Response Models¶
If you haven't already, we recommend first reading the section on Calls
Response Models in Mirascope provide a powerful way to structure and validate the output from Large Language Models (LLMs). By leveraging Pydantic's BaseModel, Response Models offer type safety, automatic validation, and easier data manipulation for your LLM responses. While we cover some details in this documentation, we highly recommend reading through Pydantic's documentation for a deeper, comprehensive dive into everything you can do with Pydantic's BaseModel.
Why Use Response Models?¶
- Structured Output: Define exactly what you expect from the LLM, ensuring consistency in responses.
- Automatic Validation: Pydantic handles type checking and validation, reducing errors in your application.
- Improved Type Hinting: Better IDE support and clearer code structure.
- Easier Data Manipulation: Work with Python objects instead of raw strings or dictionaries.
Basic Usage and Syntax¶
Let's take a look at a basic example using Mirascope vs. official provider SDKs:
Mirascope
from mirascope.core import openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import openai, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import prompt_template, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
Official SDK
from openai import OpenAI
from pydantic import BaseModel
client = OpenAI()
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
completion = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": f"Extract {text}"}],
tools=[
{
"function": {
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
},
"type": "function",
}
],
tool_choice="required",
)
if tool_calls := completion.choices[0].message.tool_calls:
return Book.model_validate_json(tool_calls[0].function.arguments)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from anthropic import Anthropic
from pydantic import BaseModel
client = Anthropic()
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
message = client.messages.create(
model="claude-3-5-sonnet-20240620",
max_tokens=1024,
messages=[{"role": "user", "content": f"Extract {text}"}],
tools=[
{
"name": "Book",
"description": "An extracted book.",
"input_schema": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
}
],
tool_choice={"type": "tool", "name": "Book"},
)
for block in message.content:
if block.type == "tool_use" and block.input is not None:
return Book.model_validate(block.input)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ToolChoice
from pydantic import BaseModel
client = MistralClient()
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
completion = client.chat(
model="mistral-large-latest",
messages=[{"role": "user", "content": f"Extract {text}"}],
tools=[
{
"function": {
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
},
"type": "function",
}
],
tool_choice=ToolChoice.any,
)
if tool_calls := completion.choices[0].message.tool_calls:
return Book.model_validate_json(tool_calls[0].function.arguments)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from google.generativeai import GenerativeModel
from google.generativeai.types import FunctionDeclaration, Tool, content_types
from proto.marshal.collections import RepeatedComposite
from pydantic import BaseModel
model = GenerativeModel("gemini-1.5-flash")
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
response = model.generate_content(
f"Extract {text}",
tools=[
Tool(
function_declarations=[
FunctionDeclaration(
**{
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
}
)
]
)
],
tool_config=content_types.to_tool_config(
{
"function_calling_config": {
"mode": "any",
"allowed_function_names": ["Book"],
}
} # pyright: ignore [reportArgumentType]
),
)
if tool_calls := [
part.function_call for part in response.parts if part.function_call.args
]:
return Book.model_validate(
{
k: v if not isinstance(v, RepeatedComposite) else list(v)
for k, v in tool_calls[0].args.items()
}
)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from groq import Groq
from pydantic import BaseModel
client = Groq()
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
completion = client.chat.completions.create(
model="llama-3.1-70b-versatile",
messages=[{"role": "user", "content": f"Extract {text}"}],
tools=[
{
"function": {
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
},
"type": "function",
}
],
tool_choice="required",
)
if tool_calls := completion.choices[0].message.tool_calls:
return Book.model_validate_json(tool_calls[0].function.arguments)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from cohere import Client
from cohere.types import Tool, ToolParameterDefinitionsValue
from pydantic import BaseModel
client = Client()
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
response = client.chat(
model="command-r-plus",
message=f"Extract {text}",
tools=[
Tool(
name="Book",
description="An extracted book.",
parameter_definitions={
"title": ToolParameterDefinitionsValue(
description=None, type="string", required=True
),
"author": ToolParameterDefinitionsValue(
description=None, type="string", required=True
),
},
)
],
)
if response.tool_calls:
return Book.model_validate(response.tool_calls[0].parameters)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from litellm import completion
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
response = completion(
model="gpt-4o-mini",
messages=[{"role": "user", "content": f"Extract {text}"}],
tools=[
{
"function": {
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
},
"type": "function",
}
],
tool_choice="required",
)
if tool_calls := response.choices[0].message.tool_calls: # pyright: ignore [reportAttributeAccessIssue]
return Book.model_validate_json(tool_calls[0].function.arguments)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import (
ChatCompletionsToolDefinition,
ChatRequestMessage,
FunctionDefinition,
)
from azure.core.credentials import AzureKeyCredential
from pydantic import BaseModel
client = ChatCompletionsClient(
endpoint="YOUR_ENDPOINT", credential=AzureKeyCredential("YOUR_KEY")
)
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
completion = client.complete(
model="gpt-4o-mini",
messages=[ChatRequestMessage({"role": "user", "content": f"Extract {text}"})],
tools=[
ChatCompletionsToolDefinition(
function=FunctionDefinition(
name="Book",
description="An extracted book.",
parameters={
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
)
)
],
tool_choices="required",
)
if tool_calls := completion.choices[0].message.tool_calls:
return Book.model_validate_json(tool_calls[0].function.arguments)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from proto.marshal.collections import RepeatedComposite
from pydantic import BaseModel
from vertexai.generative_models import (
FunctionDeclaration,
GenerativeModel,
Tool,
ToolConfig,
)
model = GenerativeModel("gemini-1.5-flash")
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
response = model.generate_content(
f"Extract {text}",
tools=[
Tool(
function_declarations=[
FunctionDeclaration(
**{
"name": "Book",
"description": "An extracted book.",
"parameters": {
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
"type": "object",
},
}
)
]
)
],
tool_config=ToolConfig(
function_calling_config=ToolConfig.FunctionCallingConfig(
mode=ToolConfig.FunctionCallingConfig.Mode.ANY,
allowed_function_names=["Book"],
),
),
)
if tool_calls := [
part.function_call
for candidate in response.candidates # pyright: ignore [reportAttributeAccessIssue]
for part in candidate.content.parts
if part.function_call.args
]:
return Book.model_validate(
{
k: v if not isinstance(v, RepeatedComposite) else list(v)
for k, v in tool_calls[0].args.items()
}
)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
import boto3
from pydantic import BaseModel
bedrock_client = boto3.client(service_name="bedrock-runtime")
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
def extract_book(text: str) -> Book:
messages = [{"role": "user", "content": [{"text": f"Extract {text}"}]}]
tool_config = {
"tools": [
{
"toolSpec": {
"name": "Book",
"description": "An extracted book.",
"inputSchema": {
"json": {
"type": "object",
"properties": {
"title": {"type": "string"},
"author": {"type": "string"},
},
"required": ["title", "author"],
}
},
}
}
],
"toolChoice": {"type": "tool", "name": "Book"},
}
response = bedrock_client.converse(
modelId="anthropic.claude-3-haiku-20240307-v1:0",
messages=messages,
toolConfig=tool_config,
)
output_message = response["output"]["message"]
messages.append(output_message)
for content_piece in output_message["content"]:
if "toolUse" in content_piece and content_piece["toolUse"].get("input"):
tool_input = content_piece["toolUse"]["input"]
return Book.model_validate(tool_input)
raise ValueError("No tool call found")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
Notice how Mirascope makes generating structured outputs significantly simpler than the official SDKs. It also greatly reduces boilerplate and standardizes the interaction across all supported LLM providers.
Tools By Default
By default, response_model will use Tools under the hood, forcing to the LLM to call that specific tool and constructing the response model from the tool's arguments.
We default to using tools because all supported providers support tools. You can also optionally set json_mode=True to use JSON Mode instead, which we cover in more detail below.
