RAG & Knowledge
helix-db
An OLTP graph database with native vector and full-text search, built in Rust on object storage.
★ 6.1k starsApache-2.0
View guide →Graph RAG conversational knowledge base built on TiDB Serverless vector storage
autoflow is an open-source rag & knowledge project by pingcap (pingcap/autoflow on GitHub). It has 2,978 GitHub stars and 194 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 27 April 2026.
Decide what knowledge you want to make conversational — typically documentation or a website that the built-in crawler can ingest via sitemap scraping.
Clone the repo and read the deployment docs linked from the README.
Deploy with Docker Compose (the docs suggest 4 CPU cores and 8 GB RAM) backed by TiDB Serverless vector storage, and configure your LLM settings.
Open the conversational search page, ask a question about your ingested content, and optionally embed the chat snippet on your site.
Retrieval is built on a knowledge-graph RAG approach using TiDB Vector storage, via LlamaIndex and DSPy.
A conversational search interface for asking questions over your knowledge base.
Crawls official and documentation sites through sitemap URL scraping to build coverage.
Drop a simple JavaScript snippet into your website to add a conversational search window, typically at the bottom right.
TiDB stores chat history, vectors, JSON and analytics in one database.
Documented deployment path with Docker Compose in the project's deployment docs.
Clone AutoFlow and enter the directory.
git clone https://github.com/pingcap/autoflow.git
cd autoflowFollow the deployment docs linked in the README for the Docker Compose deployment (4 CPU / 8 GB RAM suggested).
# see README for the exact command — deployment docs: https://autoflow.tidb.ai/deploy-with-dockerIf you want to see it before deploying, the hosted demo runs at tidb.ai.
# open https://tidb.aiPingCAP, the company behind TiDB, maintains AutoFlow as an open-source project.
The code is Apache-2.0 licensed and free to use. You will typically use TiDB Serverless and an LLM provider, whose own pricing applies.
It is an LLM-based RAG tool, so you should expect to configure an LLM provider; TiDB Serverless is used for vector storage. Check the deployment docs for the exact configuration.
AutoFlow adds a knowledge-graph (Graph RAG) layer and a ready conversational UI with crawling and an embeddable widget, rather than just a retrieval library.
Try the demo at tidb.ai, then follow the Docker Compose deployment guide linked from the README.
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