自定义重排类，实现分数阈值过滤

backend/app/engine/__init__.py

@@ -61,8 +61,8 @@ def get_chat_engine(filters=None, params:dict=None):
     react_chat_formatter = ReActChatFormatter.from_defaults(ReActChatFormatter_messages)
     agentrunner = AgentRunner.from_llm(
         llm=Settings.llm,
-        tools=tools,
-        react_chat_formatter=react_chat_formatter,
+        tools=tools,     
+        #react_chat_formatter=react_chat_formatter,
         system_prompt=system_prompt,
         verbose=True,
     )

backend/app/engine/rerank/xinferenceRerank.py

@@ -0,0 +1,75 @@
+import requests
+from llama_index.postprocessor.xinference_rerank import XinferenceRerank
+from llama_index.core.bridge.pydantic import Field
+from typing import List, Optional
+from llama_index.core.bridge.pydantic import Field
+from llama_index.core.callbacks import CBEventType, EventPayload
+from llama_index.core.instrumentation import get_dispatcher
+from llama_index.core.instrumentation.events.rerank import (
+    ReRankEndEvent,
+    ReRankStartEvent,
+)
+from llama_index.core.schema import NodeWithScore, QueryBundle, MetadataMode
+dispatcher = get_dispatcher(__name__)
+
+class CustomXinFerenceRerank(XinferenceRerank):
+    score_threshold: float = Field(default=0.3,description="分数阈值")
+
+    def _postprocess_nodes(
+        self,
+        nodes: List[NodeWithScore],
+        query_bundle: Optional[QueryBundle] = None,
+    ) -> List[NodeWithScore]:
+        dispatcher.event(
+            ReRankStartEvent(
+                query=query_bundle,
+                nodes=nodes,
+                top_n=self.top_n,
+                model_name=self.model,
+            )
+        )
+        if query_bundle is None:
+            raise ValueError("Missing query bundle.")
+        if len(nodes) == 0:
+            return []
+        with self.callback_manager.event(
+            CBEventType.RERANKING,
+            payload={
+                EventPayload.NODES: nodes,
+                EventPayload.MODEL_NAME: self.model,
+                EventPayload.QUERY_STR: self.get_query_str(query_bundle),
+                EventPayload.TOP_K: self.top_n,
+            },
+        ) as event:
+            headers = {"Content-Type": "application/json"}
+            json_data = {
+                "model": self.model,
+                "query": self.get_query_str(query_bundle),
+                "documents": [
+                    node.node.get_content(metadata_mode=MetadataMode.EMBED)
+                    for node in nodes
+                ],
+            }
+            response = requests.post(
+                url=f"{self.base_url}/v1/rerank", headers=headers, json=json_data
+            )
+            response.encoding = "utf-8"
+            if response.status_code != 200:
+                raise Exception(
+                    f"Xinference call failed with status code {response.status_code}."
+                    f"Details: {response.text}"
+                )
+            
+            rerank_nodes = []
+            for result in response.json()["results"]:
+                node = NodeWithScore(
+                    node=nodes[result["index"]].node, score=result["relevance_score"]
+                )
+                if node.score > self.score_threshold:
+                    rerank_nodes.append(node)
+            
+            if len(rerank_nodes) > self.top_n:
+                rerank_nodes = sorted(rerank_nodes,key=lambda x:x.score)[:self.top_n]
+            event.on_end(payload={EventPayload.NODES: rerank_nodes})
+        dispatcher.event(ReRankEndEvent(nodes=rerank_nodes))
+        return rerank_nodes

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')