Vector database
Explore the top vector databases for RAG workflows. Compare features, star counts, and activity levels to find the best fit for your project.
Vector database
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- semi-technologies/weaviate
semi-technologies/weaviate
16,337View on GitHubWeaviate 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
Weaviate is a cloud-native vector database explicitly optimized for retrieval-augmented generation, offering hybrid search (vector keyword metadata), scalable distributed architecture, and direct LLM integration — exactly the core capabilities this search targets.
GoHybrid Search EnginesMetadata FilteringVector Storage - activeloopai/deeplake
activeloopai/deeplake
9,175View on GitHubDeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search tha
DeepLake is a serverless vector database with hybrid search and native RAG support, designed for multimodal AI data and LLM pipelines, which exactly matches the need for a vector database to build retrieval-augmented generation applications.
C++Hybrid Search EnginesVector StorageFull Text Search - chroma-core/chroma
chroma-core/chroma
26,198View on GitHubChroma is a specialized vector database designed to index and retrieve high-dimensional data representations for semantic similarity search. It functions as a comprehensive platform for information retrieval, enabling the storage and management of unstructured documents alongside structured metadata. By mapping data into numerical representations, the system facilitates rapid similarity lookups across large datasets. The platform distinguishes itself through a hybrid search infrastructure that combines dense vector embeddings with sparse keyword and regular expression matching to balance sema
Chroma is a purpose-built vector database that provides embeddings storage, hybrid dense‑sparse search, and metadata filtering, making it a natural and widely used foundation for building Retrieval‑Augmented Generation pipelines with large language models.
RustHybrid Search EnginesHybrid Search InfrastructureMetadata Filtering - pgvector/pgvector
pgvector/pgvector
21,787View on GitHubVector similarity search extension for PostgreSQL.
pgvector is a PostgreSQL extension that turns the database into a full vector similarity search engine with ANN indexing, hybrid search, and metadata filtering, making it a natural fit for RAG pipelines and directly compatible with LLM frameworks like LangChain.
CApproximate Nearest Neighbor SearchVector Similarity Search - qdrant/qdrant
qdrant/qdrant
32,372View on GitHubQdrant is a high-performance vector similarity database designed to store, index, and search high-dimensional vectors alongside structured metadata. It functions as a distributed search engine that manages large-scale data clusters, providing low-latency retrieval and complex filtering capabilities. The system is built to serve as a specialized middleware layer, connecting machine learning pipelines and AI agents to persistent storage for intelligent information retrieval and recommendation tasks. The platform distinguishes itself through advanced retrieval techniques, including support for h
Qdrant is a purpose-built vector database for high-dimensional similarity search with distributed architecture, metadata filtering, and explicit support for AI/ML pipelines, directly matching your need for a scalable RAG backend — it stores and indexes embeddings and supports the ANN search, filtering, and LLM integration that a Retrieval-Augmented Generation pipeline requires.
RustHybrid Search EnginesMetadata FilteringVector Storage - weaviate/weaviate
weaviate/weaviate
15,620View on GitHubWeaviate is an AI-native vector database designed to store and index high-dimensional vector embeddings alongside traditional data objects. It serves as a backend infrastructure for retrieval-augmented generation, enabling applications to ground language model responses in private, context-aware data. The platform distinguishes itself by combining vector similarity search with traditional keyword filtering through a hybrid storage architecture. It integrates directly with external machine learning models to automate the generation of embeddings and perform complex inference tasks during inges
Weaviate is an AI-native vector database explicitly built for retrieval-augmented generation, combining vector similarity search with hybrid keyword filtering and direct model integrations, which makes it a comprehensive fit for building RAG pipelines.
GoHybrid Search EnginesRetrieval Augmented Generation - alibaba/zvec
alibaba/zvec
5,198View on GitHubzvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ
zvec is an embedded vector database engine that stores embeddings, performs hybrid search (vector keyword metadata), and supports LLM integration via plugins for custom embedding models and rerankers, making it a solid fit for building RAG pipelines, though its single-node architecture limits distributed scalability.
C++Approximate Nearest Neighbor SearchHybrid Search EnginesMetadata Filtering - milvus-io/milvus
milvus-io/milvus
44,804View on GitHubMilvus is a specialized vector database engine designed for the indexing, management, and high-speed similarity retrieval of high-dimensional vector embeddings. It functions as a similarity search engine capable of identifying nearest neighbors within large-scale vector spaces, supporting the storage and retrieval of billions of data points while maintaining consistent performance. The system utilizes a distributed architecture that decouples storage, query, and coordination into independent services, allowing for horizontal scaling across clusters. It employs a global indexing mechanism that
Milvus is a purpose-built vector database with distributed architecture, ANN search, and hybrid search capabilities, and its active RAG and LLM ecosystem integrations make it a leading choice for building Retrieval-Augmented Generation pipelines.
GoHybrid Search SystemsRetrieval-Augmented Generation - lance-format/lance
lance-format/lance
6,699View on GitHubLance is a columnar data format and storage layer designed for high-performance random access and the persistence of multimodal data. It functions as a vector database storage system, a multimodal data store, and a versioned dataset manager. The project distinguishes itself as a hybrid search engine that combines vector similarity search and full-text indexing on a single dataset. It provides unified storage for diverse data types including images, audio, and video, utilizing a system that lazy-loads large binary objects only when requested. The system manages dataset evolution through schem
Lance is a columnar storage format and vector database that stores embeddings, supports ANN search, hybrid vector+full-text search, and metadata filtering, making it a solid building block for RAG pipelines, though it lacks explicit LLM integration and distributed architecture.
RustHybrid Search EnginesVector StorageVector Search Indexes - vespa-engine/vespa
vespa-engine/vespa
6,961View on GitHubVespa is a distributed search engine, vector database, and machine learning ranking engine. It serves as an AI search platform designed to handle large-scale document indexing and complex query processing across a cluster of nodes, combining keyword retrieval with high-dimensional embedding storage for semantic similarity search. The platform distinguishes itself by integrating machine learning models directly into the search pipeline to perform real-time inference and ranking. It converts these models into ranking expressions to score and order results based on relevance, while providing a s
Vespa is a distributed vector database and AI search platform that natively supports hybrid (vector keyword) search, ANN, metadata filtering, and ML model ranking, making it a comprehensive fit for building scalable RAG pipelines with LLMs.
JavaVector Search Indexes - meilisearch/meilisearch
meilisearch/meilisearch
58,118View on GitHubMeilisearch is a Rust-based search engine providing typo-tolerant full-text and vector-based semantic search with real-time conversational capabilities.
Meilisearch is a high-performance search engine that now natively supports vector-based semantic search, hybrid (vector+keyword) retrieval, and metadata filtering — all delivered as a RESTful service — making it a solid, ready-to-use vector database for building RAG pipelines with LLMs.
RustDeveloper-Focused Search ToolsDocument Indexing EnginesFinite State Transducers - arangodb/arangodb
arangodb/arangodb
14,091View on GitHubThis project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals
ArangoDB is a multi-model database that integrates built-in vector search with ANN, hybrid search, metadata filtering, and distributed scaling, and is explicitly cited for supporting retrieval-augmented generation, making it a strong fit as the vector database backend for RAG pipelines.
C++Graph DatabasesMulti-Model DatabasesAI Grounding Services - surrealdb/surrealdb
surrealdb/surrealdb
32,397View on GitHubSurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer
SurrealDB is an AI-native multi-model database that natively integrates vector search, embeddings storage, and ML model execution into its declarative query language, making it a comprehensive and scalable foundation for building Retrieval-Augmented Generation pipelines with LLMs.
RustMulti-Model DatabasesAccess Control SystemsACID Transactional Cores - eto-ai/lance
eto-ai/lance
6,671View on GitHubLance is a versioned columnar data format and storage engine designed as a multimodal AI lakehouse. It serves as a vector database storage engine and a cloud object store dataset manager, organizing images, video, audio, and embeddings into a unified format optimized for machine learning workflows. The project distinguishes itself by combining a columnar layout for structured data with a specialized blob store for large multimodal tensors. It implements a hybrid search engine that integrates vector similarity search, full-text search, and SQL analytics on a single dataset, supported by a stor
Lance is a columnar data format and storage engine that functions as a vector database with built-in hybrid search (vector full-text) and embedding management, making it a viable retrieval store for RAG pipelines—though its lakehouse focus on multimodal data storage means it lacks explicit RAG orchestration tools or a distributed-nodes architecture out of the box.
