1. Understanding Retrieval-Augmented Generation (RAG)
Large Language Models suffer from knowledge cutoff dates and non-deterministic hallucinations. RAG pipelines resolve this by fetching relevant factual context from private vector databases before generating responses.
2. Vector Embeddings and Indexing
Text documents are split into semantic chunks and converted into high-dimensional embedding vectors using models like OpenAI text-embedding-3-small or open-source HuggingFace models.
import { VectorStoreIndex, SimpleDirectoryReader } from "llamaindex";
const documents = await new SimpleDirectoryReader().loadData({ directoryPath: "./data" });
const index = await VectorStoreIndex.fromDocuments(documents);
const queryEngine = index.asQueryEngine();
const response = await queryEngine.query("How do Server Actions work?");3. Sub-Second Query Execution
Using Approximate Nearest Neighbor (ANN) search algorithms like HNSW in Pinecone or Qdrant, relevant document contexts are retrieved in under 20ms.
