Graph Rag

Table of Contents Introduction Why Traditional Vector Search Falls Short for Long Contexts Enter GraphRAG: A Hybrid Retrieval Paradigm Fundamentals of Knowledge Graphs for Retrieval Architectural Blueprint of a GraphRAG System Building the Knowledge Graph: Practical Steps Indexing and Embedding Strategies Query Processing Workflow Hands‑On Example: Implementing GraphRAG with Neo4j & LangChain Performance Considerations & Scaling Evaluation Metrics for Long‑Context Retrieval Best Practices & Common Pitfalls Future Directions Conclusion Resources Introduction The explosion of large language models (LLMs) has made retrieval‑augmented generation (RAG) the de‑facto standard for building intelligent assistants, chatbots, and domain‑specific QA systems. Most RAG pipelines rely on vector search: documents are embedded into a high‑dimensional space, an approximate nearest‑neighbor (ANN) index is built, and the model retrieves the top‑k most similar chunks at inference time. ...

Introduction Traditional RAG systems treat knowledge as a collection of text chunks—embedded, indexed, and retrieved based on semantic similarity. This works well for simple factual lookup, but fails when questions require understanding relationships, dependencies, or multi-hop reasoning. Graph RAG fundamentally reimagines how knowledge is represented: instead of flat documents, information is structured as a graph of entities and relationships. This enables LLMs to traverse connections, follow dependencies, and reason about how concepts relate to each other. ...