README.md

metadata

title: Geo Spatial Multi Vector Search
emoji: 🌍
colorFrom: yellow
colorTo: red
sdk: streamlit
sdk_version: 1.52.0
app_file: app.py
pinned: false
license: mit

Geo-Spatial Chat with Qdrant & ColPali

Query geospatial burn scar data using natural language, powered by ColPali (Vidore/colSmol-500M) multi-vector embeddings and Qdrant.

✨ Features

• Natural Language Search: Ask questions like "Find burn scars larger than 500 hectares in California".
• Multi-Vector Retrieval: Uses colpali-v1.2 (via colSmol-500M) for fine-grained patch-level image retrieval.
• Spatial Filtering:
  • Geocoding Dropdown: Select US States or Canadian Provinces to automatically focus the search.
  • Radius Search: Filter results within a specified radius (km) of a location.
• Temporal Filtering: Filter burn scars by acquisition date range.
• Interactive Map: Visualize results on a Folium map with popups displaying score, area, and RGB imagery.
• Rich Results: View top matches with confidence scores, metadata, and Color (RGB) imagery.

🚀 Getting Started

Prerequisites

• Python 3.10+
• Qdrant Instance (Local or Cloud)

Installation

1. Clone the repository:

   git clone https://github.com/mahimairaja/geo-spatial-chat-qdrant.git
   cd geo-spatial-chat-qdrant
   

2. Install dependencies: Using uv (recommended):

   uv pip install -r requirements.txt
   

   Or standard pip:

   pip install -r requirements.txt
   

3. Environment Setup: Create a .env file in the root directory:

   QDRANT_URL=your_qdrant_url
   QDRANT_API_KEY=your_qdrant_api_key
   HF_TOKEN=your_huggingface_token
   

Data Ingestion

To ingest the dataset (HLS Burn Scars) into Qdrant:

python -m utils.ingest_to_qdrant

Note: This process generates ColPali embeddings and may take some time depending on your hardware (GPU recommended).

Running the App

streamlit run app.py

🛠️ Technology Stack

• Frontend: Streamlit
• Vector Database: Qdrant
• Embedding Model: ColPali (Vidore/colSmol-500M) - Optimized for document/image retrieval using Idefics3 architecture.
• Map Visualization: Folium & streamlit-folium
• Geocoding: geopy (Nominatim API)

If you are interested in dataset preparation, you can find it here:

📄 License

MIT License