RustHybrid Search EnginesHybrid Search MethodsVector Storage - lancedb/lancedb
lancedb/lancedb
9,031View on GitHubLanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters
LanceDB is a vector database designed for high-dimensional embedding storage and ANN search, with hybrid search and metadata filtering that support RAG pipelines, though it does not advertise direct LLM integrations.
HTMLApproximate Nearest Neighbor SearchHybrid Search EnginesMetadata Filtering - manticoresoftware/manticoresearch
manticoresoftware/manticoresearch
11,819View on GitHubManticoresearch is a high-performance search engine and database designed for indexing and retrieving large datasets. It functions as a full-text search engine, a vector search database, and a SQL-based search database, providing a distributed search cluster architecture. The system provides an alternative to the Elasticsearch stack, offering a compatible API for indexing and searching structured and unstructured data. It distinguishes itself by supporting multiple retrieval methods, including vector matching for similarity search, geospatial queries, and traditional full-text ranking. The p
Manticore Search is a high-performance search engine and vector database with ANN search, embeddings storage, hybrid search, and distributed architecture, making it suitable for RAG pipelines—though it does not include explicit LLM or RAG-specific integrations.
C++Full Text SearchVector Similarity Search - typesense/typesense
typesense/typesense
25,254View on GitHubTypesense is a distributed search engine designed to provide sub-millisecond query latency across massive datasets. It functions as both a high-performance indexing and retrieval engine and a comprehensive search experience platform, offering built-in typo tolerance and tools for managing relevance through synonym configuration, result curation, and complex filtering. The platform distinguishes itself by utilizing in-memory indexing to maintain high-throughput data retrieval and integrating vector database capabilities to support semantic similarity searches. It ensures data consistency and h
Typesense is a distributed search engine with vector database capabilities that supports semantic similarity search, hybrid full-text/vector queries, metadata filtering, and horizontal scaling — all of which are useful for RAG pipelines, though it lacks explicit LLM integration features.
C++Distributed Search EnginesSearch EnginesSearch Experience Platforms - pathwaycom/llm-app
pathwaycom/llm-app
59,341View on GitHubThis project is a data processing engine and AI application platform designed for building production-grade machine learning workflows. It provides a unified programming model that handles both historical batch data and live stream ingestion, enabling the development of real-time ETL pipelines and scalable data transformation workflows. The framework distinguishes itself through differential dataflow execution, which propagates only changes through a pipeline rather than recomputing entire datasets. It supports distributed state management across worker nodes and utilizes incremental stream p
Pathway's llm-app is an application platform that includes vector indexing and retrieval capabilities specifically built for RAG pipelines, directly covering embeddings storage, ANN search, and LLM integration – though its focus on real-time data processing makes it a different take from a standalone vector database, it squarely fits the tool category you are looking for.
Jupyter NotebookData Processing FrameworksDifferential Dataflow EnginesDistributed State Management - marqo-ai/marqo
marqo-ai/marqo
5,022View on GitHubMarqo is an ecommerce product discovery platform, multimodal vector database, and AI search merchandising tool. It provides infrastructure for implementing semantic search and recommendations, allowing shoppers to find products using natural language and images. The platform distinguishes itself through a hybrid ranking pipeline that combines neural semantic scores with business-defined boosting and pinning rules. It features a conversational commerce engine that uses large language models to process user intent and provides a search performance analytics suite for measuring conversion uplift
Marqo is a vector database with hybrid search and LLM-powered conversational queries, so it can serve RAG pipelines, though its primary focus is ecommerce product discovery rather than general-purpose use.
PythonMultimodal IndexersProduct Discovery EnginesSemantic Search Engines - memvid/memvid
memvid/memvid
15,679View on GitHubMemvid is an embedded memory framework designed to provide persistent, versioned context for intelligent agents. It functions as a local vector database library that stores all data within a single binary file, removing the need for external database infrastructure or network dependencies. The system distinguishes itself by integrating in-process vector indexing with append-only versioning, allowing for high-speed semantic similarity searches alongside the ability to track and roll back state changes over time. It includes built-in transparent data encryption and masking to secure sensitive i
Memvid is an embedded vector database library with in-process ANN search and versioned embeddings storage, purpose-built for agent memory and RAG pipelines; it covers core vector retrieval and LLM integration but is local-only and lacks hybrid search and distributed scalability.
RustAgent Memory EnginesAgent Memory StoresVector Databases
