https://github.com/jfraziz/adbh
Green Living Graph Based Agentic App. Your AI-powered guide for sustainable living, providing information on green spaces and environmental data across Europe. Project Submission of Arango DB Hackathon
https://github.com/jfraziz/adbh
agentic-ai agentic-rag ai arangodb hackathon llm networkx react
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Green Living Graph Based Agentic App. Your AI-powered guide for sustainable living, providing information on green spaces and environmental data across Europe. Project Submission of Arango DB Hackathon
- Host: GitHub
- URL: https://github.com/jfraziz/adbh
- Owner: JfrAziz
- Created: 2025-03-08T23:58:39.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-10T06:59:15.000Z (over 1 year ago)
- Last Synced: 2025-10-29T11:03:22.375Z (8 months ago)
- Topics: agentic-ai, agentic-rag, ai, arangodb, hackathon, llm, networkx, react
- Language: Jupyter Notebook
- Homepage: https://adbh.pages.dev
- Size: 64.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Arango DB Hackathon Submission
# Green Living Graph Based Agentic App Project
- Web Apps : [https://adbh.pages.dev](https://adbh.pages.dev/)
- Presentation: [Presentation](./docs/presentation.pdf)
- Youtube video : [https://youtu.be/UNBXhglRGzs](https://youtu.be/UNBXhglRGzs)
https://github.com/user-attachments/assets/db1c5523-fb9d-4ba1-94c6-b97c8c587b2f
## Table of Contents
- [Arango DB Hackathon Submission](#arango-db-hackathon-submission)
- [Green Living Graph Based Agentic App Project](#green-living-graph-based-agentic-app-project)
- [Table of Contents](#table-of-contents)
- [Project Overview](#project-overview)
- [Data Sources](#data-sources)
- [Data Processing and Graph Construction](#data-processing-and-graph-construction)
- [Agentic App Functionality](#agentic-app-functionality)
## Project Overview
The Green Living Data Analysis Project aims to promote sustainable living by integrating various environmental datasets into a comprehensive knowledge graph. This project leverages data from satellite imagery, mapping services, and news articles to provide insights into green infrastructure, pollution levels, and related news.
## Data Sources
- **Sentinel Copernicus Satellite Imagery**: Provides high-resolution Earth observation data for environmental monitoring.
- **OpenStreetMap (OSM)**: An open-source mapping platform offering detailed information on geographical features, including green spaces and infrastructure.
- **Event Registry**: Aggregates global news articles, enabling analysis of environmental events and trends.
## Data Processing and Graph Construction
1. **Geospatial Data Conversion**:
- Converted geospatial data into efficient formats such as Parquet and GeoPackage (GPKG) to optimize storage and access.
- Performed spatial joins to integrate various geospatial datasets, aligning features based on spatial relationships.
2. **News Data Processing**:
- Applied Named Entity Recognition (NER) techniques to extract entities like organizations, locations, and environmental terms from news articles.
- Utilized Large Language Models (LLMs) to contextualize these entities, linking them to existing graph nodes and uncovering new relationships.
3. **Graph Database Schema**:
- **Nodes**:
- *Object*: Represents entities such as Power Generators, EV Charging Stations, Greenery Lands, Public Transport Stations, and Waste Recycle Facilities.
- *ObjectType*: Categorizes objects into specific types (e.g., solar power generator, park).
- *Country* and *City*: Geographical entities with associated attributes.
- *Grid*: Represents population density and gaseous pollutant levels (CO, CH₄, NO₂).
- *News*: Contains news articles with attributes like content and date.
- *NewsEntity* and *NewsEntityType*: Extracted entities from news articles and their classifications.
- **Edges**:
- `City` → `Country`: `located_in`
- `Object` → `City`: `located_in`
- `Grid` → `City`: `located_in`
- `Object` → `ObjectType`: `is_a`
- `News` → `NewsEntity`: `mentions`
- `News` → `City`: `related_to`
- `News` → `Country`: `related_to`
- `NewsEntity` → `NewsEntityType`: `belongs_to`
## Agentic App Functionality
The Agentic App dynamically retrieves and processes natural language queries based on user intent. It offers:
- **Geospatial Data Analysis**: Provides insights into the distribution and accessibility of green infrastructure and pollutant levels.
- **News Retrieval**: Aggregates and analyzes news related to environmental issues to keep communities informed.
*Example Queries*:
- "Find EV charging stations in Berlin."
- "How many greenery lands are in Hamburg?"
- "Show me the location with the highest CO level in Bayern."
Here how it's works
