.. _rerank: ===================== Rerank ===================== Learn how to use rerank models in Xinference. Introduction ================ Given a query and a list of documents, Rerank indexes the documents from most to least semantically relevant to the query. Rerank models in Xinference can be invoked through the Rerank endpoint to rank a list of documents. Quickstart ================ We can try Rerank API out either via cURL, OpenAI Client, or Xinference's python client: .. tabs:: .. code-tab:: bash cURL curl -X 'POST' \ 'http://:/v1/rerank' \ -H 'accept: application/json' \ -H 'Content-Type: application/json' \ -d '{ "model": "", "query": "A man is eating pasta.", "documents": [ "A man is eating food.", "A man is eating a piece of bread.", "The girl is carrying a baby.", "A man is riding a horse.", "A woman is playing violin." ] }' .. code-tab:: python Xinference Python Client from xinference.client import Client client = Client("http://:") model = client.get_model() query = "A man is eating pasta." corpus = [ "A man is eating food.", "A man is eating a piece of bread.", "The girl is carrying a baby.", "A man is riding a horse.", "A woman is playing violin." ] print(model.rerank(corpus, query)) .. code-tab:: json output { "id": "480dca92-8910-11ee-b76a-c2c8e4cad3f5", "results": [{ "index": 0, "relevance_score": 0.9999247789382935, "document": "A man is eating food." }, { "index": 1, "relevance_score": 0.2564932405948639, "document": "A man is eating a piece of bread." }, { "index": 3, "relevance_score": 0.00003955026841140352, "document": "A man is riding a horse." }, { "index": 2, "relevance_score": 0.00003742107219295576, "document": "The girl is carrying a baby." }, { "index": 4, "relevance_score": 0.00003739788007806055, "document": "A woman is playing violin." }] }