Overview

Mem0 includes built-in support for various popular databases. Memory can utilize the database provided by the user, ensuring efficient use for specific needs.

Supported Vector Databases

See the list of supported vector databases below.

<Note> The following vector databases are supported in the Python implementation. The TypeScript implementation currently only supports Qdrant, Redis, Valkey, Vectorize and in-memory vector database. </Note> <CardGroup cols={3}> <Card title="Qdrant" href="/components/vectordbs/dbs/qdrant"></Card> <Card title="Chroma" href="/components/vectordbs/dbs/chroma"></Card> <Card title="PGVector" href="/components/vectordbs/dbs/pgvector"></Card> <Card title="Upstash Vector" href="/components/vectordbs/dbs/upstash-vector"></Card> <Card title="Milvus" href="/components/vectordbs/dbs/milvus"></Card> <Card title="Pinecone" href="/components/vectordbs/dbs/pinecone"></Card> <Card title="MongoDB" href="/components/vectordbs/dbs/mongodb"></Card> <Card title="Azure" href="/components/vectordbs/dbs/azure"></Card> <Card title="Redis" href="/components/vectordbs/dbs/redis"></Card> <Card title="Valkey" href="/components/vectordbs/dbs/valkey"></Card> <Card title="Elasticsearch" href="/components/vectordbs/dbs/elasticsearch"></Card> <Card title="OpenSearch" href="/components/vectordbs/dbs/opensearch"></Card> <Card title="Supabase" href="/components/vectordbs/dbs/supabase"></Card> <Card title="Vertex AI" href="/components/vectordbs/dbs/vertex_ai"></Card> <Card title="Weaviate" href="/components/vectordbs/dbs/weaviate"></Card> <Card title="FAISS" href="/components/vectordbs/dbs/faiss"></Card> <Card title="LangChain" href="/components/vectordbs/dbs/langchain"></Card> <Card title="Amazon S3 Vectors" href="/components/vectordbs/dbs/s3_vectors"></Card> <Card title="Databricks" href="/components/vectordbs/dbs/databricks"></Card> <Card title="Turbopuffer" href="/components/vectordbs/dbs/turbopuffer"></Card> </CardGroup>

Usage

To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and Qdrant will be used as the vector database.

For a comprehensive list of available parameters for vector database configuration, please refer to Config.

Common issues

Using Model with Different Dimensions

If you are using a customized model with different dimensions other than 1536 (for example, 768), you may encounter the following error:

ValueError: shapes (0,1536) and (768,) not aligned: 1536 (dim 1) != 768 (dim 0)

You can add "embedding_model_dims": 768, to the config of the vector_store to resolve this issue.