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Qdrant: a high-performance vector database (open source)

Qdrant is a Rust vector search engine with advanced payload filtering, built for semantic search at scale. An open-source alternative to Pinecone and Weaviate.

Published on May 29, 20264 min readView on GitHub

When vector search needs to scale and filter by many criteria at once, Qdrant is one of the best open-source options. It is a similarity search engine written in Rust, with advanced payload filtering, a manageable alternative to Pinecone and Weaviate.

What is Qdrant?

Qdrant uses HNSW indexing with rich JSON payload filtering during search, offers quantization to reduce memory and runs distributed. It has gRPC and REST APIs and official clients for Python, Rust, Go and JS.

Key features

  • HNSW indexing with JSON payload filtering inside the search
  • Quantization (scalar, product, binary) to reduce memory
  • Distributed and sharded deployments
  • gRPC and REST APIs, with official clients for several languages

How Reche uses it

For RAG at scale with business filters, a database like Qdrant is often the right choice. Reche designs the retrieval layer according to each product's volume and requirements, with no over-engineering or unnecessary cost.

Want to implement this in your product?

Reche's initial diagnosis defines scope, timeline, and budget. Credited to the project if you move forward.