# 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]
```
477 lines
15 KiB
Python
477 lines
15 KiB
Python
import os
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, Literal
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from pydantic import BaseModel, Field
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from arcade_core.errors import ErrorKind
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# allow for custom tool name separator
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TOOL_NAME_SEPARATOR = os.getenv("ARCADE_TOOL_NAME_SEPARATOR", ".")
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class ValueSchema(BaseModel):
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"""Value schema for input parameters and outputs."""
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val_type: Literal["string", "integer", "number", "boolean", "json", "array"]
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"""The type of the value."""
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inner_val_type: Literal["string", "integer", "number", "boolean", "json"] | None = None
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"""The type of the inner value, if the value is a list."""
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enum: list[str] | None = None
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"""The list of possible values for the value, if it is a closed list."""
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properties: dict[str, "ValueSchema"] | None = None
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"""For object types (json), the schema of nested properties."""
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inner_properties: dict[str, "ValueSchema"] | None = None
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"""For array types with json items, the schema of properties for each array item."""
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description: str | None = None
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"""Optional description of the value."""
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class InputParameter(BaseModel):
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"""A parameter that can be passed to a tool."""
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name: str = Field(..., description="The human-readable name of this parameter.")
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required: bool = Field(
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...,
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description="Whether this parameter is required (true) or optional (false).",
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)
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description: str | None = Field(
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None,
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description="A descriptive, human-readable explanation of the parameter.",
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)
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value_schema: ValueSchema = Field(
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...,
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description="The schema of the value of this parameter.",
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)
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inferrable: bool = Field(
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True,
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description="Whether a value for this parameter can be inferred by a model. Defaults to `true`.",
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)
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class ToolInput(BaseModel):
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"""The inputs that a tool accepts."""
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parameters: list[InputParameter]
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"""The list of parameters that the tool accepts."""
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tool_context_parameter_name: str | None = Field(default=None, exclude=True)
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"""
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The name of the target parameter that will contain the tool context (if any).
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This field will not be included in serialization.
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"""
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class ToolOutput(BaseModel):
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"""The output of a tool."""
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description: str | None = Field(
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None, description="A descriptive, human-readable explanation of the output."
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)
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available_modes: list[str] = Field(
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default_factory=lambda: ["value", "error", "null"],
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description="The available modes for the output.",
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)
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value_schema: ValueSchema | None = Field(
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None, description="The schema of the value of the output."
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)
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class OAuth2Requirement(BaseModel):
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"""Indicates that the tool requires OAuth 2.0 authorization."""
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scopes: list[str] | None = None
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"""The scope(s) needed for the authorized action."""
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class ToolAuthRequirement(BaseModel):
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"""A requirement for authorization to use a tool."""
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# Provider ID, Type, and ID needed for the Arcade Engine to look up the auth provider.
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# However, the developer generally does not need to set these directly.
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# Instead, they will use:
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# @tool(requires_auth=Google(scopes=["profile", "email"]))
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# or
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# client.auth.authorize(provider=AuthProvider.google, scopes=["profile", "email"])
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#
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# The Arcade SDK translates these into the appropriate provider ID (Google) and type (OAuth2).
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# The only time the developer will set these is if they are using a custom auth provider.
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provider_id: str | None = None
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"""The provider ID configured in Arcade that acts as an alias to well-known configuration."""
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provider_type: str
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"""The type of the authorization provider."""
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id: str | None = None
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"""A provider's unique identifier, allowing the tool to specify a specific authorization provider. Recommended for private tools only."""
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oauth2: OAuth2Requirement | None = None
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"""The OAuth 2.0 requirement, if any."""
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class ToolSecretRequirement(BaseModel):
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"""A requirement for a tool to run."""
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key: str
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"""The ID of the secret."""
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class ToolMetadataKey(str, Enum):
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"""Convience enum for commonly used metadata keys."""
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CLIENT_ID = "client_id"
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COORDINATOR_URL = "coordinator_url"
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@staticmethod
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def requires_auth(key: str) -> bool:
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"""Whether the key depends on the tool having an authorization requirement."""
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keys_that_require_auth = [ToolMetadataKey.CLIENT_ID]
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return key.strip().lower() in keys_that_require_auth
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class ToolMetadataRequirement(BaseModel):
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"""A requirement for a tool to run."""
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key: str
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"""The ID of the metadata."""
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class ToolRequirements(BaseModel):
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"""The requirements for a tool to run."""
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authorization: ToolAuthRequirement | None = None
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"""The authorization requirements for the tool, if any."""
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secrets: list[ToolSecretRequirement] | None = None
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"""The secret requirements for the tool, if any."""
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metadata: list[ToolMetadataRequirement] | None = None
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"""The metadata requirements for the tool, if any."""
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class ToolkitDefinition(BaseModel):
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"""The specification of a toolkit."""
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name: str
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"""The name of the toolkit."""
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description: str | None = None
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"""The description of the toolkit."""
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version: str | None = None
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"""The version identifier of the toolkit."""
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@dataclass(frozen=True)
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class FullyQualifiedName:
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"""The fully-qualified name of a tool."""
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name: str
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"""The name of the tool."""
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toolkit_name: str
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"""The name of the toolkit containing the tool."""
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toolkit_version: str | None = None
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"""The version of the toolkit containing the tool."""
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def __str__(self) -> str:
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return f"{self.toolkit_name}{TOOL_NAME_SEPARATOR}{self.name}"
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def __eq__(self, other: Any) -> bool:
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if not isinstance(other, FullyQualifiedName):
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return False
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return (
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self.name.lower() == other.name.lower()
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and self.toolkit_name.lower() == other.toolkit_name.lower()
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and (self.toolkit_version or "").lower() == (other.toolkit_version or "").lower()
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)
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def __hash__(self) -> int:
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return hash((
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self.name.lower(),
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self.toolkit_name.lower(),
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(self.toolkit_version or "").lower(),
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))
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def equals_ignoring_version(self, other: "FullyQualifiedName") -> bool:
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"""Check if two fully-qualified tool names are equal, ignoring the version."""
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return (
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self.name.lower() == other.name.lower()
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and self.toolkit_name.lower() == other.toolkit_name.lower()
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)
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@staticmethod
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def from_toolkit(tool_name: str, toolkit: ToolkitDefinition) -> "FullyQualifiedName":
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"""Creates a fully-qualified tool name from a tool name and a ToolkitDefinition."""
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return FullyQualifiedName(tool_name, toolkit.name, toolkit.version)
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class ToolDefinition(BaseModel):
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"""The specification of a tool."""
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name: str
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"""The name of the tool."""
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fully_qualified_name: str
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"""The fully-qualified name of the tool."""
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description: str
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"""The description of the tool."""
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toolkit: ToolkitDefinition
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"""The toolkit that contains the tool."""
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input: ToolInput
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"""The inputs that the tool accepts."""
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output: ToolOutput
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"""The output types that the tool can return."""
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requirements: ToolRequirements
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"""The requirements (e.g. authorization) for the tool to run."""
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deprecation_message: str | None = None
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"""The message to display when the tool is deprecated."""
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def get_fully_qualified_name(self) -> FullyQualifiedName:
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return FullyQualifiedName(self.name, self.toolkit.name, self.toolkit.version)
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class ToolReference(BaseModel):
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"""The name and version of a tool."""
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name: str
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"""The name of the tool."""
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toolkit: str
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"""The name of the toolkit containing the tool."""
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version: str | None = None
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"""The version of the toolkit containing the tool."""
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def get_fully_qualified_name(self) -> FullyQualifiedName:
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return FullyQualifiedName(self.name, self.toolkit, self.version)
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class ToolAuthorizationContext(BaseModel):
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"""The context for a tool invocation that requires authorization."""
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token: str | None = None
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"""The token for the tool invocation."""
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user_info: dict = Field(default={})
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"""
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The user information provided by the authorization server (if any).
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Some providers can provide structured user info,
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for example an internal provider-specific user ID.
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For those providers that support retrieving user info,
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the Engine can automatically pass that to tool invocations.
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"""
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class ToolSecretItem(BaseModel):
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"""The context for a tool secret."""
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key: str
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"""The key of the secret."""
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value: str
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"""The value of the secret."""
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class ToolMetadataItem(BaseModel):
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"""The context for a tool metadata."""
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key: str
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"""The key of the metadata."""
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value: str
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"""The value of the metadata."""
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class ToolContext(BaseModel):
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"""The context for a tool invocation."""
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authorization: ToolAuthorizationContext | None = None
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"""The authorization context for the tool invocation that requires authorization."""
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secrets: list[ToolSecretItem] | None = None
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"""The secrets for the tool invocation."""
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metadata: list[ToolMetadataItem] | None = None
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"""The metadata for the tool invocation."""
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user_id: str | None = None
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"""The user ID for the tool invocation (if any)."""
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def get_auth_token_or_empty(self) -> str:
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"""Retrieve the authorization token, or return an empty string if not available."""
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return self.authorization.token if self.authorization and self.authorization.token else ""
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def get_secret(self, key: str) -> str:
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"""Retrieve the secret for the tool invocation."""
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return self._get_item(key, self.secrets, "secret")
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def get_metadata(self, key: str) -> str:
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"""Retrieve the metadata for the tool invocation."""
