Track user satisfaction in your AI chatbot using a lightweight thumbs up/down feedback system integrated with Weights & Biases. This repo shows how to capture, log,and analyze user feedback to inform continuous model improvement.
- Frontend: Vue modern UI with thumbs up, thumbs down components for user Feedback
- Backend: Python with FAST Api
- Jupyter Notebooks: Sample scrapper for blog, AI Search deployment, content upload and embeddings generation
- AI Features: Azure OpenAI for text generation and embeddings (gpt-4o and text-embeddings)
- Vector Search: Vector and Semantic search functionality using Azure AI Search
- Weights and Biases: Trial Cloud Account on Weights and Biases Platform
- 👍👎 Inline thumbs feedback for chat responses
- Logs feedback and interaction metadata to Weights & Biases
- Custom W&B dashboard for insights
- Built with FastAPI + Azure AI Studio
- Lightweight frontend in Vite/Vue
- Clone the repo
- Create Weights and Biases Trial account
- Deploy gpt-4o and text-embeddings-ada-002
- Install dependencies
npm install
- Configure environment
AZURE_SEARCH_ENDPOINT=
AZURE_SEARCH_KEY=
AZURE_SEARCH_INDEX=
AZURE_OPENAI_ENDPOINT=
AZURE_OPENAI_KEY=
AZURE_OPENAI_DEPLOYMENT=
AZURE_OPENAI_EMBEDDING_DEPLOYMENT=- Get the Data or use the sample Notebook
extractblog.py
- Create Azure AI Search with Notebooks and upload content and embeddings
create_azure_ai_index.ipynb
embed_and_upload_blog_docs.ipynb
- Run Local Dev
uvicorn app.main:app --reload - Python backend
npm run dev - Vue frontend
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.