Vector Database

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 Part 2 — Ingestion Chunking. Review it first if the terminology in this part is unfamiliar. In Part 2: Data Ingestion & Atomic Chunking - Bringing Product Data into the AI Environment, we established a clean data synchronization pipeline from PostgreSQL to Qdrant via Kafka CDC. But the journey of building a standard e-commerce search engine has just begun. When a user enters: “Asus ROG Zephyrus G14 laptop under $1500 in stock” ...

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. ...