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feat(sandbox): implement sandbox runtime checks and integrate bash tool invocation in LLMNode

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This commit is contained in:
Harry
2026-01-11 22:55:57 +08:00
parent 5b753dfd6e
commit 390c805ef4
4 changed files with 207 additions and 12 deletions
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83
api/core/workflow/nodes/llm/node.py
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@ -61,6 +61,7 @@ from core.variables import (
ObjectSegment,
StringSegment,
)
from core.virtual_environment.sandbox_manager import SandboxManager
from core.workflow.constants import SYSTEM_VARIABLE_NODE_ID
from core.workflow.entities import GraphInitParams, ToolCall, ToolResult, ToolResultStatus
from core.workflow.entities.tool_entities import ToolCallResult
@ -261,18 +262,33 @@ class LLMNode(Node[LLMNodeData]):
generation_data: LLMGenerationData | None = None
structured_output: LLMStructuredOutput | None = None
# Check if tools are configured
if self.tool_call_enabled:
# Use tool-enabled invocation (Agent V2 style)
generator = self._invoke_llm_with_tools(
model_instance=model_instance,
prompt_messages=prompt_messages,
stop=stop,
files=files,
variable_pool=variable_pool,
node_inputs=node_inputs,
process_data=process_data,
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)
)
if is_sandbox_runtime:
generator = self._invoke_llm_with_sandbox(
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(
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:
# Use traditional LLM invocation
generator = LLMNode.invoke_llm(
@ -1565,7 +1581,52 @@ class LLMNode(Node[LLMNodeData]):
stream=True,
)
# Process outputs and return generation result
result = yield from self._process_tool_outputs(outputs)
return result
def _invoke_llm_with_sandbox(
self,
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
workflow_execution_id = variable_pool.system_variables.workflow_execution_id
if not workflow_execution_id:
raise LLMNodeError("workflow_execution_id is required for sandbox runtime mode")
configured_tools = self._prepare_tool_instances(variable_pool)
bash_tool = SandboxManager.get_bash_tool(
workflow_execution_id=workflow_execution_id,
tenant_id=self.tenant_id,
configured_tools=configured_tools,
)
prompt_files = self._extract_prompt_files(variable_pool)
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,
)
result = yield from self._process_tool_outputs(outputs)
return result