information-retrieval AI Models

Score relevance between queries and passages for retrieval re-ranking. Accept a JSON string containing one or many [quer...

reranking • semantic-search • text-similarity • 115 runs

Re-rank query–candidate text pairs for search and RAG by returning a relevance score per pair. Takes a JSON-encoded list...

text-reranking • information-retrieval • rag • 91.8K runs

Rerank text passages for a query by scoring text pairs and returning relevance scores. Accept a JSON list of (query, doc...

text-reranking • information-retrieval • retrieval-augmented-generation • 25 runs

Perform retrieval-augmented generation (RAG) over detailed web search results to generate accurate and well-cited summar...

📝 → 📝 • retrieval-augmented-generation • text-generation • question-answering • 38 runs

Generate text embeddings for semantic search, information retrieval, similarity, and reranking. Accepts English text (si...

text-embedding • semantic-search • information-retrieval • 37 runs

Rerank query–document pairs for information retrieval and RAG. Accepts text inputs as one or many [query, passage] pairs...

text-reranking • information-retrieval • rag • 63.2K runs

Re-rank text passages by scoring the relevance of text pairs. Accepts a JSON array of [query, document] string pairs and...

text-reranking • information-retrieval • semantic-similarity • 287 runs

Rerank documents for a text query, returning the top-k most relevant texts with relevance scores. Accepts a text query a...

reranking • semantic-search • rag • 21 runs

Score relevance between text pairs for re-ranking search results and RAG pipelines. Takes a JSON-formatted list of (quer...

text-reranking • information-retrieval • text-similarity • 8.1K runs

Reranks search results by scoring query-document pairs for improved retrieval accuracy in RAG pipelines. Uses a cross-en...

📝 → 📝 • text-embedding • text-generation • 13 runs