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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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142
api/core/agent/patterns/react.py
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@ -4,10 +4,17 @@ from __future__ import annotations
import json
from collections.abc import Generator
from typing import TYPE_CHECKING, Any, Union
from typing import TYPE_CHECKING, Any, Union, cast
from core.agent.entities import AgentLog, AgentResult, AgentScratchpadUnit, ExecutionContext
from core.agent.entities import AgentLog, AgentOutputKind, AgentResult, AgentScratchpadUnit, ExecutionContext
from core.agent.output_parser.cot_output_parser import CotAgentOutputParser
from core.agent.output_tools import (
FINAL_OUTPUT_TOOL,
FINAL_STRUCTURED_OUTPUT_TOOL,
ILLEGAL_OUTPUT_TOOL,
OUTPUT_TEXT_TOOL,
OUTPUT_TOOL_NAME_SET,
)
from core.file import File
from core.model_manager import ModelInstance
from core.model_runtime.entities import (
@ -67,6 +74,8 @@ class ReActStrategy(AgentPattern):
total_usage: dict[str, Any] = {"usage": None}
output_files: list[File] = [] # Track files produced by tools
final_text: str = ""
structured_output_payload: dict[str, Any] | None = None
output_text_payload: str | None = None
finish_reason: str | None = None
# Add "Observation" to stop sequences
@ -84,10 +93,13 @@ class ReActStrategy(AgentPattern):
)
yield round_log
# Build prompt with/without tools based on iteration
include_tools = iteration_step < max_iterations
# Build prompt with tool restrictions on last iteration
if iteration_step == max_iterations:
tools_for_prompt = [tool for tool in self.tools if tool.entity.identity.name in OUTPUT_TOOL_NAME_SET]
else:
tools_for_prompt = self.tools
current_messages = self._build_prompt_with_react_format(
prompt_messages, agent_scratchpad, include_tools, self.instruction
prompt_messages, agent_scratchpad, tools_for_prompt, self.instruction
)
model_log = self._create_log(
@ -109,18 +121,21 @@ class ReActStrategy(AgentPattern):
messages_to_use = current_messages
# Invoke model
chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = self.model_instance.invoke_llm(
prompt_messages=messages_to_use,
model_parameters=model_parameters,
stop=stop,
stream=stream,
user=self.context.user_id or "",
callbacks=[],
chunks = cast(
Union[Generator[LLMResultChunk, None, None], LLMResult],
self.model_instance.invoke_llm(
prompt_messages=messages_to_use,
model_parameters=model_parameters,
stop=stop,
stream=False,
user=self.context.user_id or "",
callbacks=[],
),
)
# Process response
scratchpad, chunk_finish_reason = yield from self._handle_chunks(
chunks, round_usage, model_log, current_messages
chunks, round_usage, model_log, current_messages, emit_chunks=False
)
agent_scratchpad.append(scratchpad)
@ -134,28 +149,46 @@ class ReActStrategy(AgentPattern):
finish_reason = chunk_finish_reason
# Check if we have an action to execute
if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
react_state = True
# Execute tool
observation, tool_files = yield from self._handle_tool_call(
scratchpad.action, current_messages, round_log
if scratchpad.action is None:
illegal_action = AgentScratchpadUnit.Action(
action_name=ILLEGAL_OUTPUT_TOOL,
action_input={"raw": scratchpad.thought or ""},
)
scratchpad.action = illegal_action
scratchpad.action_str = json.dumps(illegal_action.to_dict())
react_state = True
observation, tool_files = yield from self._handle_tool_call(illegal_action, current_messages, round_log)
scratchpad.observation = observation
# Track files produced by tools
output_files.extend(tool_files)
# Add observation to scratchpad for display
yield self._create_text_chunk(f"\nObservation: {observation}\n", current_messages)
else:
# Extract final answer
if scratchpad.action and scratchpad.action.action_input:
final_answer = scratchpad.action.action_input
if isinstance(final_answer, dict):
final_answer = json.dumps(final_answer, ensure_ascii=False)
final_text = str(final_answer)
elif scratchpad.thought:
