## Supported Vector Databases

See the list of supported vector databases below.

The following vector databases are supported in the Python implementation. The TypeScript implementation currently supports Qdrant, Redis, PGVector, Supabase, LangChain, Azure AI Search, Vectorize, Amazon S3 Vectors, Milvus, Neptune Analytics, and an in-memory store.

**Qdrant**  
 **Chroma**  
 **PGVector**  
 **Upstash Vector**  
 **Milvus**  
 **Pinecone**  
 **MongoDB**  
 **Azure**  
 **Redis**  
 **Valkey**  
 **Elasticsearch**  
 **OpenSearch**  
 **Supabase**  
 **Vertex AI**  
[**Weaviate**](https://docs.mem0.ai/components/vectordbs/dbs/weaviate)  
[**FAISS**](https://docs.mem0.ai/components/vectordbs/dbs/faiss)  
 **LangChain**  
 **Amazon S3 Vectors**  
 **Neptune Analytics**  
 **Databricks**  
 **Turbopuffer**

## 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](https://docs.mem0.ai/components/vectordbs/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.