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Easyrag

Easyrag is an MIT-licensed Python framework for retrieval-augmented question answering in automated network operations, with components for document ingestion, retrieval, reranking and language-model answer generation.

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Independent OpenViST project profile, based on linked sources.

Easyrag — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Easyrag when you want a code-based starting point for network-operations question answering and can provide the documented Python, GPU and GLM-key prerequisites.

Who it is for

  • Developers building RAG systems for network-operations knowledge.
  • Technical teams adapting document retrieval and answer-generation workflows for operational troubleshooting.

Documented uses

  • Process operational documents into searchable chunks and metadata.
  • Retrieve and combine candidate material using sparse or dense search.
  • Rerank retrieved content and generate answers grounded in the selected material.
  • Expose the documented workflow through an API and a Streamlit WebUI.
Before you choose

Requirements & limits

The public GitHub repository identifies the project as MIT-licensed.

  • The documented runtime requires Python 3.10.14 and at least one GPU with 16 GB of memory.
  • The README requires GLM keys in src/easyrag.yaml.
  • The described pipeline uses BM25 retrieval and BGE reranking alongside dense retrieval and fusion options.
  • The documented setup includes model downloads and a ZEDX-specific data-processing script.
  • The project documents API and WebUI startup, but does not detail API requests or WebUI tasks in the README.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare Easyrag and launch its documented API

Install the documented dependencies, prepare models and ZEDX data, then start the API service.

Before you start

  • Python 3.10.14
  • At least one GPU with 16 GB of memory
  • GLM keys configured in src/easyrag.yaml
  • A clone of the Easyrag repository

Steps

  1. Install the Python dependencies with pip install -r requirements.txt.

  2. Initialize Git LFS, download models with bash scripts/download.sh, and process ZEDX data with bash scripts/process.sh.

  3. From the src directory, start the API with uvicorn api:app --host 0.0.0.0 --port 8000 --workers 1.

Expected outcome

The documented preparation stages are complete and the API process is started.

Guide limitations

  • The README does not provide a request example or document API endpoints.
  • The documented data-processing command is specific to ZEDX data.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
EasyragProcess operational documents into searchable chunks and metadata.The public GitHub repository identifies the project as MIT-licensed.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
BUAADreamer/EasyRAG · Official homepage title · retained in its source language
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