refactor: move structured output support outside LLM Node (#21565)

Co-authored-by: Novice <novice12185727@gmail.com>

api/core/llm_generator/output_parser/structured_output.py

@@ -0,0 +1,374 @@
+import json
+from collections.abc import Generator, Mapping, Sequence
+from copy import deepcopy
+from enum import StrEnum
+from typing import Any, Literal, Optional, cast, overload
+
+import json_repair
+from pydantic import TypeAdapter, ValidationError
+
+from core.llm_generator.output_parser.errors import OutputParserError
+from core.llm_generator.prompts import STRUCTURED_OUTPUT_PROMPT
+from core.model_manager import ModelInstance
+from core.model_runtime.callbacks.base_callback import Callback
+from core.model_runtime.entities.llm_entities import (
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+    LLMResultChunkWithStructuredOutput,
+    LLMResultWithStructuredOutput,
+)
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+    SystemPromptMessage,
+)
+from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule
+
+
+class ResponseFormat(StrEnum):
+    """Constants for model response formats"""
+
+    JSON_SCHEMA = "json_schema"  # model's structured output mode. some model like gemini, gpt-4o,  support this mode.
+    JSON = "JSON"  # model's json mode. some model like claude support this mode.
+    JSON_OBJECT = "json_object"  # json mode's another alias. some model like deepseek-chat, qwen use this alias.
+
+
+class SpecialModelType(StrEnum):
+    """Constants for identifying model types"""
+
+    GEMINI = "gemini"
+    OLLAMA = "ollama"
+
+
+@overload
+def invoke_llm_with_structured_output(
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    json_schema: Mapping[str, Any],
+    model_parameters: Optional[Mapping] = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: Optional[list[str]] = None,
+    stream: Literal[True] = True,
+    user: Optional[str] = None,
+    callbacks: Optional[list[Callback]] = None,
+) -> Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
+
+
+@overload
+def invoke_llm_with_structured_output(
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    json_schema: Mapping[str, Any],
+    model_parameters: Optional[Mapping] = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: Optional[list[str]] = None,
+    stream: Literal[False] = False,
+    user: Optional[str] = None,
+    callbacks: Optional[list[Callback]] = None,
+) -> LLMResultWithStructuredOutput: ...
+
+
+@overload
+def invoke_llm_with_structured_output(
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    json_schema: Mapping[str, Any],
+    model_parameters: Optional[Mapping] = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: Optional[list[str]] = None,
+    stream: bool = True,
+    user: Optional[str] = None,
+    callbacks: Optional[list[Callback]] = None,
+) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]: ...
+
+
+def invoke_llm_with_structured_output(
+    provider: str,
+    model_schema: AIModelEntity,
+    model_instance: ModelInstance,
+    prompt_messages: Sequence[PromptMessage],
+    json_schema: Mapping[str, Any],
+    model_parameters: Optional[Mapping] = None,
+    tools: Sequence[PromptMessageTool] | None = None,
+    stop: Optional[list[str]] = None,
+    stream: bool = True,
+    user: Optional[str] = None,
+    callbacks: Optional[list[Callback]] = None,
+) -> LLMResultWithStructuredOutput | Generator[LLMResultChunkWithStructuredOutput, None, None]:
+    """
+    Invoke large language model with structured output
+    1. This method invokes model_instance.invoke_llm with json_schema
+    2. Try to parse the result as structured output
+
+    :param prompt_messages: prompt messages
+    :param json_schema: json schema
+    :param model_parameters: model parameters
+    :param tools: tools for tool calling
+    :param stop: stop words
+    :param stream: is stream response
+    :param user: unique user id
+    :param callbacks: callbacks
+    :return: full response or stream response chunk generator result
+    """
+
+    # handle native json schema
+    model_parameters_with_json_schema: dict[str, Any] = {
+        **(model_parameters or {}),
+    }
+
+    if model_schema.support_structure_output:
+        model_parameters = _handle_native_json_schema(
+            provider, model_schema, json_schema, model_parameters_with_json_schema, model_schema.parameter_rules
+        )
+    else:
+        # Set appropriate response format based on model capabilities
+        _set_response_format(model_parameters_with_json_schema, model_schema.parameter_rules)
+
