What is Vector Search? The Best Search Method You Didn’t Know About

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Vector search is transforming how we implement search functionality by going beyond traditional keyword and full-text search approaches. Unlike conventional methods, vector search understands semantic meaning, enabling more accurate and context-aware results.

How Vector Search Works

  1. Data Input: Text, images, or other data is fed into a Large Language Model (LLM).
  2. Vector Embedding Generation: The LLM converts each piece of data into a numerical vector (a list of numbers).
  3. Semantic Capture: These vectors represent the meaning of the data, where similar items have similar vector patterns.
  4. Search Execution: The database is queried using these embeddings to find semantically related results.

For example, searching for “quick healthy breakfast ideas” returns relevant content even if the exact keywords aren’t present.

🔗 Learn More: Vector Search Explained

You Should Know: Practical Implementation of Vector Search

1. Setting Up Vector Search with PostgreSQL (pgvector)

PostgreSQL supports vector search via the `pgvector` extension.

Installation & Setup

-- Enable the extension 
CREATE EXTENSION vector;

-- Create a table with a vector column 
CREATE TABLE documents ( 
id SERIAL PRIMARY KEY, 
content TEXT, 
embedding vector(384) -- Adjust dimensions based on model 
);

Generating Embeddings (Python Example)

import openai 
import psycopg2

Generate embeddings using OpenAI 
response = openai.Embedding.create( 
input="quick healthy breakfast ideas", 
model="text-embedding-ada-002" 
) 
embedding = response['data'][bash]['embedding']

Store in PostgreSQL 
conn = psycopg2.connect("dbname=test user=postgres") 
cur = conn.cursor() 
cur.execute("INSERT INTO documents (content, embedding) VALUES (%s, %s)", 
("Healthy smoothie recipes", embedding)) 
conn.commit()

Querying Similar Vectors

-- Find similar documents using cosine similarity 
SELECT content, 1 - (embedding <=> '[0.1, 0.2, ...]') AS similarity 
FROM documents 
ORDER BY similarity DESC 
LIMIT 5;
  1. Performance Optimization (Approximate Nearest Neighbors – ANN)
    For large datasets, use ANN algorithms like HNSW (Hierarchical Navigable Small World) to speed up searches.
-- Create an HNSW index 
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);

3. Integrating with AI Models

  • OpenAI Embeddings: `text-embedding-ada-002`
  • Hugging Face Sentence Transformers:
    from sentence_transformers import SentenceTransformer 
model = SentenceTransformer('all-MiniLM-L6-v2') 
embeddings = model.encode(["quick healthy breakfast"])

What Undercode Say

Vector search is a game-changer for semantic search, recommendation engines, and AI-driven applications. Traditional keyword searches fail to capture context, but vector embeddings enable deeper understanding.

Key Takeaways

✔ PostgreSQL + pgvector = Powerful vector database

✔ OpenAI/Hugging Face = Easy embedding generation

✔ ANN Indexing (HNSW) = Scalable performance

Relevant Linux/Windows Commands

  • Linux (Embedding Processing):
    python3 -m pip install sentence-transformers openai psycopg2-binary
  • Windows (PostgreSQL Setup):
    choco install postgresql

🔗 Further Reading: pgvector GitHub

Expected Output:

A functional vector search system integrated with PostgreSQL, returning semantically relevant results for complex queries.

-- Example Output 
SELECT content FROM documents ORDER BY embedding <=> '[0.12, 0.34, ...]' LIMIT 3;

-- Result: 
1. "5-minute avocado toast recipes" 
2. "High-protein overnight oats" 
3. "Easy smoothie bowls for busy mornings"

Vector search unlocks next-level search capabilities—start experimenting today! 🚀

References:

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Extra Hub: Undercode MoN
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