Programming Tutorials

  Building a RAG (Retrieval-Augmented Generation) pipeline using Vertex AI Agent Builder (formerly Gen App Builder) promises rapid deployment. However, the abstraction layer often hides critical failures. A common scenario for engineers is the "Infinite Spinner" of death during document ingestion, or worse, an agent that refuses to answer questions clearly defined in the uploaded FAQs. This post dissects the architecture of the underlying Discovery Engine to explain why these failures occur and provides production-grade Python solutions to fix ingestion stalls and hallucination issues caused by poor chunking. The Problem: Ingestion Hangs and Retrieval Misses Two distinct but related issues frequently plague production deployments: The Zombie Index:  You upload a batch of PDFs or a URL sitemap. The status indicator remains on "Importing" or "Indexing" indefinitely (4+ hours for small datasets). The Chunking Gap:  You have an FAQ document. A user asks a ques...

  Retrieval-Augmented Generation (RAG) systems often hit a performance plateau known as the "Naive RAG" wall. You build a prototype using a standard vector store and OpenAI embeddings, and it works flawlessly for semantic queries like "How do I reset my password?" However, when a user queries for a specific error code ("Error 0x884"), a proper noun, or a recent product SKU, the system fails. It hallucinates or retrieves irrelevant context because dense vector embeddings often struggle with exact keyword matching. To bridge the gap between semantic understanding and lexical precision, we must move beyond simple vector search. This guide details how to implement  Hybrid Search  (combining Vector and Keyword search) and a  Reranking  step using LangChain. The Root Cause: Why Vector Search Isn't Enough To fix retrieval accuracy, we must understand why it fails. Dense Vectors (Embeddings):  Models like  text-embedding-3-small  convert text into numeric...