# Improvements to Arcade TDK Error Handling
I tried my very best to not make any breaking changes in this PR. So,
you will notice various "Deprecation" notices throughout.
### Instructions for PR reviewers
1. Pull down this PR's branch
2. Pull down the Engine's tool error handling PR's branch
3. Update your installed arcadepy to have the following:
- In `arcadepy/resources/tools/tools.py`, if you want to test out
including stacktraces, then you need to update `ToolsResource.execute`
to accept a `include_error_stacktrace` argument and also include the
"include_error_stacktrace" argument to the POST to the Engine inside of
the function's execute method's body.
- In `arcadepy/types/execute_tool_response.py` add the following enum
```py
class ErrorKind(str, Enum):
"""Error kind that is comprised of
- the who (toolkit, tool, upstream)
- the when (load time, definition parsing time, runtime)
- the what (bad_definition, bad_input, bad_output, retry,
context_required, fatal, etc.)"""
TOOLKIT_LOAD_FAILED = "TOOLKIT_LOAD_FAILED"
TOOL_DEFINITION_BAD_DEFINITION = "TOOL_DEFINITION_BAD_DEFINITION"
TOOL_DEFINITION_BAD_INPUT_SCHEMA = "TOOL_DEFINITION_BAD_INPUT_SCHEMA"
TOOL_DEFINITION_BAD_OUTPUT_SCHEMA = "TOOL_DEFINITION_BAD_OUTPUT_SCHEMA"
TOOL_RUNTIME_BAD_INPUT_VALUE = "TOOL_RUNTIME_BAD_INPUT_VALUE"
TOOL_RUNTIME_BAD_OUTPUT_VALUE = "TOOL_RUNTIME_BAD_OUTPUT_VALUE"
TOOL_RUNTIME_RETRY = "TOOL_RUNTIME_RETRY"
TOOL_RUNTIME_CONTEXT_REQUIRED = "TOOL_RUNTIME_CONTEXT_REQUIRED"
TOOL_RUNTIME_FATAL = "TOOL_RUNTIME_FATAL"
UPSTREAM_RUNTIME_BAD_REQUEST = "UPSTREAM_RUNTIME_BAD_REQUEST"
UPSTREAM_RUNTIME_AUTH_ERROR = "UPSTREAM_RUNTIME_AUTH_ERROR"
UPSTREAM_RUNTIME_NOT_FOUND = "UPSTREAM_RUNTIME_NOT_FOUND"
UPSTREAM_RUNTIME_VALIDATION_ERROR = "UPSTREAM_RUNTIME_VALIDATION_ERROR"
UPSTREAM_RUNTIME_RATE_LIMIT = "UPSTREAM_RUNTIME_RATE_LIMIT"
UPSTREAM_RUNTIME_SERVER_ERROR = "UPSTREAM_RUNTIME_SERVER_ERROR"
UPSTREAM_RUNTIME_UNMAPPED = "UPSTREAM_RUNTIME_UNMAPPED"
UNKNOWN = "UNKNOWN"
```
- In `arcadepy/types/execute_tool_response.py` add the following fields
to OutputError:
```py
kind: ErrorKind
status_code: Optional[int] = None
stacktrace: Optional[str] = None
extra: Optional[dict[str, Any]] = None
```
### Example Client Usage
```py
# Example of handling an upstream rate limit
error = response.output.error
if error and error.kind == ErrorKind.UPSTREAM_RUNTIME_RATE_LIMIT:
sleep_time = error.retry_after_ms / 1000
time.sleep(sleep_time)
# and then execute again
```
```py
# Examples of determining what type of runtime error it is
error = response.output.error
if error:
is_retryable_error = error.kind == ErrorKind.TOOL_RUNTIME_RETRY
is_a_bug_in_the_tool = error.kind == ErrorKind.TOOL_RUNTIME_FATAL
is_additional_context_required = error.kind == ErrorKind.TOOL_RUNTIME_CONTEXT_REQUIRED
```
### Example Tool Usage
```py
# EXAMPLE 1 letting Arcade handle upstream error handling for you
reddit_client.post(params) # Arcade's httpx adapter will handle error handling for you!
# ------------------------------------
# EXAMPLE 2 handling upstream bad request yourself, but letting Arcade handle the rest
try:
reddit_client.post(params)
except httpx.HTTPStatusError as e:
if e.status_code == 400:
raise UpstreamError("My extra custom message) from e
raise
```
```py
# EXAMPLE 1 letting Arcade handle it for you
risky_element = my_risky_list[42] # Arcade will raise a FatalToolError for you
# ------------------------------------
# EXAMPLE 2 handling it yourself for extra flexibility
try:
risky_element = my_risky_list[42]
except IndexError as e:
raise FatalToolError("My extra custom message") from e
```
### Non-runtime Error Message Examples
Example ToolkitLoadError Messages:
```
- [TOOLKIT_LOAD_FAILED] ToolkitLoadError when loading toolkit 'sample_tool': Could not import module mock_module. Reason: Mock import error
- [TOOLKIT_LOAD_FAILED] ToolkitLoadError when loading toolkit 'test_toolkit': Tool 'ValidTool' in toolkit 'test_toolkit' already exists in the catalog.
```
Example ToolDefinitionError Messages
```
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_missing_description': Tool 'tool_missing_description' is missing a description
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_invalid_secret_type': Secret keys must be strings (error in tool ToolWithInvalidSecretType).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_empty_secret': Secrets must have a non-empty key (error in tool ToolWithEmptySecret).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_invalid_metadata_type': Metadata must be strings (error in tool ToolWithInvalidMetadataType).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_metadata_requiring_auth_without_auth': Tool ToolWithMetadataRequiringAuthWithoutAuth declares metadata key 'client_id', which requires that the tool has an auth requirement, but no auth requirement was provided. Please specify an auth requirement.
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_empty_metadata': Metadata must have a non-empty key (error in tool ToolWithEmptyMetadata).
- [TOOL_DEFINITION_BAD_DEFINITION] ToolDefinitionError in definition of tool 'tool_with_unsupported_param_type': Unsupported parameter type: <class 'test_catalog.MyFancyTestClass'>
```
Example ToolInputSchemaError Messages
```
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_missing_input_parameter_annotation': Parameter 'input_text' is missing a description
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_no_type_annotation': Parameter param has no type annotation.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_invalid_param_name': Invalid parameter name: '123invalid' is not a valid identifier. Identifiers must start with a letter or underscore, and can only contain letters, digits, or underscores.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_too_many_annotations': Parameter param: Annotated[str, 'name', 'desc', 'extra'] has too many string annotations. Expected 0, 1, or 2, got 3.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_required_union_param': Parameter param is a union type. Only optional types are supported.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_non_callable_default_factory': Default factory for parameter param: Annotated[str, 'Parameter'] = FieldInfo(annotation=NoneType, required=False, default_factory=str) is not callable.
- [TOOL_DEFINITION_BAD_INPUT_SCHEMA] ToolInputSchemaError in definition of tool 'tool_with_multiple_tool_contexts': Only one ToolContext parameter is supported, but tool tool_with_multiple_tool_contexts has multiple.
```
Example ToolOutputSchemaError Messages
```
- [TOOL_DEFINITION_BAD_OUTPUT_SCHEMA] ToolOutputSchemaError in definition of tool 'tool_missing_return_type_hint': Tool 'ToolMissingReturnTypeHint' must have a return type
- [TOOL_DEFINITION_BAD_OUTPUT_SCHEMA] ToolOutputSchemaError in definition of tool 'tool_with_unsupported_output_type': Unsupported output type '<class 'test_catalog.MyFancyTestClass'>'. Only built-in Python types, TypedDicts, Pydantic models, and standard collections are supported as tool output types.
```
### Runtime Error Message Examples
Example Tool Runtime Error Messages
```
- [TOOL_RUNTIME_FATAL] FatalToolError during execution of tool 'get_posts_in_subreddit': list index out of range
- [TOOL_RUNTIME_CONTEXT_REQUIRED] ContextRequiredToolError during execution of tool 'get_posts_in_subreddit': Ambiguous username. Please provide a more specific username
- [TOOL_RUNTIME_RETRY] RetryableToolError during execution of tool 'get_posts_in_subreddit': Retry with subreddit=learnpython or subreddit=learnprogramming
```
Example Upstream Runtime Error Messages
```
- [UPSTREAM_RUNTIME_RATE_LIMIT] UpstreamRateLimitError during execution of tool 'get_posts_in_subreddit': 429 Client Error: Too Many Requests
- [UPSTREAM_RUNTIME_BAD_REQUEST] UpstreamError during execution of tool 'get_posts_in_subreddit': 400 Client Error: Bad request. Missing 'id' parameter.