+        # handle prompt based schema
+        prompt_messages = _handle_prompt_based_schema(
+            prompt_messages=prompt_messages,
+            structured_output_schema=json_schema,
+        )
+
+    llm_result = model_instance.invoke_llm(
+        prompt_messages=list(prompt_messages),
+        model_parameters=model_parameters_with_json_schema,
+        tools=tools,
+        stop=stop,
+        stream=stream,
+        user=user,
+        callbacks=callbacks,
+    )
+
+    if isinstance(llm_result, LLMResult):
+        if not isinstance(llm_result.message.content, str):
+            raise OutputParserError(
+                f"Failed to parse structured output, LLM result is not a string: {llm_result.message.content}"
+            )
+
+        return LLMResultWithStructuredOutput(
+            structured_output=_parse_structured_output(llm_result.message.content),
+            model=llm_result.model,
+            message=llm_result.message,
+            usage=llm_result.usage,
+            system_fingerprint=llm_result.system_fingerprint,
+            prompt_messages=llm_result.prompt_messages,
+        )
+    else:
+
+        def generator() -> Generator[LLMResultChunkWithStructuredOutput, None, None]:
+            result_text: str = ""
+            prompt_messages: Sequence[PromptMessage] = []
+            system_fingerprint: Optional[str] = None
+            for event in llm_result:
+                if isinstance(event, LLMResultChunk):
+                    if isinstance(event.delta.message.content, str):
+                        result_text += event.delta.message.content
+                        prompt_messages = event.prompt_messages
+                        system_fingerprint = event.system_fingerprint
+
+                yield LLMResultChunkWithStructuredOutput(
+                    model=model_schema.model,
+                    prompt_messages=prompt_messages,
+                    system_fingerprint=system_fingerprint,
+                    delta=event.delta,
+                )
+
+            yield LLMResultChunkWithStructuredOutput(
+                structured_output=_parse_structured_output(result_text),
+                model=model_schema.model,
+                prompt_messages=prompt_messages,
+                system_fingerprint=system_fingerprint,
+                delta=LLMResultChunkDelta(
+                    index=0,
+                    message=AssistantPromptMessage(content=""),
+                    usage=None,
+                    finish_reason=None,
+                ),
+            )
+
+        return generator()
+
+
+def _handle_native_json_schema(
+    provider: str,
+    model_schema: AIModelEntity,
+    structured_output_schema: Mapping,
+    model_parameters: dict,
+    rules: list[ParameterRule],
+) -> dict:
+    """
+    Handle structured output for models with native JSON schema support.
+
+    :param model_parameters: Model parameters to update
+    :param rules: Model parameter rules
+    :return: Updated model parameters with JSON schema configuration
+    """
+    # Process schema according to model requirements
+    schema_json = _prepare_schema_for_model(provider, model_schema, structured_output_schema)
+
+    # Set JSON schema in parameters
+    model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
+
+    # Set appropriate response format if required by the model
+    for rule in rules:
+        if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
+            model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
+
+    return model_parameters
+
+
+def _set_response_format(model_parameters: dict, rules: list) -> None:
+    """
+    Set the appropriate response format parameter based on model rules.
+
+    :param model_parameters: Model parameters to update
+    :param rules: Model parameter rules
+    """
+    for rule in rules:
+        if rule.name == "response_format":
+            if ResponseFormat.JSON.value in rule.options:
+                model_parameters["response_format"] = ResponseFormat.JSON.value
+            elif ResponseFormat.JSON_OBJECT.value in rule.options:
+                model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
+
+
+def _handle_prompt_based_schema(
+    prompt_messages: Sequence[PromptMessage], structured_output_schema: Mapping
+) -> list[PromptMessage]:
+    """
+    Handle structured output for models without native JSON schema support.
+    This function modifies the prompt messages to include schema-based output requirements.
+
+    Args:
+        prompt_messages: Original sequence of prompt messages
+
+    Returns:
