Vector Search Explained - The Technology Behind SearchAF

Vector search is revolutionizing how we build answer engines and intelligent search systems. In this post, we'll explore the technology that powers SearchAF's answer generation capabilities.

What Are Vector Embeddings?#

Vector embeddings are numerical representations of data that capture semantic meaning. Instead of storing text as words, we convert it into high-dimensional vectors (arrays of numbers).

"running shoes" → [0.24, -0.18, 0.91, ... 0.33]  // 768 dimensions
"athletic footwear" → [0.26, -0.17, 0.89, ... 0.31]  // similar vector!

Notice how similar concepts have similar vectors, even though the words are different.

How Vector Search Works#

Step 1: Generate Embeddings#

When you add a product to SearchAF, we:

1. Extract text from title, description, and attributes
2. Pass it through an embedding model (like OpenAI's text-embedding-3)
3. Store the resulting vector alongside your product data

Step 2: Query Embedding#

When a user searches:

1. Convert the query to a vector using the same embedding model
2. This query vector captures the semantic intent

Step 3: Similarity Search#

Find products with vectors closest to the query vector using:

Cosine Similarity

similarity = (A · B) / (||A|| × ||B||)

This measures the angle between vectors - closer angles mean more similar meaning.

Why Vector Databases?#

Traditional databases struggle with vector operations:

• High Dimensionality: Vectors can have 768+ dimensions
• Similarity Computation: Requires specialized indexes
• Scale: Millions of products need efficient search

Antfly's Advantage#

SearchAF is built on Antfly, which provides:

• HNSW Indexes: Hierarchical navigable small world graphs for fast similarity search
• Distributed Architecture: Horizontal scaling for massive catalogs
• Hybrid Queries: Combine vector and traditional filters

SELECT * FROM products
WHERE vector_similarity(embedding, query_vector) > 0.7
  AND price < 100
  AND in_stock = true
ORDER BY vector_similarity(embedding, query_vector) DESC
LIMIT 10

Embedding Models#

Different models excel at different tasks:

OpenAI text-embedding-3-small#

• Dimensions: 1536
• Best for: General-purpose search
• Pros: High quality, widely supported
• Cons: API-dependent, cost per query

Sentence Transformers#

• Dimensions: 384-768
• Best for: Self-hosted solutions
• Pros: Free, customizable
• Cons: Need GPU for inference

Custom Fine-tuned Models#

• Dimensions: Variable
• Best for: Domain-specific search
• Pros: Optimized for your data
• Cons: Requires ML expertise

Hybrid Search: Best of Both Worlds#

Pure vector search isn't always optimal. SearchAF uses hybrid search:

final_score = α × vector_score + (1-α) × keyword_score

Where α controls the balance:

• α = 0: Pure keyword (traditional BM25)
• α = 0.5: Equal weight
• α = 1: Pure semantic

This catches:

• Exact brand names and SKUs (keyword)
• Conceptual matches (vector)
• Typos and variations (both)

Real-World Performance#

Our benchmarks show:

Metric	Traditional Search	Vector Search	Hybrid
Relevance (NDCG)	0.72	0.84	0.91
Latency (p95)	12ms	45ms	28ms
Zero Results	18%	4%	2%

Challenges and Solutions#

Challenge 1: Cold Start#

Problem: New products lack interaction data Solution: Vector embeddings work immediately from product descriptions

Challenge 2: Query Latency#

Problem: Vector operations are compute-intensive Solution: Antfly's distributed architecture and HNSW indexes

Challenge 3: Embedding Drift#

Problem: Models can become outdated Solution: Automated reindexing and A/B testing of new models

Getting Started#

Want to leverage vector search in your application?

1. Use SearchAF: Get started in minutes with our managed platform
2. Explore Antfly: Self-host for complete control
3. Read the Docs: API Reference

The Future#

We're excited about upcoming developments:

• Multimodal Search: Combine text, images, and metadata
• Retrieval Augmented Generation: Use vectors for AI chatbots
• Real-time Personalization: User-specific vector adjustments

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Want to learn more? Check out our Semantic Search Guide