Vector Databases

Fast Take: Vector databases store embeddings so AI can search and retrieve information by meaning instead of keywords.

Layer: Retrieval Status: Mature Last Updated: 2026-01-06

Decision Box

✅ Use this when:

  • You’re building RAG
  • You need semantic search
  • You store embeddings at scale
  • You need fast similarity retrieval

❌ Ignore this when:

  • Keyword search is enough
  • You have very small datasets
  • Exact matches matter more than similarity
  • You don’t need ranking or relevance scoring

⚠️ Risk if misused:

  • Poor indexing kills performance
  • Wrong distance metric gives bad results
  • No filtering = irrelevant retrieval
  • Scaling costs creep silently

Simple Explanation

⚠️ What it is:

A vector database is a specialized database designed to store and search embeddings efficiently.

Analogy:
It’s a library where books are shelved by meaning, not by title or author.

Why it matters:

  • Confusing RAG with fine-tuning (they solve different problems)
  • Assuming RAG guarantees accuracy without good data prep

Technical Breakdown

Key Concepts:

  • Vector indexing
  • Distance metrics (cosine, dot product, Euclidean)
  • Top-K retrieval
  • Metadata filtering
  • Hybrid search

Implementation Snapshot:

Embeddings → Vector Database → Similarity Search → Retrieved Chunks → LLM

Common Failure Modes:

  • No metadata filters
  • Over-retrieving (too many chunks)
  • Wrong distance metric
  • No re-ranking layer

Cost Reality:


Cost profile: Medium

  • Cost profile: Medium
  • Drivers: data volume, query rate, replication

Top Players

Company / Tool – why it matters here:

  • Pinecone
  • Weaviate
  • Qdrant
  • Milvus
  • Chroma
  • Elasticsearch (vector mode)

Go Deeper

Appears in:

AI Foundations for Builders — Module 2: The Library

This concept is covered in Module 2 – The Library (RAG & Vector Databases)

Open Module 2

Term Flow

Prerequisites:

  • Embeddings

Next Concepts:

  • Semantic Search
  • Top-K Retrieval
  • Re-ranking
  • Hybrid Search

Often Confused With:

  • Traditional databases
  • Search engines
  • Full-text indexing
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