+        list[PromptMessage]: Updated prompt messages with structured output requirements
+    """
+    # Convert schema to string format
+    schema_str = json.dumps(structured_output_schema, ensure_ascii=False)
+
+    # Find existing system prompt with schema placeholder
+    system_prompt = next(
+        (prompt for prompt in prompt_messages if isinstance(prompt, SystemPromptMessage)),
+        None,
+    )
+    structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str)
+    # Prepare system prompt content
+    system_prompt_content = (
+        structured_output_prompt + "\n\n" + system_prompt.content
+        if system_prompt and isinstance(system_prompt.content, str)
+        else structured_output_prompt
+    )
+    system_prompt = SystemPromptMessage(content=system_prompt_content)
+
+    # Extract content from the last user message
+
+    filtered_prompts = [prompt for prompt in prompt_messages if not isinstance(prompt, SystemPromptMessage)]
+    updated_prompt = [system_prompt] + filtered_prompts
+
+    return updated_prompt
+
+
+def _parse_structured_output(result_text: str) -> Mapping[str, Any]:
+    structured_output: Mapping[str, Any] = {}
+    parsed: Mapping[str, Any] = {}
+    try:
+        parsed = TypeAdapter(Mapping).validate_json(result_text)
+        if not isinstance(parsed, dict):
+            raise OutputParserError(f"Failed to parse structured output: {result_text}")
+        structured_output = parsed
+    except ValidationError:
+        # if the result_text is not a valid json, try to repair it
+        temp_parsed = json_repair.loads(result_text)
+        if not isinstance(temp_parsed, dict):
+            # handle reasoning model like deepseek-r1 got '<think>\n\n</think>\n' prefix
+            if isinstance(temp_parsed, list):
+                temp_parsed = next((item for item in temp_parsed if isinstance(item, dict)), {})
+            else:
+                raise OutputParserError(f"Failed to parse structured output: {result_text}")
+        structured_output = cast(dict, temp_parsed)
+    return structured_output
+
+
+def _prepare_schema_for_model(provider: str, model_schema: AIModelEntity, schema: Mapping) -> dict:
+    """
+    Prepare JSON schema based on model requirements.
+
+    Different models have different requirements for JSON schema formatting.
+    This function handles these differences.
+
+    :param schema: The original JSON schema
+    :return: Processed schema compatible with the current model
+    """
+
+    # Deep copy to avoid modifying the original schema
+    processed_schema = dict(deepcopy(schema))
+
+    # Convert boolean types to string types (common requirement)
+    convert_boolean_to_string(processed_schema)
+
+    # Apply model-specific transformations
+    if SpecialModelType.GEMINI in model_schema.model:
+        remove_additional_properties(processed_schema)
+        return processed_schema
+    elif SpecialModelType.OLLAMA in provider:
+        return processed_schema
+    else:
+        # Default format with name field
+        return {"schema": processed_schema, "name": "llm_response"}
+
+
+def remove_additional_properties(schema: dict) -> None:
+    """
+    Remove additionalProperties fields from JSON schema.
+    Used for models like Gemini that don't support this property.
+
+    :param schema: JSON schema to modify in-place
+    """
+    if not isinstance(schema, dict):
+        return
+
+    # Remove additionalProperties at current level
+    schema.pop("additionalProperties", None)
+
+    # Process nested structures recursively
+    for value in schema.values():
+        if isinstance(value, dict):
+            remove_additional_properties(value)
+        elif isinstance(value, list):
+            for item in value:
+                if isinstance(item, dict):
+                    remove_additional_properties(item)
+
+
+def convert_boolean_to_string(schema: dict) -> None:
+    """
+    Convert boolean type specifications to string in JSON schema.
+
+    :param schema: JSON schema to modify in-place
+    """
+    if not isinstance(schema, dict):
+        return
+
+    # Check for boolean type at current level
+    if schema.get("type") == "boolean":
+        schema["type"] = "string"
+
+    # Process nested dictionaries and lists recursively
+    for value in schema.values():
+        if isinstance(value, dict):
+            convert_boolean_to_string(value)
+        elif isinstance(value, list):
+            for item in value:
+                if isinstance(item, dict):
+                    convert_boolean_to_string(item)

