A user-friendly application that uses Retrieval-Augmented Generation (RAG) with generative AI to help you practice interview questions, compare your spoken answers against ideal responses, and receive targeted feedback.
This is how the final screen looks like to give you a visual output .png)
-
Audio Recording & Transcription
Record short audio clips (e.g. a 5-second snippet) using PyAudio and transcribe them using Whisper (local or via the OpenAI API). -
Custom Reference Documents
Upload trusted Q&A documents that serve as your ideal answer database for personalized feedback. -
Semantic Answer Retrieval
Automatically retrieves the most relevant ideal answer from your reference documents using embeddings and semantic search (via Pinecone). -
AI-Powered Feedback
Compares your transcribed answer to the ideal response and highlights key missing points, tailored for:- Solo Mode: Automated, detailed feedback for self-improvement.
- Peer Mode: Conversation prompts for mock-interview practice.
-
Interactive Web App (Streamlit)
A streamlined, web-based interface that integrates recording, transcription, retrieval, and feedback in one place.
Clone the repository and install dependencies:
git clone <your-repo-link>
cd InterviewPrep-RAG
pip install -r requirements.txtTo launch the Streamlit web app:
streamlit run app.py --server.fileWatcherType noneInterview-Prep-Using-RAG/
├── app.py # Streamlit app entry point
├── rag_interview.ipynb # Notebook for development & experimentation
├── requirements.txt # Python dependencies
├── streamlit_app/ # Folder for reference documents
- Streamlit – Interactive web interface
- PyAudio – Audio recording
- Whisper – Speech-to-text transcription (local & OpenAI API)
- Pinecone – Semantic search and retrieval
- OpenAI API – LLM for evaluation and feedback
- LangChain – Text splitting and document processing