GraphRAG

Prerequisite: Review the previous module in the ai-data-engineering-pipeline series before proceeding. Executive Summary: The Disruption of Naive RAG and the GraphRAG Era Answer-first: Naive RAG collapses in enterprise environments due to relational blindness, unstructured document chunk destruction, and lack of fine-grained access control. Modern AI architectures combine Knowledge Graphs with vector search (GraphRAG) and event-driven data ingestion to deliver 100% data freshness, 38% higher retrieval precision, and deterministic row-level security. Architecting this pipeline enforces sub-50ms P99 latency guarantees, OpenTelemetry GenAI semantic conventions, and. ...

Prerequisite: Familiarity with the concepts introduced in Executive Summary. Review it first if the terminology in this part is unfamiliar. Part 1 — Agentic GraphRAG vs. Long-Context Window: Architectural Trade-offs Answer-first: Relying exclusively on 1M+ token context windows introduces quadratic latency degradation ($O(N^2)$ attention overhead), severe token cost inflation, and needle-in-a-haystack recall loss. Agentic GraphRAG extracts focused entity subgraphs to achieve 65% faster Time-To-First-Token (TTFT) at less than 10% of the inference cost. Architecting this pipeline enforces sub-50ms P99 latency guarantees, OpenTelemetry GenAI semantic conventions, and 2026 Model Context Protocol ttlMs. ...

Prerequisite: Familiarity with the concepts introduced in Part 1 — Golang Orchestration. Review it first if the terminology in this part is unfamiliar. Data Ingestion & Atomic Chunking Product Data: Semantic Catalog Pipelines Answer-first: Data ingestion and atomic product chunking processes catalog updates into dense vector embeddings, maintaining vector index freshness and search accuracy. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. This design guarantees sub-50ms P99 latency bounds and zero-allocation memory pooling. ...

GraphRAG vs Naive RAG: Enterprise Architecture Guide Answer-first: GraphRAG outperforms naive RAG in enterprise applications by combining knowledge graph entity extraction with vector search, resolving complex multi-hop relationship queries accurately. Implementing this architecture enforces sub-50ms P99 latency guarantees, zero-allocation memory pooling with Go 1.24 unique.Handle, and fault-tolerant Dapr 1.15 component orchestration for resilient production scaling. This design guarantees sub-50ms P99 latency bounds and zero-allocation memory pooling. Most RAG (Retrieval-Augmented Generation) implementations look the same: chunk documents, embed them into vectors, store them in a vector database, retrieve by cosine similarity, and inject the top-K chunks into the LLM context. This works for simple document Q&A. It fails systematically for enterprise knowledge bases where the answer to a question depends not on a single document chunk, but on the relationships between dozens of interconnected entities. ...