api/core/llm_generator/prompts.py

@@ -291,3 +291,21 @@ Your task is to convert simple user descriptions into properly formatted JSON Sc
 
 Now, generate a JSON Schema based on my description
 """  # noqa: E501
+
+STRUCTURED_OUTPUT_PROMPT = """You’re a helpful AI assistant. You could answer questions and output in JSON format.
+constraints:
+    - You must output in JSON format.
+    - Do not output boolean value, use string type instead.
+    - Do not output integer or float value, use number type instead.
+eg:
+    Here is the JSON schema:
+    {"additionalProperties": false, "properties": {"age": {"type": "number"}, "name": {"type": "string"}}, "required": ["name", "age"], "type": "object"}
+
+    Here is the user's question:
+    My name is John Doe and I am 30 years old.
+
+    output:
+    {"name": "John Doe", "age": 30}
+Here is the JSON schema:
+{{schema}}
+"""  # noqa: E501

api/core/model_runtime/entities/llm_entities.py

@@ -1,7 +1,7 @@
-from collections.abc import Sequence
+from collections.abc import Mapping, Sequence
 from decimal import Decimal
 from enum import StrEnum
-from typing import Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel, Field
 
@@ -101,6 +101,20 @@ class LLMResult(BaseModel):
     system_fingerprint: Optional[str] = None
 
 
+class LLMStructuredOutput(BaseModel):
+    """
+    Model class for llm structured output.
+    """
+
+    structured_output: Optional[Mapping[str, Any]] = None
+
+
+class LLMResultWithStructuredOutput(LLMResult, LLMStructuredOutput):
+    """
+    Model class for llm result with structured output.
+    """
+
+
 class LLMResultChunkDelta(BaseModel):
     """
     Model class for llm result chunk delta.
@@ -123,6 +137,12 @@ class LLMResultChunk(BaseModel):
     delta: LLMResultChunkDelta
 
 
+class LLMResultChunkWithStructuredOutput(LLMResultChunk, LLMStructuredOutput):
+    """
+    Model class for llm result chunk with structured output.
+    """
+
+
 class NumTokensResult(PriceInfo):
     """
     Model class for number of tokens result.

api/core/plugin/backwards_invocation/model.py

@@ -3,7 +3,11 @@ from binascii import hexlify, unhexlify
 from collections.abc import Generator
 
 from core.model_manager import ModelManager
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import (
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkDelta,
+)
 from core.model_runtime.entities.message_entities import (
     PromptMessage,
     SystemPromptMessage,

api/core/plugin/entities/parameters.py

@@ -10,6 +10,9 @@ from core.tools.entities.common_entities import I18nObject
 class PluginParameterOption(BaseModel):
     value: str = Field(..., description="The value of the option")
     label: I18nObject = Field(..., description="The label of the option")
+    icon: Optional[str] = Field(
+        default=None, description="The icon of the option, can be a url or a base64 encoded image"
+    )
 
     @field_validator("value", mode="before")
     @classmethod

api/core/plugin/entities/request.py

@@ -82,6 +82,16 @@ class RequestInvokeLLM(BaseRequestInvokeModel):
         return v
 
 
+class RequestInvokeLLMWithStructuredOutput(RequestInvokeLLM):
+    """
+    Request to invoke LLM with structured output
+    """
+
+    structured_output_schema: dict[str, Any] = Field(
+        default_factory=dict, description="The schema of the structured output in JSON schema format"
+    )
+
+
 class RequestInvokeTextEmbedding(BaseRequestInvokeModel):
     """
     Request to invoke text embedding

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

@@ -5,11 +5,11 @@ import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Optional, cast
 
-import json_repair
-
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.file import FileType, file_manager
 from core.helper.code_executor import CodeExecutor, CodeLanguage
+from core.llm_generator.output_parser.errors import OutputParserError
+from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance, ModelManager
 from core.model_runtime.entities import (
@@ -18,7 +18,13 @@ from core.model_runtime.entities import (
     PromptMessageContentType,
     TextPromptMessageContent,
 )
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMUsage
+from core.model_runtime.entities.llm_entities import (
+    LLMResult,
+    LLMResultChunk,
+    LLMResultChunkWithStructuredOutput,
+    LLMStructuredOutput,
+    LLMUsage,
+)
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessageContentUnionTypes,
@@ -31,7 +37,6 @@ from core.model_runtime.entities.model_entities import (
     ModelFeature,
     ModelPropertyKey,
     ModelType,
-    ParameterRule,
 )
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.utils.encoders import jsonable_encoder
@@ -62,11 +67,6 @@ from core.workflow.nodes.event import (
     RunRetrieverResourceEvent,
     RunStreamChunkEvent,
 )
-from core.workflow.utils.structured_output.entities import (
-    ResponseFormat,
-    SpecialModelType,
-)
-from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
 from core.workflow.utils.variable_template_parser import VariableTemplateParser
 
