Réordonnancement#
Apprenez à utiliser le modèle de réordonnancement dans Xinference.
Introduction#
Étant donné une requête et une série de documents, Rerank réordonne les documents de la manière la plus pertinente à la moins pertinente en fonction de la pertinence sémantique avec la requête. Dans Xinference, le modèle Rerank peut être appelé via le point de terminaison Rerank pour trier une série de documents.
Démarrage rapide#
Nous pouvons essayer d’utiliser l’API Rerank via cURL, le client OpenAI ou Xinference :
curl -X 'POST' \
'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/rerank' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"model": "<MODEL_UID>",
"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."
]
}'
from xinference.client import Client
client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")
model = client.get_model(<MODEL_UID>)
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))
{
"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."
}]
}