# If no action but we have thought, use thought as final answer
final_text = scratchpad.thought
action_name = scratchpad.action.action_name
if action_name == FINAL_OUTPUT_TOOL:
if isinstance(scratchpad.action.action_input, dict):
final_text = self._format_output_text(scratchpad.action.action_input.get("text"))
else:
final_text = self._format_output_text(scratchpad.action.action_input)
observation, tool_files = yield from self._handle_tool_call(
scratchpad.action, current_messages, round_log
)
scratchpad.observation = observation
output_files.extend(tool_files)
react_state = False
else:
if action_name == OUTPUT_TEXT_TOOL and isinstance(scratchpad.action.action_input, dict):
output_text_payload = scratchpad.action.action_input.get("text")
elif action_name == FINAL_STRUCTURED_OUTPUT_TOOL and isinstance(
scratchpad.action.action_input, dict
):
data = scratchpad.action.action_input.get("data")
if isinstance(data, dict):
structured_output_payload = data
react_state = True
observation, tool_files = yield from self._handle_tool_call(
scratchpad.action, current_messages, round_log
)
scratchpad.observation = observation
output_files.extend(tool_files)
yield self._finish_log(
round_log,
@ -173,15 +206,38 @@ class ReActStrategy(AgentPattern):
from core.agent.entities import AgentResult
output_payload: str | AgentResult.StructuredOutput
if final_text:
output_payload = AgentResult.StructuredOutput(
output_kind=AgentOutputKind.FINAL_OUTPUT_ANSWER,
output_text=final_text,
output_data=structured_output_payload,
)
elif output_text_payload:
output_payload = AgentResult.StructuredOutput(
output_kind=AgentOutputKind.OUTPUT_TEXT,
output_text=str(output_text_payload),
output_data=structured_output_payload,
)
else:
output_payload = AgentResult.StructuredOutput(
output_kind=AgentOutputKind.ILLEGAL_OUTPUT,
output_text="Model failed to produce a final output.",
output_data=structured_output_payload,
)
return AgentResult(
text=final_text, files=output_files, usage=total_usage.get("usage"), finish_reason=finish_reason
output=output_payload,
files=output_files,
usage=total_usage.get("usage"),
finish_reason=finish_reason,
)
def _build_prompt_with_react_format(
self,
original_messages: list[PromptMessage],
agent_scratchpad: list[AgentScratchpadUnit],
include_tools: bool = True,
tools: list[Tool] | None,
instruction: str = "",
) -> list[PromptMessage]:
"""Build prompt messages with ReAct format."""
@ -198,9 +254,9 @@ class ReActStrategy(AgentPattern):
# Format tools
tools_str = ""
tool_names = []
if include_tools and self.tools:
if tools:
# Convert tools to prompt message tools format
prompt_tools = [tool.to_prompt_message_tool() for tool in self.tools]
prompt_tools = [tool.to_prompt_message_tool() for tool in tools]
tool_names = [tool.name for tool in prompt_tools]
# Format tools as JSON for comprehensive information
@ -253,6 +309,8 @@ class ReActStrategy(AgentPattern):
llm_usage: dict[str, Any],
model_log: AgentLog,
current_messages: list[PromptMessage],
*,
emit_chunks: bool,
) -> Generator[
LLMResultChunk | AgentLog,
None,
@ -306,14 +364,16 @@ class ReActStrategy(AgentPattern):
scratchpad.action_str = action_str
scratchpad.action = chunk
yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
if emit_chunks:
yield self._create_text_chunk(json.dumps(chunk.model_dump()), current_messages)
else:
# Text chunk
chunk_text = str(chunk)
scratchpad.agent_response = (scratchpad.agent_response or "") + chunk_text
scratchpad.thought = (scratchpad.thought or "") + chunk_text
yield self._create_text_chunk(chunk_text, current_messages)
if emit_chunks:
yield self._create_text_chunk(chunk_text, current_messages)
# Update usage
if usage_dict.get("usage"):
@ -337,6 +397,14 @@ class ReActStrategy(AgentPattern):
return scratchpad, finish_reason
@staticmethod
def _format_output_text(value: Any) -> str:
if value is None:
return ""
if isinstance(value, str):
return value
return json.dumps(value, ensure_ascii=False)
def _handle_tool_call(
self,
action: AgentScratchpadUnit.Action,