 from . import llm_utils
@@ -143,12 +143,6 @@ class LLMNode(BaseNode[LLMNodeData]):
         return "1"
 
     def _run(self) -> Generator[NodeEvent | InNodeEvent, None, None]:
-        def process_structured_output(text: str) -> Optional[dict[str, Any]]:
-            """Process structured output if enabled"""
-            if not self.node_data.structured_output_enabled or not self.node_data.structured_output:
-                return None
-            return self._parse_structured_output(text)
-
         node_inputs: Optional[dict[str, Any]] = None
         process_data = None
         result_text = ""
@@ -244,6 +238,8 @@ class LLMNode(BaseNode[LLMNodeData]):
                 stop=stop,
             )
 
+            structured_output: LLMStructuredOutput | None = None
+
             for event in generator:
                 if isinstance(event, RunStreamChunkEvent):
                     yield event
@@ -254,10 +250,12 @@ class LLMNode(BaseNode[LLMNodeData]):
                     # deduct quota
                     llm_utils.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
                     break
+                elif isinstance(event, LLMStructuredOutput):
+                    structured_output = event
+
             outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
-            structured_output = process_structured_output(result_text)
             if structured_output:
-                outputs["structured_output"] = structured_output
+                outputs["structured_output"] = structured_output.structured_output
             if self._file_outputs is not None:
                 outputs["files"] = ArrayFileSegment(value=self._file_outputs)
 
@@ -302,20 +300,40 @@ class LLMNode(BaseNode[LLMNodeData]):
         model_instance: ModelInstance,
         prompt_messages: Sequence[PromptMessage],
         stop: Optional[Sequence[str]] = None,
-    ) -> Generator[NodeEvent, None, None]:
+    ) -> Generator[NodeEvent | LLMStructuredOutput, None, None]:
-        invoke_result = model_instance.invoke_llm(
+        model_schema = model_instance.model_type_instance.get_model_schema(
-            prompt_messages=list(prompt_messages),
+            node_data_model.name, model_instance.credentials
-            model_parameters=node_data_model.completion_params,
-            stop=list(stop or []),
-            stream=True,
-            user=self.user_id,
         )
+        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()
+            invoke_result = invoke_llm_with_structured_output(
+                provider=model_instance.provider,
+                model_schema=model_schema,
+                model_instance=model_instance,
+                prompt_messages=prompt_messages,
+                json_schema=output_schema,
+                model_parameters=node_data_model.completion_params,
+                stop=list(stop or []),
+                stream=True,
+                user=self.user_id,
+            )
+        else:
+            invoke_result = model_instance.invoke_llm(
+                prompt_messages=list(prompt_messages),
+                model_parameters=node_data_model.completion_params,
+                stop=list(stop or []),
+                stream=True,
+                user=self.user_id,
+            )
 
         return self._handle_invoke_result(invoke_result=invoke_result)
 
