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refactor(parameter_extractor): implement custom error classes (#10260)

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-LAN- 2024-11-05 09:27:51 +08:00 committed by GitHub
parent 971defbbbd
commit 7a98dab6a4
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2 changed files with 86 additions and 21 deletions
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50
api/core/workflow/nodes/parameter_extractor/exc.py Normal file
View File

@ -0,0 +1,50 @@
class ParameterExtractorNodeError(ValueError):
"""Base error for ParameterExtractorNode."""
class InvalidModelTypeError(ParameterExtractorNodeError):
"""Raised when the model is not a Large Language Model."""
class ModelSchemaNotFoundError(ParameterExtractorNodeError):
"""Raised when the model schema is not found."""
class InvalidInvokeResultError(ParameterExtractorNodeError):
"""Raised when the invoke result is invalid."""
class InvalidTextContentTypeError(ParameterExtractorNodeError):
"""Raised when the text content type is invalid."""
class InvalidNumberOfParametersError(ParameterExtractorNodeError):
"""Raised when the number of parameters is invalid."""
class RequiredParameterMissingError(ParameterExtractorNodeError):
"""Raised when a required parameter is missing."""
class InvalidSelectValueError(ParameterExtractorNodeError):
"""Raised when a select value is invalid."""
class InvalidNumberValueError(ParameterExtractorNodeError):
"""Raised when a number value is invalid."""
class InvalidBoolValueError(ParameterExtractorNodeError):
"""Raised when a bool value is invalid."""
class InvalidStringValueError(ParameterExtractorNodeError):
"""Raised when a string value is invalid."""
class InvalidArrayValueError(ParameterExtractorNodeError):
"""Raised when an array value is invalid."""
class InvalidModelModeError(ParameterExtractorNodeError):
"""Raised when the model mode is invalid."""

57
api/core/workflow/nodes/parameter_extractor/parameter_extractor_node.py
View File

@ -32,6 +32,21 @@ from extensions.ext_database import db
from models.workflow import WorkflowNodeExecutionStatus
from .entities import ParameterExtractorNodeData
from .exc import (
InvalidArrayValueError,
InvalidBoolValueError,
InvalidInvokeResultError,
InvalidModelModeError,
InvalidModelTypeError,
InvalidNumberOfParametersError,
InvalidNumberValueError,
InvalidSelectValueError,
InvalidStringValueError,
InvalidTextContentTypeError,
ModelSchemaNotFoundError,
ParameterExtractorNodeError,
RequiredParameterMissingError,
)
from .prompts import (
CHAT_EXAMPLE,
CHAT_GENERATE_JSON_USER_MESSAGE_TEMPLATE,
@ -85,7 +100,7 @@ class ParameterExtractorNode(LLMNode):
model_instance, model_config = self._fetch_model_config(node_data.model)
if not isinstance(model_instance.model_type_instance, LargeLanguageModel):
raise ValueError("Model is not a Large Language Model")
raise InvalidModelTypeError("Model is not a Large Language Model")
llm_model = model_instance.model_type_instance
model_schema = llm_model.get_model_schema(
@ -93,7 +108,7 @@ class ParameterExtractorNode(LLMNode):
credentials=model_config.credentials,
)
if not model_schema:
raise ValueError("Model schema not found")
raise ModelSchemaNotFoundError("Model schema not found")
# fetch memory
memory = self._fetch_memory(
@ -155,7 +170,7 @@ class ParameterExtractorNode(LLMNode):
process_data["usage"] = jsonable_encoder(usage)
process_data["tool_call"] = jsonable_encoder(tool_call)
process_data["llm_text"] = text
except Exception as e:
except ParameterExtractorNodeError as e:
return NodeRunResult(
status=WorkflowNodeExecutionStatus.FAILED,
inputs=inputs,
@ -177,7 +192,7 @@ class ParameterExtractorNode(LLMNode):
try:
result = self._validate_result(data=node_data, result=result or {})
except Exception as e:
except ParameterExtractorNodeError as e:
error = str(e)
# transform result into standard format
@ -217,11 +232,11 @@ class ParameterExtractorNode(LLMNode):
# handle invoke result
if not isinstance(invoke_result, LLMResult):
raise ValueError(f"Invalid invoke result: {invoke_result}")
raise InvalidInvokeResultError(f"Invalid invoke result: {invoke_result}")
text = invoke_result.message.content
if not isinstance(text, str):
raise ValueError(f"Invalid text content type: {type(text)}. Expected str.")
raise InvalidTextContentTypeError(f"Invalid text content type: {type(text)}. Expected str.")
usage = invoke_result.usage
tool_call = invoke_result.message.tool_calls[0] if invoke_result.message.tool_calls else None
@ -344,7 +359,7 @@ class ParameterExtractorNode(LLMNode):
files=files,
)
else:
raise ValueError(f"Invalid model mode: {model_mode}")
raise InvalidModelModeError(f"Invalid model mode: {model_mode}")
def _generate_prompt_engineering_completion_prompt(
self,
@ -449,36 +464,36 @@ class ParameterExtractorNode(LLMNode):
Validate result.
"""
if len(data.parameters) != len(result):
raise ValueError("Invalid number of parameters")
raise InvalidNumberOfParametersError("Invalid number of parameters")
for parameter in data.parameters:
if parameter.required and parameter.name not in result:
raise ValueError(f"Parameter {parameter.name} is required")
raise RequiredParameterMissingError(f"Parameter {parameter.name} is required")
if parameter.type == "select" and parameter.options and result.get(parameter.name) not in parameter.options:
raise ValueError(f"Invalid `select` value for parameter {parameter.name}")
raise InvalidSelectValueError(f"Invalid `select` value for parameter {parameter.name}")
if parameter.type == "number" and not isinstance(result.get(parameter.name), int | float):
raise ValueError(f"Invalid `number` value for parameter {parameter.name}")
raise InvalidNumberValueError(f"Invalid `number` value for parameter {parameter.name}")
if parameter.type == "bool" and not isinstance(result.get(parameter.name), bool):
raise ValueError(f"Invalid `bool` value for parameter {parameter.name}")
raise InvalidBoolValueError(f"Invalid `bool` value for parameter {parameter.name}")
if parameter.type == "string" and not isinstance(result.get(parameter.name), str):
raise ValueError(f"Invalid `string` value for parameter {parameter.name}")
raise InvalidStringValueError(f"Invalid `string` value for parameter {parameter.name}")
if parameter.type.startswith("array"):
parameters = result.get(parameter.name)
if not isinstance(parameters, list):
raise ValueError(f"Invalid `array` value for parameter {parameter.name}")
raise InvalidArrayValueError(f"Invalid `array` value for parameter {parameter.name}")
nested_type = parameter.type[6:-1]
for item in parameters:
if nested_type == "number" and not isinstance(item, int | float):
raise ValueError(f"Invalid `array[number]` value for parameter {parameter.name}")
raise InvalidArrayValueError(f"Invalid `array[number]` value for parameter {parameter.name}")
if nested_type == "string" and not isinstance(item, str):
raise ValueError(f"Invalid `array[string]` value for parameter {parameter.name}")
raise InvalidArrayValueError(f"Invalid `array[string]` value for parameter {parameter.name}")
if nested_type == "object" and not isinstance(item, dict):
raise ValueError(f"Invalid `array[object]` value for parameter {parameter.name}")
raise InvalidArrayValueError(f"Invalid `array[object]` value for parameter {parameter.name}")
return result
def _transform_result(self, data: ParameterExtractorNodeData, result: dict) -> dict:
@ -634,7 +649,7 @@ class ParameterExtractorNode(LLMNode):
user_prompt_message = ChatModelMessage(role=PromptMessageRole.USER, text=input_text)
return [system_prompt_messages, user_prompt_message]
else:
raise ValueError(f"Model mode {model_mode} not support.")
raise InvalidModelModeError(f"Model mode {model_mode} not support.")
def _get_prompt_engineering_prompt_template(
self,
@ -669,7 +684,7 @@ class ParameterExtractorNode(LLMNode):
.replace("}γγγ", "")
)
else:
raise ValueError(f"Model mode {model_mode} not support.")
raise InvalidModelModeError(f"Model mode {model_mode} not support.")
def _calculate_rest_token(
self,
@ -683,12 +698,12 @@ class ParameterExtractorNode(LLMNode):
model_instance, model_config = self._fetch_model_config(node_data.model)
if not isinstance(model_instance.model_type_instance, LargeLanguageModel):
raise ValueError("Model is not a Large Language Model")
raise InvalidModelTypeError("Model is not a Large Language Model")
llm_model = model_instance.model_type_instance
model_schema = llm_model.get_model_schema(model_config.model, model_config.credentials)
if not model_schema:
raise ValueError("Model schema not found")
raise ModelSchemaNotFoundError("Model schema not found")
if set(model_schema.features or []) & {ModelFeature.MULTI_TOOL_CALL, ModelFeature.MULTI_TOOL_CALL}:
prompt_template = self._get_function_calling_prompt_template(node_data, query, variable_pool, None, 2000)