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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Hybrid Search Cost Calculator
Estimate the monthly infrastructure cost of running vector search and keyword search together, including an optional reranker, based on your node counts and query volume.
Keep exploring
Free tools
- Hybrid Search Cost CalculatorEstimate the monthly infrastructure cost of running vector search and keyword search together, including an optional reranker, based on your node counts and query volume.
- NetSuite Saved Search Formula GeneratorFill in your field IDs and thresholds and get copy-ready formula snippets for NetSuite saved search results and criteria.
- NetSuite Saved Search Formula Functions ReferenceEvery common SQL function for Formula (Text), (Numeric), (Date), (Currency) and (Percent) fields, with syntax and an example using real NetSuite field ids.
ERP fixes and how-tos
- How to Search for Items, Purchase Orders and Records in SyteLineFind items, purchase orders and any record in Infor SyteLine / CSI using Filter in Place, form search, lookups and query-by-example, with wildcard tips.
- How to use formula fields in a NetSuite saved searchHow to build formula (text), formula (numeric) and formula (date) fields in a NetSuite saved search, with CASE WHEN, DECODE and NVL examples that actually work.
- How to Create a BAQ in Epicor: Build, Run, Search, Export to Excel and PublishStep-by-step: create a BAQ in Epicor Query Builder, add tables and criteria, run and search it, export to Excel, and publish it as a dashboard.
Guides
- Hybrid Search: Combining BM25 and Vector Retrieval for RAGHybrid search for RAG: why pure vector retrieval misses exact matches, how BM25 fixes it, and how reciprocal rank fusion combines both reliably.
- Natural Language Reporting Over Your ERP: A Practical GuideBuild natural language reporting over your ERP the safe way: text-to-SQL guardrails, semantic layers, and why raw text-to-SQL fails on real ERP schemas.
- Choosing an Embedding Model for Enterprise Search and RAGChoose the right embedding model for enterprise RAG: dimensions, multilingual support, and domain adaptation tradeoffs that determine retrieval quality.
AI on your ERP
- AI for Power BI on ERP Data: Answers Beyond the Dashboard You Already BuiltAdd grounded natural-language answers on top of Power BI semantic models and live ERP tables, with row-level security respected and models served on-prem.
- AI for Snowflake in Manufacturing: Questions Across ERP, MES and Quality DataPut grounded natural-language AI over ERP, MES and quality data in Snowflake, with role-based access honored, and see where Cortex fits versus a private model.
Troubleshooting
- Why RAG retrieves the wrong documents, and how to fix itRAG returns irrelevant chunks when the embedding model differs between indexing and query time, chunks are too large, or queries need rewriting. Fix it here.
- Why switching embedding models breaks your vector database, and why you can't just resize the columnSwitching embedding models changes the vector dimension and breaks queries. You must re-embed the entire corpus, not just resize the column. Here is how.