https://github.com/ridenefiras/kagglexgoogle
https://github.com/ridenefiras/kagglexgoogle
embedding-models genai generative-ai llm rag
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/ridenefiras/kagglexgoogle
- Owner: RideneFiras
- Created: 2025-04-20T23:57:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-21T00:17:47.000Z (about 1 year ago)
- Last Synced: 2025-06-11T00:08:53.402Z (about 1 year ago)
- Topics: embedding-models, genai, generative-ai, llm, rag
- Language: Jupyter Notebook
- Homepage: https://www.kaggle.com/code/firasridene/5day-hopefully-final
- Size: 176 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ✈️ Airline Assistant – GenAI Capstone Project (Kaggle x Google)
---
## 📌 About This Project
This is a submission for the **Google x Kaggle 5-Day Generative AI Event**.
Our project demonstrates how to build an interactive airline assistant using **Generative AI techniques** including:
- **RAG (Retrieval-Augmented Generation)** for FAQ responses
- **Embeddings-based similarity** using `text-embedding-004`
- **Gemini 2.0 Flash** for intent classification and smart field extraction
- **Multi-turn interaction logic** with **running state**
- A clean **Gradio chat interface**
---
## 🤖 What the Assistant Can Do
The assistant interacts with users just like a real airline support agent:
- 🧠 **Understands your input** using AI-based classification
- 📘 **Answers travel questions** (e.g., "Can I bring a cat on the plane?")
- 🗃️ **Differentiates between FAQ and personal information**
- 📝 **Collects key fields**: name, flight number, issue, and email
- 🧠 **Stores information across turns** using a persistent user state
---
## ⚙️ Setup Instructions (Important!)
To run this notebook, you must add your **Google Generative AI API key**:
1. Go to **"Add-ons" > "Secrets"** (in Kaggle)
2. Add a new secret named:
```
GOOGLE_API_KEY
```
3. Paste your Gemini API key as the value (from https://makersuite.google.com/app/apikey)
---
## 🧠 How It Works
| Component | Description |
|----------|-------------|
| `text-embedding-004` | Generates vector embeddings for FAQ retrieval |
| `cosine similarity` | Finds the best-matching FAQ question |
| `Gemini Flash` | Classifies whether the user input is a question or a personal detail |
| `Field extraction` | AI-based slot-filling logic (e.g., extracting names from messy input) |
| `State tracking` | Stores each user field (name, flight, issue, email) during chat |
| `Gradio` | Provides an interactive chatbot interface for users |
---
## 💡 Why It Matters
This project simulates how a real-world **AI-powered airline assistant** might behave — blending FAQ search with intelligent form-filling, all within a lightweight notebook.
It showcases how we can combine **LLMs, embeddings, and interface tools** to build useful, responsive GenAI apps.
---
## 📁 Files
- `notebook.ipynb` – full interactive assistant
- `assets` – demo pictures
---
## 🙌 Thanks
Huge thanks to **Kaggle**, **Google**, and the GenAI team for organizing the event!
---