What is Semantic Search? Definition & Guide

What is Semantic Search?

emantic search is a search approach that finds relevant results by matching the meaning of a query rather than exact keyword strings. Semantic search uses embedding models to represent both queries and documents as vectors, then retrieves the most semantically similar results using nearest-neighbour algorithms. Semantic search is the retrieval backbone of RAG pipelines and enterprise knowledge base systems.

The practical advantage of semantic search over keyword search is dramatic for enterprise knowledge retrieval. Keyword search requires users to know the exact words used in the source document. If a customer asks 'what happens if I miss my EMI payment?' and the policy document says 'consequences of payment default', keyword search misses the match entirely. Semantic search finds it because the embedding model has learned that 'EMI payment miss' and 'payment default' are semantically equivalent. This improvement in recall — finding relevant results even when the query phrasing doesn't match the document phrasing — is the primary value of semantic search in enterprise AI.

Hybrid search — combining vector semantic search with BM25 keyword search — consistently outperforms either approach alone for most enterprise retrieval tasks. Semantic search excels at meaning-based retrieval but can miss important keyword matches (proper nouns, product codes, specific terminology) that BM25 catches reliably. Hybrid search uses a weighted combination or Reciprocal Rank Fusion (RRF) to merge results from both approaches. Production enterprise RAG systems should default to hybrid search rather than pure semantic search, especially when the knowledge base contains technical documentation, product codes, or domain-specific terminology that the embedding model may not represent well.

Also known as: Vector Search, Embedding Search

Key Points

Core idea
Semantic search finds documents that mean the same thing as the query, even when they share no exact words — dramatically improving retrieval recall for natural language queries against technical documentation.
Why it matters
Semantic search converts queries and documents to embedding vectors, then retrieves documents whose vectors are most similar to the query vector. The quality of the embedding model determines retrieval quality.
Enterprise use
Combining vector similarity search with BM25 keyword matching (hybrid search) consistently outperforms either approach alone, especially for technical documentation with specific terminology.

How It Works

How Semantic Search works

Define the purpose, inputs, and success criteria that Semantic Search must support.
Apply Semantic Search in the relevant workflow while recording its inputs, configuration, and outputs.
Evaluate the result against representative data, operational constraints, and human review before expanding production use.

How Fluid AI Uses This

Semantic search across enterprise knowledge bases.

Fluid AI's semantic search layer retrieves relevant enterprise content for AI agents in milliseconds, grounding responses in SOPs, claims files, and regulatory documents stored on-premise.

Explore Architecture

Topics Covered

semantic search enterprise AI
vector search enterprise knowledge
embedding search RAG
hybrid search enterprise RAG
semantic search vs keyword search
enterprise knowledge retrieval
semantic search banking insurance
BM25 vector hybrid search enterprise

Semantic Search