import json
from collections.abc import Mapping
from copy import deepcopy
from datetime import datetime, timezone
from mimetypes import guess_type
from typing import Any, Optional, Union
from yarl import URL
from core.app.entities.app_invoke_entities import InvokeFrom
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
from core.callback_handler.workflow_tool_callback_handler import DifyWorkflowCallbackHandler
from core.file.file_obj import FileTransferMethod
from core.ops.ops_trace_manager import TraceQueueManager
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolInvokeMessageBinary, ToolInvokeMeta, ToolParameter
from core.tools.errors import (
ToolEngineInvokeError,
ToolInvokeError,
ToolNotFoundError,
ToolNotSupportedError,
ToolParameterValidationError,
ToolProviderCredentialValidationError,
ToolProviderNotFoundError,
)
from core.tools.tool.tool import Tool
from core.tools.tool.workflow_tool import WorkflowTool
from core.tools.utils.message_transformer import ToolFileMessageTransformer
from extensions.ext_database import db
from models.model import Message, MessageFile
class ToolEngine:
"""
Tool runtime engine take care of the tool executions.
"""
@staticmethod
def agent_invoke(
tool: Tool, tool_parameters: Union[str, dict],
user_id: str, tenant_id: str, message: Message, invoke_from: InvokeFrom,
agent_tool_callback: DifyAgentCallbackHandler,
trace_manager: Optional[TraceQueueManager] = None
) -> tuple[str, list[tuple[MessageFile, bool]], ToolInvokeMeta]:
"""
Agent invokes the tool with the given arguments.
"""
# check if arguments is a string
if isinstance(tool_parameters, str):
# check if this tool has only one parameter
parameters = [
parameter for parameter in tool.get_runtime_parameters() or []
if parameter.form == ToolParameter.ToolParameterForm.LLM
]
if parameters and len(parameters) == 1:
tool_parameters = {
parameters[0].name: tool_parameters
}
else:
raise ValueError(f"tool_parameters should be a dict, but got a string: {tool_parameters}")
# invoke the tool
try:
# hit the callback handler
agent_tool_callback.on_tool_start(
tool_name=tool.identity.name,
tool_inputs=tool_parameters
)
meta, response = ToolEngine._invoke(tool, tool_parameters, user_id)
response = ToolFileMessageTransformer.transform_tool_invoke_messages(
messages=response,
user_id=user_id,
tenant_id=tenant_id,
conversation_id=message.conversation_id
)
# extract binary data from tool invoke message
binary_files = ToolEngine._extract_tool_response_binary(response)
# create message file
message_files = ToolEngine._create_message_files(
tool_messages=binary_files,
agent_message=message,
invoke_from=invoke_from,
user_id=user_id
)
plain_text = ToolEngine._convert_tool_response_to_str(response)
# hit the callback handler
agent_tool_callback.on_tool_end(
tool_name=tool.identity.name,
tool_inputs=tool_parameters,
tool_outputs=plain_text,
message_id=message.id,
trace_manager=trace_manager
)
# transform tool invoke message to get LLM friendly message
return plain_text, message_files, meta
except ToolProviderCredentialValidationError as e:
error_response = "Please check your tool provider credentials"
agent_tool_callback.on_tool_error(e)
except (
ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
) as e:
error_response = f"there is not a tool named {tool.identity.name}"
agent_tool_callback.on_tool_error(e)
except (
ToolParameterValidationError
) as e:
error_response = f"tool parameters validation error: {e}, please check your tool parameters"
agent_tool_callback.on_tool_error(e)
except ToolInvokeError as e:
error_response = f"tool invoke error: {e}"
agent_tool_callback.on_tool_error(e)
except ToolEngineInvokeError as e:
meta = e.args[0]
error_response = f"tool invoke error: {meta.error}"
agent_tool_callback.on_tool_error(e)
return error_response, [], meta
except Exception as e:
error_response = f"unknown error: {e}"
agent_tool_callback.on_tool_error(e)
return error_response, [], ToolInvokeMeta.error_instance(error_response)
@staticmethod
def workflow_invoke(tool: Tool, tool_parameters: Mapping[str, Any],
user_id: str,
workflow_tool_callback: DifyWorkflowCallbackHandler,
workflow_call_depth: int,
) -> list[ToolInvokeMessage]:
"""
Workflow invokes the tool with the given arguments.
"""
try:
# hit the callback handler
workflow_tool_callback.on_tool_start(
tool_name=tool.identity.name,
tool_inputs=tool_parameters
)
if isinstance(tool, WorkflowTool):
tool.workflow_call_depth = workflow_call_depth + 1
if tool.runtime and tool.runtime.runtime_parameters:
tool_parameters = {**tool.runtime.runtime_parameters, **tool_parameters}
response = tool.invoke(user_id=user_id, tool_parameters=tool_parameters)
# hit the callback handler
workflow_tool_callback.on_tool_end(
tool_name=tool.identity.name,
tool_inputs=tool_parameters,
tool_outputs=response,
)
return response
except Exception as e:
workflow_tool_callback.on_tool_error(e)
raise e
@staticmethod
def _invoke(tool: Tool, tool_parameters: dict, user_id: str) \
-> tuple[ToolInvokeMeta, list[ToolInvokeMessage]]:
"""
Invoke the tool with the given arguments.
"""
started_at = datetime.now(timezone.utc)
meta = ToolInvokeMeta(time_cost=0.0, error=None, tool_config={
'tool_name': tool.identity.name,
'tool_provider': tool.identity.provider,
'tool_provider_type': tool.tool_provider_type().value,
'tool_parameters': deepcopy(tool.runtime.runtime_parameters),
'tool_icon': tool.identity.icon
})
try:
response = tool.invoke(user_id, tool_parameters)
except Exception as e:
meta.error = str(e)
raise ToolEngineInvokeError(meta)
finally:
ended_at = datetime.now(timezone.utc)
meta.time_cost = (ended_at - started_at).total_seconds()
return meta, response
@staticmethod
def _convert_tool_response_to_str(tool_response: list[ToolInvokeMessage]) -> str:
"""
Handle tool response
"""
result = ''
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.TEXT:
result += response.message
elif response.type == ToolInvokeMessage.MessageType.LINK:
result += f"result link: {response.message}. please tell user to check it."
elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
result += "image has been created and sent to user already, you do not need to create it, just tell the user to check it now."
elif response.type == ToolInvokeMessage.MessageType.JSON:
result += f"tool response: {json.dumps(response.message, ensure_ascii=False)}."
else:
result += f"tool response: {response.message}."
return result
@staticmethod
def _extract_tool_response_binary(tool_response: list[ToolInvokeMessage]) -> list[ToolInvokeMessageBinary]:
"""
Extract tool response binary
"""
result = []
for response in tool_response:
if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
response.type == ToolInvokeMessage.MessageType.IMAGE:
mimetype = None
if response.meta.get('mime_type'):
mimetype = response.meta.get('mime_type')
else:
try:
url = URL(response.message)
extension = url.suffix
guess_type_result, _ = guess_type(f'a{extension}')
if guess_type_result:
mimetype = guess_type_result
except Exception:
pass
if not mimetype:
mimetype = 'image/jpeg'
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'image/jpeg'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.BLOB:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream'),
url=response.message,
save_as=response.save_as,
))
elif response.type == ToolInvokeMessage.MessageType.LINK:
# check if there is a mime type in meta
if response.meta and 'mime_type' in response.meta:
result.append(ToolInvokeMessageBinary(
mimetype=response.meta.get('mime_type', 'octet/stream') if response.meta else 'octet/stream',
url=response.message,
save_as=response.save_as,
))
return result
@staticmethod
def _create_message_files(
tool_messages: list[ToolInvokeMessageBinary],
agent_message: Message,
invoke_from: InvokeFrom,
user_id: str
) -> list[tuple[Any, str]]:
"""
Create message file
:param messages: messages
:return: message files, should save as variable
"""
result = []
for message in tool_messages:
file_type = 'bin'
if 'image' in message.mimetype:
file_type = 'image'
elif 'video' in message.mimetype:
file_type = 'video'
elif 'audio' in message.mimetype:
file_type = 'audio'
elif 'text' in message.mimetype:
file_type = 'text'
elif 'pdf' in message.mimetype:
file_type = 'pdf'
elif 'zip' in message.mimetype:
file_type = 'archive'
# ...
message_file = MessageFile(
message_id=agent_message.id,
type=file_type,
transfer_method=FileTransferMethod.TOOL_FILE.value,
belongs_to='assistant',
url=message.url,
upload_file_id=None,
created_by_role=('account'if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
created_by=user_id,
)
db.session.add(message_file)
db.session.commit()
db.session.refresh(message_file)
result.append((
message_file.id,
message.save_as
))
db.session.close()
return result