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An automated cold email generator leveraging Retrieval-Augmented Generation (RAG), combining LangChain's ChatGroq model with ChromaDB for targeted outreach. The project includes a Streamlit for intuitive job description parsing, skill matching, and personalized email generation.

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aravinda-1402/Cold_Email_Generator

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📧 Cold Email Generator

This tool is designed for service-oriented companies to streamline their outreach through cold emails. Using Groq, LangChain, and Streamlit, this application lets users input the URL of a company's careers page, extracts job postings from that page, and generates customized cold emails. Each email includes relevant portfolio links, drawn from a vector database, based on the specific job descriptions listed.


Example Scenario:

Imagine a large brand, such as a well-known sportswear company, is seeking a Principal Software Engineer and investing considerable resources in recruiting, onboarding, and training for this role.

ABC a hypothetical software development company, can step in to fulfill this need by providing a dedicated software engineer. A business development executive from ABC, such as Aravinda, could then reach out to the sportswear brand through a cold email, tailored to showcase how ABC’s services can support the brand’s goals.

Cold Email Generator Interface


Architecture Diagram

The following diagram illustrates the architecture of the tool, showing each component's role in generating tailored cold emails:

Architecture Diagram


Setup Instructions

To set up and run the Cold Email Generator, follow these steps:

  1. Generate an API Key: Obtain an API key by visiting the Groq API console. Copy your key and update the GROQ_API_KEY entry in app/.env with this key.

  2. Install Dependencies: Run the following command to install the necessary libraries:

    pip install -r requirements.txt
  3. Run the Application: Launch the application with Streamlit:

    streamlit run app/main.py

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An automated cold email generator leveraging Retrieval-Augmented Generation (RAG), combining LangChain's ChatGroq model with ChromaDB for targeted outreach. The project includes a Streamlit for intuitive job description parsing, skill matching, and personalized email generation.

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