Accessing Original Call Response¶
Every response_model that uses a Pydantic BaseModel will automatically have the original BaseCallResponse instance accessible through the _response property:
Mirascope
from typing import cast
from mirascope.core import openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(openai.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(anthropic.AnthropicCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(mistral.MistralCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(gemini.GeminiCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(groq.GroqCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(cohere.CohereCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(litellm.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(azure.AzureCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(vertex.VertexCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(bedrock.BedrockCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(openai.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(anthropic.AnthropicCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(mistral.MistralCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(gemini.GeminiCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(groq.GroqCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(cohere.CohereCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(litellm.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(azure.AzureCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(vertex.VertexCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import Messages, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(bedrock.BedrockCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import openai, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(openai.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import anthropic, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(anthropic.AnthropicCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import mistral, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(mistral.MistralCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import gemini, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(gemini.GeminiCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import groq, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(groq.GroqCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import cohere, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(cohere.CohereCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import litellm, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(litellm.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import azure, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(azure.AzureCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import prompt_template, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(vertex.VertexCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import bedrock, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(bedrock.BedrockCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(openai.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(anthropic.AnthropicCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(mistral.MistralCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(gemini.GeminiCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(groq.GroqCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(cohere.CohereCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(litellm.OpenAICallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(azure.AzureCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(vertex.VertexCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import cast
from mirascope.core import BaseMessageParam, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
response = cast(bedrock.BedrockCallResponse, book._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
Built-In Types¶
For cases where you want to extract just a single built-in type, Mirascope provides a shorthand:
from mirascope.core import openai
@openai.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import anthropic
@anthropic.call("claude-3-5-sonnet-20240620", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import mistral
@mistral.call("mistral-large-latest", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import gemini
@gemini.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import groq
@groq.call("llama-3.1-70b-versatile", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import cohere
@cohere.call("command-r-plus", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import litellm
@litellm.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import azure
@azure.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import vertex
@vertex.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import bedrock
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=list[str])
def extract_book(texts: list[str]) -> str:
return f"Extract book titles from {texts}"
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, openai
@openai.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, anthropic
@anthropic.call("claude-3-5-sonnet-20240620", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, mistral
@mistral.call("mistral-large-latest", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, gemini
@gemini.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, groq
@groq.call("llama-3.1-70b-versatile", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, cohere
@cohere.call("command-r-plus", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, litellm
@litellm.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, azure
@azure.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, vertex
@vertex.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import Messages, bedrock
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=list[str])
def extract_book(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract book titles from {texts}")
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import openai, prompt_template
@openai.call("gpt-4o-mini", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import anthropic, prompt_template
@anthropic.call("claude-3-5-sonnet-20240620", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import mistral, prompt_template
@mistral.call("mistral-large-latest", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import gemini, prompt_template
@gemini.call("gemini-1.5-flash", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import groq, prompt_template
@groq.call("llama-3.1-70b-versatile", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import cohere, prompt_template
@cohere.call("command-r-plus", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import litellm, prompt_template
@litellm.call("gpt-4o-mini", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import azure, prompt_template
@azure.call("gpt-4o-mini", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import prompt_template, vertex
@vertex.call("gemini-1.5-flash", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import bedrock, prompt_template
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=list[str])
@prompt_template("Extract book titles from {texts}")
def extract_book(texts: list[str]): ...
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, openai
@openai.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, anthropic
@anthropic.call("claude-3-5-sonnet-20240620", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, mistral
@mistral.call("mistral-large-latest", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, gemini
@gemini.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, groq
@groq.call("llama-3.1-70b-versatile", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, cohere
@cohere.call("command-r-plus", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, litellm
@litellm.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, azure
@azure.call("gpt-4o-mini", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, vertex
@vertex.call("gemini-1.5-flash", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
from mirascope.core import BaseMessageParam, bedrock
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=list[str])
def extract_book(texts: list[str]) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract book titles from {texts}")]
book = extract_book(
[
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
)
print(book)
# Output: ["The Name of the Wind", "Mistborn: The Final Empire"]
Here, we are using list[str] as the response_model, which Mirascope handles without needing to define a full BaseModel. You could of course set response_model=list[Book] as well.
Note that we have no way of attaching BaseCallResponse to built-in types, so using a Pydantic BaseModel is recommended if you anticipate needing access to the original call response.
Supported Field Types¶
While Mirascope provides a consistent interface, type support varies among providers:
| Type | Anthropic | Cohere | Gemini | Groq | Mistral | OpenAI |
|---|---|---|---|---|---|---|
| str | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| int | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| float | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| bool | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| bytes | ✓✓ | ✓✓ | -✓ | ✓✓ | ✓✓ | ✓✓ |
| list | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| set | ✓✓ | ✓✓ | -- | ✓✓ | ✓✓ | ✓✓ |
| tuple | ✓✓ | ✓✓ | -✓ | ✓✓ | ✓✓ | -✓ |
| dict | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | -✓ |
| Literal/Enum | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| BaseModel | ✓✓ | -✓ | ✓✓ | ✓✓ | ✓✓ | ✓✓ |
| Nested ($def) | ✓✓ | -- | -- | ✓✓ | ✓✓ | ✓✓ |
✓✓ : Fully Supported, -✓: Only JSON Mode Support, -- : Not supported
Validation and Error Handling¶
While response_model significantly improves output structure and validation, it's important to handle potential errors.
