anthropic.extractors¶
A class for extracting structured information using Anthropic Claude models.
AnthropicCall
¶
Bases: BaseCall[AnthropicCallResponse, AnthropicCallResponseChunk, AnthropicTool, MessageParam]
A base class for calling Anthropic's Claude models.
Example:
from mirascope.anthropic import AnthropicCall
class BookRecommender(AnthropicCall):
prompt_template = "Please recommend a {genre} book."
genre: str
response = BookRecommender(genre="fantasy").call()
print(response.content)
#> There are many great books to read, it ultimately depends...
Source code in mirascope/anthropic/calls.py
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|
call(retries=0, **kwargs)
¶
Makes a call to the model using this AnthropicCall
instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Returns:
Type | Description |
---|---|
AnthropicCallResponse
|
A |
Source code in mirascope/anthropic/calls.py
call_async(retries=0, **kwargs)
async
¶
Makes an asynchronous call to the model using this AnthropicCall
instance.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Returns:
Type | Description |
---|---|
AnthropicCallResponse
|
A |
Source code in mirascope/anthropic/calls.py
messages()
¶
Returns the template as a formatted list of messages.
stream(retries=0, **kwargs)
¶
Streams the response for a call using this AnthropicCall
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Yields:
Type | Description |
---|---|
AnthropicCallResponseChunk
|
An |
Source code in mirascope/anthropic/calls.py
stream_async(retries=0, **kwargs)
async
¶
Streams the response for an asynchronous call using this AnthropicCall
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Yields:
Type | Description |
---|---|
AsyncGenerator[AnthropicCallResponseChunk, None]
|
An |
Source code in mirascope/anthropic/calls.py
AnthropicCallParams
¶
Bases: BaseCallParams[AnthropicTool]
The parameters to use when calling d Claud API with a prompt.
Example:
from mirascope.anthropic import AnthropicCall, AnthropicCallParams
class BookRecommender(AnthropicCall):
prompt_template = "Please recommend some books."
call_params = AnthropicCallParams(
model="anthropic-3-opus-20240229",
)
Source code in mirascope/anthropic/types.py
kwargs(tool_type=None, exclude=None)
¶
Returns the keyword argument call parameters.
Source code in mirascope/anthropic/types.py
AnthropicExtractor
¶
Bases: BaseExtractor[AnthropicCall, AnthropicTool, Any, T]
, Generic[T]
A class for extracting structured information using Anthropic Claude models.
Example:
from typing import Literal, Type
from mirascope.anthropic import AnthropicExtractor
from pydantic import BaseModel
class TaskDetails(BaseModel):
title: str
priority: Literal["low", "normal", "high"]
due_date: str
class TaskExtractor(AnthropicExtractor[TaskDetails]):
extract_schema: Type[TaskDetails] = TaskDetails
prompt_template = """
Please extract the task details:
{task}
"""
task: str
task_description = "Submit quarterly report by next Friday. Task is high priority."
task = TaskExtractor(task=task_description).extract(retries=3)
assert isinstance(task, TaskDetails)
print(task)
#> title='Submit quarterly report' priority='high' due_date='next Friday'
Source code in mirascope/anthropic/extractors.py
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|
extract(retries=0, **kwargs)
¶
Extracts extract_schema
from the Anthropic call response.
The extract_schema
is converted into an AnthropicTool
, complete with a
description of the tool, all of the fields, and their types. This allows us to
take advantage of Anthropics's tool/function calling functionality to extract
information from a prompt according to the context provided by the BaseModel
schema.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
retries |
Union[int, Retrying]
|
The maximum number of times to retry the query on validation error. |
0
|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Returns:
Type | Description |
---|---|
T
|
The |
Raises:
Type | Description |
---|---|
AttributeError
|
if there is no tool in the call creation. |
ValidationError
|
if the schema cannot be instantiated from the completion. |
Source code in mirascope/anthropic/extractors.py
extract_async(retries=0, **kwargs)
async
¶
Asynchronously extracts extract_schema
from the Anthropic call response.
The extract_schema
is converted into an AnthropicTool
, complete with a
description of the tool, all of the fields, and their types. This allows us to
take advantage of Anthropic's tool/function calling functionality to extract
information from a prompt according to the context provided by the BaseModel
schema.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
retries |
Union[int, AsyncRetrying]
|
The maximum number of times to retry the query on validation error. |
0
|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Returns:
Type | Description |
---|---|
T
|
The |
Raises:
Type | Description |
---|---|
AttributeError
|
if there is no tool in the call creation. |
ValidationError
|
if the schema cannot be instantiated from the completion. |
Source code in mirascope/anthropic/extractors.py
stream(retries=0, **kwargs)
¶
Streams partial instances of extract_schema
as the schema is streamed.
The extract_schema
is converted into a partial(AnthropicTool)
, which allows
for any field (i.e.function argument) in the tool to be None
. This allows us
to stream partial results as we construct the tool from the streamed chunks.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
retries |
Union[int, Retrying]
|
The maximum number of times to retry the query on validation error. |
0
|
**kwargs |
Any
|
Additional keyword argument parameters to pass to the call. These
will override any existing arguments in |
{}
|
Yields:
Type | Description |
---|---|
T
|
The partial |
Raises:
Type | Description |
---|---|
AttributeError
|
if there is no tool in the call creation. |
ValidationError
|
if the schema cannot be instantiated from the completion. |
Source code in mirascope/anthropic/extractors.py
stream_async(retries=0, **kwargs)
async
¶
Asynchronously streams partial instances of extract_schema
as streamed.
