RAG with Qdrant
Build high-performance RAG pipelines using Qdrant's vector database. Rust-powered, filter-rich, and horizontally scalable — Qdrant delivers millisecond-latency semantic search with payload-aware filtering.
Why Qdrant for RAG
Qdrant is a purpose-built vector database written in Rust. It stores embeddings alongside JSON payloads, enabling hybrid search that combines semantic similarity with exact-match metadata filters. For RAG workloads, this means you can retrieve documents by meaning while constraining results to specific dates, categories, or access scopes — all in a single query.
Rust Core
Zero-GC pauses, predictable latency under load.
Payload Indexing
Filter on any JSON field without post-processing.
Quantization
Scalar, product, and binary quantization for memory efficiency.
gRPC Native
High-throughput ingestion with streaming upserts.