Is semantic search the same as AI search?

Semantic search is one kind of AI-assisted search, based on embeddings. Some products also generate written answers on top of it, which is retrieval-augmented generation.

Does semantic search need a large language model?

No. It needs an embedding model, which is much smaller. A large language model is needed only to write an answer from the results.

Why does semantic search miss exact numbers?

Embeddings capture meaning, and a number such as an invoice id carries little meaning, so similar-looking ids look alike. Keyword matching handles exact strings better.

What is hybrid search?

Running keyword and vector searches together and merging the results. BGE-M3’s documentation recommends hybrid retrieval followed by re-ranking.

ANVE: the private AI office, on the record. See what that means

ANVE

Private AI glossary · updated 2026-10-07

Semantic search
Finding the clause you meant, even when you used different words.

Short answer. Semantic search finds text by meaning rather than by matching words. The question and every passage are turned into embeddings, and the passages whose vectors lie closest to the question’s are returned, so a search for “ending a lease early” can find a clause headed “premature termination of tenancy”. It does not replace keyword search. BM25, the classic keyword method, was a strong baseline across the 18 datasets of the BEIR benchmark, and exact identifiers such as invoice or section numbers are matched better by keywords. Good systems combine both and re-rank the results.

How it works

Indexing happens once: split the documents into chunks, embed each chunk, and store the vectors. A search embeds the question with the same model and returns the chunks with the nearest vectors. Sentence-BERT showed why this is practical: finding the most similar pair among 10,000 sentences took about 65 hours when every pair had to be compared by BERT, and about 5 seconds when each sentence was embedded once.

The sentence-transformers documentation notes that semantic search overcomes shortcomings of lexical search and can recognise synonyms and acronyms. It also describes the weakness: a bi-encoder compares the question and passage separately, which is fast but less precise than reading them together, so results are often re-ranked.

Keyword, semantic and hybrid compared

Question	Keyword search (BM25)	Semantic search	Hybrid with re-ranking
Finds synonyms and rewordings	No	Yes	Yes
Finds exact numbers, codes and names	Strong	Weak	Strong
Works across Hindi, English and Hinglish	Only if the same words appear	If the embedding model covers the languages	Best of both
Can explain why a result matched	Yes: the matching words	Only a similarity score	Partly
Needs a vector index	No	Yes	Yes

Office examples

Query	Keyword search finds	Semantic search finds
Can we end the lease early?	Clauses containing both end and early, possibly none	The clause headed premature termination of tenancy
What did we promise the client about timelines?	Minutes that contain the word timelines	Decisions about delivery dates and milestones
Invoice INV/2026-27/00123	That exact invoice	Similar-looking invoices, not necessarily that one
किराया बढ़ाने की शर्तें (terms for raising rent)	Nothing from an English contract	The English rent-escalation clause, with a multilingual model

Making it work

Chunk by the document’s structure so each passage makes sense alone.
Use an embedding model that covers your languages, such as BGE-M3, which lists more than 100.
Add keyword matching for identifiers, and merge the two result lists.
Re-rank the top results with a cross-encoder. The BEIR study found re-ranking and late-interaction models gave the best average zero-shot results, at high computational cost.
Measure with real questions: does the right passage appear in the top five?
Filter by permission inside the search, and show the source passage with each result.

Why it matters for an office

Most office knowledge sits in files whose authors used different words from the ones you would type. Semantic search finds earlier minutes, contract clauses and policy paragraphs without exact phrases. It needs no large language model: an embedding model is enough, and it can run inside the building. A language model is added only if you want a written answer. ANVE’s document question answering searches the office’s own documents and shows the source passage with the answer.

Questions people ask

Is semantic search the same as AI search?: Semantic search is one kind of AI-assisted search, based on embeddings. Some products also generate written answers on top of it, which is retrieval-augmented generation.
Does semantic search need a large language model?: No. It needs an embedding model, which is much smaller. A large language model is needed only to write an answer from the results.
Why does semantic search miss exact numbers?: Embeddings capture meaning, and a number such as an invoice id carries little meaning, so similar-looking ids look alike. Keyword matching handles exact strings better.
What is hybrid search?: Running keyword and vector searches together and merging the results. BGE-M3’s documentation recommends hybrid retrieval followed by re-ranking.

What is not real yet

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.

Semantic searchFinding the clause you meant, even when you used different words.