Handbook - GenerationAI Hackathon - MCP Integration for Graph DBs
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GenerationAI Hackathon - MCP Integration for Graph DBs

Handbook for the GenerationAI Hackathon - MCP Integration for Graph DBs hackathon.

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Handbook

🛠️ Participant Handbook: GraphRAG & MCP Integration

This handbook provides key resources, sponsor tools, and starter ideas to help you build cutting-edge Model Context Protocol (MCP) integrations with GraphRAG databases.

🔗 Core Technologies & Setup

Neo4j for GraphRAG

Neo4j is your structured memory layer. Use it to store context, relationships, and reasoning trails.

  • Neo4j Sandbox/AuraDB: Get a free instance running quickly. [Link to Neo4j Free Tier/Sandbox to be provided]
  • Cypher Query Language: Master the basics for efficient data retrieval. [Link to Cypher Documentation to be provided]
  • Graph Data Science (GDS): Explore advanced algorithms for context ranking and relationship inference. [Link to GDS Library to be provided]

Scalingo for Deployment

Scalingo is your PaaS for deploying your API backend, RAG service, or multi-agent orchestration layer.

  • Scalingo Documentation: Learn how to deploy your Python/Node.js/etc. backend. [Link to Scalingo Docs to be provided]
  • Database Integration: Instructions on connecting your app to a managed database (if not using Neo4j Aura). [Link to Scalingo DB Docs to be provided]
  • Free Trial/Credits: Information on any special hackathon credits available to be provided.

💡 Starter Project Ideas

  1. Context-Aware Agent Orchestrator: Build a multi-agent system where agents use graph-persisted context (via MCP messages) to coordinate tasks, improving explainability.
  2. Dynamic Retrieval Pipeline: Create an RAG system where the retrieval step queries Neo4j based on the current LLM state (stored in a graph node) to fetch highly specific, relationship-aware context chunks.
  3. Interpretable Context Trail: Implement a system that visualizes the path of context retrieval from the graph, through the LLM prompt, and back into a new graph node, demonstrating explainability.
  4. Graph-Enhanced Memory: Use Neo4j to store long-term memory for an LLM, structured as knowledge graph triplets, and use MCP to inject relevant memories into the context window dynamically.

🤝 Mentorship & Support