Vector Databases & Search

What are Vector Databases?

Vector databases are specialized systems designed to store, index, and search high-dimensional vector embeddings. Unlike traditional databases that work with structured data, vector databases excel at finding similar items based on their semantic meaning.

Store Embeddings

Efficiently store millions of high-dimensional vectors

Similarity Search

Find nearest neighbors using distance metrics

Real-time Performance

Sub-second search across billions of vectors

How Vector Search Works

Interactive Vector Space Visualization

Click on the canvas to move the search query and see how distances change

SAMPLE DOCUMENTS

v1: Machine learning is amazing70%

v2: Deep learning uses neural networks80%

v3: Databases store information60%

v4: AI transforms data into insights60%

v5: Neural networks learn patterns93%

Distance Metrics

Cosine Similarity

cos(θ) = (A·B) / (||A|| × ||B||)

Measures angle between vectors

• Range: [-1, 1]
• Ignores magnitude
• Best for: Text similarity

Euclidean Distance

d = √Σ(Aᵢ - Bᵢ)²

Straight-line distance

• Range: [0, ∞)
• Considers magnitude
• Best for: Spatial data

Dot Product

A·B = Σ(Aᵢ × Bᵢ)

Unnormalized similarity

• Range: (-∞, ∞)
• Fastest to compute
• Best for: Normalized vectors

Indexing Algorithms

Algorithm	Accuracy	Speed	Memory	Description
Flat/Brute Force	100%	O(n)	Low	Exact search, compares with all vectors
LSH (Locality Sensitive Hashing)	~95%	O(1)	Medium	Hash similar vectors to same buckets
HNSW (Hierarchical NSW)	~98%	O(log n)	High	Graph-based with hierarchical layers
IVF (Inverted File Index)	~95%	O(√n)	Medium	Clusters vectors, searches nearby clusters

Popular Vector Databases

Pinecone

Managed Cloud

Key Features:

• Serverless
• Real-time updates
• Metadata filtering

Best for: Production RAG systems

Weaviate

Open Source + Cloud

Key Features:

• GraphQL API
• Hybrid search
• Multi-modal

Best for: Complex semantic search

ChromaDB

Open Source

Key Features:

• Embedded mode
• Simple API
• Python-first

Best for: Prototyping & small scale

Qdrant

Open Source + Cloud

Key Features:

• Rich filtering
• Payload storage
• Rust performance

Best for: High-performance search

Faiss

Library

Key Features:

• Facebook research
• GPU support
• Many algorithms

Best for: Research & custom solutions

Practical Example: Semantic Search

Building a Document Search System

1. Generate Embeddings

from sentence_transformers import SentenceTransformer
import chromadb

# Initialize embedding model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Sample documents
documents = [
    "Machine learning algorithms learn from data",
    "Deep learning uses neural networks",
    "Natural language processing understands text",
    "Computer vision processes images",
    "Reinforcement learning optimizes decisions"
]

# Generate embeddings
embeddings = model.encode(documents)

2. Store in Vector Database

# Initialize ChromaDB client
client = chromadb.Client()
collection = client.create_collection("documents")

# Add documents with embeddings
collection.add(
    embeddings=embeddings.tolist(),
    documents=documents,
    metadatas=[{"source": f"doc_{i}"} for i in range(len(documents))],
    ids=[f"id_{i}" for i in range(len(documents))]
)

3. Search Similar Documents

# Search query
query = "How do neural networks work?"
query_embedding = model.encode([query])

# Find similar documents
results = collection.query(
    query_embeddings=query_embedding.tolist(),
    n_results=3
)

# Display results
for i, doc in enumerate(results['documents'][0]):
    distance = results['distances'][0][i]
    print(f"Rank {i+1}: {doc}")
    print(f"Distance: {distance:.4f}\n")

Production Considerations

Performance Optimization

• Batch Operations: Insert/query multiple vectors at once
• Index Selection: Choose algorithm based on accuracy vs speed
• Dimensionality: Reduce dimensions if possible (PCA, UMAP)
• Caching: Cache frequent queries and embeddings

Scalability Strategies

• Sharding: Distribute vectors across multiple nodes
• Replication: Duplicate data for availability
• Streaming Updates: Handle real-time vector additions
• Hybrid Search: Combine vector + metadata filtering

Common Challenges & Solutions

Challenge: Curse of Dimensionality

High-dimensional spaces make distance metrics less meaningful

Solution: Use dimensionality reduction, normalize vectors, choose appropriate metrics

Challenge: Index Building Time

Building indexes for large datasets can be slow

Solution: Use incremental indexing, parallel processing, or pre-built indexes

Challenge: Memory Requirements

Storing millions of high-dimensional vectors requires significant RAM

Solution: Use quantization, disk-based indexes, or managed services

Next Steps

Now that you understand vector databases and search, you're ready to explore how they enable powerful AI applications through Retrieval-Augmented Generation (RAG).

Continue to RAG Architecture