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https://github.com/langgenius/dify.git
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refactor apps
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takatost
@ -1,262 +0,0 @@
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import json
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import logging
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import time
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from typing import Any, Optional, Union, cast
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from langchain.agents import openai_functions_agent, openai_functions_multi_agent
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from langchain.callbacks.base import BaseCallbackHandler
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from langchain.schema import AgentAction, AgentFinish, BaseMessage, LLMResult
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from core.app.app_queue_manager import AppQueueManager, PublishFrom
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from core.callback_handler.entity.agent_loop import AgentLoop
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from core.entities.application_entities import ModelConfigEntity
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from core.model_runtime.entities.llm_entities import LLMResult as RuntimeLLMResult
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from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
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from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from extensions.ext_database import db
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from models.model import Message, MessageAgentThought, MessageChain
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class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
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"""Callback Handler that prints to std out."""
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raise_error: bool = True
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def __init__(self, model_config: ModelConfigEntity,
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queue_manager: AppQueueManager,
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message: Message,
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message_chain: MessageChain) -> None:
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"""Initialize callback handler."""
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self.model_config = model_config
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self.queue_manager = queue_manager
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self.message = message
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self.message_chain = message_chain
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model_type_instance = self.model_config.provider_model_bundle.model_type_instance
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self.model_type_instance = cast(LargeLanguageModel, model_type_instance)
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self._agent_loops = []
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self._current_loop = None
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self._message_agent_thought = None
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@property
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def agent_loops(self) -> list[AgentLoop]:
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return self._agent_loops
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def clear_agent_loops(self) -> None:
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self._agent_loops = []
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self._current_loop = None
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self._message_agent_thought = None
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@property
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def always_verbose(self) -> bool:
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"""Whether to call verbose callbacks even if verbose is False."""
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return 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 True
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def on_llm_before_invoke(self, prompt_messages: list[PromptMessage]) -> None:
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if not self._current_loop:
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# Agent start with a LLM query
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self._current_loop = AgentLoop(
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position=len(self._agent_loops) + 1,
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prompt="\n".join([prompt_message.content for prompt_message in prompt_messages]),
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status='llm_started',
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started_at=time.perf_counter()
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)
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def on_llm_after_invoke(self, result: RuntimeLLMResult) -> None:
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if self._current_loop and self._current_loop.status == 'llm_started':
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self._current_loop.status = 'llm_end'
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if result.usage:
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self._current_loop.prompt_tokens = result.usage.prompt_tokens
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else:
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self._current_loop.prompt_tokens = self.model_type_instance.get_num_tokens(
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model=self.model_config.model,
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credentials=self.model_config.credentials,
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prompt_messages=[UserPromptMessage(content=self._current_loop.prompt)]
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)
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completion_message = result.message
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if completion_message.tool_calls:
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self._current_loop.completion \
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= json.dumps({'function_call': completion_message.tool_calls})
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else:
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self._current_loop.completion = completion_message.content
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if result.usage:
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self._current_loop.completion_tokens = result.usage.completion_tokens
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else:
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self._current_loop.completion_tokens = self.model_type_instance.get_num_tokens(
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model=self.model_config.model,
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credentials=self.model_config.credentials,
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prompt_messages=[AssistantPromptMessage(content=self._current_loop.completion)]
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)
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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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pass
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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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pass
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def on_llm_end(self, response: LLMResult, **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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logging.debug("Agent on_llm_error: %s", error)
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self._agent_loops = []
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self._current_loop = None
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self._message_agent_thought = None
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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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# kwargs={'color': 'green', 'llm_prefix': 'Thought:', 'observation_prefix': 'Observation: '}
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# input_str='action-input'
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# serialized={'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'name': 'Search'}
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pass
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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 = json.dumps({"query": action.tool_input}
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if isinstance(action.tool_input, str) else action.tool_input)
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completion = None
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if isinstance(action, openai_functions_agent.base._FunctionsAgentAction) \
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or isinstance(action, openai_functions_multi_agent.base._FunctionsAgentAction):
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thought = action.log.strip()
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completion = json.dumps({'function_call': action.message_log[0].additional_kwargs['function_call']})
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else:
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action_name_position = action.log.index("Action:") if action.log else -1
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thought = action.log[:action_name_position].strip() if action.log else ''
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if self._current_loop and self._current_loop.status == 'llm_end':
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self._current_loop.status = 'agent_action'
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self._current_loop.thought = thought
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self._current_loop.tool_name = tool
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self._current_loop.tool_input = tool_input
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if completion is not None:
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self._current_loop.completion = completion
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self._message_agent_thought = self._init_agent_thought()
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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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# kwargs={'name': 'Search'}
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# llm_prefix='Thought:'
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# observation_prefix='Observation: '
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# output='53 years'
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if self._current_loop and self._current_loop.status == 'agent_action' and output and output != 'None':
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self._current_loop.status = 'tool_end'
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self._current_loop.tool_output = output
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self._current_loop.completed = True
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self._current_loop.completed_at = time.perf_counter()
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self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
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self._complete_agent_thought(self._message_agent_thought)
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self._agent_loops.append(self._current_loop)
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self._current_loop = None
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self._message_agent_thought = None
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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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logging.debug("Agent on_tool_error: %s", error)
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self._agent_loops = []
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self._current_loop = None
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self._message_agent_thought = None
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def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
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"""Run on agent end."""
