Semantic Search

Search byMeaning

Vector-powered semantic search that understands what users mean — not just what they type. Plug into your knowledge base and find relevant content in milliseconds.

Semantic vs Keyword Search

See the difference — search by intent, not exact match

Blizzer Semantic Search
How do I open a bank account in Cambodia?

Requirements for Opening a Cambodian Bank Account

Documents needed include a valid passport or national ID, proof of address, and initial deposit...

0.97

score

NBC Guidelines for Digital Banking Onboarding

The National Bank of Cambodia requires eKYC verification for all new digital account holders...

0.91

score

ABA Bank Account Types & Benefits

Choose from savings, current, and USD accounts. Minimum deposit starts from $10...

0.88

score

Vector Search, Simplified

No ML expertise required. Plug in your data and go.

Vector Embeddings

State-of-the-art multilingual embeddings — including Khmer. Index millions of documents in minutes.

Millisecond Latency

Sub-10ms search on indexed collections. Built on optimized approximate nearest neighbor algorithms.

Khmer Language Native

First-class support for Khmer semantic search. Users can query in Khmer and find results across any language.

Simple API

Index data and run semantic queries with 5 lines of code. SDKs for Python, JavaScript, and REST.

Hybrid Search

Combine semantic similarity with keyword filters, date ranges, and metadata for precision results.

Relevance Tuning

Fine-tune ranking models on your domain-specific data to maximize result quality for your use case.

Power Any Search Experience

From customer support bots to internal knowledge bases — semantic search makes your content findable.

  • Customer support knowledge base
  • Product catalog & e-commerce search
  • Legal & regulatory document retrieval
  • Internal HR & policy document search
  • News & content recommendation
  • RAG (Retrieval Augmented Generation) pipelines

Quick Start

import blizzer
import blizzer

client = blizzer.SemanticSearch(api_key="...")

# Index your documents
client.index([
  {"id": "1", "text": "ធនាគារ ABA..."},
  {"id": "2", "text": "How to open..."},
])

# Search by meaning
results = client.search(
  query="open bank account",
  top_k=5
)

print(results[0].score)  # 0.97

Build Smarter Search Today

Free indexing tier. No ML expertise needed.