Let's take a look at an example where we want to validate that all fields are uppercase:
from typing import Annotated, cast
from mirascope.core import openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import Messages, bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import openai, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import anthropic, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import mistral, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import gemini, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import groq, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import cohere, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import litellm, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import azure, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import prompt_template, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import bedrock, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
print(f"Error: {str(e)}")
# Error: 2 validation errors for Book
# title
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='The Name of the Wind', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
# author
# Assertion failed, Field must be uppercase [type=assertion_error, input_value='Patrick Rothfuss', input_type=str]
# For further information visit https://errors.pydantic.dev/2.7/v/assertion_error
Without additional prompt engineering, this call will fail every single time. It's important to engineer your prompts to reduce errors, but LLMs are far from perfect, so always remember to catch and handle validation errors gracefully.
We highly recommend taking a look at our section on retries to learn more about automatically retrying and re-inserting validation errors, which enables retrying the call such that the LLM can learn from its previous mistakes.
Accessing Original Call Response On Error¶
In case of a ValidationError, you can access the original response for debugging:
from typing import Annotated, cast
from mirascope.core import openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(openai.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(anthropic.AnthropicCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(mistral.MistralCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(gemini.GeminiCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(groq.GroqCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(cohere.CohereCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(litellm.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(azure.AzureCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(vertex.VertexCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> str:
return f"Extract {text}"
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(bedrock.BedrockCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(openai.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(anthropic.AnthropicCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(mistral.MistralCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(gemini.GeminiCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(groq.GroqCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(cohere.CohereCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(litellm.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(azure.AzureCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(vertex.VertexCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import Messages, bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(bedrock.BedrockCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import openai, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(openai.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import anthropic, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(anthropic.AnthropicCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import mistral, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(mistral.MistralCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import gemini, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(gemini.GeminiCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import groq, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(groq.GroqCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import cohere, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(cohere.CohereCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import litellm, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(litellm.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import azure, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(azure.AzureCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import prompt_template, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(vertex.VertexCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import bedrock, prompt_template
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(bedrock.BedrockCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, openai
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@openai.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(openai.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, anthropic
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(anthropic.AnthropicCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, mistral
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@mistral.call("mistral-large-latest", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(mistral.MistralCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, gemini
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@gemini.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(gemini.GeminiCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, groq
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@groq.call("llama-3.1-70b-versatile", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(groq.GroqCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, cohere
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@cohere.call("command-r-plus", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(cohere.CohereCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, litellm
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@litellm.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(litellm.OpenAICallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, azure
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@azure.call("gpt-4o-mini", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(azure.AzureCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, vertex
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@vertex.call("gemini-1.5-flash", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(vertex.VertexCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
from typing import Annotated, cast
from mirascope.core import BaseMessageParam, bedrock
from pydantic import AfterValidator, BaseModel, ValidationError
def validate_upper(v: str) -> str:
assert v.isupper(), "Field must be uppercase"
return v
class Book(BaseModel):
"""An extracted book."""
title: Annotated[str, AfterValidator(validate_upper)]
author: Annotated[str, AfterValidator(validate_upper)]
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Book)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
try:
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
except ValidationError as e:
response = cast(bedrock.BedrockCallResponse, e._response) # pyright: ignore[reportAttributeAccessIssue]
print(response.model_dump())
# > {'metadata': {}, 'response': {'id': ...}, ...}
This allows you to gracefully handle errors as well as inspect the original LLM response when validation fails.
JSON Mode¶
By default, response_model uses Tools under the hood. You can instead use JSON Mode in conjunction with response_model by setting json_mode=True:
from mirascope.core import openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> str:
return f"Extract {text}"
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import openai, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import prompt_template, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, json_mode=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, openai
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, anthropic
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, mistral
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, gemini
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, groq
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, cohere
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, litellm
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, azure
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, vertex
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, bedrock
from pydantic import BaseModel
class Book(BaseModel):
"""An extracted book."""
