Programming Tutorials

  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...

  In the early stages of building a Retrieval-Augmented Generation (RAG) pipeline, vector search feels like magic. You insert a few thousand PDF chunks, run a cosine similarity search, and get relevant context in milliseconds. Then, you scale. Your dataset grows from 10,000 vectors to 10 million. Suddenly, that snappy 50ms query latency spikes to 2 seconds. Your LLM is left waiting for context, user retention drops, and your database CPU usage sits at 100%. This is the "Vector Latency Cliff." It occurs when your dataset exceeds the capability of exact nearest neighbor search, forcing the database to perform full table scans. This article details how to transition from brute-force search to optimized Approximate Nearest Neighbor (ANN) search using PostgreSQL and  pgvector . We will focus on tuning the Hierarchical Navigable Small World (HNSW) algorithm, the current industry standard for high-performance RAG. The Root Cause: Why Exact Search Fails at Scale To understand the fix...