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feat: support structured output in sandbox and tool mode

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Signed-off-by: Stream <Stream_2@qq.com>
This commit is contained in:
Stream
2026-01-30 06:46:38 +08:00
parent d3fc457331
commit 7926024569
22 changed files with 629 additions and 117 deletions
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6
api/core/workflow/nodes/llm/entities.py
View File

@ -7,7 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_valid
from core.agent.entities import AgentLog, AgentResult
from core.file import File
from core.model_runtime.entities import ImagePromptMessageContent, LLMMode
from core.model_runtime.entities.llm_entities import LLMUsage
from core.model_runtime.entities.llm_entities import LLMStructuredOutput, LLMUsage
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
from core.tools.entities.tool_entities import ToolProviderType
from core.workflow.entities import ToolCall, ToolCallResult
@ -156,6 +156,9 @@ class LLMGenerationData(BaseModel):
finish_reason: str | None = Field(None, description="Finish reason from LLM")
files: list[File] = Field(default_factory=list, description="Generated files")
trace: list[LLMTraceSegment] = Field(default_factory=list, description="Streaming trace in emitted order")
structured_output: LLMStructuredOutput | None = Field(
default=None, description="Structured output from tool-only agent runs"
)
class ThinkTagStreamParser:
@ -284,6 +287,7 @@ class AggregatedResult(BaseModel):
files: list[File] = Field(default_factory=list)
usage: LLMUsage = Field(default_factory=LLMUsage.empty_usage)
finish_reason: str | None = None
structured_output: LLMStructuredOutput | None = None
class AgentContext(BaseModel):

