Defining and extracting schemas¶
Mirascope's extraction functionality is built on top of Pydantic. We will walk through the high-level concepts you need to know to get started extracting structured information with LLMs. We recommend reading their docs for more detailed explanations of everything that you can do with Pydantic.
Model¶
Defining the schema for extraction is done via models, which are classes that inherit from pydantic.BaseModel. We can then use an extractor to extract this schema:
from typing import Type
from mirascope.openai import OpenAIExtractor
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
class BookExtractor(OpenAIExtractor[Book]):
extract_schema: Type[Book] = Book
prompt_template = "The Name of the Wind by Patrick Rothfuss."
book = BookExtractor().extract()
assert isinstance(book, Book)
print(book)
#> title='The Name of the Wind' author='Patrick Rothfuss'
You can use tool classes like OpenAITool directly if you want to extract a single tool instead of just a schema (which is useful for calling attached functions).
Field¶
You can also use pydantic.Fields to add additional information for each field in your schema. Again, this information will be included in the prompt, and we can take advantage of that:
from typing import Type
from mirascope.openai import OpenAIPrompt
from pydantic import BaseModel, Field
class Book(BaseModel):
title: str
author: str = Field(..., description="Last, First")
class BookExtractor(OpenAIExtractor[Book]):
extract_schema: Type[Book] = Book
prompt_template = "The Name of the Wind by Patrick Rothfuss."
book = BookExtractor().extract()
assert isinstance(book, Book)
print(book)
#> title='The Name of the Wind' author='Rothfuss, Patrick'
Notice how instead of “Patrick Rothfuss” the extracted author is “Rothfuss, Patrick” as desired.
Retries¶
Sometimes the model will fail to extract the schema. This can often be a result of the prompt; however, sometimes it’s simply a failure of the model. If you want to retry the extraction some number of times, you can set retries equal to the number of runs (defaults to 1). Alternatively, you can pass in tenacity.Retrying so that you can customize the behavior of retries. Mirascope will automatically pass in the error to the next call to give context.
from tenacity import Retrying, stop_after_attempt
retries = Retrying(
stop=stop_after_attempt(3),
)
task_details = TaskExtractor(task=task).extract(retries=retries)
As you can see, Mirascope makes extraction extremely simple. Under the hood, Mirascope uses the provided schema to extract the generated content and validate it (see Validation for more details).
Generating Synthetic Data¶
In the above examples, we’re extracting information present in the prompt text into structured form. We can also use extract to generate structured information from a prompt:
from mirascope.openai import OpenAIPrompt
from pydantic import BaseModel
class Book(BaseModel):
"""A science fiction book."""
title: str
author: str
class BookRecommender(OpenAIPrompt[Book]):
extract_schema: Type[Book] = Book
prompt_template = "Please recommend a book."
book = BookRecommender().extract()
assert isinstance(book, Book)
print(book)
#> title='Dune' author='Frank Herbert'
Notice that the docstring for the Book schema specified a science fiction book, which resulted in the model recommending a science fiction book. The docstring gets included with the prompt as part of the schema definition, and you can use this to your advantage for better prompting.