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Successfully developed an interview preparation guide using Langchain which can effectively guide users in their interview preparation process and job search journeys by providing valuable insights and feedback regarding their performance. It generates a comprehensive list of questions pertaining to a user query as well.

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AI-Powered Interview Preparation Guide using Langchain 🎤🤖

Welcome to the AI-Powered Interview Preparation Guide using Langchain! This repository provides a comprehensive tool designed to assist users in preparing for interviews and navigating their job search journeys. By leveraging the power of Langchain, this guide offers valuable insights and feedback, helping users enhance their performance during interviews.

Download Releases

Table of Contents

Introduction

In today's competitive job market, preparation is key. This guide serves as a powerful tool that generates tailored interview questions based on user queries. It also provides feedback on responses, helping users refine their answers and approach. Whether you're preparing for a technical role or a general interview, this guide has you covered.

Features

  • Tailored Questions: Generate a list of questions based on specific user queries.
  • Performance Feedback: Receive insights on your responses to improve your performance.
  • User-Friendly Interface: A clean and intuitive interface that makes preparation easy.
  • Comprehensive Resources: Access a variety of resources to enhance your interview skills.

Technologies Used

This project utilizes several cutting-edge technologies, including:

  • Langchain: A framework for building applications powered by language models.
  • Anthropic Claude: A language model designed for conversational tasks.
  • ChatGPT: OpenAI's powerful conversational agent.
  • Deepgram API: For speech-to-text transcription.
  • Google Gemini AI: Advanced AI capabilities for enhanced performance.
  • Streamlit: A framework for creating web applications.

Additional topics include:

  • Generative AI
  • LLM Applications
  • Multi-Prompt Chain
  • Output Parsers
  • Prompt Engineering
  • Prompt Templates
  • Speech-to-Text Transcription
  • Text Generation

Installation

To set up the project locally, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/Daaruk/AI-Powered-Interview-Preparation-Guide-using-Langchain.git
    cd AI-Powered-Interview-Preparation-Guide-using-Langchain
  2. Install Dependencies: Make sure you have Python installed. Then, install the required packages:

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

    streamlit run app.py

Usage

Once the application is running, follow these steps to use the guide:

  1. Input Your Query: Enter the type of interview you are preparing for.
  2. Receive Questions: The guide will generate a list of relevant questions.
  3. Practice Responses: Answer the questions and receive feedback on your performance.
  4. Iterate: Continue practicing until you feel confident.

For additional resources and updates, check the Releases section.

Contributing

We welcome contributions to enhance this project. If you would like to contribute, please follow these steps:

  1. Fork the Repository: Click on the "Fork" button at the top right corner.
  2. Create a Branch:
    git checkout -b feature/YourFeature
  3. Make Your Changes: Implement your feature or fix.
  4. Commit Your Changes:
    git commit -m "Add Your Feature"
  5. Push to Your Branch:
    git push origin feature/YourFeature
  6. Open a Pull Request: Go to the original repository and click on "New Pull Request".

License

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

Contact

For questions or suggestions, feel free to reach out:

Explore the Releases for the latest updates and features!


Thank you for checking out the AI-Powered Interview Preparation Guide using Langchain! Happy interviewing!

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Successfully developed an interview preparation guide using Langchain which can effectively guide users in their interview preparation process and job search journeys by providing valuable insights and feedback regarding their performance. It generates a comprehensive list of questions pertaining to a user query as well.

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