Resume-Bot

This is an implementation of a Retrieval Augmented Generation (RAG) pipeline for the Qwen-2.5 model to anlyze job descriptions in context of job descriptions. The RAG pipeline creates a vector database of the uploaded files and responds with relevant context it retrieves from the knowledge base.

Directories

• src/: contains the source code for the RAG.
• src/ingest.py: script to ingest documents, create a vector database and add/remove documents.
• src/retrieval.py: script to for the LLM to retrieve the relevant context from the knowledge base.
• src/augment.py: script to augment the user prompt for the LLM and the UI for user interaction.
• src/scrapper.py script for retrieval of HTML scripts from URLs to enable web-searches (work in progress).
• src/test.py: script for unit testing of the pipeline.
• src/test.pdf:* pdf files to test the ingestions of the database.
• requirements.txt: text file for the requirements of the project.

Requirements

1. GPU: to run the model locally
2. requirements.txt: this lists all the dependencies such as Ollama, LangChain, Pytorch, TensorRT, etc.
3. Python: version >= 3.12.7

How to run

1. Create a python virtual environment

    python -m venv venv

2. Activate the virtual environment:

    source venv/bin/activate

3. Install the dependencies:

    pip install -r requirements.txt

4. Run the UI application:

    streamlit run src/augment.py

5. Interact and use the UI with features to upload and clear resumes and job description contexts