GraphRAG in Production

The era of flat retrieval is ending. It is time to give your AI agents a brain, not just a search bar.
For the past two years, the industry has relied on Vector RAG to ground Large Language Models. We chopped documents into chunks, embedded them, and hoped that semantic similarity would be enough to answer every user question. For simple queries, it worked. But as we push LLMs into complex domains like financial forensics, legal discovery, and scientific research, the cracks are showing. Vector databases are excellent at finding needles in haystacks, but they fail to understand how the hay is stacked. They struggle with multi-hop reasoning, often missing the critical "disconnected facts" that link a cause to an effect across different documents.
GraphRAG in Production is the engineering manual for the next generation of intelligent systems.
This book is not a theoretical academic text. It is a hands-on guide for software architects and data scientists who are ready to move beyond simple prototypes. It details the architectural shift from retrieving data based on what it looks like (vectors) to retrieving data based on what it is connected to (graphs). You will learn how to construct a dual-memory system that fuses the fuzzy flexibility of vectors with the rigid logic of knowledge graphs, enabling your agents to traverse complex relationships and answer questions that stump standard RAG systems.
Inside, you will engineer:
The Hybrid Search Engine: Implement Reciprocal Rank Fusion (RRF) to mathematically combine vector similarity scores with graph centrality metrics, ensuring your retrieval is both broad and precise.
The Agentic Control Plane: Move beyond brittle linear chains. Build stateful, self-correcting agents using LangGraph that can plan research, write and fix their own Cypher queries, and retry failed searches automatically.
The Indexing Pipeline: Stop paying premium prices for menial tasks. Architect a tiered ingestion system that uses small, fine-tuned models for entity extraction and large reasoning models for synthesis, reducing your token costs by up to 80%.
Advanced Graph Algorithms: Implement the Leiden algorithm for community detection and "Map-Reduce" style global summarization, allowing your system to answer high-level thematic questions across your entire dataset.
Production Infrastructure: Deploy a containerized stack using Docker, comparing the trade-offs between Neo4j, FalkorDB, and Memgraph for specific LLM workloads.
Who This Book Is For
This text is written for Python-proficient engineers, AI architects, and technical leads. If you have hit the ceiling of what standard RAG can do and are looking for a reliable, scalable way to implement structured reasoning, this is your blueprint.
Stop building chatbots that guess. Start building agents that know.