Knowledge Graphs Are a Multi-Tool Problem
You know knowledge graphs are the answer to RAG hallucinations, multi-hop reasoning, and structured AI memory. But the path from "I should build a knowledge graph" to "my knowledge graph is in production, monitored, and queryable" runs through five different toolchains, three architectural decisions you can't undo, and a visualization library that rewrites its API every quarter.
This toolkit is the complete reference shelf. One download, everything you need.
What's Inside
Part 1: Build — Neo4j + LLM Integration Guide (58 pages)
Ship a hybrid RAG system that combines Neo4j graph traversal with vector search. Query routing, entity resolution, context serialization, incremental updates, performance runbook. Benchmarks: 89% accuracy on multi-hop questions vs 62% with vector-only.
Part 2: Ship — GraphRAG Production Playbook v1.4 (70 pages)
From prototype to production. Caching strategies, monitoring with OpenTelemetry, CI/CD for graph schemas, cost optimization, evaluation frameworks (MRR, NDCG, LLM-as-judge), and sovereign AI deployment on DGX Spark. Based on real production incidents, not blog posts.
Part 3: Understand — Knowledge Graph Fundamentals (50 pages)
RDF, SPARQL, property graphs, ontology design, and the architectural decisions that determine whether your KG succeeds or stalls. Start here if you're new to knowledge graphs.
Part 4: Optimize — Graph Theory for Software Engineers (52 pages)
Not abstract math. Dijkstra, PageRank, community detection, centrality — implemented and deployed on real graph data. Understand why your traversal query is slow before you optimize it.
Part 5: Visualize — Graph Visualization with Sigma.js (100 pages)
Ship interactive graph visualizations that render 100K nodes without dropping frames. Force layouts, WebGL rendering, dynamic filtering, production dashboard patterns.
Part 6: Operate — Observability Stack: KG Edition
Prometheus metrics, Grafana dashboards, Loki log aggregation — purpose-built for knowledge graph infrastructure. Know when query latency spikes, when Neo4j connection pools saturate, or when node growth stalls.
What You'll Be Able To Do
- Design a knowledge graph schema that survives production — from ontology decisions to indexing strategy
- Build hybrid RAG that answers multi-hop questions pure vector search can't touch
- Deploy with monitoring, backup, and CI/CD — not just a prototype on your laptop
- Visualize 100K-node graphs interactively in the browser
- Optimize traversal queries using graph theory fundamentals
- Explain your architecture to your team using 330 pages of shared reference
- Run sovereign — deploy your LLM and GraphRAG pipeline entirely on-premises with DGX Spark
The Complete Table of Contents
| Component | Pages | What It Solves | |-----------|-------|----------------| | Neo4j + LLM Integration Guide | 58 | How to connect graph retrieval to your LLM | | GraphRAG Production Playbook | 70 | How to ship and operate at scale | | Knowledge Graph Fundamentals | 50 | How to design your schema | | Graph Theory for Software Engineers | 52 | How to optimize your queries | | Sigma.js Visualization | 100 | How to show your graph to stakeholders | | Observability Stack | — | How to know when things break | | Grafana Dashboards | — | How to see the health at a glance |
Total: 330 pages + production infrastructure.
Format & Delivery
ZIP archive containing all guides in PDF, ePub, and Mobi formats (read on any device), plus the Observability Stack Docker Compose and Grafana dashboard JSON. Immediate digital download. Lifetime updates included.
