Using Neo4j with LangChain

In this course, you will learn how to integrate Neo4j into your LangChain applications, enabling you to leverage the power of graph databases in your Generative AIiGo to glossary for generative AI (opens in a new tab)Models that produce new content rather than classifying or scoring content that already exists. workflows.

You will learn how to:

1. Use the langchain_neo4j package to interact with Neo4j through a LangChain application.
2. Create RAGiFull definition for retrieval-augmented generation (opens in a new tab)Fetching relevant context from an external data source as additional context to inform a language model's response. and GraphRAGiFull definition for GraphRAG (opens in a new tab)Retrieval-augmented generation whose context comes from a knowledge graph, so the model can follow the relationships between facts. retrieversiFull definition for retriever (opens in a new tab)A component that searches a data source and returns the information relevant to a query. Often used to provide context for a language model..
3. Implement and customize a text-to-Cypher retriever.
4. Create a simple LangChain agent that interacts with Neo4j.

This course uses models from OpenAI, although you can use the model and supplier of your choice.

Prerequisites

Before taking this course, you should have:

• A basic understanding of Graph Databases and Neo4j
• Knowledge of Python and basic familiarity with LangChain

We recommend taking the following courses:

• Neo4j Fundamentals.
• Neo4j & GenAI Fundamentals.

If you are new to LangChain, we recommend completing the LangChain Build a simple LLM application and Build a Chatbot tutorials.

To complete the practical tasks within this course, you will need an OpenAI API key.