An AI-powered voice assistant that enables users to call and interact with a customer service agent in real time. The assistant answers account-related questions, follows company guidelines via RAG (Retrieval-Augmented Generation), and provides fast, natural conversations using state-of-the-art speech and language models.
- Voice Interaction: Real-time speech-to-text and text-to-speech using Deepgram APIs.
- AI-Powered Conversations: Uses LLaMA3 model via Groq API for fast and intelligent responses.
- RAG Implementation: Ensures accurate, compliant answers by referencing company guidelines and user information.
- Python-Only Implementation: Entire pipeline (speech, RAG, AI responses) built in Python — no frontend required.
- Scalable Design: Modular architecture to extend to more services or integrate with enterprise systems.
- Core Language: Python
- AI Backend: Groq API + LLaMA3
- Speech Processing: Deepgram Speech-to-Text & Text-to-Speech APIs
- Knowledge Integration: Retrieval-Augmented Generation (RAG)
git clone https://github.com/YoshaM09/AIVoiceAssistant.git
cd AIVoiceAssistantpython -m venv venv
source venv/bin/activate # On Windows use venv\Scripts\activatepip install -r requirements.txt- Create a .env file in the project root with your API keys:
GROQ_API_KEY=your_groq_api_key
DEEPGRAM_API_KEY=your_deepgram_api_keypython demo.py- Start the Python script
python demo.py- Speak into your microphone — your voice will be transcribed via Deepgram STT.
- The AI assistant (LLaMA3 via Groq API + RAG) processes your query and generates a response.
- The response is converted back to speech using Deepgram TTS and played in real time.
- Contributions are welcome! Please submit a pull request or open an issue for suggestions.
- This project is licensed under the MIT License.