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