Constructing Knowledge Graphs with Neo4j GraphRAG for Python
Course Description
In this hands-on course, you will learn how to create knowledge graphs using Neo4j GraphRAG for Python.
You will:
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Use the
neo4j_graphragPython package to build knowledge graphs from unstructured data. -
Add structured data to the knowledge graph to improve LLM responses.
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Create retrievers to search the knowledge graph.
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Learn how you can customize the build process to suit your data and use case.
Finally, you will use what you have learned to build a knowledge graph from your data.
Prerequisites
This is an advanced course and you should:
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Understand graph and Neo4 fundamental concepts - Neo4j and Graph Fundamentals.
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Have an understanding of how Generative AI, LLMs, and vector indexes are related to Neo4j - Neo4j & GenerativeAI Fundamentals.
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Be able to read and write simple Cypher queries - Cypher Fundamentals.
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Understand how you can use an LLM to generate a knowledge graph - https://graphacademy.neo4j.com/courses/llm-knowledge-graph-construction/^.
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Have experience with programming in Python.
Duration
2 hours
What you will learn
How to:
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Use the Neo4j GraphRAG for Python package to create a knowledge graph from unstructured data.
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Enhance a knowledge graph by adding structured data.
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Create Retrievers to search a knowledge graph.
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Customize the knowledge graph build process to suit your data and use case.
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Model a knowledge graph of both structured and unstructured data.
This course includes
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16 lessons
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7 hands-on challenges
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8 simple quizzes to support your learning
Get Support
If you find yourself stuck at any stage then our friendly community will be happy to help. You can reach out for help on the Neo4j Community Site, or head over to the Neo4j Discord server for real-time discussions.
Feedback
If you have any comments or feedback on this course you can email us on graphacademy@neo4j.com.