跳轉到主要內容
POST
/
v1
/
rerank
curl -X POST "https://api.lemondata.cc/v1/rerank" \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "BAAI/bge-reranker-v2-m3",
    "query": "What is machine learning?",
    "documents": [
      "Machine learning is a subset of AI",
      "The weather is nice today",
      "Deep learning uses neural networks"
    ],
    "top_n": 2,
    "return_documents": true
  }'

{
  "results": [
    {
      "index": 0,
      "relevance_score": 0.95,
      "document": "Machine learning is a subset of AI"
    },
    {
      "index": 2,
      "relevance_score": 0.82,
      "document": "Deep learning uses neural networks"
    }
  ],
  "model": "BAAI/bge-reranker-v2-m3",
  "usage": {
    "prompt_tokens": 45,
    "total_tokens": 45
  }
}
使用语义相似度模型对文档进行重排。适用于优化搜索结果和 RAG 应用。

​
请求体

​
model
string
必填
要使用的重排模型 ID（例如 BAAI/bge-reranker-v2-m3，qwen3-rerank）。
​
query
string
必填
用于对文档进行排名的查询语句。
​
documents
array
必填
需要重排的文档列表（字符串数组）。
​
top_n
integer
返回的前几个结果的数量。默认为所有文档。
​
return_documents
boolean
預設值:"false"
是否在响应中包含原始文档文本。

​
响应

​
results
array
带有评分的已排序文档列表。每个结果包含：
  • index (integer): 原始文档索引
  • relevance_score (number): 相关性评分 (0-1)
  • document (string): 原始文本（如果 return_documents=true）
​
model
string
用于重排的模型。
​
usage
object
Token 使用统计。
curl -X POST "https://api.lemondata.cc/v1/rerank" \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "BAAI/bge-reranker-v2-m3",
    "query": "What is machine learning?",
    "documents": [
      "Machine learning is a subset of AI",
      "The weather is nice today",
      "Deep learning uses neural networks"
    ],
    "top_n": 2,
    "return_documents": true
  }'

{
  "results": [
    {
      "index": 0,
      "relevance_score": 0.95,
      "document": "Machine learning is a subset of AI"
    },
    {
      "index": 2,
      "relevance_score": 0.82,
      "document": "Deep learning uses neural networks"
    }
  ],
  "model": "BAAI/bge-reranker-v2-m3",
  "usage": {
    "prompt_tokens": 45,
    "total_tokens": 45
  }
}