# LLM-RAG-WEB

**Repository Path**: zhichong8/LLM-RAG-WEB

## Basic Information

- **Project Name**: LLM-RAG-WEB
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-07
- **Last Updated**: 2024-08-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# LLM-RAG-WEB
LLM Model connection LangChain RAG Connection to Streamlit Web


## 1、Brief description

Technology stack:

- Large Language Model: chatglm2
- File processing + faiss: langchain
- Visual interface: streamlit

Code framework description:

- web.py: Project entry, web page
- model. py: interconnects with the model interface
- split.py: document splitting
- configs.py: configures

## 2、Running steps:

1) Modify configuration

  Add embedding model local address to the configs.py file
  
  ```
  ##model address：line 8
  embedding_model_address = "" ## "shibing624/text2vec-base-chinese",Download the local address where the model is saved
  llm_service_url_address = "" ## fschat address, http://*****:21002
  ```

2) Deploy the model

model deploy : faschat(Reference: https://github.com/lm-sys/FastChat)

  run:
  
    1.python -m fastchat.serve.controller
    
    2.python -m fastchat.serve.model_worker --model-path ./chatglm2-6b --num-gpus 2 --host=0.0.0.0 --port=21002

3) Run the web
  
  streamlit run  web.py 


![alt text](https://github.com/lonngxiang/LLM-RAG-WEB/blob/main/web.png)