Self-querying retriever
Learn about how the self-querying retriever works here.
📄️ Deep Lake
Deep Lake is a multimodal database for building AI applications
📄️ Chroma
Chroma is a database for building AI applications with embeddings.
📄️ DashVector
DashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.
📄️ Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine.
📄️ Milvus
Milvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.
📄️ MyScale
MyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.
📄️ OpenSearch
OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on Apache Lucene.
📄️ Pinecone
Pinecone is a vector database with broad functionality.
📄️ Qdrant
Qdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.
📄️ Redis
Redis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.
📄️ Supabase
Supabase is an open-source Firebase alternative.
📄️ Timescale Vector (Postgres) self-querying
Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.
📄️ Vectara
Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation
📄️ Weaviate
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from