mirror of
https://github.com/langgenius/dify.git
synced 2026-04-27 14:08:18 +08:00
add few workflow run codes
This commit is contained in:
takatost
0
api/core/callback_handler/__init__.py
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0
api/core/callback_handler/__init__.py
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@ -1,157 +0,0 @@
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import os
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import sys
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from typing import Any, Optional, Union
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.input import print_text
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from langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult
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class DifyStdOutCallbackHandler(BaseCallbackHandler):
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"""Callback Handler that prints to std out."""
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def __init__(self, color: Optional[str] = None) -> None:
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"""Initialize callback handler."""
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self.color = color
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def on_chat_model_start(
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self,
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serialized: dict[str, Any],
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messages: list[list[BaseMessage]],
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**kwargs: Any
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) -> Any:
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print_text("\n[on_chat_model_start]\n", color='blue')
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for sub_messages in messages:
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for sub_message in sub_messages:
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print_text(str(sub_message) + "\n", color='blue')
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def on_llm_start(
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self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any
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) -> None:
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"""Print out the prompts."""
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print_text("\n[on_llm_start]\n", color='blue')
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print_text(prompts[0] + "\n", color='blue')
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def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
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"""Do nothing."""
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print_text("\n[on_llm_end]\nOutput: " + str(response.generations[0][0].text) + "\nllm_output: " + str(
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response.llm_output) + "\n", color='blue')
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
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"""Do nothing."""
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pass
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def on_llm_error(
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
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) -> None:
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"""Do nothing."""
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print_text("\n[on_llm_error]\nError: " + str(error) + "\n", color='blue')
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def on_chain_start(
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self, serialized: dict[str, Any], inputs: dict[str, Any], **kwargs: Any
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) -> None:
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"""Print out that we are entering a chain."""
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chain_type = serialized['id'][-1]
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print_text("\n[on_chain_start]\nChain: " + chain_type + "\nInputs: " + str(inputs) + "\n", color='pink')
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def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
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"""Print out that we finished a chain."""
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print_text("\n[on_chain_end]\nOutputs: " + str(outputs) + "\n", color='pink')
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def on_chain_error(
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
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) -> None:
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"""Do nothing."""
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print_text("\n[on_chain_error]\nError: " + str(error) + "\n", color='pink')
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def on_tool_start(
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self,
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serialized: dict[str, Any],
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input_str: str,
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**kwargs: Any,
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) -> None:
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"""Do nothing."""
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print_text("\n[on_tool_start] " + str(serialized), color='yellow')
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def on_agent_action(
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self, action: AgentAction, color: Optional[str] = None, **kwargs: Any
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) -> Any:
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"""Run on agent action."""
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tool = action.tool
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tool_input = action.tool_input
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try:
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action_name_position = action.log.index("\nAction:") + 1 if action.log else -1
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thought = action.log[:action_name_position].strip() if action.log else ''
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except ValueError:
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thought = ''
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log = f"Thought: {thought}\nTool: {tool}\nTool Input: {tool_input}"
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print_text("\n[on_agent_action]\n" + log + "\n", color='green')
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def on_tool_end(
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self,
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output: str,
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color: Optional[str] = None,
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observation_prefix: Optional[str] = None,
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llm_prefix: Optional[str] = None,
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**kwargs: Any,
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) -> None:
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"""If not the final action, print out observation."""
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print_text("\n[on_tool_end]\n", color='yellow')
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if observation_prefix:
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print_text(f"\n{observation_prefix}")
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print_text(output, color='yellow')
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if llm_prefix:
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print_text(f"\n{llm_prefix}")
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print_text("\n")
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def on_tool_error(
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self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
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) -> None:
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"""Do nothing."""
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print_text("\n[on_tool_error] Error: " + str(error) + "\n", color='yellow')
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def on_text(
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self,
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text: str,
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color: Optional[str] = None,
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end: str = "",
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**kwargs: Optional[str],
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) -> None:
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"""Run when agent ends."""
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print_text("\n[on_text] " + text + "\n", color=color if color else self.color, end=end)
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def on_agent_finish(
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self, finish: AgentFinish, color: Optional[str] = None, **kwargs: Any
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) -> None:
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"""Run on agent end."""
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print_text("[on_agent_finish] " + finish.return_values['output'] + "\n", color='green', end="\n")
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@property
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def ignore_llm(self) -> bool:
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"""Whether to ignore LLM callbacks."""
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return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
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@property
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def ignore_chain(self) -> bool:
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"""Whether to ignore chain callbacks."""
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return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
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@property
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def ignore_agent(self) -> bool:
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"""Whether to ignore agent callbacks."""
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return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
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@property
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def ignore_chat_model(self) -> bool:
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"""Whether to ignore chat model callbacks."""
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return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true'
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class DifyStreamingStdOutCallbackHandler(DifyStdOutCallbackHandler):
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"""Callback handler for streaming. Only works with LLMs that support streaming."""
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def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
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"""Run on new LLM token. Only available when streaming is enabled."""
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sys.stdout.write(token)
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sys.stdout.flush()
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45
api/core/callback_handler/workflow_event_trigger_callback.py
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45
api/core/callback_handler/workflow_event_trigger_callback.py
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@ -0,0 +1,45 @@
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from core.app.app_queue_manager import AppQueueManager, PublishFrom
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from core.workflow.callbacks.base_callback import BaseWorkflowCallback
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from models.workflow import WorkflowRun, WorkflowNodeExecution
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class WorkflowEventTriggerCallback(BaseWorkflowCallback):
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def __init__(self, queue_manager: AppQueueManager):
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self._queue_manager = queue_manager
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def on_workflow_run_started(self, workflow_run: WorkflowRun) -> None:
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"""
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Workflow run started
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"""
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self._queue_manager.publish_workflow_started(
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workflow_run_id=workflow_run.id,
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pub_from=PublishFrom.TASK_PIPELINE
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)
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def on_workflow_run_finished(self, workflow_run: WorkflowRun) -> None:
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"""
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Workflow run finished
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"""
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self._queue_manager.publish_workflow_finished(
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workflow_run_id=workflow_run.id,
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pub_from=PublishFrom.TASK_PIPELINE
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)
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def on_workflow_node_execute_started(self, workflow_node_execution: WorkflowNodeExecution) -> None:
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"""
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Workflow node execute started
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"""
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self._queue_manager.publish_node_started(
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workflow_node_execution_id=workflow_node_execution.id,
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pub_from=PublishFrom.TASK_PIPELINE
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)
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def on_workflow_node_execute_finished(self, workflow_node_execution: WorkflowNodeExecution) -> None:
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"""
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Workflow node execute finished
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"""
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self._queue_manager.publish_node_finished(
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workflow_node_execution_id=workflow_node_execution.id,
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pub_from=PublishFrom.TASK_PIPELINE
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)
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