## Overview

In the first four modules of this lab, you'll work with multiple heterogeneous data stores (such as MySQL, Milvus, and ElasticSearch) to address different requirements in a GenAI application. This will require several Python packages, allowing you to experience the challenges of development and maintenance. In Module 5, you'll use just one Python package - pytidb - and one data store - TiDB - as the unified data storage for GenAI. Here, you'll see firsthand how much simpler and more efficient it is to power your GenAI applications with TiDB.

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> [!IMPORTANT]
> Please be sure to read the following information before starting the lab.
> + Before you start this lab, you need to register a billable OpenAI account and get the `OPENAI_API_KEY`. You can find the guidance of getting your keys from this [document from OpenAI](https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key).
> + This lab only supports Chrome browser.
> + Currently, TiDB Full-Text Search with SQL is only available in TiDB Serverless Frankfurt (eu-central-1) and Singapore (ap-southeast-1). If you use your own free-tier TiDB Cloud database for this lab, make sure your TiDB cluster is deployed in either Frankfurt or Singapore.

![70%](https://lab-static.pingcap.com/quick-demo/Demo_422_SplashImage_20250625.png)

Generative AI is currently a cutting-edge field that has attracted many developers eager to explore it. However, several barriers often hinder their entry into this domain, such as:

- A lack of foundational AI knowledge, leaving them unsure where to begin.
- The absence of comprehensive, step-by-step, and interactive guides for building generative AI applications.
- Limited access to essential infrastructure needed for learning, such as databases and large model APIs.

To address these challenges, we developed a series of labs, including this one, through which you will learn:

- How to write an AI application in a traditional way, based on these storages below to build a full-featured application:

  - Relational Database: e.g. `MySQL`
  - Vector Database: e.g. `Milvus`
  - Full-Text Search Engine: e.g. `ElasticSearch`

- How to use `pytidb` to leverage **TiDB Cloud Serverless** as a unified data storage layer to simplify your AI application.

> [!NOTE]
> In this lab, we'll use Streamlit as a rapid application development framework to streamline and accelerate data application building.
> Whether you choose your own TiDB Cloud Serverless cluster or the sandbox TiDB Cloud Serverless option to start the lab, a dedicated database will be created for this hands-on lab.

### Tasks

1. Build your first simple AI application: Call the LLM and provide an answer to a question.
2. Improve your AI application using RAG, giving your application a memory:

    - Use MySQL as storage for text chunks.
    - Use Milvus as a vector storage and search engine.

3. Enhance your AI application's performance by introducing an additional retrieval channel. Full-text search is a good choice, as it will provide your AI application with a different search perspective:

    - Use ElasticSearch as the full-text search engine.

4. **Rewrite your application using TiDB to simplify your overall architecture.**

> [!TIP]
> Click **Start** to launch the lab environment.
> Once the environment is ready, the resources assigned to your hands-on session will be populated with the actual values, so you can follow the lab guide step-by-step to complete the practice.