Vector Stores#

Vector stores contain embedding vectors of ingested document chunks (and sometimes the document chunks as well).

Simple Vector Store#

By default, LlamaIndex uses a simple in-memory vector store thatโ€™s great for quick experimentation. They can be persisted to (and loaded from) disk by calling vector_store.persist() (and SimpleVectorStore.from_persist_path(...) respectively).

Vector Store Options & Feature Support#

LlamaIndex supports over 20 different vector store options. We are actively adding more integrations and improving feature coverage for each.

Vector Store

Type

Metadata Filtering

Hybrid Search

Delete

Store Documents

Async

Apache Cassandraยฎ

self-hosted / cloud

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Astra DB

cloud

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Azure Cognitive Search

cloud

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Azure CosmosDB MongoDB

cloud

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ChatGPT Retrieval Plugin

aggregator

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Chroma

self-hosted

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DashVector

cloud

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Deeplake

self-hosted / cloud

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DocArray

aggregator

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DynamoDB

cloud

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Elasticsearch

self-hosted / cloud

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FAISS

in-memory

txtai

in-memory

Jaguar

self-hosted / cloud

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LanceDB

cloud

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Lantern

self-hosted / cloud

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Metal

cloud

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MongoDB Atlas

self-hosted / cloud

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MyScale

cloud

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Milvus / Zilliz

self-hosted / cloud

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Neo4jVector

self-hosted / cloud

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OpenSearch

self-hosted / cloud

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Pinecone

cloud

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Postgres

self-hosted / cloud

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pgvecto.rs

self-hosted / cloud

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Qdrant

self-hosted / cloud

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Redis

self-hosted / cloud

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Simple

in-memory

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SingleStore

self-hosted / cloud

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Supabase

self-hosted / cloud

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Tair

cloud

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TencentVectorDB

cloud

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Timescale

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Typesense

self-hosted / cloud

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Upstash

cloud

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Weaviate

self-hosted / cloud

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For more details, see Vector Store Integrations.