Vector Databases for AI Cheat Sheet

Explains embeddings, approximate nearest neighbor search, and indexing strategies like HNSW, with code for storing and querying vectors using common libraries.

2 PagesIntermediateMar 20, 2026

Core Concepts

Foundations of vector search.

Embedding- A dense numeric vector representing the semantic meaning of text, images, or other data, produced by a model
Similarity metric- Cosine similarity, dot product, or Euclidean (L2) distance used to compare vectors; must match how the embedding model was trained
Approximate Nearest Neighbor (ANN)- Trades a small amount of recall for large speedups over exact nearest-neighbor search at scale
HNSW (Hierarchical Navigable Small World)- Graph-based ANN index offering strong recall/speed trade-offs; the most common index type in production vector DBs
IVF (Inverted File Index)- Clusters vectors into partitions (via k-means) and searches only the nearest partitions, common in FAISS
Metadata filtering- Combining vector similarity search with structured filters (e.g., date, category) in the same query

Local ANN Search with FAISS

Build and query an in-memory HNSW index.

python

import faissimport numpy as npdim = 384index = faiss.IndexHNSWFlat(dim, 32)   # 32 = M, neighbors per nodeindex.hnsw.efConstruction = 200vectors = np.random.rand(10000, dim).astype('float32')index.add(vectors)query = np.random.rand(1, dim).astype('float32')distances, indices = index.search(query, k=5)  # top-5 nearest neighbors

Storing and Querying with Chroma

A lightweight vector DB for embedding-based retrieval (e.g., RAG).

python

import chromadbclient = chromadb.Client()collection = client.create_collection("docs")collection.add(    ids=["doc1", "doc2"],    documents=["Paris is the capital of France.", "The Eiffel Tower is in Paris."],    metadatas=[{"source": "wiki"}, {"source": "wiki"}],)results = collection.query(query_texts=["What city is the Eiffel Tower in?"], n_results=2)print(results["documents"])

Choosing a Vector Database

Key differentiators across common options.

FAISS- Library, not a server; fastest for local/in-memory search but no built-in persistence, filtering, or multi-tenancy
Chroma- Lightweight, easy local setup, popular for prototyping RAG applications
Pinecone- Fully managed cloud service with metadata filtering, namespaces, and horizontal scaling built in
pgvector- Postgres extension adding vector columns/indexes, useful when you want vectors alongside existing relational data
Weaviate / Milvus / Qdrant- Self-hostable or managed vector DBs with hybrid (vector + keyword) search and filtering support

Pro Tip

Always use the same distance metric the embedding model was trained/normalized for (usually cosine similarity) -- mixing, say, an L2 index with cosine-normalized embeddings silently degrades retrieval quality without throwing an error.

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