https://github.com/annnieglez/genai-travel-guide
This project is an AI-powered chatbot that provides real-time travel advice about Iceland. It utilizes Retrieval-Augmented Generation (RAG) by storing document embeddings in ChromaDB and retrieving relevant information to generate responses using a Large Language Model (LLM).
https://github.com/annnieglez/genai-travel-guide
beautifulsoup chromadb embeddings langchain llm llms matplotlib openai pandas rag reportlab selenium tiktoken
Last synced: 7 months ago
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This project is an AI-powered chatbot that provides real-time travel advice about Iceland. It utilizes Retrieval-Augmented Generation (RAG) by storing document embeddings in ChromaDB and retrieving relevant information to generate responses using a Large Language Model (LLM).
- Host: GitHub
- URL: https://github.com/annnieglez/genai-travel-guide
- Owner: annnieglez
- License: mit
- Created: 2025-03-31T12:02:33.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-04-03T15:10:46.000Z (7 months ago)
- Last Synced: 2025-04-03T16:25:28.185Z (7 months ago)
- Topics: beautifulsoup, chromadb, embeddings, langchain, llm, llms, matplotlib, openai, pandas, rag, reportlab, selenium, tiktoken
- Language: Jupyter Notebook
- Homepage:
- Size: 51.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Iceland Travel AI Assistant ✈️
## Overview
This project is an AI-powered chatbot that provides real-time travel advice about Iceland. It utilizes **Retrieval-Augmented Generation (RAG)** by storing document embeddings in **ChromaDB** and retrieving relevant information to generate responses using a **Large Language Model (LLM)**.
## Features
- **ChromaDB for Embeddings**: Stores and retrieves document embeddings (OpenAI's `text-embedding-3-large`) for relevant travel insights.
- **Streamlit**: Provides a user-friendly interface for travelers to ask questions.
- **AI-Powered Responses**: Uses an LLM ([OpenAI's](https://platform.openai.com/api-keys) `gpt-4o`) to generate accurate and meaningful travel answers.
- **Performance Evaluation**: Responses were tested using an LLM as a judge, with a **9/10** accuracy rating being the most common score.
## Data Sources
The data used for this project includes:
- **Travel Guides**: Publicly available travel guides about Iceland.
- **Tourism Websites**: Information from Icelandic tourism websites.
- **MG Trip PLanner**: Iceland on a budget information.
- **Custom Data**: Curated datasets collected via paid APIs (not included in the repository due to privacy concerns).
The data was preprocessed and cleaned using the `data_cleaning.ipynb` notebook, and document chunks were created using the `chunks.ipynb` notebook. These notebooks are included for reference but can not be run, since they need the curated dataset collected via paid APIs.
## Project Structure
Below is the project structure with a brief explanation of each component:
```bash
genai-travel-guide/
├── app/
│ ├── images/ # Static assets like images or icons for the app
│ ├── _pages/ # Directory containing the main page components of the app
│ │ ├── about.py # About page of the app
│ │ ├── chat.py # Chat interface for user interactions
│ │ └── home.py # Home page of the app
│ └── app.py # Main Streamlit app for user interaction
├── chroma_db/
│ ├── chromas.sqlite3 # SQLite database for storing document embeddings
│ └── query_embeddings_cache.pkl # Cache file for query embeddings
├── genai_scripts/
│ ├── fonts/ # Folder for storing custom font files used in visualizations
│ │ └── Montserrat-Regular.ttf # Custom font Montserrat-Regular
│ ├── __init__.py # Initialization file for the custom module
│ ├── data_check.py # Script for checking data integrity
│ ├── data_cleaning.py # Script for cleaning and preprocessing data
│ ├── data_storage.py # Script for creating chunks, embeddings and storing data in ChromaDB
│ ├── eda.py # Script for exploratory data analysis in the chunks
│ └── GenAI_RAG.py # Script for generating responses using RAG approach and LLM
├── images/ # Folder for storing visual assets
├── notebooks/
│ ├── chunks.ipynb # Notebook for creating document chunks and saving chunk embeddings in ChromaDB
│ ├── data_cleaning.ipynb # Notebook for cleaning and preprocessing data
│ └── GenAI_RAG.ipynb # Notebook for testing the RAG system
├── reports/
│ └── Iceland_travel_AI_assistant.pdf # Final project report
├── .gitignore # Specifies files/folders for Git to ignore (e.g., temporary files, credentials)
├── LICENSE # License file for the project
├── pyproject.toml # Configuration file for project setup
├── README.md # Project documentation
├── requirements.txt # List of Python dependencies
├── setup.py # Setup script for installing the project
└── update_vscode.py # Script for updating VSCode settings
```
## Installation & Setup
### 1. Clone the Repository
```bash
git clone https://github.com/annnieglez/genai-travel-guide
cd genai-travel-guide
```
### 2. Create a Virtual Environment (Optional but Recommended)
```bash
python -m venv rag_chat
source rag_chat/bin/activate # On macOS/Linux
source rag_chat\Scripts\activate # On Windows
```
### 3. Update VSCode Settings (Optional)
Run the following script to configure VSCode for the project:
```bash
python update_vscode.py
```
### 4. Install the Project in Editable Mode
From the main directory, run:
```bash
pip install -e .
```
to install the custom scripts.
### 5. Install Dependencies
```bash
pip install -r requirements.txt
```
### 6. Configure your OpenAI API Key
Create a `.env` file in the main directory and add your OpenAI API key:
```bash
echo OPENAI_API_KEY="your_openai_api_key" > .env
```
Replace `your_openai_api_key` with your actual API key.
### 7. Run the Streamlit App
Navigate to the `app` folder and run:
```bash
streamlit run app.py
```
Ensure you are inside the `app` folder for correct retrieval of the ChromaDB database.
## Usage
1. Run the Streamlit app in your browser.
```bash
streamlit run app.py
```
Alternatively, you can run the retrieval system directly from the `GENAI_RAG.ipynb` notebook for testing and experimentation.
## Conclusion
This AI assistant has demonstrated high accuracy and effectiveness, offering meaningful insights for travelers. The RAG system ensures responses are well-informed, and testing with an LLM judge confirms a **9/10 accuracy score** in most cases.
## Future Improvements
- Expand the knowledge base with more travel datasets.
- Improve response personalization based on user preferences.
- Implement voice-based interaction for hands-free queries.
## Contributing
Pull requests are welcome! If you'd like to contribute, please fork the repository and create a new branch for your changes.
## License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the `LICENSE` file for details.
## Author
This project was created by Annie Meneses Gonzalez. Feel free to connect with me on [LinkedIn](https://www.linkedin.com/in/annie-meneses-gonzalez-57bb9b145/).
---
💙 *Happy travels with AI-powered insights!*