pingcap/autoflow is a Graph RAG based and conversational knowledge base tool built with TiDB Serverless Vector Storage. Demo: https://tidb.ai
npm install autoflowAutoFlow
[!WARNING] Autoflow is still in the early stages of development. And we are actively working on it, the next move is to make it to a python package and make it a RAG solution e.g.
pip install autoflow-ai. If you have any questions or suggestions, please feel free to contact us on Discussion.
Introduction
AutoFlow is an open source graph rag (graphrag: knowledge graph rag) based knowledge base tool built on top of TiDB Vector and LlamaIndex and DSPy.
- Live Demo: https://tidb.ai
- Deployment Docs: Deployment Docs
Features
- Perplexity-style Conversational Search page: Our platform features an advanced built-in website crawler, designed to elevate your browsing experience. This crawler effortlessly navigates official and documentation sites, ensuring comprehensive coverage and streamlined search processes through sitemap URL scraping.
- Embeddable JavaScript Snippet: Integrate our conversational search window effortlessly into your website by copying and embedding a simple JavaScript code snippet. This widget, typically placed at the bottom right corner of your site, facilitates instant responses to product-related queries.
Deploy
- Deploy with Docker Compose (with: 4 CPU cores and 8GB RAM)
Tech Stack
- TiDB – Database to store chat history, vector, json, and analytic
- LlamaIndex - RAG framework
- DSPy - The framework for programming—not prompting—foundation models
- Next.js – Framework
- Tailwind CSS – CSS framework
- shadcn/ui - Design
Contributing
We welcome contributions from the community. If you are interested in contributing to the project, please read the Contributing Guidelines.
License
AutoFlow is open-source under the Apache License, Version 2.0. You can find it here.
Contact
You can reach out to us on Discord.