The Search Revolution: From Keywords to Meaning
Imagine searching for “how to fix a leaking tap” and finding results about plumbing repairs, DIY solutions, and home maintenance — even if those exact words never appear in the content. This is the power of semantic search, and it’s transforming how we interact with data.
Traditional keyword-based search has hit its limits. As AI applications explode, we need systems that understand meaning, not just keywords. Enter vector search — the technology powering modern AI applications from chatbots to recommendation engines.
In this guide, we’ll dive deep into Hierarchical Navigable Small World (HNSW) graphs, the algorithm behind today’s most efficient vector search systems, and build a production-ready semantic search engine in Python.
Why Traditional Search Falls Short
The Keyword Problem
# Traditional search - exact matches only
def keyword_search(query, documents):
matches = []
for doc in documents:
if query.lower() in doc.lower():
matches.append(doc)
return matches# What happens when we search?
documents = [
"How to repair a dripping faucet",
"Fixing leaky taps in your kitchen",
"Plumbing solutions for home owners"
]
results = keyword_search("leaking tap", documents)
# Returns: [] - No matches!