# Run Local RAG using Langchain-Chatchat on Intel GPU

[chatchat-space/Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) is a Knowledge Base QA application using RAG pipeline; by porting it to [`ipex-llm`](https://github.com/intel-analytics/ipex-llm), users can now easily ***local RAG pipelines*** using [Langchain-Chatchat](https://github.com/intel-analytics/Langchain-Chatchat) with LLMs and Embedding models on Intel GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max); 

*See the demos of running LLaMA2-7B (English) and ChatGLM-3-6B (Chinese) on an Intel Core Ultra laptop below.*

<table border="1" width="100%">
  <tr>
    <td align="center" width="50%">English</td>
    <td align="center" width="50%">简体中文</td>
  </tr>
  <tr>
    <td><video src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-en.mp4" width="100%" controls></video></td>
    <td><video src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-cn.mp4" width="100%" controls></video></td>
</tr>
</table>


>You can change the UI language in the left-side menu. We currently support **English** and **简体中文** (see video demos below). 

## Langchain-Chatchat Architecture

See the Langchain-Chatchat architecture below ([source](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/img/langchain%2Bchatglm.png)).

<img src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-arch.png" height="50%" />

## Quickstart


  
### Install and Run

 Follow the guide that corresponds to your specific system and GPU type from the links provided below:

- For systems with Intel Core Ultra integrated GPU: [Windows Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_win_mtl.md#)
- For systems with Intel Arc A-Series GPU: [Windows Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_win_arc.md#) | [Linux Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_linux_arc.md#)
- For systems with Intel Data Center Max Series GPU: [Linux Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_linux_max.md#)


### How to use RAG

#### Step 1: Create Knowledge Base

- Select `Manage Knowledge Base` from the menu on the left, then choose `New Knowledge Base` from the dropdown menu on the right side.
  <p align="center"><img src="https://llm-assets.readthedocs.io/en/latest/_images/new-kb.png" alt="image1" width="70%" align="center"></p>
- Fill in the name of your new knowledge base (example: "test") and press the `Create` button. Adjust any other settings as needed. 
  <p align="center"><img src="https://llm-assets.readthedocs.io/en/latest/_images/create-kb.png" alt="image1" width="70%" align="center"></p>
- Upload knowledge files from your computer and allow some time for the upload to complete. Once finished, click on `Add files to Knowledge Base` button to build the vector store. Note: this process may take several minutes.
  <p align="center"><img src="https://llm-assets.readthedocs.io/en/latest/_images/build-kb.png" alt="image1" width="70%" align="center"></p>


#### Step 2: Chat with RAG

You can now click `Dialogue` on the left-side menu to return to the chat UI. Then in `Knowledge base settings` menu, choose the Knowledge Base you just created, e.g, "test". Now you can start chatting. 

<p align="center"><img src="https://llm-assets.readthedocs.io/en/latest/_images/rag-menu.png" alt="rag-menu" width="60%" align="center"></p>

<br/>

For more information about how to use Langchain-Chatchat, refer to Official Quickstart guide in [English](./README_en.md#), [Chinese](./README_chs.md#), or the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/).



### Trouble Shooting & Tips

#### 1. Version Compatibility

Ensure that you have installed `ipex-llm>=2.1.0b20240327`. To upgrade `ipex-llm`, use
```bash
pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
```

#### 2. Prompt Templates

In the left-side menu, you have the option to choose a prompt template. There're several pre-defined templates - those ending with '_cn' are Chinese templates, and those ending with '_en' are English templates. You can also define your own prompt templates in `configs/prompt_config.py`. Remember to restart the service to enable these changes.