GraphAcademy GenAI Workshop

GraphAcademy is Neo4j's free online resource of self-paced, hands-on courses and certification training. It covers graph technology, Neo4j graph database development, designing and optimizing graph-powered applications, and GraphRAG. The courses are provided by the people who build Neo4j.

Agent Context Workshop is a hands-on workshop resource for building and evaluating context layers for coding agents. It uses selected crates from the Qdrant Rust codebase to extract syntax with tree-sitter, resolve symbols and relationships with rustdoc JSON, store the resulting code knowledge graph in Neo4j, and expose graph tools such as search, blast-radius analysis, and implementor lookup to an agent. The workshop compares graph-based agents with grep-style agents using gold-verified questions, deterministic scoring, cached evaluation results, tool-adoption measurements, and trace inspection. Additional notebooks cover static Python resolution with Griffe and an optional Fenic-and-LLM semantic layer. The GitHub repository maintains the workshop through ordered Jupyter notebooks, typed extraction and graph code, evaluation harnesses, tests, committed Qdrant seed data, and a committed evaluation cache; it is licensed under Apache-2.0.

An open-source Python package from Neo4j for building graph retrieval-augmented generation applications with Neo4j. It provides knowledge-graph construction pipelines that process text or PDFs, use an LLM to extract schema-constrained entities and relationships, generate embeddings, and populate a Neo4j database. The package includes vector-index utilities, embedding and LLM integrations, GraphRAG generation, and retrievers for vector, hybrid, graph-traversal, and text-to-Cypher retrieval. Its `Pipeline` supports extensive customization, while `SimpleKGPipeline` provides a simpler abstraction; optional dependencies add support for providers and external vector stores including Weaviate, Pinecone, and Qdrant. It is installable with pip or conda and supports Python 3.10 through 3.14, with experimental knowledge-graph features identified as unsuitable for production use.

AI Engineering Lab is a free, self-paced 24-week AI engineering course developed by Zorost Intelligence AI Lab. It takes learners from Python and machine learning through deep learning, LLM internals, prompt and context engineering, retrieval-augmented generation with vector search, quantization, LoRA and DPO fine-tuning, evaluation harnesses, coding-agent harnesses, AI agents, MCP, and cloud and Databricks platforms including Azure AI Foundry, Google Vertex AI, AWS Bedrock, and Databricks. The curriculum uses one continuous fictional freight-company case study. Each week pairs a concept with a runnable Python, SQL, or PySpark artifact; from Week 3 onward, the work includes a metric and an error note. The repository contains 43 runnable notebooks, publishes the full curriculum online, requires no signup or paid API key, and states that the first eight weeks require no GPU. It is MIT licensed.

Production Agentic RAG Course is a GitHub-hosted course on building production Retrieval-Augmented Generation systems through a seven-week, hands-on arXiv Paper Curator project. Its learning path covers Docker-based infrastructure, FastAPI, PostgreSQL, OpenSearch, Airflow, academic-paper ingestion with the arXiv API and Docling, BM25 keyword search, section-aware chunking, Jina embeddings, hybrid retrieval with reciprocal-rank fusion, local LLM serving with Ollama, streaming responses, Gradio, Langfuse tracing, Redis caching, and LangGraph-based agentic RAG. The agentic RAG stage uses guardrail, retrieval, document-grading, query-rewriting, and generation nodes coordinated in a state-based LangGraph workflow. It includes adaptive retrieval, semantic relevance evaluation, domain-boundary detection, reasoning-step tracking, and an asynchronous Telegram bot interface. The course is organized through weekly notebooks, blog posts, and tagged code releases in the repository. The project is distributed under the MIT License and is designed to run locally with Docker Compose, Python 3.12 or later, and the UV package manager; optional external services include Jina embeddings, Langfuse, and Telegram.