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zhangwei 1b1107315c 根据4.7日更新代码，解决冲突 
新增ernie系列模型作为embedding model,默认依旧使用GanymedeNil/text2vec-large-chinese
显存不足时可尝试使用ernie-tiny作为embedding模型
调用方式为embeddings = HuggingFaceEmbeddings(model_name=embedding_model_dict["ernie-tiny"], )
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README_en.md

ChatGLM Application Based on Local Knowledge

Introduction

🌍 中文文档

🤖️ A local knowledge based LLM Application with ChatGLM-6B and langchain.

💡 Inspired by document.ai by GanymedeNil and ChatGLM-6B Pull Request by AlexZhangji.

✅ In this project, GanymedeNil/text2vec-large-chinese is used as Embedding Model，and ChatGLM-6B used as LLM。Based on those models，this project can be deployed offline with all open source models。

Update

[2023/04/07]

  1. Fix bug which costs twice gpu memory (Thanks to @suc16 and @myml).
  2. Add gpu memory clear function after each call of ChatGLM.

Usage

Hardware Requirements

  • ChatGLM Hardware Requirements

    Quantization Level GPU Memory
    FP16（no quantization） 13 GB
    INT8 10 GB
    INT4 6 GB

  • Embedding Hardware Requirements

    The default Embedding model in this repo is GanymedeNil/text2vec-large-chinese, 3GB GPU Memory required when running on GPU.

1. install python packages

pip install -r requirements

Attention: With langchain.document_loaders.UnstructuredFileLoader used to connect with local knowledge file, you may need some other dependencies as mentioned in langchain documentation

2. Run knowledge_based_chatglm.py script

python knowledge_based_chatglm.py

Roadmap

  • local knowledge based application with langchain + ChatGLM-6B
  • unstructured files loaded with langchain
  • more different file format loaded with langchain
  • implement web ui DEMO with gradio/streamlit
  • implement API with fastapi，and web ui DEMO with API