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Mirascope

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Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock.

Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create powerful, robust applications.

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30 Second Quickstart

Install Mirascope, specifying the provider(s) you intend to use, and set your API key:

pip install "mirascope[openai]"

export OPENAI_API_KEY=XXXXX

pip install "mirascope[anthropic]"

export ANTHROPIC_API_KEY=XXXXX

pip install "mirascope[mistral]"

export MISTRAL_API_KEY=XXXXX

pip install "mirascope[gemini]"

export GOOGLE_API_KEY=XXXXX

pip install "mirascope[groq]"

export GROQ_API_KEY=XXXXX

pip install "mirascope[cohere]"

export CO_API_KEY=XXXXX

pip install "mirascope[litellm]"

export OPENAI_API_KEY=XXXXX  # set keys for providers you will use

pip install "mirascope[azure]"

export AZURE_INFERENCE_ENDPOINT=XXXXX
export AZURE_INFERENCE_CREDENTIAL=XXXXX

pip install "mirascope[vertex]"

gcloud init
gcloud auth application-default login

pip install "mirascope[bedrock]"

aws configure

pip install "mirascope[openai]"

set OPENAI_API_KEY=XXXXX

pip install "mirascope[anthropic]"

set ANTHROPIC_API_KEY=XXXXX

pip install "mirascope[mistral]"

set MISTRAL_API_KEY=XXXXX

pip install "mirascope[gemini]"

set GOOGLE_API_KEY=XXXXX

pip install "mirascope[groq]"

set GROQ_API_KEY=XXXXX

pip install "mirascope[cohere]"

set CO_API_KEY=XXXXX

pip install "mirascope[litellm]"

set OPENAI_API_KEY=XXXXX  # set keys for providers you will use

pip install "mirascope[azure]"

set AZURE_INFERENCE_ENDPOINT=XXXXX
set AZURE_INFERENCE_CREDENTIAL=XXXXX

pip install "mirascope[vertex]"

gcloud init
gcloud auth application-default login

pip install "mirascope[bedrock]"

aws configure

Make your first call to an LLM to extract the title and author of a book from the given text:

Mirascope

from mirascope.core import openai
from pydantic import BaseModel


class Book(BaseModel):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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):
    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'

import os
from typing import cast

from mistralai import Mistral
from pydantic import BaseModel

client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])


class Book(BaseModel):
    """An extracted book."""

    title: str
    author: str


def extract_book(text: str) -> Book:
    completion = client.chat.complete(
        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="any",
    )
    if (
        completion
        and (choices := completion.choices)
        and (tool_calls := choices[0].message.tool_calls)
    ):
        return Book.model_validate_json(cast(str, 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'

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