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
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, andQdrant 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.