Semantic Search

Find works by meaning using AI embeddings. Best for paragraph-length queries.

Semantic search returns works whose meaning is closest to your query, even when the wording differs. A query about “predicting drug toxicity from molecular structure” finds papers using “computational toxicology” or “QSAR” — words your search never mentioned.

https://api.openalex.org/works?search.semantic=predicting drug toxicity from molecular structure

Long-text queries

Semantic search shines when you have a longer description — an abstract, a grant aim, or a paragraph from a paper you’re writing. The richer the input, the better the matches.

# Paste your grant aim verbatim (URL-encoded)
https://api.openalex.org/works?search.semantic=We propose to integrate single-cell RNA-seq with spatial transcriptomics to map T-cell exhaustion in solid tumors and identify novel checkpoint targets.

Up to 2,000 characters are used for matching; longer input is truncated.

Combining with filters

Most filters and the select parameter work as usual:

https://api.openalex.org/works?search.semantic=mRNA vaccine immunogenicity in older adults&filter=publication_year:>2020,is_oa:true&select=id,title,relevance_score

Two filters are not supported on semantic search — they would require pre-filtering hundreds of millions of vectors and time out:

• last_known_institutions.country_code (and the country_code shorthand)
• cited_by_count

How it works

OpenAlex embeds the title and abstract of every work using GTE Large EN, an open-source embedding model from Alibaba DAMO Academy, into a 1,024-dimensional vector. At query time we embed your query the same way and return the works closest by cosine similarity.

Limits

Constraint	Value
Max input length	2,000 characters
Max results	50 per query
Rate limit	1 request per second
Pricing	See pricing by endpoint

Only one search parameter is allowed per request: search, search.exact, or search.semantic.