Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Building Intelligent Knowledge Graphs: A Practical Guide to Neo4j and Ollama Integration: Master Graph Database Architecture, Local AI Models, and Sem

Building Intelligent Knowledge Graphs: A Practical Guide to Neo4j and Ollama Integration: Master Graph Database Architecture, Local AI Models, and Sem

Vireo, Isla

Paperback

General Computers

Currently unavailable to order

ISBN13: 9798273142213
Publisher: Independently Published
Published: Nov 5 2025
Pages: 218
Weight: 0.85
Height: 0.46 Width: 7.00 Depth: 10.00
Language: English
Unlock the power of connected data and artificial intelligence with this comprehensive guide to building intelligent knowledge graphs using Neo4j and Ollama. This hands-on resource bridges the gap between graph database technology and modern AI, showing you how to create sophisticated, queryable knowledge systems that leverage local large language models.

Whether you're a data engineer, software developer, AI enthusiast, or technical architect, this book provides step-by-step instruction on designing graph schemas, implementing Cypher queries, integrating Ollama AI models, and building production-ready knowledge graph applications.

Learn to transform unstructured data into meaningful relationships, enhance semantic search capabilities, implement vector embeddings, and create RAG (Retrieval-Augmented Generation) systems that combine the best of graph databases and generative AI.

Topics covered include Neo4j fundamentals, graph data modeling, Cypher query language, property graphs, graph algorithms, natural language processing, entity extraction, knowledge graph construction, embeddings and vector search, AI-powered graph traversal, Ollama model deployment, prompt engineering for graphs, real-time data pipelines, graph visualization techniques, and enterprise-scale implementations.

Perfect for practitioners seeking to build intelligent data systems, enhance search and recommendation engines, create conversational AI interfaces, or develop knowledge management platforms. All examples include practical Python code, Docker configurations, and production deployment strategies.

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