The extract_schema
is converted into a partial(AnthropicTool)
, which allows
for any field (i.e.function argument) in the tool to be None
. This allows us
to stream partial results as we construct the tool from the streamed chunks.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
retries |
Union[int, AsyncRetrying]
|
The maximum number of times to retry the query on validation error. |
0
|
**kwargs |
Any
|
Additional keyword arguments parameters to pass to the call. These
will override any existing arguments in |
{}
|
Yields:
Type | Description |
---|---|
AsyncGenerator[T, None]
|
The partial |
Raises:
Type | Description |
---|---|
AttributeError
|
if there is no tool in the call creation. |
ValidationError
|
if the schema cannot be instantiated from the completion. |
Source code in mirascope/anthropic/extractors.py
AnthropicTool
¶
Bases: BaseTool[ToolUseBlock]
A base class for easy use of tools with the Anthropic Claude client.
AnthropicTool
internally handles the logic that allows you to use tools with
simple calls such as AnthropicCallResponse.tool
or AnthropicTool.fn
, as seen in
the example below.
Example:
from mirascope import AnthropicCall, AnthropicCallParams
def animal_matcher(fav_food: str, fav_color: str) -> str:
"""Tells you your most likely favorite animal from personality traits.
Args:
fav_food: your favorite food.
fav_color: your favorite color.
Returns:
The animal most likely to be your favorite based on traits.
"""
return "Your favorite animal is the best one, a frog."
class AnimalMatcher(AnthropicCall):
prompt_template = """
Tell me my favorite animal if my favorite food is {food} and my
favorite color is {color}.
"""
food: str
color: str
call_params = AnthropicCallParams(tools=[animal_matcher])
response = AnimalMatcher(food="pizza", color="red").call
tool = response.tool
print(tool.fn(**tool.args))
#> Your favorite animal is the best one, a frog.
Source code in mirascope/anthropic/tools.py
from_base_type(base_type)
classmethod
¶
Constructs a AnthropicTool
type from a BaseType
type.
from_fn(fn)
classmethod
¶
from_model(model)
classmethod
¶
Constructs a AnthropicTool
type from a BaseModel
type.
from_tool_call(tool_call)
classmethod
¶
Extracts an instance of the tool constructed from a tool call response.
Given the tool call contents in a Message
from an Anthropic call response,
this method parses out the arguments of the tool call and creates an
AnthropicTool
instance from them.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
tool_call |
ToolUseBlock
|
The list of |
required |
Returns:
Type | Description |
---|---|
AnthropicTool
|
An instance of the tool constructed from the tool call. |
Raises:
Type | Description |
---|---|
ValidationError
|
if the tool call doesn't match the tool schema. |
Source code in mirascope/anthropic/tools.py
tool_schema()
classmethod
¶
Constructs JSON tool schema for use with Anthropic's Claude API.
Source code in mirascope/anthropic/tools.py
AnthropicToolStream
¶
Bases: BaseToolStream[AnthropicCallResponseChunk, AnthropicTool]
A base class for streaming tools from response chunks.
Source code in mirascope/anthropic/types.py
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|
from_async_stream(async_stream, allow_partial=False)
async
classmethod
¶
Yields partial tools from the given stream of chunks asynchronously.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stream |
The async generator of chunks from which to stream tools. |
required | |
allow_partial |
Whether to allow partial tools. |
False
|
Raises:
Type | Description |
---|---|
RuntimeError
|
if a tool in the stream is of an unknown type. |
Source code in mirascope/anthropic/types.py
from_stream(stream, allow_partial=False)
classmethod
¶
Yields partial tools from the given stream of chunks.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
stream |
The generator of chunks from which to stream tools. |
required | |
allow_partial |
Whether to allow partial tools. |
False
|
Raises:
Type | Description |
---|---|
RuntimeError
|
if a tool in the stream is of an unknown type. |
Source code in mirascope/anthropic/types.py
BaseExtractor
¶
Bases: BasePrompt
, Generic[BaseCallT, BaseToolT, BaseToolStreamT, ExtractedTypeT]
, ABC
The base abstract interface for extracting structured information using LLMs.
Source code in mirascope/base/extractors.py
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|
extract(retries=0)
abstractmethod
¶
extract_async(retries=0)
abstractmethod
async
¶
from_prompt(prompt_type, call_params, *, extract_schema=None)
classmethod
¶
Returns an extractor_type generated dynamically from this base extractor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
prompt_type |
type[BasePromptT]
|
The prompt class to use for the extractor. Properties and class variables of this class will be used to create the new extractor class. Must be a class that can be instantiated. |
required |
call_params |
BaseCallParams
|
The call params to use for the extractor. |
required |
extract_schema |
Optional[ExtractedType]
|
The extract schema to use for the extractor. If none, the extractor will use the class' extract_schema. |
None
|
Returns:
Type | Description |
---|---|
type[BasePromptT]
|
A new extractor class with new extractor type. |