API Reference¶
Source code in instructor/client.py
Validator
¶
Bases: OpenAISchema
Validate if an attribute is correct and if not, return a new value with an error message
Source code in instructor/dsl/validators.py
llm_validator(statement, client, allow_override=False, model='gpt-3.5-turbo', temperature=0)
¶
Create a validator that uses the LLM to validate an attribute
Usage¶
from instructor import llm_validator
from pydantic import BaseModel, Field, field_validator
class User(BaseModel):
name: str = Annotated[str, llm_validator("The name must be a full name all lowercase")
age: int = Field(description="The age of the person")
try:
user = User(name="Jason Liu", age=20)
except ValidationError as e:
print(e)
1 validation error for User
name
The name is valid but not all lowercase (type=value_error.llm_validator)
Note that there, the error message is written by the LLM, and the error type is value_error.llm_validator
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
statement | str | The statement to validate | required |
model | str | The LLM to use for validation (default: "gpt-3.5-turbo-0613") | 'gpt-3.5-turbo' |
temperature | float | The temperature to use for the LLM (default: 0) | 0 |
openai_client | OpenAI | The OpenAI client to use (default: None) | required |
Source code in instructor/dsl/validators.py
openai_moderation(client)
¶
Validates a message using OpenAI moderation model.
Should only be used for monitoring inputs and outputs of OpenAI APIs Other use cases are disallowed as per: https://platform.openai.com/docs/guides/moderation/overview
Example:
from instructor import OpenAIModeration
class Response(BaseModel):
message: Annotated[str, AfterValidator(OpenAIModeration(openai_client=client))]
Response(message="I hate you")
ValidationError: 1 validation error for Response
message
Value error, `I hate you.` was flagged for ['harassment'] [type=value_error, input_value='I hate you.', input_type=str]
client (OpenAI): The OpenAI client to use, must be sync (default: None)
Source code in instructor/dsl/validators.py
IterableModel(subtask_class, name=None, description=None)
¶
Dynamically create a IterableModel OpenAISchema that can be used to segment multiple tasks given a base class. This creates class that can be used to create a toolkit for a specific task, names and descriptions are automatically generated. However they can be overridden.
Usage¶
from pydantic import BaseModel, Field
from instructor import IterableModel
class User(BaseModel):
name: str = Field(description="The name of the person")
age: int = Field(description="The age of the person")
role: str = Field(description="The role of the person")
MultiUser = IterableModel(User)
Result¶
class MultiUser(OpenAISchema, MultiTaskBase):
tasks: List[User] = Field(
default_factory=list,
repr=False,
description="Correctly segmented list of `User` tasks",
)
@classmethod
def from_streaming_response(cls, completion) -> Generator[User]:
'''
Parse the streaming response from OpenAI and yield a `User` object
for each task in the response
'''
json_chunks = cls.extract_json(completion)
yield from cls.tasks_from_chunks(json_chunks)
Parameters:
Name | Type | Description | Default |
---|---|---|---|
subtask_class | Type[OpenAISchema] | The base class to use for the MultiTask | required |
name | Optional[str] | The name of the MultiTask class, if None then the name of the subtask class is used as | None |
description | Optional[str] | The description of the MultiTask class, if None then the description is set to | None |
Returns:
Name | Type | Description |
---|---|---|
schema | OpenAISchema | A new class that can be used to segment multiple tasks |
Source code in instructor/dsl/iterable.py
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|
Partial
¶
Bases: Generic[T_Model]
Generate a new class which has PartialBase as a base class.
Notes
This will enable partial validation of the model while streaming.
Example
Partial[SomeModel]
Source code in instructor/dsl/partial.py
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__class_getitem__(wrapped_class)
¶
Convert model to one that inherits from PartialBase.
We don't make the fields optional at this point, we just wrap them with Partial
so the names of the nested models will be Partial{ModelName}
. We want the output of model_json_schema()
to reflect the name change, but everything else should be the same as the original model. During validation, we'll generate a true partial model to support partially defined fields.
Source code in instructor/dsl/partial.py
__init_subclass__(*args, **kwargs)
¶
Cannot subclass.
Raises:
Type | Description |
---|---|
TypeError | Subclassing not allowed. |
__new__(*args, **kwargs)
¶
Cannot instantiate.
Raises:
Type | Description |
---|---|
TypeError | Direct instantiation not allowed. |
Source code in instructor/dsl/partial.py
PartialBase
¶
Bases: Generic[T_Model]
Source code in instructor/dsl/partial.py
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get_partial_model()
cached
classmethod
¶
Return a partial model we can use to validate partial results.
Source code in instructor/dsl/partial.py
MaybeBase
¶
Bases: BaseModel
, Generic[T]
Extract a result from a model, if any, otherwise set the error and message fields.
Source code in instructor/dsl/maybe.py
Maybe(model)
¶
Create a Maybe model for a given Pydantic model. This allows you to return a model that includes fields for result
, error
, and message
for sitatations where the data may not be present in the context.
Usage¶
from pydantic import BaseModel, Field
from instructor import Maybe
class User(BaseModel):
name: str = Field(description="The name of the person")
age: int = Field(description="The age of the person")
role: str = Field(description="The role of the person")
MaybeUser = Maybe(User)
Result¶
class MaybeUser(BaseModel):
result: Optional[User]
error: bool = Field(default=False)
message: Optional[str]
def __bool__(self):
return self.result is not None
Parameters:
Name | Type | Description | Default |
---|---|---|---|
model | Type[BaseModel] | The Pydantic model to wrap with Maybe. | required |
Returns:
Name | Type | Description |
---|---|---|
MaybeModel | Type[BaseModel] | A new Pydantic model that includes fields for |
Source code in instructor/dsl/maybe.py
OpenAISchema
¶
Bases: BaseModel
Source code in instructor/function_calls.py
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from_response(completion, validation_context=None, strict=None, mode=Mode.TOOLS)
classmethod
¶
Execute the function from the response of an openai chat completion
Parameters:
Name | Type | Description | Default |
---|---|---|---|
completion | ChatCompletion | The response from an openai chat completion | required |
throw_error | bool | Whether to throw an error if the function call is not detected | required |
context | dict | The context to use for validating the response | required |
strict | bool | Whether to use strict json parsing | None |
mode | Mode | The openai completion mode | TOOLS |
Returns:
Name | Type | Description |
---|---|---|
cls | OpenAISchema | An instance of the class |
Source code in instructor/function_calls.py
openai_schema()
¶
Return the schema in the format of OpenAI's schema as jsonschema
Note
Its important to add a docstring to describe how to best use this class, it will be included in the description attribute and be part of the prompt.
Returns:
Name | Type | Description |
---|---|---|
model_json_schema | dict | A dictionary in the format of OpenAI's schema as jsonschema |