EasyRAG is a lightweight and efficient Retrieval-Augmented Generation (RAG) framework designed for automated network operations. It targets developers and researchers seeking a performant RAG solution that prioritizes accurate question answering, simple deployment, and fast inference without requiring model fine-tuning. The framework achieved top rankings in the CCF AIOps International Challenge 2024.

How It Works

EasyRAG employs a dual-route retrieval strategy: sparse retrieval (BM25) for coarse ranking, followed by an LLM-based reranker (BGE) for fine-grained reordering. This approach, combined with a specific data processing workflow and an LLM for answer generation and optimization, aims for high accuracy. The framework is designed for ease of deployment, requiring no model fine-tuning and minimal VRAM, and offers plug-and-play acceleration schemes to reduce inference latency across the RAG pipeline.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.10.14, 1 GPU with 16GB VRAM. Requires GLM API keys.
• Setup: Run bash scripts/download.sh to download models and bash scripts/process.sh to process data.
• Docs: Project README

Highlighted Details

• Achieved top rankings in the GLM4 track of the CCF AIOps International Challenge 2024.
• Employs BM25 for coarse retrieval and BGE-reranker for reranking.
• Offers flexible RAG pipeline customization with various retrieval and generation strategies.
• Includes efficient inference acceleration for reduced latency.

Maintenance & Community

The project is associated with the CCF AIOps International Challenge 2024. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. The project is presented as a solution for a challenge, and its use for commercial or closed-source applications would require clarification on licensing terms.

Limitations & Caveats

The framework requires specific hardware (16GB GPU) and relies on external API keys (GLM). The primary focus is on automated network operations, and its generalizability to other domains may require further customization.