Semantic search

What Is Semantic Search?

Semantic search finds the right document even when the searcher and the author use different words. A query for "laptop won't turn on" should surface a help article titled "Troubleshooting power failures", even though the two share no terms.

Most systems built today do this with embeddings. A model reads each passage and outputs a vector, a fixed-length list of numbers, positioned so that passages about similar things land close together. At query time, the engine converts the query the same way and returns the passages whose vectors sit nearest to it.

Older approaches tried to achieve the same goal with hand-built thesauri and entity models. Some current systems use learned sparse models instead, which expand a query with related terms and then run it against a classic keyword index. What unites them is the target: the intent behind the query.

Semantic search is a retrieval technique, so it usually functions inside something larger. Retrieval-augmented generation uses it to pick the passages a language model reads before answering, and a knowledge graph can supply explicit relationships that vector similarity only approximates.

Why Is Search Moving From Keywords to Meaning?

Keyword search matches spellings, and users rarely spell their needs the way a catalog or knowledge base does.

• Users stop guessing the catalog's vocabulary. Lexical engines rank documents by shared terms, usually with the BM25 scoring formula. This means that a query in everyday language misses content written in product or clinical terminology. As people started typing full questions, the problem grew: when Google added the BERT language model to ranking in 2019, it said the change would help Search better understand one in 10 searches in the U.S. in English. Semantic search scores the intent of the whole query, so exact phrasing carries less weight.

• One retrieval layer serves the search box and AI features. An LLM feature relies on the same embedding index that powers a search page to find source material. Building it once as a shared retrieval service lets customer-facing search and an internal support assistant draw on the same index, instead of a separate index for each AI project.

How Does Semantic Search Turn a Query Into Ranked Results?

Semantic search converts documents and queries into vectors with the same model. Then, it ranks documents by how close their vectors sit to the query's.

• Chunking the content. Documents are split into passages, often a few paragraphs each. This is because one vector for a 40-page manual averages every topic in it into a blur. Chunk size is a tuning decision: smaller chunks match precisely, larger ones keep more context around each match.

• Embedding each chunk. An embedding model turns every chunk into a vector. OpenAI's text-embedding-3-small, for example, outputs 1,536 numbers per input by default. The model learned during training to place related text close together, so "refund" and "money back" end up near each other without a synonym list.

• Indexing for fast lookup. Comparing a query against every stored vector gives exact results. It also slows each query as the collection grows. Most systems use approximate nearest neighbor (ANN) indexes instead, such as the Hierarchical Navigable Small World (HNSW) graph. It's a layered graph whose search cost scales logarithmically with collection size. The index accepts a small recall loss, meaning it occasionally misses a close match, in exchange for much faster queries.

• Scoring the query. At search time, the query passes through the same embedding model. The index returns the stored vectors nearest to it, measured by cosine similarity (the angle between two vectors). Metadata filters, such as language or product line, narrow the candidates before or after this step.

• Fusing and reranking. Production systems often run a keyword query in parallel and merge the two ranked lists with reciprocal rank fusion. It rewards documents that rank high in either list. A cross-encoder reranker, a slower model that reads the query and each candidate together, can then reorder the top results for precision.

What Tools Do Teams Use to Build Semantic Search?

A semantic search stack needs an embedding model and a vector store, and many teams add a hybrid engine or a reranker on top.

• Embedding models: OpenAI text-embedding-3, Cohere Embed, and the open-source Sentence Transformers library, which runs thousands of pretrained models on your own hardware. Model choice shapes result quality more than any other decision, and the MTEB leaderboard compares models task by task.

• Vector databases and extensions: Pinecone as a managed service, Qdrant as an open-source engine you can self-host or run in its cloud, and pgvector, a PostgreSQL extension that stores vectors next to existing relational data.

• Hybrid search and reranking: Elasticsearch and Weaviate both combine keyword scoring with vector search in one query, and Cohere Rerank reorders a candidate list by semantic relevance.

What Defines How a Semantic Search System Behaves?

Semantic search ranks by closeness, which gives it strengths a keyword engine lacks.

• Every query gets an answer. Some vector always sits nearest to the query, so the engine returns a ranked list even for gibberish or out-of-scope questions. Applications that need to say "nothing relevant found" set a minimum similarity score, and the right cut-off differs from one model to the next.

• The model decides what counts as similar. Two passages are close only if the embedding model learned to place them close. A general-purpose model may treat "discharge" in a hospital record and in a battery datasheet as neighbors, while a model trained on clinical text keeps them apart.

• Results are approximate by design. ANN indexes trade a little recall for speed, so an exhaustive scan of the same collection could surface a match the index skipped. Index settings let teams shift that balance in either direction.

• Any content an encoder can map into vectors becomes searchable. A photo can serve as the query for a product image catalog. Sentence Transformers, for instance, support image embeddings alongside text.

What Do Products Gain From Semantic Search?

Semantic search makes more of a collection findable with less manual tuning of the index.

• Untagged content becomes findable. Support tickets and call transcripts rarely carry clean titles or tags. Embeddings index them by what they discuss. This lets teams search years of unstructured records without running a tagging project.

• Queries cross languages. Multilingual embedding models place a sentence and its translation near each other, so a Spanish query can retrieve an English document. Cohere's multilingual models, for example, support over 100 languages.

• Synonym files shrink. An embedding model covers everyday synonyms by default, so the team maintains only domain-specific exceptions. Keyword engines, by contrast, handle vocabulary differences with hand-curated synonym lists and query-rewriting rules.

• "More like this" comes with the index. Every document already has a vector. Finding items similar to a given article or ticket is the same nearest-neighbor lookup, using that document's vector as the starting point. That supports related-content panels and duplicate detection without a separate system.

What Trade-Offs Come With Semantic Search?

Semantic search broadens what loosely worded queries can reach. It comes with weak spots, and each fix adds cost or complexity elsewhere.

• Exact identifiers slip. Embeddings capture topic well and literal strings poorly, so a search for part number "AX-4410" may rank "AX-4401" first. Hybrid search fixes this by running BM25 alongside the vector query, and the price is a second index to keep in sync, plus fusion weights someone has to tune again as content changes.

• Accuracy drops outside the training domain. The BEIR benchmark tested retrieval models zero-shot across 18 datasets and found BM25 a strong baseline that dense retrievers often failed to beat. Rerankers scored best on average at high computational cost. Fine-tuning on your own content recovers much of the lost accuracy but needs labeled query–document pairs, while a reranker adds latency and compute to every query.

• The index is tied to one model. Vectors from different embedding models live in incompatible spaces, so upgrading the model means re-embedding the whole corpus. Teams usually build the new index in parallel and switch traffic once it tests well, which means paying for two indexes and a full embedding run during the migration.

• Vectors take space. At 3,072 dimensions stored as 32-bit floats, one text-embedding-3-large vector takes 12,288 bytes, so 10 million chunks need about 123 GB before any index overhead. OpenAI's models accept a dimensions parameter that shortens vectors, and quantization (storing each number in fewer bits) cuts size further. Both trade some retrieval accuracy for size savings.

How Is Semantic Search Different From Keyword Search?

Embedding-based semantic search matches a query's meaning, while keyword search matches its exact terms.