Is Flexible GraphRAG customizable for different environments?

Absolutely. Its configurable architecture supports various vector/graph databases, search engines, and LLM providers (e.g., OpenAI, Ollama), allowing seamless integration into diverse tech stacks.

What is Flexible GraphRAG?

Flexible GraphRAG is a platform that builds knowledge graphs from diverse content to power hybrid search (vector, full-text, graph) and AI-powered Q&A capabilities.

How does its hybrid search work?

It combines vector similarity search, BM25 full-text search, and knowledge graph traversal to provide comprehensive and highly relevant document retrieval.

What document types can Flexible GraphRAG process?

It handles over 15 formats, including PDFs, Microsoft Office files, HTML, XML, Markdown, and image formats (with OCR), ingested from filesystems, CMIS, or Alfresco.

What are the main benefits of using Flexible GraphRAG?

It enhances retrieval accuracy with multi-modal search, enables deeper insights via knowledge graphs, supports AI-driven Q&A, and integrates seamlessly with existing enterprise systems.

Flexible GraphRAG

Name: Flexible GraphRAG
Author: stevereiner

bystevereiner

•

114

•

Developer Tools

Data Science & ML

Content Management

Enables hybrid search and AI-powered Q&A by building knowledge graphs from diverse content sources using a configurable architecture.

Flexible GraphRAG is a comprehensive platform designed to streamline document processing, automate knowledge graph construction, and facilitate both RAG (Retrieval Augmented Generation) and GraphRAG capabilities. It offers a highly configurable hybrid search system that combines vector similarity, full-text search (BM25), and knowledge graph traversal across multiple data sources. Built with LlamaIndex, it provides robust backend services via FastAPI and an MCP server, alongside intuitive Angular, React, and Vue UI clients, allowing for seamless ingestion and querying of information.

Key Features

01Automated knowledge graph extraction from documents with LlamaIndex for graph-based reasoning.

021 GitHub stars

03Hybrid search combining vector, full-text (BM25), and graph traversal for comprehensive retrieval.

04FastAPI backend with REST API and MCP server providing specialized tools for AI assistant workflows.

05Configurable architecture supporting various vector databases, graph databases, search engines, and LLM providers.

06Multi-source document ingestion from filesystems, CMIS repositories, and Alfresco systems.

Use Cases

01Performing comprehensive research and fact-checking across large document corpuses.

02Building and leveraging knowledge graphs for advanced data reasoning and retrieval.

03Generating AI-powered answers to natural language questions based on internal documents.