Riordinamento#
Impara come utilizzare i modelli di riordino in Xinference.
introduzione#
Dato un query e una serie di documenti, Rerank riordina i documenti dal più rilevante al meno rilevante in base alla correlazione semantica con il query. In Xinference, è possibile ordinare una serie di documenti chiamando il modello Rerank tramite l’endpoint Rerank.
Guida rapida#
Possiamo provare a utilizzare l’API Rerank tramite cURL, OpenAI Client o 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."
}]
}