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book = extract_book("The Name of the Wind by Patrick Rothfuss")
print(book)
# Output: title='The Name of the Wind' author='Patrick Rothfuss'
Few-Shot Examples¶
Adding few-shot examples to your response model can improve results by demonstrating exactly how to adhere to your desired output.
We take advantage of Pydantic's Field and ConfigDict to add these examples to response models:
from mirascope.core import openai
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import anthropic
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import mistral
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import groq
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import cohere
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import litellm
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import azure
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import bedrock
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> str:
return f"Extract {text}. Match example format excluding 'examples' key."
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, openai
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, anthropic
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, mistral
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, groq
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, cohere
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, litellm
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, azure
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import Messages, bedrock
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> Messages.Type:
return Messages.User(
f"Extract {text}. Match example format excluding 'examples' key."
)
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import openai, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import anthropic, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import mistral, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import groq, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import cohere, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import litellm, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import azure, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import bedrock, prompt_template
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
@prompt_template("Extract {text}. Match example format excluding 'examples' key.")
def extract_book(text: str): ...
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, openai
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@openai.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, anthropic
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, mistral
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@mistral.call("mistral-large-latest", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, groq
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@groq.call("llama-3.1-70b-versatile", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, cohere
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@cohere.call("command-r-plus", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, litellm
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@litellm.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, azure
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@azure.call("gpt-4o-mini", response_model=Book, json_mode=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
from mirascope.core import BaseMessageParam, bedrock
from pydantic import BaseModel, ConfigDict, Field
class Book(BaseModel):
title: str = Field(..., examples=["THE NAME OF THE WIND"])
author: str = Field(..., examples=["Rothfuss, Patrick"])
model_config = ConfigDict(
json_schema_extra={
"examples": [
{"title": "THE NAME OF THE WIND", "author": "Rothfuss, Patrick"}
]
}
)
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, json_mode=True
)
def extract_book(text: str) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user",
content=f"Extract {text}. Match example format excluding 'examples' key.",
)
]
book = extract_book("The Way of Kings by Brandon Sanderson")
print(book)
# Output: title='THE NAME OF THE WIND' author='Rothfuss, Patrick'
Streaming Response Models¶
If you set stream=True when response_model is set, your LLM call will return an Iterable where each item will be a partial version of your response model representing the current state of the streamed information. The final model returned by the iterator will be the full response model.
from mirascope.core import openai
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import vertex
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, stream=True
)
def extract_book(text: str) -> str:
return f"Extract {text}"
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, openai
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, anthropic
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, mistral
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, gemini
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, groq
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, cohere
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, litellm
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, azure
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, vertex
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import Messages, bedrock
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, stream=True
)
def extract_book(text: str) -> Messages.Type:
return Messages.User(f"Extract {text}")
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import openai, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import anthropic, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import mistral, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import gemini, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import groq, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import cohere, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import litellm, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import azure, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import prompt_template, vertex
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, stream=True)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import bedrock, prompt_template
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, stream=True
)
@prompt_template("Extract {text}")
def extract_book(text: str): ...
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, openai
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@openai.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, anthropic
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, mistral
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@mistral.call("mistral-large-latest", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, gemini
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@gemini.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, groq
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@groq.call("llama-3.1-70b-versatile", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, cohere
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@cohere.call("command-r-plus", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, litellm
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@litellm.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, azure
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@azure.call("gpt-4o-mini", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, vertex
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@vertex.call("gemini-1.5-flash", response_model=Book, stream=True)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
from mirascope.core import BaseMessageParam, bedrock
from pydantic import BaseModel
class Book(BaseModel):
title: str
author: str
@bedrock.call(
"anthropic.claude-3-haiku-20240307-v1:0", response_model=Book, stream=True
)
def extract_book(text: str) -> list[BaseMessageParam]:
return [BaseMessageParam(role="user", content=f"Extract {text}")]
book_stream = extract_book("The Name of the Wind by Patrick Rothfuss")
for partial_book in book_stream:
print(partial_book)
# Output:
# title=None author=None
# title='' author=None
# title='The' author=None
# title='The Name' author=None
# title='The Name of' author=None
# title='The Name of the' author=None
# title='The Name of the Wind' author=None
# title='The Name of the Wind' author=''
# title='The Name of the Wind' author='Patrick'
# title='The Name of the Wind' author='Patrick Roth'
# title='The Name of the Wind' author='Patrick Rothf'
# title='The Name of the Wind' author='Patrick Rothfuss'
Once exhausted, you can access the final, full response model through the constructed_response_model property of the structured stream. Note that this will also give you access to the ._response property that every BaseModel receives.
