Semantic Search

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

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