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LlamaIndex RAG Example

This example demonstrates how to use the LlamaIndex and TiDB Serverless to build a simple RAG(Retrival-Augmented Generation) application. It crawl an example webpage and index the content to TiDB Serverless with LlamaIndex, then use the LlamaIndex to search the content and generate the answer with OpenAI.

Prerequisites

  • A running TiDB Serverless cluster with vector search enabled
  • Python 3.8 or later
  • OpenAI API key

Run the example

Clone this repo

git clone https://github.com/pingcap/tidb-vector-python.git

Create a virtual environment

cd tidb-vector-python/examples/llamaindex-tidb-vector
python3 -m venv .venv
source .venv/bin/activate

Install dependencies

pip install -r requirements.txt

Set the environment variables

Get the OPENAI_API_KEY from OpenAI

Get the TIDB_HOST, TIDB_USERNAME, and TIDB_PASSWORD from the TiDB Cloud console, as described in the Prerequisites section.

export OPENAI_API_KEY="sk-*******"
export TIDB_HOST="gateway01.*******.shared.aws.tidbcloud.com"
export TIDB_USERNAME="****.root"
export TIDB_PASSWORD="****"

Run this example

$ python chat_with_url.py --help
Usage: chat_with_url.py [OPTIONS]

Options:
  --url TEXT  URL you want to talk to,
              default=https://docs.pingcap.com/tidb/stable/overview
  --help      Show this message and exit.
$
$ python chat_with_url.py
Enter your question: tidb vs mysql
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability. TiDB is designed to provide users with a one-stop database solution that covers OLTP, OLAP, and HTAP services. It offers easy horizontal scaling, financial-grade high availability, real-time HTAP capabilities, cloud-native features, and compatibility with the MySQL protocol and ecosystem.
Enter your question: