refactor: Refactors Knowledge Retrieval Node for enhanced modularity

Signed-off-by: -LAN- <laipz8200@outlook.com>

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

@@ -1,10 +1,10 @@
 from collections.abc import Sequence
-from typing import Any, Literal, Optional
+from typing import Literal, Optional
 
 from pydantic import BaseModel, Field
 
 from core.workflow.nodes.base import BaseNodeData
-from core.workflow.nodes.llm.entities import VisionConfig
+from core.workflow.nodes.llm.entities import ModelConfig, VisionConfig
 
 
 class RerankingModelConfig(BaseModel):
@@ -56,17 +56,6 @@ class MultipleRetrievalConfig(BaseModel):
     weights: Optional[WeightedScoreConfig] = None
 
 
-class ModelConfig(BaseModel):
-    """
-    Model Config.
-    """
-
-    provider: str
-    name: str
-    mode: str
-    completion_params: dict[str, Any] = {}
-
-
 class SingleRetrievalConfig(BaseModel):
     """
     Single Retrieval Config.
@@ -129,7 +118,7 @@ class KnowledgeRetrievalNodeData(BaseNodeData):
     multiple_retrieval_config: Optional[MultipleRetrievalConfig] = None
     single_retrieval_config: Optional[SingleRetrievalConfig] = None
     metadata_filtering_mode: Optional[Literal["disabled", "automatic", "manual"]] = "disabled"
-    metadata_model_config: Optional[ModelConfig] = None
+    metadata_model_config: ModelConfig
     metadata_filtering_conditions: Optional[MetadataFilteringCondition] = None
     vision: VisionConfig = Field(default_factory=VisionConfig)
 

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

@@ -4,7 +4,7 @@ import re
 import time
 from collections import defaultdict
 from collections.abc import Mapping, Sequence
-from typing import Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
 
 from sqlalchemy import Float, and_, func, or_, text
 from sqlalchemy import cast as sqlalchemy_cast
@@ -15,20 +15,30 @@ from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEnti
 from core.entities.agent_entities import PlanningStrategy
 from core.entities.model_entities import ModelStatus
 from core.model_manager import ModelInstance, ModelManager
-from core.model_runtime.entities.message_entities import PromptMessageRole
-from core.model_runtime.entities.model_entities import ModelFeature, ModelType
+from core.model_runtime.entities.message_entities import (
+    PromptMessageRole,
+)
+from core.model_runtime.entities.model_entities import (
+    ModelFeature,
+    ModelType,
+)
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.prompt.simple_prompt_transform import ModelMode
 from core.rag.datasource.retrieval_service import RetrievalService
 from core.rag.entities.metadata_entities import Condition, MetadataCondition
 from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
 from core.rag.retrieval.retrieval_methods import RetrievalMethod
-from core.variables import StringSegment
+from core.variables import (
+    StringSegment,
+)
 from core.variables.segments import ArrayObjectSegment
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionStatus
+from core.workflow.nodes.base import BaseNode
 from core.workflow.nodes.enums import NodeType
-from core.workflow.nodes.event.event import ModelInvokeCompletedEvent
+from core.workflow.nodes.event import (
+    ModelInvokeCompletedEvent,
+)
 from core.workflow.nodes.knowledge_retrieval.template_prompts import (
     METADATA_FILTER_ASSISTANT_PROMPT_1,
     METADATA_FILTER_ASSISTANT_PROMPT_2,
@@ -38,7 +48,8 @@ from core.workflow.nodes.knowledge_retrieval.template_prompts import (
     METADATA_FILTER_USER_PROMPT_2,
     METADATA_FILTER_USER_PROMPT_3,
 )
-from core.workflow.nodes.llm.entities import LLMNodeChatModelMessage, LLMNodeCompletionModelPromptTemplate
+from core.workflow.nodes.llm.entities import LLMNodeChatModelMessage, LLMNodeCompletionModelPromptTemplate, ModelConfig
+from core.workflow.nodes.llm.file_saver import FileSaverImpl, LLMFileSaver
 from core.workflow.nodes.llm.node import LLMNode
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
@@ -46,7 +57,7 @@ from libs.json_in_md_parser import parse_and_check_json_markdown
 from models.dataset import Dataset, DatasetMetadata, Document, RateLimitLog
 from services.feature_service import FeatureService
 
-from .entities import KnowledgeRetrievalNodeData, ModelConfig
+from .entities import KnowledgeRetrievalNodeData
 from .exc import (
     InvalidModelTypeError,
     KnowledgeRetrievalNodeError,
@@ -56,6 +67,10 @@ from .exc import (
     ModelQuotaExceededError,
 )
 
+if TYPE_CHECKING:
+    from core.file.models import File
+    from core.workflow.graph_engine import Graph, GraphInitParams, GraphRuntimeState
+
 logger = logging.getLogger(__name__)
 
 default_retrieval_model = {
@@ -67,10 +82,51 @@ default_retrieval_model = {
 }
 
 
-class KnowledgeRetrievalNode(LLMNode):
-    _node_data_cls = KnowledgeRetrievalNodeData  # type: ignore
+class KnowledgeRetrievalNode(BaseNode):
     _node_type = NodeType.KNOWLEDGE_RETRIEVAL
 
+    node_data: KnowledgeRetrievalNodeData
+
+    # Instance attributes specific to LLMNode.
+    # Output variable for file
+    _file_outputs: list["File"]
+
+    _llm_file_saver: LLMFileSaver
+
+    def __init__(
+        self,
+        id: str,
+        config: Mapping[str, Any],
+        graph_init_params: "GraphInitParams",
+        graph: "Graph",
+        graph_runtime_state: "GraphRuntimeState",
+        previous_node_id: Optional[str] = None,
+        thread_pool_id: Optional[str] = None,
+        *,
+        llm_file_saver: LLMFileSaver | None = None,
+    ) -> None:
+        super().__init__(
+            id=id,
+            config=config,
+            graph_init_params=graph_init_params,
+            graph=graph,
+            graph_runtime_state=graph_runtime_state,
+            previous_node_id=previous_node_id,
+            thread_pool_id=thread_pool_id,
+        )
+        # LLM file outputs, used for MultiModal outputs.
+        self._file_outputs: list[File] = []
+
+        if llm_file_saver is None:
+            llm_file_saver = FileSaverImpl(
+                user_id=graph_init_params.user_id,
+                tenant_id=graph_init_params.tenant_id,
+            )
+        self._llm_file_saver = llm_file_saver
+
+    def from_dict(self, data: Mapping[str, Any]) -> None:
+        self.node_data = KnowledgeRetrievalNodeData(**data)
+
     @classmethod
     def version(cls):
         return "1"
@@ -448,7 +504,7 @@ class KnowledgeRetrievalNode(LLMNode):
             metadata_fields=all_metadata_fields,
             query=query or "",
         )
-        prompt_messages, stop = self._fetch_prompt_messages(
+        prompt_messages, stop = LLMNode.fetch_prompt_messages(
             prompt_template=prompt_template,
             sys_query=query,
             memory=None,
@@ -458,16 +514,22 @@ class KnowledgeRetrievalNode(LLMNode):
             vision_detail=node_data.vision.configs.detail,
             variable_pool=self.graph_runtime_state.variable_pool,
             jinja2_variables=[],
+            tenant_id=self.tenant_id,
         )
 
         result_text = ""
         try:
             # handle invoke result
-            generator = self._invoke_llm(
-                node_data_model=node_data.metadata_model_config,  # type: ignore
+            generator = LLMNode.invoke_llm(
+                node_data_model=node_data.metadata_model_config,
                 model_instance=model_instance,
                 prompt_messages=prompt_messages,
                 stop=stop,
+                user_id=self.user_id,
+                structured_output_enabled=self.node_data.structured_output_enabled,
+                file_saver=self._llm_file_saver,
+                file_outputs=self._file_outputs,
+                node_id=self.node_id,
             )
 
             for event in generator:

api/core/workflow/nodes/llm/entities.py

@@ -1,4 +1,4 @@
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field, field_validator
@@ -65,7 +65,7 @@ class LLMNodeData(BaseNodeData):
     memory: Optional[MemoryConfig] = None
     context: ContextConfig
     vision: VisionConfig = Field(default_factory=VisionConfig)
-    structured_output: dict | None = None
+    structured_output: Mapping[str, Any] | None = None
     # We used 'structured_output_enabled' in the past, but it's not a good name.
     structured_output_switch_on: bool = Field(False, alias="structured_output_enabled")
 

api/core/workflow/nodes/llm/node.py

@@ -191,7 +191,10 @@ class LLMNode(BaseNode):
                 node_inputs["#context#"] = context
 
             # fetch model config
-            model_instance, model_config = self._fetch_model_config(self.node_data.model)
+            model_instance, model_config = LLMNode._fetch_model_config(
+                node_data_model=self.node_data.model,
+                tenant_id=self.tenant_id,
+            )
 
             # fetch memory
             memory = llm_utils.fetch_memory(
@@ -209,7 +212,7 @@ class LLMNode(BaseNode):
                 ):
                     query = query_variable.text
 
-            prompt_messages, stop = self._fetch_prompt_messages(
+            prompt_messages, stop = LLMNode.fetch_prompt_messages(
                 sys_query=query,
                 sys_files=files,
                 context=context,
@@ -221,14 +224,20 @@ class LLMNode(BaseNode):
                 vision_detail=self.node_data.vision.configs.detail,
                 variable_pool=variable_pool,
                 jinja2_variables=self.node_data.prompt_config.jinja2_variables,
+                tenant_id=self.tenant_id,
             )
 
             # handle invoke result
-            generator = self._invoke_llm(
+            generator = LLMNode.invoke_llm(
                 node_data_model=self.node_data.model,
                 model_instance=model_instance,
                 prompt_messages=prompt_messages,
                 stop=stop,
+                user_id=self.user_id,
+                structured_output_enabled=self.node_data.structured_output_enabled,
+                file_saver=self._llm_file_saver,
+                file_outputs=self._file_outputs,
+                node_id=self.node_id,
             )
 
             structured_output: LLMStructuredOutput | None = None
@@ -298,12 +307,18 @@ class LLMNode(BaseNode):
                 )
             )
 
-    def _invoke_llm(
-        self,
+    @staticmethod
+    def invoke_llm(
+        *,
         node_data_model: ModelConfig,
         model_instance: ModelInstance,
         prompt_messages: Sequence[PromptMessage],
         stop: Optional[Sequence[str]] = None,
+        user_id: str,
+        structured_output_enabled: bool,
+        file_saver: LLMFileSaver,
+        file_outputs: list["File"],
+        node_id: str,
     ) -> Generator[NodeEvent | LLMStructuredOutput, None, None]:
         model_schema = model_instance.model_type_instance.get_model_schema(
             node_data_model.name, model_instance.credentials
@@ -311,8 +326,8 @@ class LLMNode(BaseNode):
         if not model_schema:
             raise ValueError(f"Model schema not found for {node_data_model.name}")
 
-        if self.node_data.structured_output_enabled:
-            output_schema = self._fetch_structured_output_schema()
+        if structured_output_enabled:
+            output_schema = LLMNode.fetch_structured_output_schema()
             invoke_result = invoke_llm_with_structured_output(
                 provider=model_instance.provider,
                 model_schema=model_schema,
@@ -322,7 +337,7 @@ class LLMNode(BaseNode):
                 model_parameters=node_data_model.completion_params,
                 stop=list(stop or []),
                 stream=True,
-                user=self.user_id,
+                user=user_id,
             )
         else:
             invoke_result = model_instance.invoke_llm(
@@ -330,17 +345,31 @@ class LLMNode(BaseNode):
                 model_parameters=node_data_model.completion_params,
                 stop=list(stop or []),
                 stream=True,
-                user=self.user_id,
+                user=user_id,
             )
 
-        return self._handle_invoke_result(invoke_result=invoke_result)
+        return LLMNode.handle_invoke_result(
+            invoke_result=invoke_result,
+            file_saver=file_saver,
+            file_outputs=file_outputs,
+            node_id=node_id,
+        )
 
-    def _handle_invoke_result(
-        self, invoke_result: LLMResult | Generator[LLMResultChunk | LLMStructuredOutput, None, None]
+    @staticmethod
+    def handle_invoke_result(
+        *,
+        invoke_result: LLMResult | Generator[LLMResultChunk | LLMStructuredOutput, None, None],
+        file_saver: LLMFileSaver,
+        file_outputs: list["File"],
+        node_id: str,
     ) -> Generator[NodeEvent | LLMStructuredOutput, None, None]:
         # For blocking mode
         if isinstance(invoke_result, LLMResult):
-            event = self._handle_blocking_result(invoke_result=invoke_result)
+            event = LLMNode.handle_blocking_result(
+                invoke_result=invoke_result,
+                saver=file_saver,
+                file_outputs=file_outputs,
+            )
             yield event
             return
 
@@ -358,11 +387,13 @@ class LLMNode(BaseNode):
                     yield result
                 if isinstance(result, LLMResultChunk):
                     contents = result.delta.message.content
-                    for text_part in self._save_multimodal_output_and_convert_result_to_markdown(contents):
+                    for text_part in LLMNode._save_multimodal_output_and_convert_result_to_markdown(
+                        contents=contents,
+                        file_saver=file_saver,
+                        file_outputs=file_outputs,
+                    ):
                         full_text_buffer.write(text_part)
-                        yield RunStreamChunkEvent(
-                            chunk_content=text_part, from_variable_selector=[self.node_id, "text"]
-                        )
+                        yield RunStreamChunkEvent(chunk_content=text_part, from_variable_selector=[node_id, "text"])
 
                     # Update the whole metadata
                     if not model and result.model:
@@ -380,7 +411,8 @@ class LLMNode(BaseNode):
 
         yield ModelInvokeCompletedEvent(text=full_text_buffer.getvalue(), usage=usage, finish_reason=finish_reason)
 
-    def _image_file_to_markdown(self, file: "File", /):
+    @staticmethod
+    def _image_file_to_markdown(file: "File", /):
         text_chunk = f"![]({file.generate_url()})"
         return text_chunk
 
@@ -541,11 +573,14 @@ class LLMNode(BaseNode):
 
         return None
 
+    @staticmethod
     def _fetch_model_config(
-        self, node_data_model: ModelConfig
+        *,
+        node_data_model: ModelConfig,
+        tenant_id: str,
     ) -> tuple[ModelInstance, ModelConfigWithCredentialsEntity]:
         model, model_config_with_cred = llm_utils.fetch_model_config(
-            tenant_id=self.tenant_id, node_data_model=node_data_model
+            tenant_id=tenant_id, node_data_model=node_data_model
         )
         completion_params = model_config_with_cred.parameters
 
@@ -558,8 +593,8 @@ class LLMNode(BaseNode):
         node_data_model.completion_params = completion_params
         return model, model_config_with_cred
 
-    def _fetch_prompt_messages(
-        self,
+    @staticmethod
+    def fetch_prompt_messages(
         *,
         sys_query: str | None = None,
         sys_files: Sequence["File"],
@@ -572,13 +607,14 @@ class LLMNode(BaseNode):
         vision_detail: ImagePromptMessageContent.DETAIL,
         variable_pool: VariablePool,
         jinja2_variables: Sequence[VariableSelector],
+        tenant_id: str,
     ) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
         prompt_messages: list[PromptMessage] = []
 
         if isinstance(prompt_template, list):
             # For chat model
             prompt_messages.extend(
-                self._handle_list_messages(
+                LLMNode.handle_list_messages(
                     messages=prompt_template,
                     context=context,
                     jinja2_variables=jinja2_variables,
@@ -604,7 +640,7 @@ class LLMNode(BaseNode):
                     edition_type="basic",
                 )
                 prompt_messages.extend(
-                    self._handle_list_messages(
+                    LLMNode.handle_list_messages(
                         messages=[message],
                         context="",
                         jinja2_variables=[],
@@ -733,7 +769,7 @@ class LLMNode(BaseNode):
             )
 
         model = ModelManager().get_model_instance(
-            tenant_id=self.tenant_id,
+            tenant_id=tenant_id,
             model_type=ModelType.LLM,
             provider=model_config.provider,
             model=model_config.model,
@@ -837,8 +873,8 @@ class LLMNode(BaseNode):
             },
         }
 
-    def _handle_list_messages(
-        self,
+    @staticmethod
+    def handle_list_messages(
         *,
         messages: Sequence[LLMNodeChatModelMessage],
         context: Optional[str],
@@ -899,9 +935,19 @@ class LLMNode(BaseNode):
 
         return prompt_messages
 
-    def _handle_blocking_result(self, *, invoke_result: LLMResult) -> ModelInvokeCompletedEvent:
+    @staticmethod
+    def handle_blocking_result(
+        *,
+        invoke_result: LLMResult,
+        saver: LLMFileSaver,
+        file_outputs: list["File"],
+    ) -> ModelInvokeCompletedEvent:
         buffer = io.StringIO()
-        for text_part in self._save_multimodal_output_and_convert_result_to_markdown(invoke_result.message.content):
+        for text_part in LLMNode._save_multimodal_output_and_convert_result_to_markdown(
+            contents=invoke_result.message.content,
+            file_saver=saver,
+            file_outputs=file_outputs,
+        ):
             buffer.write(text_part)
 
         return ModelInvokeCompletedEvent(
@@ -910,7 +956,12 @@ class LLMNode(BaseNode):
             finish_reason=None,
         )
 
-    def _save_multimodal_image_output(self, content: ImagePromptMessageContent) -> "File":
+    @staticmethod
+    def save_multimodal_image_output(
+        *,
+        content: ImagePromptMessageContent,
+        file_saver: LLMFileSaver,
+    ) -> "File":
         """_save_multimodal_output saves multi-modal contents generated by LLM plugins.
 
         There are two kinds of multimodal outputs:
@@ -920,19 +971,14 @@ class LLMNode(BaseNode):
 
         Currently, only image files are supported.
         """
-        # Inject the saver somehow...
-        _saver = self._llm_file_saver
-
-        # If this
         if content.url != "":
-            saved_file = _saver.save_remote_url(content.url, FileType.IMAGE)
+            saved_file = file_saver.save_remote_url(content.url, FileType.IMAGE)
         else:
-            saved_file = _saver.save_binary_string(
+            saved_file = file_saver.save_binary_string(
                 data=base64.b64decode(content.base64_data),
                 mime_type=content.mime_type,
                 file_type=FileType.IMAGE,
             )
-        self._file_outputs.append(saved_file)
         return saved_file
 
     def _fetch_model_schema(self, provider: str) -> AIModelEntity | None:
@@ -950,16 +996,20 @@ class LLMNode(BaseNode):
         model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
         return model_schema
 
-    def _fetch_structured_output_schema(self) -> dict[str, Any]:
+    @staticmethod
+    def fetch_structured_output_schema(
+        *,
+        structured_output: Mapping[str, Any] | None = None,
+    ) -> dict[str, Any]:
         """
         Fetch the structured output schema from the node data.
 
         Returns:
             dict[str, Any]: The structured output schema
         """
-        if not self.node_data.structured_output:
+        if not structured_output:
             raise LLMNodeError("Please provide a valid structured output schema")
-        structured_output_schema = json.dumps(self.node_data.structured_output.get("schema", {}), ensure_ascii=False)
+        structured_output_schema = json.dumps(structured_output.get("schema", {}), ensure_ascii=False)
         if not structured_output_schema:
             raise LLMNodeError("Please provide a valid structured output schema")
 
@@ -971,9 +1021,12 @@ class LLMNode(BaseNode):
         except json.JSONDecodeError:
             raise LLMNodeError("structured_output_schema is not valid JSON format")
 
+    @staticmethod
     def _save_multimodal_output_and_convert_result_to_markdown(
-        self,
+        *,
         contents: str | list[PromptMessageContentUnionTypes] | None,
+        file_saver: LLMFileSaver,
+        file_outputs: list["File"],
     ) -> Generator[str, None, None]:
         """Convert intermediate prompt messages into strings and yield them to the caller.
 
@@ -996,9 +1049,12 @@ class LLMNode(BaseNode):
                 if isinstance(item, TextPromptMessageContent):
                     yield item.data
                 elif isinstance(item, ImagePromptMessageContent):
-                    file = self._save_multimodal_image_output(item)
-                    self._file_outputs.append(file)
-                    yield self._image_file_to_markdown(file)
+                    file = LLMNode.save_multimodal_image_output(
+                        content=item,
+                        file_saver=file_saver,
+                    )
+                    file_outputs.append(file)
+                    yield LLMNode._image_file_to_markdown(file)
                 else:
                     logger.warning("unknown item type encountered, type=%s", type(item))
                     yield str(item)

api/core/workflow/nodes/question_classifier/question_classifier_node.py

@@ -1,6 +1,6 @@
 import json
 from collections.abc import Mapping, Sequence
-from typing import Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
 
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.memory.token_buffer_memory import TokenBufferMemory
@@ -12,6 +12,7 @@ from core.prompt.simple_prompt_transform import ModelMode
 from core.prompt.utils.prompt_message_util import PromptMessageUtil
 from core.workflow.entities.node_entities import NodeRunResult
 from core.workflow.entities.workflow_node_execution import WorkflowNodeExecutionMetadataKey, WorkflowNodeExecutionStatus
+from core.workflow.nodes.base.node import BaseNode
 from core.workflow.nodes.enums import NodeType
 from core.workflow.nodes.event import ModelInvokeCompletedEvent
 from core.workflow.nodes.llm import (
@@ -20,6 +21,7 @@ from core.workflow.nodes.llm import (
     LLMNodeCompletionModelPromptTemplate,
     llm_utils,
 )
+from core.workflow.nodes.llm.file_saver import FileSaverImpl, LLMFileSaver
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 from libs.json_in_md_parser import parse_and_check_json_markdown
 
@@ -35,11 +37,53 @@ from .template_prompts import (
     QUESTION_CLASSIFIER_USER_PROMPT_3,
 )
 
+if TYPE_CHECKING:
+    from core.file.models import File
+    from core.workflow.graph_engine import Graph, GraphInitParams, GraphRuntimeState
 
-class QuestionClassifierNode(LLMNode):
-    _node_data_cls = QuestionClassifierNodeData  # type: ignore
+
+class QuestionClassifierNode(BaseNode):
     _node_type = NodeType.QUESTION_CLASSIFIER
 
