fix: metadata filter not work (#19020)

Co-authored-by: 金鹏程 <jinpengcheng01@corp.netease.com>
Co-authored-by: crazywoola <427733928@qq.com>

api/core/agent/cot_agent_runner.py

@@ -69,6 +69,13 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         tool_instances, prompt_messages_tools = self._init_prompt_tools()
         self._prompt_messages_tools = prompt_messages_tools
 
+        # fix metadata filter not work
+        if app_config.dataset is not None:
+            metadata_filtering_conditions = app_config.dataset.retrieve_config.metadata_filtering_conditions
+            for key, dataset_retriever_tool in tool_instances.items():
+                if hasattr(dataset_retriever_tool, "retrieval_tool"):
+                    dataset_retriever_tool.retrieval_tool.metadata_filtering_conditions = metadata_filtering_conditions
+
         function_call_state = True
         llm_usage: dict[str, Optional[LLMUsage]] = {"usage": None}
         final_answer = ""

api/core/agent/fc_agent_runner.py

@@ -45,6 +45,13 @@ class FunctionCallAgentRunner(BaseAgentRunner):
         # convert tools into ModelRuntime Tool format
         tool_instances, prompt_messages_tools = self._init_prompt_tools()
 
+        # fix metadata filter not work
+        if app_config.dataset is not None:
+            metadata_filtering_conditions = app_config.dataset.retrieve_config.metadata_filtering_conditions
+            for key, dataset_retriever_tool in tool_instances.items():
+                if hasattr(dataset_retriever_tool, "retrieval_tool"):
+                    dataset_retriever_tool.retrieval_tool.metadata_filtering_conditions = metadata_filtering_conditions
+
         assert app_config.agent
 
         iteration_step = 1

api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py

@@ -4,6 +4,7 @@ from pydantic import BaseModel, Field
 
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.entities.context_entities import DocumentContext
+from core.rag.entities.metadata_entities import MetadataCondition
 from core.rag.models.document import Document as RetrievalDocument
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
 from core.tools.utils.dataset_retriever.dataset_retriever_base_tool import DatasetRetrieverBaseTool
@@ -33,6 +34,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
     args_schema: type[BaseModel] = DatasetRetrieverToolInput
     description: str = "use this to retrieve a dataset. "
     dataset_id: str
+    metadata_filtering_conditions: MetadataCondition
 
     @classmethod
     def from_dataset(cls, dataset: Dataset, **kwargs):
@@ -46,6 +48,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
             tenant_id=dataset.tenant_id,
             dataset_id=dataset.id,
             description=description,
+            metadata_filtering_conditions=MetadataCondition(),
             **kwargs,
         )
 
@@ -65,6 +68,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
                 dataset_id=dataset.id,
                 query=query,
                 external_retrieval_parameters=dataset.retrieval_model,
+                metadata_condition=self.metadata_filtering_conditions,
             )
             for external_document in external_documents:
                 document = RetrievalDocument(