- [UPSTREAM_RUNTIME_BAD_REQUEST] UpstreamError during execution of tool 'search_files': Upstream Google API error: Invalid value '-23'. Values must be within the range: [value: 1\n, value: 1000\n]
```
123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
from typing import Any, TypeVar
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from pydantic import BaseModel
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from arcade_core.errors import ErrorKind
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from arcade_core.schema import ToolCallError, ToolCallLog, ToolCallOutput
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from arcade_core.utils import coerce_empty_list_to_none
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T = TypeVar("T")
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class ToolOutputFactory:
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"""
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Singleton pattern for unified return method from tools.
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"""
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def success(
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self,
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*,
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data: T | None = None,
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logs: list[ToolCallLog] | None = None,
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) -> ToolCallOutput:
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# Extract the result value
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"""
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Extracts the result value for the tool output.
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The executor guarantees that `data` is either a string, a dict, or None.
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"""
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value: str | int | float | bool | dict | list | None
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if data is None:
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value = ""
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elif hasattr(data, "result"):
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result = getattr(data, "result", "")
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# Handle None result the same way as None data
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if result is None:
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value = ""
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# If the result is a BaseModel (e.g., from TypedDict conversion), convert to dict
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elif isinstance(result, BaseModel):
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value = result.model_dump()
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# If the result is a list, check if it contains BaseModel objects
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elif isinstance(result, list):
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value = [
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item.model_dump() if isinstance(item, BaseModel) else item for item in result
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]
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else:
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value = result
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elif isinstance(data, BaseModel):
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value = data.model_dump()
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elif isinstance(data, (str, int, float, bool, list, dict)):
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value = data
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else:
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raise ValueError(f"Unsupported data output type: {type(data)}")
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logs = coerce_empty_list_to_none(logs)
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return ToolCallOutput(
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value=value,
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logs=logs,
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)
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def fail(
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self,
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*,
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message: str,
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developer_message: str | None = None,
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stacktrace: str | None = None,
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logs: list[ToolCallLog] | None = None,
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additional_prompt_content: str | None = None,
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retry_after_ms: int | None = None,
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kind: ErrorKind = ErrorKind.UNKNOWN,
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can_retry: bool = False,
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status_code: int | None = None,
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extra: dict[str, Any] | None = None,
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) -> ToolCallOutput:
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return ToolCallOutput(
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error=ToolCallError(
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message=message,
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developer_message=developer_message,
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can_retry=can_retry,
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additional_prompt_content=additional_prompt_content,
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retry_after_ms=retry_after_ms,
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stacktrace=stacktrace,
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kind=kind,
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status_code=status_code,
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extra=extra,
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),
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logs=coerce_empty_list_to_none(logs),
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)
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def fail_retry(
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self,
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*,
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message: str,
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developer_message: str | None = None,
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additional_prompt_content: str | None = None,
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retry_after_ms: int | None = None,
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stacktrace: str | None = None,
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logs: list[ToolCallLog] | None = None,
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kind: ErrorKind = ErrorKind.TOOL_RUNTIME_RETRY,
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status_code: int = 500,
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extra: dict[str, Any] | None = None,
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) -> ToolCallOutput:
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"""
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DEPRECATED: Use ToolOutputFactory.fail instead.
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This method will be removed in version 3.0.0
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"""
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return ToolCallOutput(
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error=ToolCallError(
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message=message,
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developer_message=developer_message,
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can_retry=True,
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additional_prompt_content=additional_prompt_content,
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retry_after_ms=retry_after_ms,
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stacktrace=stacktrace,
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kind=kind,
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status_code=status_code,
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extra=extra,
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),
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logs=coerce_empty_list_to_none(logs),
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)
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output_factory = ToolOutputFactory()
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