GraphRAG Vectors

This package provides vector store implementations for GraphRAG with support for multiple backends including LanceDB, Azure AI Search, and Azure Cosmos DB. It offers both a convenient configuration-driven API and direct factory access for creating and managing vector stores with flexible index schema definitions.

Basic usage with the utility function (recommended)

This demonstrates the recommended approach to create a vector store using the create_vector_store convenience function with configuration objects that specify the store type and index schema. The example shows setting up a LanceDB vector store with a defined index configuration, then connecting to it and creating the index for vector operations.

Open the notebook to explore the basic usage with utility function example code

Basic usage implementing the factory directly

This example shows a different approach to create vector stores by directly using the vector_store_factory with enum types and dictionary-based initialization arguments. This method provides more direct control over the factory creation process while bypassing the convenience function layer.

Open the notebook to explore the basic usage using factory directly example code

Supported Vector Stores

• LanceDB: Local vector database
• Azure AI Search: Azure's managed search service with vector capabilities
• Azure Cosmos DB: Azure's NoSQL database with vector search support

Custom Vector Store

You can register custom vector store implementations:

Open the notebook to explore the custom vector example code

Configuration

Vector stores are configured using:

• VectorStoreConfig: baseline parameters for the store
• IndexSchema: Schema configuration for the specific index to create/connect to (index name, field names, vector size)