​
Available Components

Astra Vector Store

Suitable for serverless vector database applications using DataStax Astra DB

Chroma Vector Store

Suitable for open-source embedding database needs with efficient querying capabilities

Doc Store Vector Stores

Enables efficient document search and retrieval from a Document Store

Elasticsearch Vector Store

Provides distributed search and analytics with advanced text and vector search capabilities

Faiss Vector Store

Enables efficient similarity search and clustering of dense vectors at scale

In-Memory Vector Store

Provides fast in-memory operations for small to medium-sized datasets

Meilisearch

Combines keyword and semantic search for fast and relevant search results

MongoDB Atlas Vector Store

Offers managed, cloud-based vector database capabilities in MongoDB environments

OpenSearch Vector Store

Enables large-scale distributed search and analytics with real-time capabilities

Pinecone Vector Store

Offers managed vector database capabilities for large-scale similarity search

Postgres Vector Store

Integrates vector search capabilities with PostgreSQL using pgvector extension

Qdrant Vector Store

Provides scalable vector search with efficient storage and retrieval of embeddings

Redis Vector Store

Enables fast, in-memory vector similarity searches with low latency

SingleStore Vector Store

Provides local vector store implementation using LlamaIndex for quick setup

Supabase Vector Store

Offers managed PostgreSQL database with vector capabilities using pgvector

Upstash Vector Store

Enables fast vector operations in serverless environments

Vectara Vector Store

Provides LLM-powered search-as-a-service with advanced retrieval options

Weaviate Vector Store

Enables scalable vector search for semantic search and recommendations

Zep Collection - Cloud

Provides cloud-based vector store capabilities for scalable LLM applications

Zep Collection - Open Source

Offers open-source vector store implementation for LLM applications
These vector store components cater to a wide range of use cases, from local development to large-scale production environments, offering various options for storing, indexing, and retrieving vector embeddings efficiently.