     def _handle_invoke_result(
-        self, invoke_result: LLMResult | Generator[LLMResultChunk, None, None]
+        self, invoke_result: LLMResult | Generator[LLMResultChunk | LLMStructuredOutput, None, None]
-    ) -> Generator[NodeEvent, None, None]:
+    ) -> Generator[NodeEvent | LLMStructuredOutput, None, None]:
         # For blocking mode
         if isinstance(invoke_result, LLMResult):
             event = self._handle_blocking_result(invoke_result=invoke_result)
@@ -329,23 +347,32 @@ class LLMNode(BaseNode[LLMNodeData]):
         usage = LLMUsage.empty_usage()
         finish_reason = None
         full_text_buffer = io.StringIO()
-        for result in invoke_result:
+        # Consume the invoke result and handle generator exception
-            contents = result.delta.message.content
+        try:
-            for text_part in self._save_multimodal_output_and_convert_result_to_markdown(contents):
+            for result in invoke_result:
-                full_text_buffer.write(text_part)
+                if isinstance(result, LLMResultChunkWithStructuredOutput):
-                yield RunStreamChunkEvent(chunk_content=text_part, from_variable_selector=[self.node_id, "text"])
+                    yield result
-
+                if isinstance(result, LLMResultChunk):
-            # Update the whole metadata
+                    contents = result.delta.message.content
-            if not model and result.model:
+                    for text_part in self._save_multimodal_output_and_convert_result_to_markdown(contents):
-                model = result.model
+                        full_text_buffer.write(text_part)
-            if len(prompt_messages) == 0:
+                        yield RunStreamChunkEvent(
-                # TODO(QuantumGhost): it seems that this update has no visable effect.
+                            chunk_content=text_part, from_variable_selector=[self.node_id, "text"]
-                # What's the purpose of the line below?
+                        )
-                prompt_messages = list(result.prompt_messages)
+
-            if usage.prompt_tokens == 0 and result.delta.usage:
+                    # Update the whole metadata
-                usage = result.delta.usage
+                    if not model and result.model:
-            if finish_reason is None and result.delta.finish_reason:
+                        model = result.model
-                finish_reason = result.delta.finish_reason
+                    if len(prompt_messages) == 0:
+                        # TODO(QuantumGhost): it seems that this update has no visable effect.
+                        # What's the purpose of the line below?
+                        prompt_messages = list(result.prompt_messages)
+                    if usage.prompt_tokens == 0 and result.delta.usage:
+                        usage = result.delta.usage
+                    if finish_reason is None and result.delta.finish_reason:
+                        finish_reason = result.delta.finish_reason
+        except OutputParserError as e:
+            raise LLMNodeError(f"Failed to parse structured output: {e}")
 
         yield ModelInvokeCompletedEvent(text=full_text_buffer.getvalue(), usage=usage, finish_reason=finish_reason)
 
@@ -522,12 +549,6 @@ class LLMNode(BaseNode[LLMNodeData]):
         if not model_schema:
             raise ModelNotExistError(f"Model {node_data_model.name} not exist.")
 
-        if self.node_data.structured_output_enabled:
-            if model_schema.support_structure_output:
-                completion_params = self._handle_native_json_schema(completion_params, model_schema.parameter_rules)
-            else:
-                # Set appropriate response format based on model capabilities
-                self._set_response_format(completion_params, model_schema.parameter_rules)
         model_config_with_cred.parameters = completion_params
         # NOTE(-LAN-): This line modify the `self.node_data.model`, which is used in `_invoke_llm()`.
         node_data_model.completion_params = completion_params
@@ -719,32 +740,8 @@ class LLMNode(BaseNode[LLMNodeData]):
         )
         if not model_schema:
             raise ModelNotExistError(f"Model {model_config.model} not exist.")
-        if self.node_data.structured_output_enabled:
-            if not model_schema.support_structure_output:
-                filtered_prompt_messages = self._handle_prompt_based_schema(
-                    prompt_messages=filtered_prompt_messages,
-                )
         return filtered_prompt_messages, model_config.stop
 
-    def _parse_structured_output(self, result_text: str) -> dict[str, Any]:
-        structured_output: dict[str, Any] = {}
-        try:
-            parsed = json.loads(result_text)
-            if not isinstance(parsed, dict):
-                raise LLMNodeError(f"Failed to parse structured output: {result_text}")
-            structured_output = parsed
-        except json.JSONDecodeError as e:
-            # if the result_text is not a valid json, try to repair it
-            parsed = json_repair.loads(result_text)
-            if not isinstance(parsed, dict):
-                # handle reasoning model like deepseek-r1 got '<think>\n\n</think>\n' prefix
-                if isinstance(parsed, list):
-                    parsed = next((item for item in parsed if isinstance(item, dict)), {})
-                else:
-                    raise LLMNodeError(f"Failed to parse structured output: {result_text}")
-            structured_output = parsed
-        return structured_output
-
     @classmethod
     def _extract_variable_selector_to_variable_mapping(
         cls,
@@ -934,104 +931,6 @@ class LLMNode(BaseNode[LLMNodeData]):
         self._file_outputs.append(saved_file)
         return saved_file
 
-    def _handle_native_json_schema(self, model_parameters: dict, rules: list[ParameterRule]) -> dict:
-        """
-        Handle structured output for models with native JSON schema support.
-
-        :param model_parameters: Model parameters to update
-        :param rules: Model parameter rules
-        :return: Updated model parameters with JSON schema configuration
-        """
-        # Process schema according to model requirements
-        schema = self._fetch_structured_output_schema()
-        schema_json = self._prepare_schema_for_model(schema)
-
-        # Set JSON schema in parameters
-        model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
-
-        # Set appropriate response format if required by the model
-        for rule in rules:
-            if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
-                model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
-
-        return model_parameters
-
-    def _handle_prompt_based_schema(self, prompt_messages: Sequence[PromptMessage]) -> list[PromptMessage]:
-        """
-        Handle structured output for models without native JSON schema support.
-        This function modifies the prompt messages to include schema-based output requirements.
-
-        Args:
-            prompt_messages: Original sequence of prompt messages
-
-        Returns:
-            list[PromptMessage]: Updated prompt messages with structured output requirements
-        """
-        # Convert schema to string format
-        schema_str = json.dumps(self._fetch_structured_output_schema(), ensure_ascii=False)
-
-        # Find existing system prompt with schema placeholder
-        system_prompt = next(
-            (prompt for prompt in prompt_messages if isinstance(prompt, SystemPromptMessage)),
-            None,
-        )
-        structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str)
-        # Prepare system prompt content
-        system_prompt_content = (
-            structured_output_prompt + "\n\n" + system_prompt.content
-            if system_prompt and isinstance(system_prompt.content, str)
-            else structured_output_prompt
-        )
-        system_prompt = SystemPromptMessage(content=system_prompt_content)
-
-        # Extract content from the last user message
-
-        filtered_prompts = [prompt for prompt in prompt_messages if not isinstance(prompt, SystemPromptMessage)]
-        updated_prompt = [system_prompt] + filtered_prompts
-
-        return updated_prompt
-
-    def _set_response_format(self, model_parameters: dict, rules: list) -> None:
-        """
-        Set the appropriate response format parameter based on model rules.
-
-        :param model_parameters: Model parameters to update
-        :param rules: Model parameter rules
-        """
-        for rule in rules:
-            if rule.name == "response_format":
-                if ResponseFormat.JSON.value in rule.options:
-                    model_parameters["response_format"] = ResponseFormat.JSON.value
-                elif ResponseFormat.JSON_OBJECT.value in rule.options:
-                    model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
-
-    def _prepare_schema_for_model(self, schema: dict) -> dict:
-        """
-        Prepare JSON schema based on model requirements.
-
-        Different models have different requirements for JSON schema formatting.
-        This function handles these differences.
-
-        :param schema: The original JSON schema
-        :return: Processed schema compatible with the current model
-        """
-
-        # Deep copy to avoid modifying the original schema
-        processed_schema = schema.copy()
-
-        # Convert boolean types to string types (common requirement)
-        convert_boolean_to_string(processed_schema)
-
-        # Apply model-specific transformations
-        if SpecialModelType.GEMINI in self.node_data.model.name:
-            remove_additional_properties(processed_schema)
-            return processed_schema
-        elif SpecialModelType.OLLAMA in self.node_data.model.provider:
-            return processed_schema
-        else:
-            # Default format with name field
-            return {"schema": processed_schema, "name": "llm_response"}
-
     def _fetch_model_schema(self, provider: str) -> AIModelEntity | None:
         """
         Fetch model schema
@@ -1243,49 +1142,3 @@ def _handle_completion_template(
     )
     prompt_messages.append(prompt_message)
     return prompt_messages
-
-
-def remove_additional_properties(schema: dict) -> None:
-    """
-    Remove additionalProperties fields from JSON schema.
-    Used for models like Gemini that don't support this property.
-
-    :param schema: JSON schema to modify in-place
-    """
-    if not isinstance(schema, dict):
-        return
-
-    # Remove additionalProperties at current level
-    schema.pop("additionalProperties", None)
-
-    # Process nested structures recursively
-    for value in schema.values():
-        if isinstance(value, dict):
-            remove_additional_properties(value)
-        elif isinstance(value, list):
-            for item in value:
-                if isinstance(item, dict):
-                    remove_additional_properties(item)
-
-
-def convert_boolean_to_string(schema: dict) -> None:
-    """
-    Convert boolean type specifications to string in JSON schema.
-
-    :param schema: JSON schema to modify in-place
-    """
-    if not isinstance(schema, dict):
-        return
-
-    # Check for boolean type at current level
-    if schema.get("type") == "boolean":
-        schema["type"] = "string"
-
-    # Process nested dictionaries and lists recursively
-    for value in schema.values():
-        if isinstance(value, dict):
-            convert_boolean_to_string(value)
-        elif isinstance(value, list):
-            for item in value:
-                if isinstance(item, dict):
-                    convert_boolean_to_string(item)

api/core/workflow/utils/structured_output/entities.py

@@ -1,16 +0,0 @@
-from enum import StrEnum
-
-
-class ResponseFormat(StrEnum):
-    """Constants for model response formats"""
-
-    JSON_SCHEMA = "json_schema"  # model's structured output mode. some model like gemini, gpt-4o,  support this mode.
-    JSON = "JSON"  # model's json mode. some model like claude support this mode.
-    JSON_OBJECT = "json_object"  # json mode's another alias. some model like deepseek-chat, qwen use this alias.
-
-
-class SpecialModelType(StrEnum):
-    """Constants for identifying model types"""
-
-    GEMINI = "gemini"
-    OLLAMA = "ollama"

api/core/workflow/utils/structured_output/prompt.py

@@ -1,17 +0,0 @@
-STRUCTURED_OUTPUT_PROMPT = """You’re a helpful AI assistant. You could answer questions and output in JSON format.
-constraints:
-    - You must output in JSON format.
-    - Do not output boolean value, use string type instead.
-    - Do not output integer or float value, use number type instead.
-eg:
-    Here is the JSON schema:
-    {"additionalProperties": false, "properties": {"age": {"type": "number"}, "name": {"type": "string"}}, "required": ["name", "age"], "type": "object"}
-
-    Here is the user's question:
-    My name is John Doe and I am 30 years old.
-
-    output:
-    {"name": "John Doe", "age": 30}
-Here is the JSON schema:
-{{schema}}
-"""  # noqa: E501

api/tests/integration_tests/workflow/nodes/test_llm.py

@@ -9,6 +9,7 @@ from unittest.mock import MagicMock, patch
 import pytest
 
 from core.app.entities.app_invoke_entities import InvokeFrom
+from core.llm_generator.output_parser.structured_output import _parse_structured_output
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.entities.message_entities import AssistantPromptMessage
 from core.workflow.entities.variable_pool import VariablePool
@@ -277,29 +278,6 @@ def test_execute_llm_with_jinja2(flask_req_ctx, setup_code_executor_mock):
 
 
 def test_extract_json():
-    node = init_llm_node(
-        config={
-            "id": "llm",
-            "data": {
-                "title": "123",
-                "type": "llm",
-                "model": {"provider": "openai", "name": "gpt-3.5-turbo", "mode": "chat", "completion_params": {}},
-                "prompt_config": {
-                    "structured_output": {
-                        "enabled": True,
-                        "schema": {
-                            "type": "object",
-                            "properties": {"name": {"type": "string"}, "age": {"type": "number"}},
-                        },
-                    }
-                },
-                "prompt_template": [{"role": "user", "text": "{{#sys.query#}}"}],
-                "memory": None,
-                "context": {"enabled": False},
-                "vision": {"enabled": False},
-            },
-        },
-    )
     llm_texts = [
         '<think>\n\n</think>{"name": "test", "age": 123',  # resoning model (deepseek-r1)
         '{"name":"test","age":123}',  # json schema model (gpt-4o)
@@ -308,4 +286,4 @@ def test_extract_json():
         '{"name":"test",age:123}',  # without quotes (qwen-2.5-0.5b)
     ]
     result = {"name": "test", "age": 123}
-    assert all(node._parse_structured_output(item) == result for item in llm_texts)
+    assert all(_parse_structured_output(item) == result for item in llm_texts)