The Power of Vector Search
Standard search looks for keywords. SearchJet looks for meaning. Use our interactive playground below to see how semantic understanding transforms the search experience.
Vector Search Sandbox
Experience the power of SearchJet's AI-driven semantic search. See how it understands meaning, not just keywords.
Try these semantic examples
Concept Search
Unlike standard search that looks for exact words, SearchJet maps your query into a high-dimensional vector space to find the underlying concept.
- Truly semantic results
- Cross-lingual understanding
- Scalable to millions of documents
Typo Tolerance
Stop losing users to fast fingers. Our vector search engine handles misspellings natively without needing complex dictionary lookups.
Powered by SearchJet Vector Engine v2.4.0
Why settle for matching words when you can match intent?
Beyond Exact Match
Keyword search fails when users use different terminology. Vector search understands that "pricing", "cost", and "how much" all refer to the same intent.
Native Typo Tolerance
Traditional fuzzy matching is slow and prone to errors. Vector-based search handles misspellings as a natural part of the mathematical similarity calculation.
Cross-Lingual Support
Our embeddings can bridge the gap between languages, allowing users to find English content using Arabic queries and vice-versa (depending on the model).
# SearchJet Vector Logic
query = "how to setup search"
query_vector = model.embed(query)
# Mathematical similarity vs indexing
results = db.search(
vector=query_vector,
limit=5,
min_score=0.85
)
for hit in results:
print(f"Match: {hit.title}")
# Finds "Installation Guide"
# despite 0 word overlap!