semi-technologies/weaviate
Weaviate
Weaviate is a cloud-native vector database and distributed vector store designed to save high-dimensional vectors alongside structured data. It functions as a hybrid search engine that combines vector similarity, keyword matching, and structured metadata filtering within a single query.
The system is optimized for retrieval-augmented generation, integrating vector search with generative AI and reranking to power question-and-answer workflows. It distinguishes itself through the ability to merge semantic search with traditional keyword queries and structured metadata filters to improve result precision.
The platform covers broad capability areas including enterprise data retrieval with role-based access control, multi-tenant data partitioning for horizontal scaling, and memory optimization via vector data compression. It also provides tools for managing the data lifecycle through automated expiration policies and external vectorizer integration for embedding ingestion.
Features
- Hybrid Search Engines - Functions as a hybrid search engine combining vector similarity, keyword matching, and metadata filtering.
- Retrieval-Augmented Generation - Powers generative AI by retrieving relevant document context from a vector database to inform model responses.
- Search-Enhanced Generation - Integrates retrieval-augmented generation and reranking to power generative question-and-answer workflows directly within the database.
- Distributed Vector Stores - Implements a horizontally scalable, replicated data system for managing large-scale vector embeddings.
- Graph-Based Indexing - Implements HNSW graph-based indexing for fast approximate nearest neighbor searches.
- Horizontal Scaling - Distributes data across multiple nodes using replication and multi-tenancy to handle production workloads.
- Hybrid Search - Combines vector similarity search with keyword matching and metadata filtering to refine search results.
- Metadata Filtering - Provides structured metadata filtering to refine the precision of vector similarity search results.
- Multi-Tenant Data Management - Isolates customer data into separate logical namespaces to ensure security and horizontal scalability.
- Semantic Search - Implements semantic search using vector embeddings to match meanings and concepts instead of exact keywords.
- Vector Databases - Provides a system for storing and scaling high-dimensional embeddings with memory optimization.
- RAG Optimizations - Provides a vector store optimized for RAG, integrating similarity search with generative AI workflows.
- Vector Similarity Search - Locates similar data objects by calculating the distance between high-dimensional embeddings.
- Vector Storage - Saves high-dimensional vectors alongside structured data objects to enable both semantic and filtered retrieval.
- Vector-Aware Data Ingestion - Supports the conversion of raw data into vector embeddings via external models during the ingestion pipeline.
- Enterprise Data Platforms - Provides an enterprise-grade platform with role-based access control and multi-tenancy for secure organizational search.
- Product Quantization - Reduces memory usage of high-dimensional vectors through product quantization and centroid-based approximation.
- Log-Structured Merge-Trees - Utilizes log-structured merge-trees to optimize write throughput and store structured metadata.
- Vector Quantization - Implements vector quantization and multi-vector encoding to reduce the memory footprint of high-dimensional embeddings.
- External Integrations - Provides integration with remote machine learning models via API to generate embeddings during data ingestion.
- Raft Consensus Implementations - Uses the Raft consensus protocol to coordinate data replication and cluster state for high availability.
- Role-Based Access Control - Provides role-based access control to restrict data access and administrative actions based on defined user roles.
- Databases - Vector search engine with a GraphQL interface.
- Vector Databases - Vector search engine that uses machine learning for data storage.
Star history
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