Pinecone
Managed vector database for AI applications
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Discover top open-source software, updated regularly with real-world adoption signals.
Pythonic vector database with CRUD, sharding, and replication
A lean Python vector database built on DocArray and Jina, offering full CRUD operations, flexible deployment from local to cloud, and seamless scalability through sharding and replication.
vectordb is a Pythonic vector database designed for developers who need efficient semantic search without unnecessary complexity. Built on DocArray's retrieval engine and Jina's scalability framework, it delivers a complete suite of CRUD operations with straightforward deployment options spanning local, on-premise, and cloud environments.
Define schemas using DocArray's dataclass syntax, then index and search embeddings with pre-built backends like InMemoryExactNN or HNSW. The unified API supports both embedded library usage and client-server architectures over gRPC, HTTP, and WebSocket protocols. Horizontal scaling through sharding and replication ensures production readiness, while Jina AI Cloud integration enables one-command deployment with managed infrastructure.
Start with local prototyping using the in-process library, then transition to a served instance with configurable replicas and shards. Deploy to Jina AI Cloud via the vectordb deploy command for globally accessible endpoints. The consistent API across deployment modes eliminates code rewrites when moving from development to production.
When teams consider VectorDB, these hosted platforms usually appear on the same shortlist.
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LLM Context Retrieval
Enrich language model prompts by retrieving semantically relevant documents from indexed embeddings, improving generation quality with contextual grounding.
Multimodal Content Discovery
Enable users to search across text, image, and audio embeddings using a unified schema, delivering cross-modal similarity results through a single API.
Recommendation Systems
Index user and item embeddings to power real-time recommendation engines, scaling horizontally with sharding as catalog and traffic grow.
Rapid Prototype to Production
Develop and test vector search logic locally, then deploy to Jina AI Cloud with a single command, maintaining identical code across environments.
vectordb includes pre-built backends such as InMemoryExactNNVectorDB for brute-force exact search and HNSWVectorDB for approximate nearest neighbor search using the HNSW algorithm.
Yes. You can use vectordb as a local library or self-host the service on your own infrastructure using the serve method with gRPC, HTTP, or WebSocket protocols.
Use the replicas parameter for vertical load distribution and the shards parameter for horizontal data partitioning when calling the serve method.
DocArray provides the vector search algorithms and schema definitions, Jina handles scalable serving and deployment, and vectordb wraps both into a cohesive database experience.
vectordb provides full CRUD operations including index, search, update, and delete methods, all accessible through the same unified API.
Project at a glance
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