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return self._get_item(key, self.metadata, "metadata")
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def _get_item(
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self,
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key: str,
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items: list[ToolMetadataItem] | list[ToolSecretItem] | None,
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item_name: str,
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) -> str:
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if not key or not key.strip():
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raise ValueError(
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f"{item_name.capitalize()} key passed to get_{item_name} cannot be empty."
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)
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if not items:
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raise ValueError(f"{item_name.capitalize()}s not found in context.")
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normalized_key = key.lower()
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for item in items:
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if item.key.lower() == normalized_key:
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return item.value
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raise ValueError(f"{item_name.capitalize()} {key} not found in context.")
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def set_secret(self, key: str, value: str) -> None:
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"""Set a secret for the tool invocation."""
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if not self.secrets:
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self.secrets = []
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secret = ToolSecretItem(key=str(key), value=str(value))
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self.secrets.append(secret)
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class ToolCallRequest(BaseModel):
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"""The request to call (invoke) a tool."""
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run_id: str | None = None
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"""The globally-unique run ID provided by the Engine."""
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execution_id: str | None = None
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"""The globally-unique ID for this tool execution in the run."""
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created_at: str | None = None
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"""The timestamp when the tool invocation was created."""
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tool: ToolReference
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"""The fully-qualified name and version of the tool."""
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inputs: dict[str, Any] | None = None
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"""The inputs for the tool."""
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context: ToolContext = Field(default_factory=ToolContext)
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"""The context for the tool invocation."""
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class ToolCallLog(BaseModel):
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"""A log that occurred during the tool invocation."""
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message: str
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"""The user-facing warning message."""
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level: Literal[
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"debug",
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"info",
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"warning",
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"error",
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]
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"""The level of severity for the log."""
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subtype: Literal["deprecation"] | None = None
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"""Optional field for further categorization of the log."""
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class ToolCallError(BaseModel):
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"""The error that occurred during the tool invocation."""
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message: str
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"""The user-facing error message."""
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kind: ErrorKind
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"""The error kind that uniquely identifies the kind of error."""
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developer_message: str | None = None
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"""The developer-facing error details."""
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can_retry: bool = False
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"""Whether the tool call can be retried."""
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additional_prompt_content: str | None = None
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"""Additional content to be included in the retry prompt."""
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retry_after_ms: int | None = None
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"""The number of milliseconds (if any) to wait before retrying the tool call."""
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stacktrace: str | None = None
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"""The stacktrace information for the tool call."""
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status_code: int | None = None
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"""The HTTP status code of the error."""
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extra: dict[str, Any] | None = None
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"""Additional information about the error."""
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@property
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def is_toolkit_error(self) -> bool:
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"""Check if this error originated from loading a toolkit."""
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return self.kind.name.startswith("TOOLKIT_")
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@property
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def is_tool_error(self) -> bool:
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"""Check if this error originated from a tool."""
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return self.kind.name.startswith("TOOL_")
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@property
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def is_upstream_error(self) -> bool:
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"""Check if this error originated from an upstream service."""
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return self.kind.name.startswith("UPSTREAM_")
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class ToolCallRequiresAuthorization(BaseModel):
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"""The authorization requirements for the tool invocation."""
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authorization_url: str | None = None
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"""The URL to redirect the user to for authorization."""
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authorization_id: str | None = None
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"""The ID for checking the status of the authorization."""
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scopes: list[str] | None = None
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"""The scopes that are required for authorization."""
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status: str | None = None
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"""The status of the authorization."""
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class ToolCallOutput(BaseModel):
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"""The output of a tool invocation."""
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value: str | int | float | bool | dict | list | None = None
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"""The value returned by the tool."""
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logs: list[ToolCallLog] | None = None
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"""The logs that occurred during the tool invocation."""
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error: ToolCallError | None = None
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"""The error that occurred during the tool invocation."""
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requires_authorization: ToolCallRequiresAuthorization | None = None
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"""The authorization requirements for the tool invocation."""
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model_config = {
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"json_schema_extra": {
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"oneOf": [
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{"required": ["value"]},
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{"required": ["error"]},
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{"required": ["requires_authorization"]},
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{"required": ["artifact"]},
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]
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}
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}
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class ToolCallResponse(BaseModel):
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"""The response to a tool invocation."""
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execution_id: str
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"""The globally-unique ID for this tool execution."""
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finished_at: str
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"""The timestamp when the tool execution finished."""
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duration: float
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"""The duration of the tool execution in milliseconds (ms)."""
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success: bool
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"""Whether the tool execution was successful."""
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output: ToolCallOutput | None = None
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"""The output of the tool invocation."""
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