---
title: 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>
</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](./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.
