agent-framework/docs/design/vector-stores.md

Vector Stores and Embedding Clients

A vector store is component that provides a unified interface for interacting with different vector databases, similar to model clients. It exposes indexing and querying methods, including vector, text-based and hybrid queries.

The details can be filled in based on the existing vector abstraction in Semantic Kernel.

The framework provides pre-built vector stores (already exist in Semantic Kernel):

• Azure AI Search
• Cosmos DB
• Chroma
• Couchbase
• Elasticsearch
• Faiss
• In-memory
• JDBC
• MongoDB
• Pinecone
• Postgres
• Qdrant
• Redis
• SQL Server
• SQLite
• Volatile
• Weaviate

Many vector store implementations will require embedding clients to function. An embedding client is a component that implements a unified interface to interact with different embedding models.

The framework provides a set of pre-built embedding clients:

• TBD.