78
api/core/workflow/nodes/llm/node.py
View File

@ -12,7 +12,8 @@ from typing import TYPE_CHECKING, Any, Literal, cast
from sqlalchemy import select
from core.agent.entities import AgentEntity, AgentLog, AgentResult, AgentToolEntity, ExecutionContext
from core.agent.entities import AgentEntity, AgentLog, AgentOutputKind, AgentResult, AgentToolEntity, ExecutionContext
from core.agent.output_tools import build_agent_output_tools
from core.agent.patterns import StrategyFactory
from core.app.entities.app_asset_entities import AppAssetFileTree
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
@ -20,6 +21,7 @@ from core.app_assets.constants import AppAssetsAttrs
from core.file import File, FileTransferMethod, 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.file_ref import convert_file_refs_in_output
from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
from core.memory.base import BaseMemory
from core.model_manager import ModelInstance, ModelManager
@ -62,6 +64,7 @@ from core.skill.entities.skill_document import SkillDocument
from core.skill.entities.tool_dependencies import ToolDependencies, ToolDependency
from core.skill.skill_compiler import SkillCompiler
from core.tools.__base.tool import Tool
from core.tools.entities.tool_entities import ToolInvokeFrom
from core.tools.signature import sign_upload_file
from core.tools.tool_manager import ToolManager
from core.variables import (
@ -355,6 +358,8 @@ class LLMNode(Node[LLMNodeData]):
reasoning_content = ""
usage = generation_data.usage
finish_reason = generation_data.finish_reason
if generation_data.structured_output:
structured_output = generation_data.structured_output
# Unified process_data building
process_data = {
@ -1900,7 +1905,7 @@ class LLMNode(Node[LLMNodeData]):
prompt_messages=list(prompt_messages),
model_parameters=self._node_data.model.completion_params,
stop=list(stop or []),
stream=True,
stream=False,
)
result = yield from self._process_tool_outputs(outputs)
@ -1921,11 +1926,22 @@ class LLMNode(Node[LLMNodeData]):
with SandboxBashSession(sandbox=sandbox, node_id=self.id, tools=tool_dependencies) as session:
prompt_files = self._extract_prompt_files(variable_pool)
model_features = self._get_model_features(model_instance)
structured_output_schema = None
if self._node_data.structured_output_enabled:
structured_output_schema = LLMNode.fetch_structured_output_schema(
structured_output=self._node_data.structured_output or {},
)
output_tools = build_agent_output_tools(
tenant_id=self.tenant_id,
invoke_from=self.invoke_from,
tool_invoke_from=ToolInvokeFrom.WORKFLOW,
structured_output_schema=structured_output_schema,
)
strategy = StrategyFactory.create_strategy(
model_features=model_features,
model_instance=model_instance,
tools=[session.bash_tool],
tools=[session.bash_tool, *output_tools],
files=prompt_files,
max_iterations=self._node_data.max_iterations or 100,
agent_strategy=AgentEntity.Strategy.FUNCTION_CALLING,
@ -1936,7 +1952,7 @@ class LLMNode(Node[LLMNodeData]):
prompt_messages=list(prompt_messages),
model_parameters=self._node_data.model.completion_params,
stop=list(stop or []),
stream=True,
stream=False,
)
result = yield from self._process_tool_outputs(outputs)
@ -2011,6 +2027,20 @@ class LLMNode(Node[LLMNodeData]):
logger.warning("Failed to load tool %s: %s", tool, str(e))
continue
structured_output_schema = None
if self._node_data.structured_output_enabled:
structured_output_schema = LLMNode.fetch_structured_output_schema(
structured_output=self._node_data.structured_output or {},
)
tool_instances.extend(
build_agent_output_tools(
tenant_id=self.tenant_id,
invoke_from=self.invoke_from,
tool_invoke_from=ToolInvokeFrom.WORKFLOW,
structured_output_schema=structured_output_schema,
)
)
return tool_instances
def _extract_prompt_files(self, variable_pool: VariablePool) -> list[File]:
@ -2480,6 +2510,7 @@ class LLMNode(Node[LLMNodeData]):
finish_reason=aggregate.finish_reason,
files=aggregate.files,
trace=trace_state.trace_segments,
structured_output=aggregate.structured_output,
)
def _process_tool_outputs(
@ -2494,19 +2525,54 @@ class LLMNode(Node[LLMNodeData]):
if isinstance(output, AgentLog):
yield from self._handle_agent_log_output(output, state.stream, state.trace, state.agent)
else:
yield from self._handle_llm_chunk_output(output, state.stream, state.trace, state.aggregate)
continue
except StopIteration as exception:
if isinstance(getattr(exception, "value", None), AgentResult):
state.agent.agent_result = exception.value
if state.agent.agent_result:
state.aggregate.text = state.agent.agent_result.text or state.aggregate.text
output_payload = state.agent.agent_result.output
structured_output_data: Mapping[str, Any] | None = None
if isinstance(output_payload, AgentResult.StructuredOutput):
output_kind = output_payload.output_kind
if output_kind == AgentOutputKind.ILLEGAL_OUTPUT:
raise ValueError("Agent returned illegal output")
if output_kind in {AgentOutputKind.FINAL_OUTPUT_ANSWER, AgentOutputKind.OUTPUT_TEXT}:
if not output_payload.output_text:
raise ValueError("Agent returned empty text output")
state.aggregate.text = output_payload.output_text
elif output_kind == AgentOutputKind.FINAL_STRUCTURED_OUTPUT:
if output_payload.output_data is None:
raise ValueError("Agent returned empty structured output")
else:
raise ValueError("Agent returned unsupported output kind")
if output_payload.output_data is not None:
if not isinstance(output_payload.output_data, Mapping):
raise ValueError("Agent returned invalid structured output")
structured_output_data = output_payload.output_data
else:
if not output_payload:
raise ValueError("Agent returned empty output")
state.aggregate.text = str(output_payload)
state.aggregate.files = state.agent.agent_result.files
if state.agent.agent_result.usage:
state.aggregate.usage = state.agent.agent_result.usage
if state.agent.agent_result.finish_reason:
state.aggregate.finish_reason = state.agent.agent_result.finish_reason
if structured_output_data is not None:
output_schema = LLMNode.fetch_structured_output_schema(
structured_output=self._node_data.structured_output or {},
)
converted_output = convert_file_refs_in_output(
output=structured_output_data,
json_schema=output_schema,
tenant_id=self.tenant_id,
)
state.aggregate.structured_output = LLMStructuredOutput(structured_output=converted_output)
yield from self._flush_remaining_stream(state.stream, state.trace, state.aggregate)
yield from self._close_streams()