兼容解析重排序服务返回score字段

internal/models/rerank/reranker.go

@@ -2,9 +2,10 @@ package rerank
 
 import (
 	"context"
+	"encoding/json"
 	"fmt"
 	"strings"
-
+    
 	"github.com/Tencent/WeKnora/internal/types"
 )
 
@@ -25,6 +26,33 @@ type RankResult struct {
 	Document       DocumentInfo `json:"document"`
 	RelevanceScore float64      `json:"relevance_score"`
 }
+//Handles the RelevanceScore field by checking if RelevanceScore exists first, otherwise falls back to Score field
+func (r *RankResult) UnmarshalJSON(data []byte) error {
+
+	var temp struct {
+		Index          int           `json:"index"`
+		Document       DocumentInfo  `json:"document"`
+		RelevanceScore *float64      `json:"relevance_score"`
+		Score          *float64      `json:"score"`
+	}
+
+
+	if err := json.Unmarshal(data, &temp); err != nil {
+		return fmt.Errorf("failed to unmarshal rank result: %w", err)
+	}
+
+	r.Index = temp.Index
+	r.Document = temp.Document
+
+	if temp.RelevanceScore != nil {
+		r.RelevanceScore = *temp.RelevanceScore
+	} else if temp.Score != nil {
+		r.RelevanceScore = *temp.Score
+	}
+
+
+	return nil
+}
 
 type DocumentInfo struct {
 	Text string `json:"text"`

rerank_server_demo.py

@@ -0,0 +1,102 @@
+import torch
+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel, Field
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from typing import List
+
+# --- 1. 定义API的请求和响应数据结构 ---
+
+# 请求体结构保持不变
+class RerankRequest(BaseModel):
+    query: str
+    documents: List[str]
+
+# --- 修改开始：定义测试用的响应结构，字段名为 "score" ---
+
+# DocumentInfo 结构保持不变
+class DocumentInfo(BaseModel):
+    text: str
+
+# 将原来的 GoRankResult 修改为 TestRankResult
+# 核心改动：将 "relevance_score" 字段重命名为 "score"
+class TestRankResult(BaseModel):
+    index: int
+    document: DocumentInfo
+    score: float  # <--- 【关键修改点】字段名已从 relevance_score 改为 score
+
+# 最终响应体结构，其 "results" 列表包含的是 TestRankResult
+class TestFinalResponse(BaseModel):
+    results: List[TestRankResult]
+
+# --- 修改结束 ---
+
+
+# --- 2. 加载模型 (在服务启动时执行一次) ---
+print("正在加载模型，请稍候...")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"使用的设备: {device}")
+try:
+    # 请确保这里的路径是正确的
+    model_path = '/data1/home/lwx/work/Download/rerank_model_weight'
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    model.to(device)
+    model.eval()
+    print("模型加载成功！")
+except Exception as e:
+    print(f"模型加载失败: {e}")
+    # 在测试环境中，如果模型加载失败，可以考虑退出以避免运行一个无效的服务
+    exit()
+
+# --- 3. 创建FastAPI应用 ---
+app = FastAPI(
+    title="Reranker API (Test Version)",
+    description="一个返回 'score' 字段以测试Go客户端兼容性的API服务",
+    version="1.0.1"
+)
+
+# --- 4. 定义API端点 ---
+# --- 修改开始：将 response_model 指向新的测试用响应结构 ---
+@app.post("/rerank", response_model=TestFinalResponse) # <--- 【关键修改点】response_model 改为 TestFinalResponse
+def rerank_endpoint(request: RerankRequest):
+    # --- 修改结束 ---
+
+    pairs = [[request.query, doc] for doc in request.documents]
+
+    with torch.no_grad():
+        inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=1024).to(device)
+        scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+
+    # --- 修改开始：按照测试用的结构来构建结果 ---
+    results = []
+    for i, (text, score_val) in enumerate(zip(request.documents, scores)):
+        
+        # 1. 创建嵌套的 document 对象
+        doc_info = DocumentInfo(text=text)
+        
+        # 2. 创建 TestRankResult 对象
+        #    注意字段名：index, document, score
+        test_result = TestRankResult(
+            index=i,
+            document=doc_info,
+            score=score_val.item()  # <--- 【关键修改点】赋值给 "score" 字段
+        )
+        results.append(test_result)
+
+    # 3. 排序 (key 也要相应修改为 score)
+    sorted_results = sorted(results, key=lambda x: x.score, reverse=True)
+    # --- 修改结束 ---
+    
+    # 返回一个字典，FastAPI 会根据 response_model (TestFinalResponse) 来验证和序列化它
+    # 最终生成的 JSON 会是 {"results": [{"index": ..., "document": ..., "score": ...}]}
+    return {"results": sorted_results}
+
+@app.get("/")
+def read_root():
+    return {"status": "Reranker API (Test Version) is running"}
+
+# --- 5. 启动服务 ---
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+