Vector Databases Explained: The Foundation of AI Search

Vector databases power modern AI search. Here’s what you need to know.

What Is a Vector Database?

A vector database stores and searches embeddings—numerical representations of data (text, images, etc.) that capture meaning.

Traditional Database:
"The cat sat on the mat" → Stored as text string

Vector Database:
"The cat sat on the mat" → [0.23, -0.41, 0.87, ...] (1536 numbers)

Why Vectors Matter

Semantic Understanding

Traditional search: Find exact matches Vector search: Find similar meaning

Query: “feline resting on carpet”

Keyword search: No match
Vector search: Finds “The cat sat on the mat” ✓

How It Works

Step 1: Generate Embeddings

Use an embedding model to convert data:

Text → Embedding Model → Vector
"Hello world" → [0.12, -0.34, 0.56, ...]

Step 2: Store Vectors

Vector database indexes vectors for fast search:

ID: 1, Vector: [0.12, -0.34, ...], Metadata: {source: "doc1"}
ID: 2, Vector: [0.45, -0.12, ...], Metadata: {source: "doc2"}

Step 3: Search by Similarity

Query vector compared to stored vectors:

Query: "greeting" → [0.11, -0.32, ...]
Most similar: ID 1 (cosine similarity: 0.95)

Key Concepts

Embeddings

Dense numerical representations capturing semantic meaning:

Text embeddings (OpenAI, Cohere)
Image embeddings (CLIP)
Audio embeddings (Whisper)

Similarity Metrics

How to measure “closeness”:

Metric	Use Case
Cosine	Text similarity
Euclidean	General purpose
Dot product	Normalized vectors

Indexing

Structures for fast search:

HNSW (common, balanced)
IVF (good for large scale)
Flat (exact, small datasets)

Popular Vector Databases

Managed Services

Database	Strengths
Pinecone	Easy to use, fully managed
Weaviate	Hybrid search, open source
Qdrant	Performance, open source
Milvus	Scale, open source

Cloud Provider Options

Service	Provider
Azure AI Search	Microsoft
Vertex AI Vector Search	Google
Amazon OpenSearch	AWS

Self-Hosted

Option	Best For
Chroma	Local development
pgvector	PostgreSQL users
Elasticsearch	Existing ES users

Use Cases

1. RAG (Retrieval-Augmented Generation)

Store knowledge base, retrieve relevant context for LLMs:

User question → Vector search → Relevant docs → LLM → Answer

2. Semantic Search

Search by meaning, not keywords:

Product search
Documentation search
Support ticket search

3. Recommendation

Find similar items:

Similar products
Related content
Matching profiles

4. Deduplication

Find near-duplicates:

Document deduplication
Image matching
Record linking

Building with Vector Databases

Basic Pattern

# Pseudocode
# 1. Create embedding
embedding = embed("User query text")

# 2. Search similar
results = vector_db.search(
    embedding,
    top_k=5,
    filter={"category": "products"}
)

# 3. Use results
for result in results:
    print(result.text, result.score)

RAG Pattern

# 1. Embed user query
query_embedding = embed(user_query)

# 2. Search relevant documents
docs = vector_db.search(query_embedding, top_k=3)

# 3. Build prompt with context
prompt = f"""
Based on these documents:
{docs}

Answer: {user_query}
"""

# 4. Generate answer
answer = llm.generate(prompt)

Choosing a Vector Database

Consider:

Factor	Questions
Scale	How many vectors?
Latency	Speed requirements?
Features	Filtering, metadata?
Operations	Self-host or managed?
Cost	Budget constraints?

Quick Guide

Prototype: Chroma (local, free)
Startup: Pinecone (easy, managed)
Enterprise: Weaviate or Qdrant (flexible, scalable)
Existing DB: pgvector (PostgreSQL extension)

Performance Tips

Choose right embedding model - Size vs. quality tradeoff
Optimize chunk size - Too small: noise; too big: irrelevant
Use metadata filters - Narrow search space
Monitor and tune - Index parameters matter
Cache common queries - Reduce latency

Need help implementing vector search? Let’s discuss your architecture.