리랭킹#

Xinference에서 재정렬 모델을 사용하는 방법을 학습합니다.

소개#

주어진 쿼리와 일련의 문서가 있을 때, Rerank는 쿼리와의 의미적 관련성에 따라 문서를 가장 관련성 높은 순에서 낮은 순으로 재정렬합니다. Xinference에서는 Rerank 엔드포인트를 통해 Rerank 모델을 호출하여 일련의 문서를 정렬할 수 있습니다.

빠른 시작#

cURL, OpenAI Client 또는 Xinference를 통해 Rerank API를 사용해 볼 수 있습니다:

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."
    }]
}