Build a Semantic Knowledge Graph with GraphRAG, PostgreSQL 16, and Apache AGE in Docker for Advanced Cypher Queries and AI Integration

Discover how to integrate GraphRAG with PostgreSQL and Apache AGE in a Docker environment to build a semantically rich knowledge graph. This solution supports Cypher queries and AI agents, enabling interactive, context-aware data retrieval from unstructured sources—all in under 15 minutes! Unique :

Unlocking GraphRAG & PostgreSQL Integration with Docker and AI Agents

If you’re into AI, databases, and graph queries, this new integration is a game-changer. In just 15 minutes, you can spin up a Cypher-powered knowledge graph that’s semantically rich and interactive. Let’s dive into what makes this setup so exciting for tech enthusiasts and developers alike.

What’s New: GraphRAG Meets PostgreSQL with Docker

GraphRAG transforms messy, unstructured data like text files into structured knowledge graphs. This means you can query complex relationships and get precise insights. The twist? Instead of juggling files and blob storage, this solution runs GraphRAG directly on data stored in PostgreSQL, enhanced with Apache AGE for native Cypher query support.

“GraphRAG extracts structured knowledge from raw, unstructured data, enabling more precise and context-aware retrieval.”

The entire stack is containerized using Docker, making deployment smooth and modular. You get PostgreSQL 16 with AGE, Python, Jupyter notebooks, semantic-kernel, and all necessary modules bundled together. This setup supports building AI agents that leverage the graph’s rich data.

Major Updates: Why PostgreSQL + AGE Over Neo4j?

Neo4j is popular but comes with drawbacks like Java dependencies and licensing concerns. PostgreSQL with AGE offers a more flexible, relational-friendly environment. Plus, AGE’s Cypher support lets you run powerful graph queries without leaving the PostgreSQL ecosystem.

“Running Neo4j requires a pre-installed Java Virtual Machine; PostgreSQL with AGE avoids this and supports relational operations better.”

This integration simplifies pipelines by removing intermediate storage steps. Data flows smoothly between the database and GraphRAG services inside Docker containers, easing development and iteration.

How to Get Started: Step-by-Step Docker Workflow

The GitHub repo (link here) provides everything you need. Here’s a quick rundown:

Step 0: Insert raw .txt data into your database (optional if data already exists).
Step 1: Build the Docker image with all dependencies.
Step 2: Launch the PostgreSQL service inside Docker.
Step 3: Load data from DB to Docker folder for GraphRAG input.
Step 4: Build the GraphRAG index, generating embeddings and graph files.
Step 5: Write the index output back to the database for backup.
Step 6: Build the graph in PostgreSQL using AGE for Cypher queries.
Step 7: Run queries and AI agents via Jupyter notebooks inside Docker.

This modular approach lets you update code or configs without rebuilding the entire image, speeding up your workflow.

Why This Matters for AI and Data Engineering

Combining GraphRAG’s semantic extraction with PostgreSQL’s robust graph querying opens new doors. You can now build AI agents that understand complex relationships in your data, from product catalogs to podcast transcripts.

Imagine querying multi-hop relationships across scattered data sources in seconds. This integration streamlines that process, making it accessible without complex infrastructure.

Final Thoughts

This project exemplifies how open-source tools and cloud-native tech can empower developers to build smarter, more connected AI solutions. Whether you’re a data engineer or AI researcher, this integration offers a powerful way to turn unstructured data into actionable insights.

Ready to explore? Check out the GitHub repo, follow the quickstart, and start querying your own knowledge graph today!

Leverages PostgreSQL 16 with Apache AGE for native Cypher query support in a Docker container.

Eliminates intermediate blob storage by directly using databases for input and output.

Includes modular Docker services for data loading, indexing, graph building, querying, and AI agent integration.

Supports multi-hop queries to link related data points across complex product or content datasets.

Offers a ready-to-use Jupyter notebook for running Cypher, GraphRAG, and vector search queries interactively.

From the New blog articles in Microsoft Community Hub