You can also use the stream property to access the BaseStream instance and all of it's properties.
FromCallArgs¶
Fields annotated with FromCallArgs will be populated with the corresponding argument from the function call rather than expecting it from the LLM's response. This enables seamless validation of LLM outputs against function inputs:
Mirascope
from typing import Annotated
from mirascope.core import FromCallArgs, openai
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@openai.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, anthropic
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, mistral
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@mistral.call("mistral-large-latest", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, gemini
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@gemini.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, groq
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@groq.call("llama-3.1-70b-versatile", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, cohere
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@cohere.call("command-r-plus", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, litellm
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@litellm.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, azure
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@azure.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, vertex
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@vertex.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, bedrock
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Books)
def extract_books(texts: list[str]) -> str:
return f"Extract the books from these texts: {texts}"
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, openai
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@openai.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, anthropic
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, mistral
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@mistral.call("mistral-large-latest", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, gemini
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@gemini.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, groq
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@groq.call("llama-3.1-70b-versatile", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, cohere
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@cohere.call("command-r-plus", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, litellm
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@litellm.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, azure
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@azure.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, vertex
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@vertex.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, Messages, bedrock
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Books)
def extract_books(texts: list[str]) -> Messages.Type:
return Messages.User(f"Extract the books from these texts: {texts}")
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, openai, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@openai.call("gpt-4o-mini", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, anthropic, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, mistral, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@mistral.call("mistral-large-latest", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, gemini, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@gemini.call("gemini-1.5-flash", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, groq, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@groq.call("llama-3.1-70b-versatile", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, cohere, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@cohere.call("command-r-plus", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, litellm, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@litellm.call("gpt-4o-mini", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, azure, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@azure.call("gpt-4o-mini", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, prompt_template, vertex
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@vertex.call("gemini-1.5-flash", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import FromCallArgs, bedrock, prompt_template
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Books)
@prompt_template("Extract the books from these texts: {texts}")
def extract_books(texts: list[str]): ...
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, openai
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@openai.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, anthropic
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@anthropic.call("claude-3-5-sonnet-20240620", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, mistral
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@mistral.call("mistral-large-latest", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, gemini
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@gemini.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, groq
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@groq.call("llama-3.1-70b-versatile", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, cohere
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@cohere.call("command-r-plus", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, litellm
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@litellm.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, azure
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@azure.call("gpt-4o-mini", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, vertex
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@vertex.call("gemini-1.5-flash", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
from typing import Annotated
from mirascope.core import BaseMessageParam, FromCallArgs, bedrock
from pydantic import BaseModel, model_validator
from typing_extensions import Self
class Book(BaseModel):
title: str
author: str
class Books(BaseModel):
texts: Annotated[list[str], FromCallArgs()]
books: list[Book]
@model_validator(mode="after")
def validate_output_length(self) -> Self:
if len(self.texts) != len(self.books):
raise ValueError("length mismatch...")
return self
@bedrock.call("anthropic.claude-3-haiku-20240307-v1:0", response_model=Books)
def extract_books(texts: list[str]) -> list[BaseMessageParam]:
return [
BaseMessageParam(
role="user", content=f"Extract the books from these texts: {texts}"
)
]
texts = [
"The Name of the Wind by Patrick Rothfuss",
"Mistborn: The Final Empire by Brandon Sanderson",
]
print(extract_books(texts))
# Output: texts=['The Name of the Wind by Patrick Rothfuss', 'Mistborn: The Final Empire by Brandon Sanderson'] books=[Book(title='The Name of the Wind', author='Patrick Rothfuss'), Book(title='Mistborn: The Final Empire', author='Brandon Sanderson')]
Next Steps¶
By following these best practices and leveraging Response Models effectively, you can create more robust, type-safe, and maintainable LLM-powered applications with Mirascope.
Next, we recommend taking a lookg at one of: