fix page content is empty

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -19,6 +19,7 @@ from core.workflow.entities.node_entities import NodeRunResult, NodeType
 from core.workflow.entities.variable_pool import VariablePool
 from core.workflow.nodes.base_node import BaseNode
 from core.workflow.nodes.knowledge_retrieval.entities import KnowledgeRetrievalNodeData
+from core.workflow.nodes.knowledge_retrieval.multi_dataset_function_call_router import FunctionCallMultiDatasetRouter
 from core.workflow.nodes.knowledge_retrieval.structed_multi_dataset_router_agent import ReactMultiDatasetRouter
 from extensions.ext_database import db
 from models.dataset import Dataset, Document, DocumentSegment
@@ -214,32 +215,19 @@ class KnowledgeRetrievalNode(BaseNode):
             if ModelFeature.TOOL_CALL in features \
                     or ModelFeature.MULTI_TOOL_CALL in features:
                 planning_strategy = PlanningStrategy.ROUTER
-
+        dataset_id = None
         if planning_strategy == PlanningStrategy.REACT_ROUTER:
             react_multi_dataset_router = ReactMultiDatasetRouter()
-            return react_multi_dataset_router.invoke(query, tools, node_data, model_config, model_instance,
+            dataset_id = react_multi_dataset_router.invoke(query, tools, node_data, model_config, model_instance,
-                                                     self.user_id, self.tenant_id)
+                                                           self.user_id, self.tenant_id)
 
-        prompt_messages = [
+        elif planning_strategy == PlanningStrategy.ROUTER:
-            SystemPromptMessage(content='You are a helpful AI assistant.'),
+            function_call_router = FunctionCallMultiDatasetRouter()
-            UserPromptMessage(content=query)
+            dataset_id = function_call_router.invoke(query, tools, model_config, model_instance)
-        ]
+        if dataset_id:
-        result = model_instance.invoke_llm(
-            prompt_messages=prompt_messages,
-            tools=tools,
-            stream=False,
-            model_parameters={
-                'temperature': 0.2,
-                'top_p': 0.3,
-                'max_tokens': 1500
-            }
-        )
-
-        if result.message.tool_calls:
             # get retrieval model config
-            function_call_name = result.message.tool_calls[0].function.name
             dataset = db.session.query(Dataset).filter(
-                Dataset.id == function_call_name
+                Dataset.id == dataset_id
             ).first()
             if dataset:
                 retrieval_model_config = dataset.retrieval_model \

api/core/workflow/nodes/knowledge_retrieval/multi_dataset_function_call_router.py

@@ -0,0 +1,58 @@
+from collections.abc import Generator, Sequence
+from typing import Optional, Union
+
+from langchain import PromptTemplate
+from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
+from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+
+from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.llm_entities import LLMUsage
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageRole, PromptMessageTool, \
+    SystemPromptMessage, UserPromptMessage
+from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
+from core.prompt.entities.advanced_prompt_entities import ChatModelMessage
+from core.workflow.nodes.knowledge_retrieval.entities import KnowledgeRetrievalNodeData
+from core.workflow.nodes.llm.llm_node import LLMNode
+
+
+class FunctionCallMultiDatasetRouter:
+
+    def invoke(
+            self,
+            query: str,
+            dataset_tools: list[PromptMessageTool],
+            model_config: ModelConfigWithCredentialsEntity,
+            model_instance: ModelInstance,
+
+    ) -> Union[str, None]:
+        """Given input, decided what to do.
+        Returns:
+            Action specifying what tool to use.
+        """
+        if len(dataset_tools) == 0:
+            return None
+        elif len(dataset_tools) == 1:
+            return dataset_tools[0].name
+
+        try:
+            prompt_messages = [
+                SystemPromptMessage(content='You are a helpful AI assistant.'),
+                UserPromptMessage(content=query)
+            ]
+            result = model_instance.invoke_llm(
+                prompt_messages=prompt_messages,
+                tools=dataset_tools,
+                stream=False,
+                model_parameters={
+                    'temperature': 0.2,
+                    'top_p': 0.3,
+                    'max_tokens': 1500
+                }
+            )
+            if result.message.tool_calls:
+                # get retrieval model config
+                return result.message.tool_calls[0].function.name
+            return None
+        except Exception as e:
+            return None

api/core/workflow/nodes/knowledge_retrieval/structed_multi_dataset_router_agent.py

@@ -2,8 +2,11 @@ from collections.abc import Generator, Sequence
 from typing import Optional, Union
 
 from langchain import PromptTemplate
+from langchain.agents import AgentOutputParser
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParserWithRetries
 from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+from langchain.schema import AgentAction
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.model_manager import ModelInstance
@@ -13,6 +16,7 @@ from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage
 from core.workflow.nodes.knowledge_retrieval.entities import KnowledgeRetrievalNodeData
 from core.workflow.nodes.llm.llm_node import LLMNode
+from pydantic import Field
 
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
@@ -126,7 +130,13 @@ class ReactMultiDatasetRouter:
             user_id=user_id,
             tenant_id=tenant_id
         )
-        return result_text
+        output_parser: AgentOutputParser = Field(
+            default_factory=StructuredChatOutputParserWithRetries
+        )
+        agent_decision = output_parser.parse(result_text)
+        if isinstance(agent_decision, AgentAction):
+            tool_inputs = agent_decision.tool_input
+        return tool_inputs
 
     def _invoke_llm(self, node_data: KnowledgeRetrievalNodeData,
                     model_instance: ModelInstance,
@@ -197,7 +207,7 @@ class ReactMultiDatasetRouter:
     ) -> list[ChatModelMessage]:
         tool_strings = []
         for tool in tools:
-            tool_strings.append(f"{tool.name}: {tool.description}")
+            tool_strings.append(f"dataset_{tool.name}: {tool.description}, args: {{'query': {{'title': 'Query', 'description': 'Query for the dataset to be used to retrieve the dataset.', 'type': 'string'}}}}")
         formatted_tools = "\n".join(tool_strings)
         unique_tool_names = set(tool.name for tool in tools)
         tool_names = ", ".join('"' + name + '"' for name in unique_tool_names)