[!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

1. 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.

2. 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.