8/27/2024

Setting Up Ollama with PostGIS

In today’s ever-evolving data-driven world, the intersection of AI & geospatial data represents a massive frontier for exploration. One of the exciting tools you can explore is Ollama, which allows for running powerful large language models locally, combined with the strengths of PostGIS, an extension of PostgreSQL designed for spatial data. In this blog post, we'll dive deep into setting up Ollama with PostGIS, enabling you to create intelligent applications that can analyze and retrieve geospatial information effortlessly.

What is Ollama?

Ollama is a desktop application that provides a straightforward way to run open-source large language models directly on your local machine. Thanks to its innovative Command Line Interface (CLI), users can communicate with models seamlessly via terminal or through REST API calls. Ollama supports various models, including Llama2–7B and Mistral-7B, making it flexible for different use cases, including chatbots, content generation, and more.

However, even AI models need a robust backend to power them with data, especially when it comes to dynamic, complex queries involving geographical data. This is where PostGIS kicks in!

What is PostGIS?

PostGIS is a popular spatial database extender for PostgreSQL, which enables the database system to be able to store, query, and manipulate spatial data effectively. It offers capabilities that are essential for any location-based application, from simple point data (think locations on a map) to complex geometries.

Why Combine Ollama & PostGIS?

Combining Ollama & PostGIS opens doors to a treasure trove of opportunities:

Dynamic Data Handling: Integrate live geospatial databases for real-time analysis.
Enhanced User Interaction: Develop AI applications capable of understanding and processing complex queries regarding geographical data.
Cost-Effective Solutions: As both Ollama and PostGIS are open-source, you can run robust AI & spatial analysis systems without the costs often associated with cloud solutions.

Setting Up Your Environment

Prerequisites

Before diving in, make sure you have:

A local machine that runs on macOS or Linux (Windows is possible, but may require additional steps).
Docker installed on your machine to run PostgreSQL & PostGIS easily.
Sufficient RAM (minimum 8GB recommended) to handle your models effectively.
Basic familiarity with command-line interfaces and SQL.

Installing Ollama

To start with Ollama, follow these steps:

Download the Ollama application for your operating system.
Install Ollama using the provided installation instructions. After installation, you can confirm it's working by running a simple command in your terminal:
1 2bash ollama run mistral
This should pull the Mistral model to your local system.

Setting Up PostgreSQL with PostGIS Using Docker

To run PostgreSQL with PostGIS, all you need is a preconfigured Docker image. Here’s a simple Dockerfile to get started:

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dockerfile
FROM ankane/pgvector
COPY *.sql /docker-entrypoint-initdb.d/

In this setup, ensure you create an

init.sql

file to initialize your database, which will contain commands to activate the

PostGIS

extension:

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sql
CREATE EXTENSION IF NOT EXISTS postgis;
CREATE EXTENSION IF NOT EXISTS pgvector;

Next, here is a sample

docker-compose.yaml

file: ```yaml version: '3.9' services: postgres: image: postgis/postgis:latest ports:

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- "5432:5432"
environment:
  POSTGRES_USER: postgres
  POSTGRES_PASSWORD: password
  POSTGRES_DB: mydatabase
volumes:
  - pg_data:/var/lib/postgresql/data

volumes: pg_data:

Now start your PostGIS service with:

bash docker-compose up ```

Confirm PostGIS Installation

You can confirm the installation of PostGIS by connecting to the database and checking its version:

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bash
psql -h localhost -U postgres -d mydatabase -c "SELECT PostGIS_Version();"

Integrating Ollama with PostGIS

Creating Your Schema

Let’s create a spatial table in your PostgreSQL database to store geometries. Execute the following SQL statement to set everything up:

sql
CREATE TABLE locations (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100),
  geom GEOMETRY(Point, 4326)
);

This schema will allow us to store point data such as geographic coordinates efficiently.

Loading Data into PostGIS

You can now start loading your geospatial data into the

locations

table. Here’s an example inserting a geographical point:

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sql
INSERT INTO locations (name, geom) VALUES ('Central Park', ST_SetSRID(ST_MakePoint(-73.9654, 40.7851), 4326));

Querying Data from Ollama

With your data in place, it’s time to build an interaction interface using Ollama’s capabilities. Here’s how you can establish a communication channel between your AI and the database.

Generating embeddings for queries:
1 2 3 4 5javascript const response = await ollama.chat({ prompt: 'Which parks are in New York?', model: 'mistral', });
This will provide you with an AI-generated response.
Using the embeddings in PostGIS Queries:
1 2sql SELECT name FROM locations WHERE ST_DWithin(geom, ST_SetSRID(ST_MakePoint(-73.9654, 40.7851), 4326), 500);

This SQL command retrieves parks within a 500-meter radius of Central Park. The combination of Ollama's AI queries & PostGIS's geospatial analysis allows you to build powerful applications.

Building a Retrieval-Augmented Generation (RAG) System

The concept behind Retrieval-Augmented Generation is to enhance AI responses using external data. To achieve this with Ollama and PostGIS, you need:

Embedding Generation: Use Ollama to create embeddings for your prompts.
Geospatial Queries: Utilize PostGIS to retrieve data based on the embeddings.
AI Response Generation: Convert the results back into a meaningful response for the user.

Implementing RAG

For the RAG model implementation, you can set up your API calls similar to the following:

javascript
const vectorResults = await getEmbedding(userPrompt);
const locationResults = await queryDatabase(vectorResults);
const answer = await ollama.chat(locationResults);
console.log(answer);

This code shows how you can capture user prompts, generate embeddings, perform spatial queries, and get optimized responses using Ollama.

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Conclusion

Setting up Ollama with PostGIS paves the way for creating intelligent applications that can handle complex geospatial data. From foundational installations to effectively querying geospatial data, merging Ollama's language modeling capabilities with PostGIS's spatial analysis opens doors to innovative solutions that enhance our understanding of geographical analytics. Embrace this technology and start building smarter applications that respond effectively to your queries based on solid data foundations.

Happy querying! 🌍