NEOPOLIS AKADEMY
Graph RAG with LangChain and Neo4j
Explore Graph RAG with LangChain and Neo4j: combine hybrid retrieval and graph memory to enrich responses, structure reasoning and maintain a relational knowledge history.
View this course on Akademy ↗Enrolment and practical details are available on Neopolis Akademy.
What you will explore
This course examines integrating LangChain with Neo4j to represent relations and contexts within a RAG pipeline.
It studies hybrid retrieval mixing vectors and graph queries, long-term memory construction and using the graph to guide information assembly.
The course shows how exploiting relational structure can improve relevance, traceability and coherence of generated answers.
STEP BY STEP
Course programme
01Graph RAG with LangChain and Neo4j
Learn to build a Graph RAG using LangChain and Neo4j to enhance information retrieval. The course covers creating nodes and relationships, lexical graphs, hybridizing with vector search, and entity-resolution techniques to improve RAG reliability.
Fundamentals: how graphs relate to Retrieval-Augmented Generation (RAG).
Hands-on creation of nodes and relationships in Neo4j, and storing graph-linked documents.
Lexical graph models: breaking content into acts and building hierarchical lexical graphs.
Improving retrieval: entity resolution, using extracted entities, graph-based resolution methods and evaluation with RAGAS.
Explore this module on Akademy ↗Programme source: Neopolis Akademy. Original course page