Programming Knowledge Graphs with Python : Model, Build, and Query AI-Ready Knowledge with Cypher, Neo4j, and AI Agents

Lingua: inglese

Editore: Gitforgits, 2026

8117136090 / 9788117136096

Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

Venditore con 5 stelle

Venditore AbeBooks dal 14 agosto 2006

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Descrizione dell’articolo da parte del venditore

nach der Bestellung gedruckt Neuware - Printed after ordering - Get practical hands-on to build your own knowledge graph, and use it across retrieval, analytics and agentsLLMs sound confident, but they forget context, invent facts and can't show their reasoning. A knowledge graph gives them structured, traceable facts to work from. What you'll do in this book is grow a single Neo4j graph from the Python Enhancement Proposals, which are the documents that shaped the language. It starts with getting to grips with nodes and relationships and writing Cypher like a pro. Then you design a schema in Pydantic, chunk documents into a lexical graph and extract entities with spaCy and local LLMs. Then, you add vector embeddings and indexes, and combine full-text search, vector search and graph traversal into GraphRAG retrieval that cites its sources.The rest of the book goes into more detail. You can rank and cluster nodes with Graph Data Science, infer new facts with rules, import ontologies and validate data with SHACL. You also give LangGraph agents guarded Cypher tools, serve the graph over MCP, store agent memory with Graphiti and trace every decision. At the end of the day, you keep the graph healthy with regular refreshes, time-bound facts, evaluation runs, query tuning and backups.Key LearningsWrite expressive, parameterized Cypher queries from Python.Design graph schemas with Pydantic, constraints and indexes.Chunk documents into lexical graphs with batched ingestion pipelines.Extract entities and relationships using spaCy rules and schema-guided local LLMs.Store embeddings in Neo4j and query them through vector indexes and similarity metrics.Build GraphRAG retrievers for full-text, vector search and subgraph expansion.Rank, cluster and predict links with PageRank, Leiden and GDS pipelines.Infer facts with Cypher rules, SKOS ontologies and SHACL shape validation.Connect LangGraph agents to graphs through guarded Cypher tools, MCP and Graphiti memory.Operate graphs with incremental refreshes, temporal facts, evaluation runs, tuning and backups.Table of ContentThinking in Nodes and RelationshipsWriting Cypher with ConfidenceDesigning Graph SchemaPreparing Data and ChunksExtracting Entities and RelationshipsStructuring Embeddings in GraphsRetrieving with Vectors and StructureAnalyzing Graph StructureReasoning Symbolically over KnowledgeConnecting AI Agents to GraphsKeeping Knowledge Graphs Healthy.…

Codice articolo 9788117136096

Titolo
Programming Knowledge Graphs with Python : Model, Build, and Query AI-Ready Knowledge with Cypher, Neo4j, and AI Agents
Autore
Ryein Twardo
Editore
Gitforgits
Anno di pubblicazione
2026
Condizione
Neu
Rilegatura
Taschenbuch
Lingua
inglese
ISBN 10
8117136090
ISBN 13
9788117136096
Peso dell'articolo
485 grammi
Dimensioni
235x191x14 mm

AHA-BUCH GmbH

Einbeck, Germania

Venditore con 5 stelle

Venditore AbeBooks dal 14 agosto 2006

Tariffe di spedizione da Germania a U.S.A.

ArticoloDa 7 a 10 giorni lavorativiDa 5 a 7 giorni lavorativi
Primo articoloEUR 35,00EUR 45,00
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