+    node_data: QuestionClassifierNodeData
+
+    _file_outputs: list["File"]
+    _llm_file_saver: LLMFileSaver
+
+    def __init__(
+        self,
+        id: str,
+        config: Mapping[str, Any],
+        graph_init_params: "GraphInitParams",
+        graph: "Graph",
+        graph_runtime_state: "GraphRuntimeState",
+        previous_node_id: Optional[str] = None,
+        thread_pool_id: Optional[str] = None,
+        *,
+        llm_file_saver: LLMFileSaver | None = None,
+    ) -> None:
+        super().__init__(
+            id=id,
+            config=config,
+            graph_init_params=graph_init_params,
+            graph=graph,
+            graph_runtime_state=graph_runtime_state,
+            previous_node_id=previous_node_id,
+            thread_pool_id=thread_pool_id,
+        )
+        # LLM file outputs, used for MultiModal outputs.
+        self._file_outputs: list[File] = []
+
+        if llm_file_saver is None:
+            llm_file_saver = FileSaverImpl(
+                user_id=graph_init_params.user_id,
+                tenant_id=graph_init_params.tenant_id,
+            )
+        self._llm_file_saver = llm_file_saver
+
+    def from_dict(self, data: Mapping[str, Any]) -> None:
+        self.node_data = QuestionClassifierNodeData(**data)
+
     @classmethod
     def version(cls):
         return "1"
@@ -53,7 +97,10 @@ class QuestionClassifierNode(LLMNode):
         query = variable.value if variable else None
         variables = {"query": query}
         # fetch model config
-        model_instance, model_config = self._fetch_model_config(node_data.model)
+        model_instance, model_config = LLMNode._fetch_model_config(
+            node_data_model=node_data.model,
+            tenant_id=self.tenant_id,
+        )
         # fetch memory
         memory = llm_utils.fetch_memory(
             variable_pool=variable_pool,
@@ -91,7 +138,7 @@ class QuestionClassifierNode(LLMNode):
         # If both self._get_prompt_template and self._fetch_prompt_messages append a user prompt,
         # two consecutive user prompts will be generated, causing model's error.
         # To avoid this, set sys_query to an empty string so that only one user prompt is appended at the end.
-        prompt_messages, stop = self._fetch_prompt_messages(
+        prompt_messages, stop = LLMNode.fetch_prompt_messages(
             prompt_template=prompt_template,
             sys_query="",
             memory=memory,
@@ -101,6 +148,7 @@ class QuestionClassifierNode(LLMNode):
             vision_detail=node_data.vision.configs.detail,
             variable_pool=variable_pool,
             jinja2_variables=[],
+            tenant_id=self.tenant_id,
         )
 
         result_text = ""
@@ -109,11 +157,16 @@ class QuestionClassifierNode(LLMNode):
 
         try:
             # handle invoke result
-            generator = self._invoke_llm(
+            generator = LLMNode.invoke_llm(
                 node_data_model=node_data.model,
                 model_instance=model_instance,
                 prompt_messages=prompt_messages,
                 stop=stop,
+                user_id=self.user_id,
+                structured_output_enabled=False,
+                file_saver=self._llm_file_saver,
+                file_outputs=self._file_outputs,
+                node_id=self.node_id,
             )
 
             for event in generator:

api/tests/unit_tests/core/workflow/nodes/llm/test_node.py

@@ -540,7 +540,10 @@ class TestLLMNodeSaveMultiModalImageOutput:
             size=9,
         )
         mock_file_saver.save_binary_string.return_value = mock_file
-        file = llm_node._save_multimodal_image_output(content=content)
+        file = llm_node.save_multimodal_image_output(
+            content=content,
+            file_saver=mock_file_saver,
+        )
         assert llm_node._file_outputs == [mock_file]
         assert file == mock_file
         mock_file_saver.save_binary_string.assert_called_once_with(
@@ -566,7 +569,10 @@ class TestLLMNodeSaveMultiModalImageOutput:
             size=9,
         )
         mock_file_saver.save_remote_url.return_value = mock_file
-        file = llm_node._save_multimodal_image_output(content=content)
+        file = llm_node.save_multimodal_image_output(
+            content=content,
+            file_saver=mock_file_saver,
+        )
         assert llm_node._file_outputs == [mock_file]
         assert file == mock_file
         mock_file_saver.save_remote_url.assert_called_once_with(content.url, FileType.IMAGE)