Neo4j & GenerativeAI Fundamentals
2h
Learn how knowledge graphs, vector search, and GraphRAG ground LLMs in facts. Build RAG pipelines and retrievers in Python to stop hallucination.
Role
Context Engineer
Build GenAI applications and AI agents on Neo4j knowledge graphs: vector search, GraphRAG retrieval, LLM graph construction, and custom MCP tools.
An LLM is only as good as the context you give it. Context engineers build the layer between an organisation's data and its AI applications: knowledge graphs that hold facts and their connections, vector indexes that make unstructured text searchable, and GraphRAG retrieval pipelines that ground a model's answers in data it can cite. Start with Neo4j & GenerativeAI Fundamentals, then learn embeddings and semantic search in Introduction to Vector Indexes and Unstructured Data before building knowledge graphs, agents, and MCP tools in the courses below.
10 courses~18h
Building Agents in Neo4j Aura
2h
Build agents on Neo4j Aura without code. Design an agent, add Cypher Template, Text2Cypher, and Similarity Search tools, then publish it over MCP.
Introduction to Vector Indexes and Unstructured Data
1h
Learn semantic search with Neo4j vector indexes. Create embeddings with LLMs, chunk unstructured data with LangChain, and build a knowledge graph.
Building Knowledge Graphs with LLMs
1h
Start building knowledge graphs with LLMs. Use the Neo4j LLM Graph Builder to turn unstructured text into a knowledge graph and query it with Cypher.
Constructing Knowledge Graphs with Neo4j GraphRAG for Python
2h
Construct knowledge graphs from unstructured data with Neo4j GraphRAG for Python. Define schemas, tune chunking, and build GraphRAG retrievers.
Using Neo4j with LangChain
1h
Integrate Neo4j with LangChain to build GraphRAG applications: vector retrievers, graph-enhanced retrieval, text-to-Cypher, and an LLM agent.
Context Graphs: Agent Memory with Neo4j
2h 30m
Give AI agents persistent memory with Neo4j context graphs. Build short-term, long-term, and reasoning memory, then query the agent's full trace.
Developing with Neo4j MCP Tools
2h
Learn to connect AI agents to Neo4j with the Model Context Protocol. Install the Neo4j MCP server, query graphs in natural language, and build apps.
Building GraphRAG Python MCP tools
2h
Build a custom MCP server in Python with FastMCP. Create Neo4j graph-backed tools, resources, and prompts that ground AI agents with GraphRAG.
Building GraphRAG TypeScript MCP tools
2h
Create a Model Context Protocol server in TypeScript. Define type-safe Zod tools, resources, and prompts that connect AI agents to Neo4j graph data.