What Is Semantic Search?
Also known as: vector search, meaning-based search, neural search
Definition
Semantic search retrieves results by meaning rather than exact word match, using embeddings to find text that is conceptually close to the query. A search for "supplier delivered damaged goods" can find records that say "vendor shipment arrived broken".
Semantic Search Explained
Traditional ERP search is keyword based: it finds records containing the typed characters, or filters on fields. If a nonconformance was described as "cracked housing" and you search "damaged casting", you get nothing. Semantic search encodes both the query and each record as vectors using an embedding model, then returns the nearest neighbours in vector space.
In manufacturing it is most useful on free text: nonconformance reports, corrective action descriptions, service tickets, engineering change notes, buyer comments, inspection remarks and maintenance logs. These fields hold years of tribal knowledge that structured filters cannot reach.
Semantic search is weak on exact identifiers. A part number like 4471-A2 has little meaning to an embedding model, so production systems use hybrid search that combines keyword matching with vector similarity, then optionally reranks the results. Structured filters on site, date, status or item family narrow the candidate set first.
It is also the retrieval half of RAG. The same index that powers a search box feeds relevant passages to an LLM that writes a grounded answer with citations.
Why It Matters
- Finds relevant history in free-text fields that keyword search misses, such as prior failures with similar symptoms.
- Reduces repeat problems by surfacing earlier corrective actions.
- Works across languages when a multilingual embedding model is used, useful for global plants.
- Can be added alongside the ERP without changing it, by indexing exported or replicated data.
In Practice
A quality engineer searches "intermittent leak after pressure test on manifold". The index holds 18,000 nonconformance records. Semantic search returns 12 close matches, including reports worded as "seep at port weld" and "weeping at fitting after hydro". Three share one supplier lot and one process change, which a keyword search for the engineer's exact words would never have connected.
Frequently Asked Questions
Does semantic search replace keyword search?
No. The best systems combine both, because keywords handle exact part and document numbers while vectors handle meaning.
Does it need the data to leave my network?
No. Embedding models run locally, and the vector index can sit on your own servers or database.
Can it respect ERP permissions?
It can if every indexed record carries access metadata such as site or role and the search filters on the user's rights. This must be designed in from the start.
How is it different from a generative AI answer?
Semantic search returns the matching records. A RAG system adds an LLM that reads those records and writes an answer with citations.
Related Terms
Embedding
An embedding is a list of numbers, typically several hundred to a few thousand dimensions, that represents the meaning of a piece of text, image, or code so that semantically similar items produce vectors that sit close together.
Vector Database
A vector database is a system that stores high-dimensional embedding vectors and retrieves the ones most similar to a query vector, enabling semantic search where results match meaning rather than exact keywords.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an architecture that searches your own document store for passages relevant to a question, inserts them into the LLM prompt, and instructs the model to answer only from that retrieved evidence.
Reranking
Reranking is a second retrieval stage in RAG where a more precise model re-scores the top candidate passages from the first search and reorders them, so the LLM receives only the most relevant context.
Hallucination
A hallucination is output from an AI model that is fluent and confident but factually wrong or entirely fabricated, produced because language models generate statistically plausible text rather than retrieving verified facts.
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