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.
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
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.
