Vector Databases and Semantic Search: An Engineer's Guide to LLMs

Beyond Exact Keyword Matches

Traditional SQL LIKE queries and inverted indexes struggle with synonyms, typos, and semantic intent. Vector embeddings transform text, code, and images into high-dimensional numerical vectors where semantic meaning translates to spatial proximity.

Building an Embeddings Pipeline

Here is how a production Retrieval-Augmented Generation (RAG) system functions:

1. Chunking: Split raw documents into coherent paragraphs with overlapping tokens.
2. Embedding Generation: Pass chunks through an embedding model (e.g. OpenAI text-embedding-3-small) to produce 1536-dimensional arrays.
3. Index & Search: Store in a vector database like Pinecone, PgVector, or Qdrant using Hierarchical Navigable Small World (HNSW) graphs.
4. Augmentation: Inject the top-K relevant passages into the LLM system prompt to generate grounded, hallucination-free answers.