The Problem

If you have ever tried to build a RAG system that understands locations, you know standard vector databases fail completely. If you ask an AI to find "infrastructure damage within 50km of Lagos", a normal vector database just gives you articles about infrastructure damage from all over the world because it ignores the map.

I built GeoRAG to fix this. It is a python package that combines PostGIS mapping with pgvector semantic search in a single database, this means your AI only gets information that actually makes sense for the specific location you are asking about.

What it does

• Hybrid Search: It filters out the wrong map locations first and then it finds the best matching text.
• Smart Clustering: It uses Scikit-Learn to group your data points into real geographic regions so your AI does not get confused by scattered coordinates.
• One Database: Everything lives in PostgreSQL, you do not need to sync a separate vector database with your main backend.

Quickstart

from georag import GeoRAGPipeline

# Set up the pipeline with your Django model
pipeline = GeoRAGPipeline(chunk_model=MySpatialChunk, max_clusters=3)

# Get context ready for your LLM
context = pipeline.generate_context(
    query_embedding=my_vector,
    target_longitude=3.3792,
    target_latitude=6.5244,
    radius_km=50.0
)

Author & Next Steps

Feel free to open an issue or reach out at makindeadedeji500@gmail.com if you have questions.

Upcoming Roadmap

• Interactive Demo: Hosting a live instance on Hugging Face to visualize the spatial routing algorithm.
• Architecture Breakdown: Publishing a Hashnode article detailing the exact PostGIS and Scikit-Learn backend integration.