root/local/: graphrag-vectors-3.0.2 metadata and description

Simple index

GraphRAG vector store package.

author Mónica Carvajal
author_email Alonso Guevara Fernández <alonsog@microsoft.com>, Andrés Morales Esquivel <andresmor@microsoft.com>, Chris Trevino <chtrevin@microsoft.com>, David Tittsworth <datittsw@microsoft.com>, Dayenne de Souza <ddesouza@microsoft.com>, Derek Worthen <deworthe@microsoft.com>, Gaudy Blanco Meneses <gaudyb@microsoft.com>, Ha Trinh <trinhha@microsoft.com>, Jonathan Larson <jolarso@microsoft.com>, Josh Bradley <joshbradley@microsoft.com>, Kate Lytvynets <kalytv@microsoft.com>, Kenny Zhang <zhangken@microsoft.com>, Nathan Evans <naevans@microsoft.com>, Rodrigo Racanicci <rracanicci@microsoft.com>, Sarah Smith <smithsarah@microsoft.com>
classifiers
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3.11
  • Programming Language :: Python :: 3.12
  • Programming Language :: Python :: 3.13
description_content_type text/markdown
license MIT
metadata_version 2.4
project_urls
  • Source, https://github.com/microsoft/graphrag
requires_dist
  • azure-core~=1.32
  • azure-cosmos~=4.9
  • azure-identity~=1.25
  • azure-search-documents~=11.6
  • graphrag-common==3.0.2
  • lancedb~=0.24.1
  • numpy~=2.1
  • pyarrow~=22.0
  • pydantic~=2.10
requires_python <3.14,>=3.11

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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

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: