Course
Graph RAG with LangChain and Neo4j
AdvancedSkill Level
Updated 03/2026PythonArtificial Intelligence3 hr11 videos37 Exercises3,100 XPStatement of Accomplishment
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Level-Up Your RAG Applications with Graphs
Are you bored of vectors, embeddings, and vector RAG applications yet? Look no further! In this course, you'll discover how Graph RAG can greatly improve the accuracy and reliability of RAG applications by storing and querying information in the form of nodes and relationships. Combine Neo4j graph databases with LangChain and you get a truly awesome way of retrieving and integrating external data with LLMs.Create Neo4j Graph Databases from Unstructured Text
What if my dataset is messy unstructured text rather than a graph? Don't panic—you'll learn how to use LLMs with structured outputs to extract entities and relationships from text, and create new nodes and relationships for your graph database. You'll utilize the Pydantic library to define strict data structures for your LLM to populate with extracted text data.Combine Vectors and Graphs for Hybrid RAG
You don't have to choose between vectors vs. graphs—you can have the best of both worlds! Discover how you can retrieve from both data sources in a single workflow and carefully construct prompts to integrate them into a hybrid RAG application.Integrate Long-Term Chatbot Memory
Graph databases like Neo4j aren't only useful as knowledge bases for retrieval, you can also store long-term information like user facts and preferences as graphs! This long-term memory can then be queried just like any other graph database to integrate these preferences and personalize your applications.Prerequisites
Retrieval Augmented Generation (RAG) with LangChain1
Getting Started with Graph RAG and Neo4j
Learn how Graph RAG can improve the accuracy and reliability of RAG applications! Store information as nodes and edges in a Neo4j database, and give your LLM the ability to query it so it can retrieve entity and relational information to provide informed answers.
2
Graph Models and Hybrid RAG
Text-to-Cypher applications work well in many circumstances, but we can do better than that! Discover how to construct graph databases using different graph models including lexical and domain graphs. Create Neo4j vector indexes so that you can have the best of both worlds and run graph and vector retrieval simultaneously!
3
Improving Retrieval Quality
Although Graph RAG applications are generally more reliable than vector RAG, they aren't totally infallible. In this chapter, you'll learn to evaluate your Graph RAG applications, spot incorrect or duplicate graph nodes, and integrate long-term memory so user preferences can be learned over time.
Graph RAG with LangChain and Neo4j
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FAQs
How does Graph RAG differ from standard vector-based RAG?
Graph RAG stores information as nodes and relationships in a graph database instead of vector embeddings, improving accuracy for queries that involve entity connections and relational data.
What tools and libraries are used in this course?
You will use LangChain for orchestration, Neo4j as the graph database, and the OpenAI API for LLM integration, building Graph RAG applications across three chapters.
What prior experience with RAG and LLMs do I need?
You should have completed courses on LangChain, embeddings, prompt engineering, and basic RAG with LangChain. Strong Python skills and OpenAI API experience are also expected.
Does the course cover combining graph and vector approaches?
Yes. The second chapter teaches hybrid RAG, which combines graph-based retrieval with vector-based methods for more comprehensive and accurate information retrieval.
What is Cypher and will I learn to use it?
Cypher is the query language for Neo4j graph databases. The course teaches text-to-Cypher techniques that let your LLM automatically generate graph queries from natural language.
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