What Is Semantic Search and How Does It Work?

Semantic search is a retrieval method that finds documents by meaning rather than by exact word overlap. Instead of matching the literal keywords in a query against the literal words in a document, it converts both into numeric vectors (embeddings) and returns the items whose vectors are closest to the query vector. Use it when users phrase questions in natural language and the relevant content may use different vocabulary — for example, a query like "how do I cancel my subscription" should surface a page titled "Ending your plan." It is less suited to lookups where exact tokens matter, such as order IDs, error codes, or rare proper nouns, unless you pair it with keyword matching.

How semantic search differs from keyword search

Keyword (lexical) search, the kind behind classic inverted indexes, scores a document by how often and how distinctively the query terms appear in it. It is precise, fast, and explainable: if the query says invoice_4471, you get documents containing that string.

Semantic search scores by proximity in an embedding space. Two passages that mean the same thing land near each other even with zero shared words. The trade-off is the reverse: it can miss exact strings and can return "topically related but wrong" results.

Most production systems combine both — often called hybrid search — and merge the two ranked lists.

The core pipeline

Semantic search is a short, repeatable loop:

1. Chunk each source document into passages small enough to embed and retrieve usefully (a paragraph, a section, a table).
2. Embed every chunk with an embedding model, producing one vector per chunk.
3. Store those vectors in a vector database, usually alongside the original text and metadata.
4. Embed the query with the same model used for the documents.
5. Retrieve the nearest vectors (nearest-neighbor search), typically the top-k by cosine similarity or dot product.
6. Return the matching chunks — and, in a RAG setup, pass them to an LLM to write the answer.

The critical constraint: the document embedding model and the query embedding model must be the same, or the vectors live in incompatible spaces and similarity becomes meaningless.

How semantic search powers RAG and AI chat

Retrieval Augmented Generation (RAG) uses semantic search as its retrieval step. The flow is: retrieve the most relevant passages for a user question, then instruct the LLM to answer using only those passages. This grounds the answer in your data and reduces hallucination, because the model is summarizing retrieved evidence rather than relying on memory.

A platform like Agentset illustrates the pieces this requires in practice. Its documented features include:

• Model-agnostic design — you select your own vector database, embedding model, and LLM, so the retrieval and generation layers are swappable.
• Multimodal support — it works with images, graphs, and tables as well as text, so answers can draw on every part of a knowledge base.
• Citations — answers automatically cite their sources, letting users inspect the retrieved evidence.
• Metadata filtering — answers can be restricted to a subset of the data, which is how you scope a search to one department, date range, or document type.
• Developer tooling — JavaScript and Python SDKs, 22+ supported file formats, an MCP server, and AI SDK integration.

A minimal ingestion call in their SDK looks like this:

import { Agentset } from "agentset";

const agentset = new Agentset({ apiKey: "agentset_xxx" });
const namespace = agentset.namespace("ns_1234");

const ingestJob = await namespace.ingestion.create({
  payload: {
    type: "FILE",
    fileUrl: "https://example.com/document.pdf",
    fileName: "my-document.pdf",
  },
  config: { metadata: { foo: "bar" } },
});

The metadata you attach here is what later enables filtered retrieval.

Practical considerations

Embedding model choice. Models differ in quality, dimensionality, cost, and language coverage. Changing the model means re-embedding the entire corpus, so treat it as a durable decision.

Chunking. Chunks that are too large dilute the embedding and waste context; too small and they lose the surrounding meaning. Overlap between chunks helps preserve continuity across boundaries.

Metadata filters. Attaching structured fields (source, date, category) to each chunk lets you narrow retrieval before or alongside vector search — essential when the corpus spans domains that shouldn't mix.

Hybrid search. Because pure semantic search is weak on exact terms, combining vector similarity with keyword matching is the common fix. Route or blend the two depending on the query.

Common failure modes

• Poor recall on exact terms. Product codes, IDs, and rare names may not surface, because the embedding blurs them into general meaning. Mitigate with keyword or hybrid search.
• Semantic drift. A query returns passages that are about the topic but don't answer it. Better chunking and reranking help.
• Stale index. Documents updated after embedding are no longer represented accurately; you need a re-ingestion path.
• Mismatched models. Embedding queries with a different model than the documents silently breaks similarity.

When to use semantic search — and when not to

Use it when users ask questions in their own words, when your content is prose-heavy, and when synonyms and paraphrase are common. Combine it with lexical search when exact tokens matter or when you need high precision on identifiers. If your queries are almost always exact strings, a keyword index alone may be simpler and more accurate.