Pinecone
Managed vector database for AI applications
VectorDB
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.
Overview
Purpose and Audience
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.
Core Capabilities
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.
Deployment Flexibility
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.
Highlights
Full CRUD operations (index, search, update, delete) with DocArray schema definitions
Multiple backends including InMemoryExactNN and HNSW for different performance profiles
Native sharding and replication for horizontal scalability
One-command deployment to Jina AI Cloud with gRPC/HTTP/WebSocket protocols
Pros
- Unified API for local and remote usage eliminates deployment friction
- Pythonic design with DocArray dataclass schemas for type-safe document modeling
- Built-in serving capabilities with configurable replicas and shards
- Seamless cloud deployment through Jina AI Cloud integration
Considerations
- Depends on DocArray and Jina ecosystem; less flexibility for standalone use
- Pre-built backends may limit algorithmic customization compared to lower-level libraries
- Cloud deployment tied to Jina AI Cloud platform
- Documentation focuses on basic examples; advanced tuning guidance is limited
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Fit guide
Great for
- Python developers building semantic search into applications without infrastructure overhead
- Teams prototyping locally who need a clear path to production deployment
- Projects requiring multimodal embedding search with straightforward CRUD workflows
- Use cases needing rapid cloud deployment with managed scaling
Not ideal when
- Applications requiring deep customization of vector indexing algorithms
- Teams committed to non-Python technology stacks
- Projects needing vendor-neutral cloud deployment across multiple providers
- Use cases demanding sub-millisecond latency guarantees at extreme scale
How teams use it
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.
Tech snapshot
Python94%
Shell6%
Dockerfile1%
Frequently asked questions
What vector indexing algorithms does vectordb support?
vectordb includes pre-built backends such as InMemoryExactNNVectorDB for brute-force exact search and HNSWVectorDB for approximate nearest neighbor search using the HNSW algorithm.
Can I run vectordb without deploying to Jina AI Cloud?
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.
How do I scale vectordb for production workloads?
Use the replicas parameter for vertical load distribution and the shards parameter for horizontal data partitioning when calling the serve method.
What is the relationship between vectordb, DocArray, and Jina?
DocArray provides the vector search algorithms and schema definitions, Jina handles scalable serving and deployment, and vectordb wraps both into a cohesive database experience.
Does vectordb support updates and deletes, or only indexing and search?
vectordb provides full CRUD operations including index, search, update, and delete methods, all accessible through the same unified API.
Project at a glance
Dormant- Stars
- 640
- Watchers
- 640
- Forks
- 49
LicenseApache-2.0
Repo age2 years old
Last commit2 years ago
Self-hostingSupported
Primary languagePython
Last synced 4 hours ago