Jupyter Notebook Writing

# Milvus Integration Tutorial Format Specification

This document outlines the standard format for Milvus integration tutorials in the Milvus bootcamp repository.

## File Naming
- Use lowercase with underscores
- Keep provider names simple and recognizable
- This guide can be used for both markdown and ipynb files.


## Document Structure
### 0. Badges
Add two badges to the top of your notebook. Note that the suffix of the notebook path should be `ipynb`, even if it is a markdown file since we will finally convert it to a jupyter notebook file.
```html
<a href="https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/bootcamp/relative_path/to/your_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>   <a href="https://github.com/milvus-io/bootcamp/blob/master/bootcamp/relative_path/to/your_notebook.ipynb" target="_blank">
    <img src="https://img.shields.io/badge/View%20on%20GitHub-555555?style=flat&logo=github&logoColor=white" alt="GitHub Repository"/>
```
### 1. Header Section
- **Title**
  - Start with "# " as the markdown heading level.
- **Introduction**
  - One or two paragraphs explaining the purpose of the tutorial, the integration provider and the key technologies.
### 2. Prerequisites/Preparation
- **Dependencies**:
  - Pip install command with all required packages, always include `--upgrade` flag
  - After installing pip in Google Colab, always add this note:
```
> If you are using Google Colab, to enable dependencies just installed, you may need to **restart the runtime** (click on the "Runtime" menu at the top of the screen, and select "Restart session" from the dropdown menu).
```
- **API Keys**:
  - Instructions for obtaining API keys
  - Environment variable setup code block
  - Use placeholder format: `"sk-***********"` or `"***********"`

### 3. Main Content Sections
This is the main content of the tutorial. It should be divided into several hierarchical sections, each section should be a logical unit of the tutorial.

Before each code block, add a short introduction to the code block, which can avoid the reader to be confused about the code block.

(Optional) In the text description, if there are concepts or terms that are not familiar to the reader, you can properly add links to the text.

At the end of the article, the conclusion section should be as simple as possible, such as several sentences to summarize the tutorial.

---
# Example

Below this divider, here is an example of a Milvus integration tutorial:
---

<a href="https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/integration/langchain/rag_with_milvus_and_langchain.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>   <a href="https://github.com/milvus-io/bootcamp/blob/master/integration/langchain/rag_with_milvus_and_langchain.ipynb" target="_blank">
    <img src="https://img.shields.io/badge/View%20on%20GitHub-555555?style=flat&logo=github&logoColor=white" alt="GitHub Repository"/>
</a>


# Retrieval-Augmented Generation (RAG) with Milvus and LangChain

This guide demonstrates how to build a Retrieval-Augmented Generation (RAG) system using LangChain and Milvus.

The RAG system combines a retrieval system with a generative model to generate new text based on a given prompt. The system first retrieves relevant documents from a corpus using Milvus, and then uses a generative model to generate new text based on the retrieved documents.

[LangChain](https://www.langchain.com/) is a framework for developing applications powered by large language models (LLMs). [Milvus](https://milvus.io/) is the world's most advanced open-source vector database, built to power embedding similarity search and AI applications.


## Prerequisites

Before running this notebook, make sure you have the following dependencies installed:

```python
! pip install --upgrade langchain langchain-core lan