{"product_id":"graphrag-in-production-building-reliable-knowledge-driven-agents-with-hybrid-search-9798278116318","title":"GraphRAG in Production: Building Reliable Knowledge-Driven Agents with Hybrid Search","description":"\u003cp\u003e • Author(s): Max Reedwell\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Data Science - Data Modeling \u0026amp; Design\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003cb\u003eThe era of flat retrieval is ending. It is time to give your AI agents a brain, not just a search bar.\u003c\/b\u003e\u003c\/b\u003e\u003cbr\u003eFor 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.\u003cbr\u003e\u003cb\u003e\u003cb\u003eGraphRAG in Production\u003c\/b\u003e\u003c\/b\u003e is the engineering manual for the next generation of intelligent systems.\u003cbr\u003eThis 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.\u003cbr\u003e\u003cb\u003e\u003cb\u003eInside, you will engineer: \u003c\/b\u003e\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003cb\u003eThe Hybrid Search Engine: \u003c\/b\u003e\u003c\/b\u003e Implement Reciprocal Rank Fusion (RRF) to mathematically combine vector similarity scores with graph centrality metrics, ensuring your retrieval is both broad and precise.\u003cbr\u003e\u003cb\u003e\u003cb\u003eThe Agentic Control Plane: \u003c\/b\u003e\u003c\/b\u003e Move beyond brittle linear chains. Build stateful, self-correcting agents using \u003cb\u003e\u003cb\u003eLangGraph\u003c\/b\u003e\u003c\/b\u003e that can plan research, write and fix their own \u003cb\u003e\u003cb\u003eCypher\u003c\/b\u003e\u003c\/b\u003e queries, and retry failed searches automatically.\u003cbr\u003e\u003cb\u003e\u003cb\u003eThe Indexing Pipeline: \u003c\/b\u003e\u003c\/b\u003e 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%.\u003cbr\u003e\u003cb\u003e\u003cb\u003eAdvanced Graph Algorithms: \u003c\/b\u003e\u003c\/b\u003e Implement the \u003cb\u003e\u003cb\u003eLeiden algorithm\u003c\/b\u003e\u003c\/b\u003e for community detection and \"Map-Reduce\" style global summarization, allowing your system to answer high-level thematic questions across your entire dataset.\u003cbr\u003e\u003cb\u003e\u003cb\u003eProduction Infrastructure: \u003c\/b\u003e\u003c\/b\u003e Deploy a containerized stack using \u003cb\u003e\u003cb\u003eDocker\u003c\/b\u003e\u003c\/b\u003e, comparing the trade-offs between \u003cb\u003e\u003cb\u003eNeo4j\u003c\/b\u003e\u003c\/b\u003e, \u003cb\u003e\u003cb\u003eFalkorDB\u003c\/b\u003e\u003c\/b\u003e, and \u003cb\u003e\u003cb\u003eMemgraph\u003c\/b\u003e\u003c\/b\u003e for specific LLM workloads.\u003cbr\u003e\u003cb\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003c\/b\u003e\u003cbr\u003eThis 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.\u003cbr\u003e\u003cb\u003e\u003cb\u003eStop building chatbots that guess. Start building agents that know.\u003c\/b\u003e\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":46861555138711,"sku":"9798278116318","price":2155.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798278116318.webp?v=1769962545","url":"https:\/\/atlanticbooks.com\/products\/graphrag-in-production-building-reliable-knowledge-driven-agents-with-hybrid-search-9798278116318","provider":"Atlantic Books","version":"1.0","type":"link"}