qdrant-vector-search

Hybrid search combines dense vector embeddings with complex metadata filtering to improve retrieval accuracy. This approach facilitates building production-ready RAG pipelines that require low latency and high retrieval precision across large-scale knowledge bases.

Scaling vector search across millions of documents requires distributed horizontal scaling with sharding, replication, and Raft consensus. This architecture ensures sub-millisecond search performance and high availability for real-time recommendation engines.

To set up a vector database for semantic search, initialize a new collection specifying the vector size and distance metric, such as cosine distance. This prepares the storage structure for efficient similarity search and retrieval operations.

Yes, memory-efficient quantization is supported to manage high-dimensional vector data. This reduces the memory footprint of stored embeddings while maintaining the ability to perform fast vector similarity search.

Yes, you can filter search results using complex metadata alongside dense vector embeddings. This hybrid search capability restricts the vector similarity search space to documents matching specific metadata criteria, improving accuracy.

Horizontal scaling is needed for vector similarity search when your workload exceeds a single machine's capacity. It supports distributed environments by sharding data and replicating collections to maintain low latency under heavy query loads.