/ResumeChat-AI

Llama RAG app to chat with Applicants' Resume and Extension for LinkedIn

Primary LanguagePythonMIT LicenseMIT


ResumeChat AI
ResumeChat AI

A RAG app to chat with Applicants' Resume and Chrome extension to connect on LinkedIn.

Overview • Chatbot • Chrome Extension • Architecture • Build from Source • Contact

📋 Overview

A Retrieval-Augmented Generation (RAG) app for HRs to chat with Applicants' Resume and Chrome extension to connect on LinkedIn.

💬 Chatbot

resumechat_ai.mp4

Frontend Features

  • Upload applicants' Resumes as PDF files via File Uploader (accepts multiple files).
  • Chat and ask questions about the Resumes to gain valuable insights about the candidates.
  • It is developed with streamlit and uses sseclient to generate streamed response.

Backend Features

  • Utilizes all-MiniLM-L6-v2 embedding model to split and convert the PDF docs to chromadb vector database.
  • Retrieves contexts from chat queries and uses Ollama LLMs to generate contextually accurate response.
  • The backend was developed with fastapi and langchain to produce streamed output.

🌐 Chrome Extension

resumechat_extension.mp4

Key Features

  • Load the chrome extension and let AI reply to LinkedIn posts with just a click!
  • It is developed with pure javascript and uses the same API as chatbot to complete response.

💡 Architecture

architecture

⚙️ Build from Source

Serve Ollama

  1. Download and install Ollama from https://ollama.com/download

  2. Pull required open-source LLMs (here we use mistral, you can use other models like llama3, llama2-uncensored, etc.)

ollama pull mistral
  1. Serve Ollama locally (by default Ollama is served from http://localhost:11434)
ollama serve

Note: If this command results in an error, make sure to quit any running Ollama background processes.

Setup Server and Chatbot

  1. Clone the repository
git clone https://github.com/zzarif/ResumeChat-AI.git
cd ResumeChat-AI/
  1. Install necessary dependencies
poetry install
  1. Activate virtual environment
poetry shell
  1. Start chatbot backend server (served from http://localhost:8000)
python chatbot/backend/api.py
  1. Launch the chatbot (served from http://localhost:8501)
streamlit run chatbot/main.py

Load Chrome Extension

  1. Go to chrome://extensions/, or, Chrome ▶ Manage Extensions
  2. Turn on the Developer mode
  3. Click Load Unpacked
  4. Select the extension directory
  5. Go to https://www.linkedin.com/feed/ and start commenting!

Note: Everytime you make changes to the extension code you must first ⟳ reload it from Manage Extensions and then ⟳ reload https://www.linkedin.com/feed/

✉️ Contact:

LinkedIn Mail