Resume Database Chatbot Using Local LLM

Playing with RAG using Ollama, Langchain, and Streamlit.

Project Description

This project aims to demonstrate how a recruiter or HR personnel can benefit from a chatbot that answers questions regarding candidates.

Data

https://www.kaggle.com/datasets/gauravduttakiit/resume-dataset/data

Reference

https://www.jeremymorgan.com/blog/generative-ai/how-to-run-llm-local-windows/ https://levelup.gitconnected.com/talk-to-your-csv-llama2-how-to-use-llama2-and-langchain-69012c5ff653 https://blog.duy-huynh.com/build-your-own-rag-and-run-them-locally/

https://ollama.ai/library

Usage

Written in Python 3.9.9. Some technologies used:

• Ollama
• Lanchain
• Streamlit

To see the project in action, install the required libraries with

pip install langchain langchain-community chromadb fastembed streamlit streamlit_chat

and execute streamlit run app.py.

Meta

Ednalyn C. De Dios – @ecdedios

Distributed under the MIT license. See LICENSE for more information.

• LinkedIn: in/ecdedios/
• Resumé: http://ednalyn.com
• Data Science Projects https://datasciencenerd.us

Contributing

1. Fork it (https://github.com/ecdedios/resume-chatbot-local-llm/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request

2024