In this course, you will learn how to integrate Neo4j with Generative AI models using Langchain.
You will learn why graph databases are a reliable option for grounding Large Language Models (LLMs), using Neo4j to provide factual, reliable information to stop the LLM from giving false information, also known as hallucination.
You will use Langchain and Python to interact with an LLM and Neo4j. Langchain provides a robust basis for AI application development and comes with Neo4j integrations for Cypher and Vector Indexes.
This course focuses on OpenAI, although Langchain allows you to work with the LLM of your choice.
Before taking this course, you should have:
A basic understanding of Graph Databases and Neo4j
Knowledge of Python and capable of reading simple programs
We recommend taking the Neo4j Fundamentals course.
Although not essential, you may also benefit from taking the Building Neo4j Applications with Python course to understand how the Neo4j Python Driver works.
To complete the practical tasks within this course, you will need an OpenAI billing account and API key.
What you will learn
Retrieval Augmented Generation (RAG) and its role in grounding LLM generated content
Using Vector and full text indexes in Neo4j to perform similarity and keyword search
Coordinating your LLM interactions with Langchain chains, agents and tools
This course includes
3 short hands-on challenges
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.
If you have any comments or feedback on this course you can email us on email@example.com.