Neuordnung#
Lernen Sie, wie Sie das Re-Ranking-Modell in Xinference verwenden.
Introduktion#
Gegeben eine Abfrage und eine Reihe von Dokumenten, sortiert Rerank die Dokumente basierend auf ihrer semantischen Relevanz zur Abfrage neu, von der relevantesten zur am wenigsten relevanten. In Xinference kann ein Rerank-Modell über den Rerank-Endpunkt aufgerufen werden, um eine Reihe von Dokumenten zu sortieren.
Schnellstart#
Wir können den Rerank-API über cURL, OpenAI Client oder Xinference ausprobieren:
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."
}]
}