9a3a21e4fc
* axolotl_quickstart.md * benchmark_quickstart.md * bigdl_llm_migration.md * chatchat_quickstart.md * continue_quickstart.md * deepspeed_autotp_fastapi_quickstart.md * dify_quickstart.md * fastchat_quickstart.md * adjust tab style * fix link * fix link * add video preview * Small fixes * Small fix --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
83 lines
5.2 KiB
Markdown
83 lines
5.2 KiB
Markdown
# Run Local RAG using Langchain-Chatchat on Intel CPU and GPU
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[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 run ***local RAG pipelines*** using [Langchain-Chatchat](https://github.com/intel-analytics/Langchain-Chatchat) with LLMs and Embedding models on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max).
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*See the demos of running LLaMA2-7B (English) and ChatGLM-3-6B (Chinese) on an Intel Core Ultra laptop below.*
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<table border="1" width="100%">
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<tr>
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<td align="center" width="50%">English</td>
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<td align="center" width="50%">简体中文</td>
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</tr>
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<tr>
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<td><a href="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-en.mp4"><img src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-en.png"/></a></td>
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<td><a href="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-cn.mp4"><img src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-chatchat-cn.png"/></a></td>
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</tr>
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</table>
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> [!NOTE]
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> You can change the UI language in the left-side menu. We currently support **English** and **简体中文** (see video demos below).
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## Langchain-Chatchat Architecture
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See the Langchain-Chatchat architecture below ([source](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/img/langchain%2Bchatglm.png)).
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/langchain-arch.png" height="50%" />
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## Quickstart
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### Install and Run
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Follow the guide that corresponds to your specific system and device from the links provided below:
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- For systems with Intel Core Ultra integrated GPU: [Windows Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_win_mtl.md#) | [Linux Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_linux_mtl.md#)
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- 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#)
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- 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#)
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- For systems with Xeon-Series CPU: [Linux Guide](https://github.com/intel-analytics/Langchain-Chatchat/blob/ipex-llm/INSTALL_linux_xeon.md#)
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### How to use RAG
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#### Step 1: Create Knowledge Base
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- Select `Manage Knowledge Base` from the menu on the left, then choose `New Knowledge Base` from the dropdown menu on the right side.
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/new-kb.png" target="_blank">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/new-kb.png" alt="rag-menu" width="100%" align="center">
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</a>
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- Fill in the name of your new knowledge base (example: "test") and press the `Create` button. Adjust any other settings as needed.
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/create-kb.png" target="_blank">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/create-kb.png" alt="rag-menu" width="100%" align="center">
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</a>
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- 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.
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/build-kb.png" target="_blank">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/build-kb.png" alt="rag-menu" width="100%" align="center">
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</a>
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#### Step 2: Chat with RAG
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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.
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<a href="https://llm-assets.readthedocs.io/en/latest/_images/rag-menu.png" target="_blank">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/rag-menu.png" alt="rag-menu" width="100%" align="center">
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</a>
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<br/>
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For more information about how to use Langchain-Chatchat, refer to Official Quickstart guide in [English](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/README_en.md#), [Chinese](https://github.com/chatchat-space/Langchain-Chatchat/blob/master/README.md#), or the [Wiki](https://github.com/chatchat-space/Langchain-Chatchat/wiki/).
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### Trouble Shooting & Tips
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#### 1. Version Compatibility
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Ensure that you have installed `ipex-llm>=2.1.0b20240327`. To upgrade `ipex-llm`, use
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```bash
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pip install --pre --upgrade ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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#### 2. Prompt Templates
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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.
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