vearch
Vearch is a distributed vector search engine designed for AI-native applications, enabling scalable and efficient similarity search across large datasets.
About this tool
vearch
Vearch is a cloud-native distributed vector database designed for efficient similarity search of embedding vectors in AI applications.
- Website/Source: https://github.com/vearch/vearch
- Category: vector-database-engines
- Tags: open-source, distributed, vector-search, ai
- License: Apache-2.0
Features
- Distributed & Cloud-Native: Built for scalable, distributed deployments.
- Hybrid Search: Supports both vector search and scalar filtering.
- Performance: Fast vector retrieval, capable of searching millions of objects in milliseconds.
- Scalability & Reliability: Supports replication and elastic scaling out.
- Replication: Raft-based partition replication for reliability.
- RESTful APIs: Provides APIs for upsert, delete, search, and query operations.
- Multiple SDKs: Official SDKs for Python and Go; Java SDK under development.
- Integration with AI Frameworks:
- Langchain (Python)
- LlamaIndex
- Langchaingo (Go)
- LangChain4j (Java)
- Visual Search Support: Can be used to build large-scale visual search systems (e.g., indexing billions of images).
- Core Engine: Uses Gamma, based on Faiss, for vector storage, indexing, and retrieval.
- Easy Deployment:
- Kubernetes (Helm charts)
- Docker Compose (standalone and cluster modes)
- Docker and source compilation supported
- Component Architecture:
- Master: Schema management, metadata, resource coordination
- Router: REST API, request routing, result merging
- PartitionServer (PS): Hosts document partitions with replication
- OpenAPI Support: For API documentation and integration
- Backup & Compression: Supports backup with zstd compression
- Flamegraph UI: Web UI support for performance flamegraphs
Pricing
Vearch is open-source and free to use under the Apache-2.0 license. No paid plans are mentioned.
Usage Examples
- As a memory backend for AI and RAG (retrieval-augmented generation) applications
- As a vector store for frameworks like Langchain and LlamaIndex
- Building large-scale image and visual search systems
Deployment
- Kubernetes: via Helm repository or local charts
- Docker Compose: standalone and cluster modes
- Docker and source compilation options
References
- Vearch Documentation
- API Documentation
- Academic Paper: "The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform"
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