Qdrant
Qdrant is a vector database that stores embeddings as points and retrieves the most similar ones at query time, with payload-based partitioning so one shared instance can isolate each user's or tenant's vectors.
Description
Qdrant is an open-source vector search engine written in Rust. It stores vectors (embeddings) together with JSON payloads and returns the nearest neighbours for a query vector, which is how agents and RAG pipelines retrieve semantically similar memories or documents. Search can be filtered by payload conditions and combined across dense and sparse vectors. For multi-user deployments, Qdrant supports payload-based partitioning within a single collection so each tenant can only access their own vectors.
Solution
Qdrant is the retrieval backend an agent calls, not an agent loop. Documents or memories are embedded and upserted as points with payloads; at query time the agent embeds its query and asks Qdrant for the top-k nearest points, optionally constrained by a payload filter. In multi-tenant use the query carries a tenant identifier filter so the search only ranges over that tenant's vectors, and the returned items are fed back into the agent's context.
Primary use cases
- storing embeddings for semantic retrieval
- nearest-neighbour vector search for RAG and agent memory
- payload-filtered and hybrid search
- multi-tenant vector isolation
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