AI-Powered Job Extraction and Cold Email Generator

This project leverages cutting-edge AI tools and frameworks to automate the process of extracting job postings from web pages, and generating personalized cold emails for job applications based on user input and a customizable portfolio of skills. It integrates Groq-powered LLM (Language Learning Model), Langchain prompts, web scraping, and vector databases to streamline the end-to-end process of job application creation.

Features

• Web Scraping: Automatically extracts job postings from dynamic web pages using WebBaseLoader and cleans the extracted data to eliminate noise.
• Job Posting Extraction: Utilizes Langchain's LLM prompt chains to extract and format relevant job details (role, experience, skills, description) into valid JSON objects.
• Personalized Cold Emails: Generates tailored cold emails using a Groq-powered LLM, customizable with the candidate's skills and relevant portfolio links.
• Vector Database Integration: Stores tech stack and portfolio metadata using Chromadb for efficient querying of relevant links for the cold email.
• Dynamic Email Generation: Creates personalized, results-driven emails that align with job descriptions and highlight the most relevant experience and projects.

Technologies Used

• LLM Integration (Groq): ChatGroq (Llama-3.1-70B) model for high-accuracy text generation.
• Langchain: Used for creating dynamic prompt templates and chaining LLMs to generate responses.
• Flask: Backend server to handle email generation requests and manage job scraping and LLM interaction.
• React.js: Frontend for collecting user input (e.g., username) and triggering the email generation process.
• Chromadb: Vector database for efficient metadata storage and querying.
• Pandas: Used for handling portfolio CSV data and processing it for storage in Chromadb.
• Flask-CORS: Enabling CORS for the Flask backend.

Setup Instructions

Prerequisites

• Python 3
• Node.js and npm (for React frontend)
• Groq API key (for LLM integration)

Installation

1. Clone the repository:

   git clone https://github.com/IMRANDIL/cold_email_generator_llm.git
   cd cold_email_generator_llm

2. Backend Setup (Flask):

   • Create and activate a virtual environment:

     python3 -m venv venv
     source venv/bin/activate

   • Install the required Python dependencies:

     pip install -r requirements.txt

   • Set up environment variables for the Groq API key:

     export GROQ_API_KEY="your-api-key"

   • Start the Flask server:

     python app.py

3. Frontend Setup (React):

   • Navigate to the frontend directory:

     cd frontend

   • Install the dependencies:

     npm install

   • Start the React development server:

     npm run dev

Usage

• Visit the frontend interface to input the username and trigger the email generation process.
• The backend will scrape job postings, process them with Langchain prompts, query the vector database, and generate customized cold emails for each job.
• Emails are returned in JSON format and are displayed in a well-formatted code block with a "Copy to Clipboard" feature.

Example Output

{
  "subject": "Application for Lead Software Engineer at XYZ Inc.",
  "body": "Dear Hiring Manager, I am writing to express my interest in the Lead Software Engineer position..."
}

Future Enhancements

• Dynamic Web Scraping: Extend web scraping capabilities to handle more complex and dynamic job posting websites.
• Advanced Querying: Improve the vector database querying mechanism to handle multiple skill sets more efficiently.
• UI Enhancements: Further enhance the React.js frontend for better user experience and support for multiple email templates.
• Multiple API Integration: Explore integration with other LLM models (OpenAI, etc.) for increased versatility.

License

This project is licensed under the MIT License. See the LICENSE file for details.