自定义重排类，实现分数阈值过滤

backend/app/settings.py

@@ -5,10 +5,10 @@ from llama_index.core.constants import DEFAULT_TEMPERATURE
 from llama_index.core.settings import Settings
 from llama_index.embeddings.xinference import XinferenceEmbedding
 #from llama_index.llms.xinference import Xinference
-from app.engine.model.xinfeng import XinfengModel
-#from llama_index.embeddings.xinference import XinferenceEmbedding
+from app.engine.model.xinference import XinferenceModel
+from app.engine.rerank.xinferenceRerank import CustomXinFerenceRerank
 from llama_index.llms.xinference.base import DEFAULT_XINFERENCE_TEMP
-from llama_index.postprocessor.xinference_rerank import XinferenceRerank
+
 
 from app.engine.loaders import getProjectInfos
 from app.api.routers.request.base import ProjectInfo
@@ -97,7 +97,7 @@ class XinferencePlatform(ModelPlatform):
         model = os.getenv("MODEL")
         max_tokens = int(os.getenv("LLM_MAX_TOKENS")) if os.getenv("LLM_MAX_TOKENS") is not None else None
         temperature = float(os.getenv("LLM_TEMPERATURE", DEFAULT_XINFERENCE_TEMP))
-        return XinfengModel(model_uid = model,endpoint = base_url,temperature = temperature,max_tokens = max_tokens)
+        return XinferenceModel(model_uid = model,endpoint = base_url,temperature = temperature,max_tokens = max_tokens)
     
     def embedding(self):
         base_url = os.getenv("BASE_URL")
@@ -116,7 +116,7 @@ class XinferencePlatform(ModelPlatform):
         rerank_threshold = os.getenv("RERANK_THRESHOLD")
         postprocess = None
         if rerank_model is not None:
-            postprocess = [XinferenceRerank(model = rerank_model, base_url = rerank_url, top_n=rerank_top_n)]
+            postprocess = [CustomXinFerenceRerank(model = rerank_model, base_url = rerank_url, top_n=rerank_top_n,score_threshold=rerank_threshold)]
         return postprocess
 
 @register(ModelPlateCategory,'openai')