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# Final Answer
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if self._current_loop and (self._current_loop.status == 'llm_end' or self._current_loop.status == 'agent_action'):
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self._current_loop.status = 'agent_finish'
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self._current_loop.completed = True
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self._current_loop.completed_at = time.perf_counter()
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self._current_loop.latency = self._current_loop.completed_at - self._current_loop.started_at
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self._current_loop.thought = '[DONE]'
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self._message_agent_thought = self._init_agent_thought()
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self._complete_agent_thought(self._message_agent_thought)
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self._agent_loops.append(self._current_loop)
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self._current_loop = None
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self._message_agent_thought = None
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elif not self._current_loop and self._agent_loops:
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self._agent_loops[-1].status = 'agent_finish'
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def _init_agent_thought(self) -> MessageAgentThought:
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message_agent_thought = MessageAgentThought(
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message_id=self.message.id,
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message_chain_id=self.message_chain.id,
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position=self._current_loop.position,
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thought=self._current_loop.thought,
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tool=self._current_loop.tool_name,
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tool_input=self._current_loop.tool_input,
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message=self._current_loop.prompt,
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message_price_unit=0,
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answer=self._current_loop.completion,
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answer_price_unit=0,
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created_by_role=('account' if self.message.from_source == 'console' else 'end_user'),
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created_by=(self.message.from_account_id
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if self.message.from_source == 'console' else self.message.from_end_user_id)
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)
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db.session.add(message_agent_thought)
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db.session.commit()
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self.queue_manager.publish_agent_thought(message_agent_thought, PublishFrom.APPLICATION_MANAGER)
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return message_agent_thought
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def _complete_agent_thought(self, message_agent_thought: MessageAgentThought) -> None:
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loop_message_tokens = self._current_loop.prompt_tokens
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loop_answer_tokens = self._current_loop.completion_tokens
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# transform usage
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llm_usage = self.model_type_instance._calc_response_usage(
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self.model_config.model,
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self.model_config.credentials,
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loop_message_tokens,
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loop_answer_tokens
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)
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message_agent_thought.observation = self._current_loop.tool_output
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message_agent_thought.tool_process_data = '' # currently not support
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message_agent_thought.message_token = loop_message_tokens
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message_agent_thought.message_unit_price = llm_usage.prompt_unit_price
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message_agent_thought.message_price_unit = llm_usage.prompt_price_unit
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message_agent_thought.answer_token = loop_answer_tokens
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message_agent_thought.answer_unit_price = llm_usage.completion_unit_price
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message_agent_thought.answer_price_unit = llm_usage.completion_price_unit
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message_agent_thought.latency = self._current_loop.latency
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message_agent_thought.tokens = self._current_loop.prompt_tokens + self._current_loop.completion_tokens
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message_agent_thought.total_price = llm_usage.total_price
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message_agent_thought.currency = llm_usage.currency
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db.session.commit()
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@ -1,23 +0,0 @@
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from pydantic import BaseModel
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class AgentLoop(BaseModel):
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position: int = 1
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thought: str = None
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tool_name: str = None
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tool_input: str = None
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tool_output: str = None
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prompt: str = None
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prompt_tokens: int = 0
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completion: str = None
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completion_tokens: int = 0
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latency: float = None
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status: str = 'llm_started'
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completed: bool = False
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started_at: float = None
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completed_at: float = None
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@ -1,6 +1,6 @@
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from core.app.app_queue_manager import AppQueueManager, PublishFrom
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from core.entities.application_entities import InvokeFrom
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from core.app.entities.app_invoke_entities import InvokeFrom
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from core.rag.models.document import Document
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from extensions.ext_database import db
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from models.dataset import DatasetQuery, DocumentSegment
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