Ragflow

Ragflow API Client

Universal client for Ragflow — self-hosted RAG (Retrieval-Augmented Generation) platform.

Features

• Dataset management — Create, list, delete knowledge bases
• Document upload — Upload files or text content
• Chat queries — Run RAG queries against datasets
• Chunk management — Trigger parsing, list chunks

Usage

# List datasets
node {baseDir}/scripts/ragflow.js datasets

# Create dataset
node {baseDir}/scripts/ragflow.js create-dataset --name "My Knowledge Base"

# Upload document
node {baseDir}/scripts/ragflow.js upload --dataset DATASET_ID --file article.md

# Chat query
node {baseDir}/scripts/ragflow.js chat --dataset DATASET_ID --query "What is stroke?"

# List documents in dataset
node {baseDir}/scripts/ragflow.js documents --dataset DATASET_ID

Configuration

Set environment variables in your .env:

RAGFLOW_URL=https://your-ragflow-instance.com
RAGFLOW_API_KEY=your-api-key

API

This skill wraps Ragflow's REST API:

• GET /api/v1/datasets — List datasets
• POST /api/v1/datasets — Create dataset
• DELETE /api/v1/datasets/{id} — Delete dataset
• POST /api/v1/datasets/{id}/documents — Upload document
• POST /api/v1/datasets/{id}/chunks — Trigger parsing
• POST /api/v1/datasets/{id}/retrieval — RAG query

Full API docs: https://ragflow.io/docs

Examples

// Programmatic usage
const ragflow = require('{baseDir}/lib/api.js');

// Upload and parse
await ragflow.uploadDocument(datasetId, './article.md', { filename: 'article.md' });
await ragflow.triggerParsing(datasetId, [documentId]);

// Query
const answer = await ragflow.chat(datasetId, 'What are the stroke guidelines?');