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FEAT: NEW WORKFLOW ENGINE (#3160)

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Co-authored-by: Joel <iamjoel007@gmail.com>
Co-authored-by: Yeuoly <admin@srmxy.cn>
Co-authored-by: JzoNg <jzongcode@gmail.com>
Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: jyong <jyong@dify.ai>
Co-authored-by: nite-knite <nkCoding@gmail.com>
Co-authored-by: jyong <718720800@qq.com>
This commit is contained in:
takatost
2024-04-08 18:51:46 +08:00
committed by GitHub
parent 2fb9850af5
commit 7753ba2d37
1161 changed files with 103836 additions and 10327 deletions
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8
api/core/tools/tool/api_tool.py
View File

@@ -8,7 +8,8 @@ import requests
import core.helper.ssrf_proxy as ssrf_proxy
from core.tools.entities.tool_bundle import ApiBasedToolBundle
from core.tools.entities.tool_entities import ToolInvokeMessage
from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
from core.tools.entities.user_entities import UserToolProvider
from core.tools.errors import ToolInvokeError, ToolParameterValidationError, ToolProviderCredentialValidationError
from core.tools.tool.tool import Tool
@@ -34,7 +35,7 @@ class ApiTool(Tool):
api_bundle=self.api_bundle.copy() if self.api_bundle else None,
runtime=Tool.Runtime(**meta)
)
def validate_credentials(self, credentials: dict[str, Any], parameters: dict[str, Any], format_only: bool = False) -> str:
"""
validate the credentials for Api tool
@@ -49,6 +50,9 @@ class ApiTool(Tool):
# validate response
return self.validate_and_parse_response(response)
def tool_provider_type(self) -> ToolProviderType:
return UserToolProvider.ProviderType.API
def assembling_request(self, parameters: dict[str, Any]) -> dict[str, Any]:
headers = {}
credentials = self.runtime.credentials or {}

5
api/core/tools/tool/builtin_tool.py
View File

@@ -1,6 +1,8 @@
from core.model_runtime.entities.llm_entities import LLMResult
from core.model_runtime.entities.message_entities import PromptMessage, SystemPromptMessage, UserPromptMessage
from core.tools.entities.tool_entities import ToolProviderType
from core.tools.entities.user_entities import UserToolProvider
from core.tools.model.tool_model_manager import ToolModelManager
from core.tools.tool.tool import Tool
from core.tools.utils.web_reader_tool import get_url
@@ -40,6 +42,9 @@ class BuiltinTool(Tool):
prompt_messages=prompt_messages,
)
def tool_provider_type(self) -> ToolProviderType:
return UserToolProvider.ProviderType.BUILTIN
def get_max_tokens(self) -> int:
"""
get max tokens

20
api/core/tools/tool/dataset_retriever_tool.py
View File

@@ -2,11 +2,18 @@ from typing import Any
from langchain.tools import BaseTool
from core.app.app_config.entities import DatasetRetrieveConfigEntity
from core.app.entities.app_invoke_entities import InvokeFrom
from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
from core.entities.application_entities import DatasetRetrieveConfigEntity, InvokeFrom
from core.features.dataset_retrieval.dataset_retrieval import DatasetRetrievalFeature
from core.rag.retrieval.dataset_retrieval import DatasetRetrieval
from core.tools.entities.common_entities import I18nObject
from core.tools.entities.tool_entities import ToolDescription, ToolIdentity, ToolInvokeMessage, ToolParameter
from core.tools.entities.tool_entities import (
ToolDescription,
ToolIdentity,
ToolInvokeMessage,
ToolParameter,
ToolProviderType,
)
from core.tools.tool.tool import Tool
@@ -30,7 +37,7 @@ class DatasetRetrieverTool(Tool):
if retrieve_config is None:
return []
feature = DatasetRetrievalFeature()
feature = DatasetRetrieval()
# save original retrieve strategy, and set retrieve strategy to SINGLE
# Agent only support SINGLE mode
@@ -52,7 +59,7 @@ class DatasetRetrieverTool(Tool):
for langchain_tool in langchain_tools:
tool = DatasetRetrieverTool(
langchain_tool=langchain_tool,
identity=ToolIdentity(author='', name=langchain_tool.name, label=I18nObject(en_US='', zh_Hans='')),
identity=ToolIdentity(provider='', author='', name=langchain_tool.name, label=I18nObject(en_US='', zh_Hans='')),
parameters=[],
is_team_authorization=True,
description=ToolDescription(
@@ -76,6 +83,9 @@ class DatasetRetrieverTool(Tool):
required=True,
default=''),
]
def tool_provider_type(self) -> ToolProviderType:
return ToolProviderType.DATASET_RETRIEVAL
def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
"""

5
api/core/tools/tool/model_tool.py
View File

@@ -11,7 +11,7 @@ from core.model_runtime.entities.message_entities import (
UserPromptMessage,
)
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.tools.entities.tool_entities import ModelToolPropertyKey, ToolInvokeMessage
from core.tools.entities.tool_entities import ModelToolPropertyKey, ToolInvokeMessage, ToolProviderType
from core.tools.tool.tool import Tool
VISION_PROMPT = """## Image Recognition Task
@@ -79,6 +79,9 @@ class ModelTool(Tool):
"""
pass
def tool_provider_type(self) -> ToolProviderType:
return ToolProviderType.BUILT_IN
def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
"""
"""

96
api/core/tools/tool/tool.py
View File

@@ -2,14 +2,14 @@ from abc import ABC, abstractmethod
from enum import Enum
from typing import Any, Optional, Union
from pydantic import BaseModel
from pydantic import BaseModel, validator
from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
from core.tools.entities.tool_entities import (
ToolDescription,
ToolIdentity,
ToolInvokeMessage,
ToolParameter,
ToolProviderType,
ToolRuntimeImageVariable,
ToolRuntimeVariable,
ToolRuntimeVariablePool,
@@ -22,8 +22,13 @@ class Tool(BaseModel, ABC):
parameters: Optional[list[ToolParameter]] = None
description: ToolDescription = None
is_team_authorization: bool = False
agent_callback: Optional[DifyAgentCallbackHandler] = None
use_callback: bool = False
@validator('parameters', pre=True, always=True)
def set_parameters(cls, v, values):
if not v:
return []
return v
class Runtime(BaseModel):
"""
@@ -45,15 +50,10 @@ class Tool(BaseModel, ABC):
def __init__(self, **data: Any):
super().__init__(**data)
if not self.agent_callback:
self.use_callback = False
else:
self.use_callback = True
class VARIABLE_KEY(Enum):
IMAGE = 'image'
def fork_tool_runtime(self, meta: dict[str, Any], agent_callback: DifyAgentCallbackHandler = None) -> 'Tool':
def fork_tool_runtime(self, meta: dict[str, Any]) -> 'Tool':
"""
fork a new tool with meta data
@@ -65,9 +65,16 @@ class Tool(BaseModel, ABC):
parameters=self.parameters.copy() if self.parameters else None,
description=self.description.copy() if self.description else None,
runtime=Tool.Runtime(**meta),
agent_callback=agent_callback
)
@abstractmethod
def tool_provider_type(self) -> ToolProviderType:
"""
get the tool provider type
:return: the tool provider type
"""
def load_variables(self, variables: ToolRuntimeVariablePool):
"""
load variables from database
@@ -174,50 +181,22 @@ class Tool(BaseModel, ABC):
return result
def invoke(self, user_id: str, tool_parameters: Union[dict[str, Any], str]) -> list[ToolInvokeMessage]:
# check if tool_parameters is a string
if isinstance(tool_parameters, str):
# check if this tool has only one parameter
parameters = [parameter for parameter in self.parameters 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}")
def invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
# update tool_parameters
if self.runtime.runtime_parameters:
tool_parameters.update(self.runtime.runtime_parameters)
# hit callback
if self.use_callback:
self.agent_callback.on_tool_start(
tool_name=self.identity.name,
tool_inputs=tool_parameters
)
# try parse tool parameters into the correct type
tool_parameters = self._transform_tool_parameters_type(tool_parameters)
try:
result = self._invoke(
user_id=user_id,
tool_parameters=tool_parameters,
)
except Exception as e:
if self.use_callback:
self.agent_callback.on_tool_error(e)
raise e
result = self._invoke(
user_id=user_id,
tool_parameters=tool_parameters,
)
if not isinstance(result, list):
result = [result]
# hit callback
if self.use_callback:
self.agent_callback.on_tool_end(
tool_name=self.identity.name,
tool_inputs=tool_parameters,
tool_outputs=self._convert_tool_response_to_str(result)
)
return result
def _convert_tool_response_to_str(self, tool_response: list[ToolInvokeMessage]) -> str:
@@ -242,6 +221,31 @@ class Tool(BaseModel, ABC):
result += f"tool response: {response.message}."
return result
def _transform_tool_parameters_type(self, tool_parameters: dict[str, Any]) -> dict[str, Any]:
"""
Transform tool parameters type
"""
for parameter in self.parameters:
if parameter.name in tool_parameters:
if parameter.type in [
ToolParameter.ToolParameterType.SECRET_INPUT,
ToolParameter.ToolParameterType.STRING,
ToolParameter.ToolParameterType.SELECT,
] and not isinstance(tool_parameters[parameter.name], str):
tool_parameters[parameter.name] = str(tool_parameters[parameter.name])
elif parameter.type == ToolParameter.ToolParameterType.NUMBER \
and not isinstance(tool_parameters[parameter.name], int | float):
if isinstance(tool_parameters[parameter.name], str):
try:
tool_parameters[parameter.name] = int(tool_parameters[parameter.name])
except ValueError:
tool_parameters[parameter.name] = float(tool_parameters[parameter.name])
elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
if not isinstance(tool_parameters[parameter.name], bool):
tool_parameters[parameter.name] = bool(tool_parameters[parameter.name])
return tool_parameters
@abstractmethod
def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]: