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refactor(sandbox): remove unused bash tool methods and streamline sandbox session handling in LLMNode

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This commit is contained in:
Harry
2026-01-12 00:09:40 +08:00
parent 390c805ef4
commit bc2ffa39fc
4 changed files with 155 additions and 60 deletions
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58
api/core/workflow/nodes/llm/node.py
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@ -13,6 +13,7 @@ from sqlalchemy import select
from core.agent.entities import AgentLog, AgentResult, AgentToolEntity, ExecutionContext
from core.agent.patterns import StrategyFactory
from core.agent.sandbox_session import SandboxSession
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.file import File, FileTransferMethod, FileType, file_manager
from core.helper.code_executor import CodeExecutor, CodeLanguage
@ -264,9 +265,8 @@ class LLMNode(Node[LLMNodeData]):
if self.tool_call_enabled:
workflow_execution_id = variable_pool.system_variables.workflow_execution_id
is_sandbox_runtime = (
workflow_execution_id is not None
and SandboxManager.is_sandbox_runtime(workflow_execution_id)
is_sandbox_runtime = workflow_execution_id is not None and SandboxManager.is_sandbox_runtime(
workflow_execution_id
)
if is_sandbox_runtime:
@ -274,10 +274,7 @@ class LLMNode(Node[LLMNodeData]):
model_instance=model_instance,
prompt_messages=prompt_messages,
stop=stop,
files=files,
variable_pool=variable_pool,
node_inputs=node_inputs,
process_data=process_data,
)
else:
generator = self._invoke_llm_with_tools(
@ -1589,10 +1586,7 @@ class LLMNode(Node[LLMNodeData]):
model_instance: ModelInstance,
prompt_messages: Sequence[PromptMessage],
stop: Sequence[str] | None,
files: Sequence[File],
variable_pool: VariablePool,
node_inputs: dict[str, Any],
process_data: dict[str, Any],
) -> Generator[NodeEventBase, None, LLMGenerationData]:
from core.agent.entities import AgentEntity
@ -1602,32 +1596,38 @@ class LLMNode(Node[LLMNodeData]):
configured_tools = self._prepare_tool_instances(variable_pool)
bash_tool = SandboxManager.get_bash_tool(
result: LLMGenerationData | None = None
with SandboxSession(
workflow_execution_id=workflow_execution_id,
tenant_id=self.tenant_id,
configured_tools=configured_tools,
)
user_id=self.user_id,
tools=configured_tools,
) as sandbox_session:
prompt_files = self._extract_prompt_files(variable_pool)
prompt_files = self._extract_prompt_files(variable_pool)
strategy = StrategyFactory.create_strategy(
model_features=[],
model_instance=model_instance,
tools=[sandbox_session.bash_tool],
files=prompt_files,
max_iterations=self._node_data.max_iterations or 10,
context=ExecutionContext(user_id=self.user_id, app_id=self.app_id, tenant_id=self.tenant_id),
agent_strategy=AgentEntity.Strategy.CHAIN_OF_THOUGHT,
)
strategy = StrategyFactory.create_strategy(
model_features=[],
model_instance=model_instance,
tools=[bash_tool],
files=prompt_files,
max_iterations=self._node_data.max_iterations or 10,
context=ExecutionContext(user_id=self.user_id, app_id=self.app_id, tenant_id=self.tenant_id),
agent_strategy=AgentEntity.Strategy.CHAIN_OF_THOUGHT,
)
outputs = strategy.run(
prompt_messages=list(prompt_messages),
model_parameters=self._node_data.model.completion_params,
stop=list(stop or []),
stream=True,
)
outputs = strategy.run(
prompt_messages=list(prompt_messages),
model_parameters=self._node_data.model.completion_params,
stop=list(stop or []),
stream=True,
)
result = yield from self._process_tool_outputs(outputs)
if result is None:
raise LLMNodeError("SandboxSession exited unexpectedly")
result = yield from self._process_tool_outputs(outputs)
return result
def _get_model_features(self, model_instance: ModelInstance) -> list[ModelFeature]: