HiRAG

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HiRAG is an open-source research tool for retrieval-augmented generation with hierarchical knowledge. It significantly improves RAG performance compared to other methods by leveraging hierarchical knowledge structures.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is HiRAG?

HiRAG is an official repository for the paper "Retrieval-Augmented Generation with Hierarchical Knowledge," accepted to EMNLP 2025 Findings. This tool provides a robust framework for implementing and evaluating hierarchical knowledge in RAG systems. It allows users to index context, perform hierarchical searches, and integrate with various LLMs like DeepSeek, ChatGLM, and OpenAI. The repository includes examples for quick start, evaluation procedures, and comparative results against other RAG approaches, demonstrating substantial improvements in comprehensiveness, empowerment, and diversity of generated answers.

Best used for

Ideal for AI researchers and NLP engineers who need to develop and evaluate advanced Retrieval-Augmented Generation (RAG) systems, implement hierarchical knowledge structures, and compare performance against existing RAG methods. Especially valuable for academic research and experimental development in natural language processing.

Common actions

improve RAG performance

evaluate RAG models

implement hierarchical knowledge

integrate LLMs

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Capabilities

Key features

Hierarchical knowledge integration
Context indexing
Multi-LLM integration
Comparative evaluation
Customizable search modes
Graph database insertion

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the primary benefit of using HiRAG over traditional RAG methods?

HiRAG significantly improves the comprehensiveness, empowerment, and diversity of generated answers by incorporating hierarchical knowledge. This approach allows for more nuanced and contextually rich retrieval, leading to higher quality outputs compared to naive RAG, GraphRAG, LightRAG, FastGraphRAG, and KAG.

Which Large Language Models (LLMs) can be integrated with HiRAG?

HiRAG is designed for flexible integration with various LLMs. The repository provides examples and configurations for using DeepSeek, ChatGLM, and OpenAI APIs. Users can set up their API keys and LLM configurations within the `config.yaml` file to utilize these models.

How can I evaluate the performance of HiRAG?

The repository includes a comprehensive evaluation procedure, demonstrated with the Mix dataset. Users can extract context, insert it into a graph database, and test different HiRAG approaches (e.g., hi, naive, hi_nobridge, hi_local, hi_global, hi_bridge) using provided scripts. Evaluation results can then be generated and compared.

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