Semantic Search
Semantic search retrieves information based on meaning rather than relying only on exact word matches.
It allows a search system to recognize that different phrases can express similar ideas, making it useful for natural-language questions, paraphrases, synonyms, and concept-based search.
What Is Semantic Search?
Traditional search often depends heavily on the words used in the query.
Semantic search focuses on what the query means.
For example:
Query: "Can I bring my dog?"
Document: "Pets are allowed in designated rooms."
The query and source do not use the same words, but the meaning is closely related.
A semantic search system can identify that relationship and retrieve the document.
How Semantic Search Works
Modern semantic search commonly uses embeddings.
An embedding is a numerical representation of content that captures aspects of its meaning.
The process usually looks like this:
- Convert searchable content into embeddings.
- Convert the user's query into an embedding.
- Compare the query representation with stored content representations.
- Identify the closest matches.
- Rank the results by semantic similarity.
Content with similar meanings tends to be represented closer together in the embedding space.
Semantic Search and Vector Search
Semantic search is often implemented using vector search.
The content and query are represented as vectors, and the system searches for vectors that are close to the query vector.
This is why the terms are frequently associated.
They are not exactly the same.
Vector search describes the mechanism for finding similar vectors.
Semantic search describes the search objective: retrieving content based on meaning.
Vector search can also be used for non-text data such as images, audio, or other numerical representations.
Semantic Search Example
Consider a hotel's information page containing:
"Complimentary luggage storage is available after checkout."
A guest asks:
"Can you hold my bags after I leave the room?"
There may be little exact keyword overlap.
Semantic search can recognize relationships between:
- "hold my bags" and "luggage storage"
- "after I leave the room" and "after checkout"
This makes semantic retrieval particularly useful for conversational search.
Semantic Search vs. Lexical Search
Lexical search relies on textual similarity.
Semantic search relies on similarity in meaning.
Consider another example:
Query: EV charging
A lexical system may strongly prioritize documents containing the exact term "EV."
A semantic system may retrieve content mentioning "electric vehicle charging stations" even if the acronym does not appear.
The semantic result can be useful, but exact lexical matching still matters.
If a user enters a specific product code or unusual brand name, lexical search may outperform semantic search.
Semantic Search vs. Keyword Search
Keyword search typically requires the user to provide terms that are likely to appear in the source.
Semantic search allows users to phrase the request naturally.
Instead of searching:
refund damaged item
a user can ask:
"What happens if my order arrives broken?"
Semantic search can match the intent even if the source uses different terminology.
Semantic Search vs. Hybrid Search
Hybrid search combines semantic and lexical retrieval.
This is useful because semantic search does not replace every advantage of exact text matching.
Hybrid retrieval can preserve:
- Exact product names
- Codes
- Acronyms
- Domain terminology
- Exact phrases
while also understanding:
- Paraphrases
- Synonyms
- Natural-language questions
- Conceptual similarity
For many business knowledge collections, the two methods complement each other.
What Are Embeddings?
Embeddings are numerical representations generated by a model.
Text with related meaning tends to receive representations that are closer together.
For example, phrases such as:
- "book an appointment"
- "reserve a time"
- "find an available slot"
may produce similar representations even though the wording differs.
These representations allow the search system to compare meaning mathematically.
What Semantic Search Is Good At
Semantic search is useful for:
Natural-Language Questions
Users can ask complete questions instead of predicting the exact keywords used in the source.
Synonyms
Different words can express similar concepts.
Paraphrases
The query can be phrased differently from the source.
Concept Search
Users can search for an idea rather than a literal phrase.
Multilingual and Cross-Lingual Search
Depending on the embedding model, semantic representations can support relationships across languages.
Conversational Interfaces
Users interacting with an AI agent naturally phrase requests in many different ways.
Semantic retrieval helps connect those requests with relevant business information.
Limitations of Semantic Search
Semantic search can struggle with queries where exact terms matter more than general meaning.
Examples include:
- Product codes
- Policy numbers
- Model numbers
- Rare acronyms
- Proper nouns
- Short ambiguous queries
- Highly specialized terminology
Semantic similarity can also retrieve content that appears conceptually related but is not precise enough for the task.
This is one reason many systems combine semantic and lexical retrieval.
Semantic Search for AI Agents
An AI agent can use semantic search to retrieve relevant knowledge when a user's wording does not match the source.
For example, a customer may say:
"I need to push my visit to next week."
The underlying document might use the phrase:
"Appointments can be rescheduled..."
Semantic retrieval can connect the two concepts.
The retrieved information can then be supplied to the agent for answering or deciding what should happen next.
Semantic Search in AskHandle
AskHandle uses semantic retrieval as one component of hybrid search in Document Search and Data Search.
Semantic retrieval helps the system find information based on meaning, while lexical retrieval helps preserve exact textual matches.
Using both signals allows AskHandle to handle natural-language questions without giving up the precision needed for exact business terminology.
Evaluating Semantic Search
A semantic search system should be evaluated using representative queries from the actual domain.
Useful test cases include:
- Paraphrased questions
- Synonym-heavy queries
- Exact terminology
- Ambiguous requests
- Short queries
- Long natural-language questions
- Queries with irrelevant semantic neighbors
The goal is not to retrieve content that is merely related. The goal is to retrieve information that is useful for answering the actual query.