and seamless dynamic scaling of the collections
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### Stand-alone
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Qdrant operates independently, without reliance on external databases or orchestration controllers, simplifying configuration.
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### Highlighted Features
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* **Query Planning and Payload Indexes** - leverages stored payload information to optimize query execution strategy.
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* **SIMD Hardware Acceleration** - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
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* **Async I/O** - uses `io_uring` to maximize disk throughput utilization even on a network-attached storage.
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* **Write-Ahead Logging** - ensures data persistence with update confirmation, even during power outages.
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# Integrations
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