diff --git a/docs/blog/posts/openai-multimodal.md b/docs/blog/posts/openai-multimodal.md new file mode 100644 index 000000000..4d7a9cb9c --- /dev/null +++ b/docs/blog/posts/openai-multimodal.md @@ -0,0 +1,87 @@ +--- +authors: +- jxnl +categories: +- OpenAI +- Audio +comments: true +date: 2025-10-17 +description: Explore the new audio capabilities in OpenAI's Chat Completions API using the gpt-4o-audio-preview model. +draft: false +tags: +- OpenAI +- Audio Processing +- API +- Machine Learning +--- + +# Audio Support in OpenAI's Chat Completions API + +OpenAI has recently introduced audio support in their Chat Completions API, opening up exciting new possibilities for developers working with audio and text interactions. This feature is powered by the new `gpt-4o-audio-preview` model, which brings advanced voice capabilities to the familiar Chat Completions API interface. + +## Key Features + +The new audio support in the Chat Completions API offers several compelling features: + +1. **Flexible Input Handling**: The API can now process any combination of text and audio inputs, allowing for more versatile applications. + +2. **Natural, Steerable Voices**: Similar to the Realtime API, developers can use prompting to shape various aspects of the generated audio, including language, pronunciation, and emotional range. + +3. **Tool Calling Integration**: The audio support seamlessly integrates with existing tool calling functionality, enabling complex workflows that combine audio, text, and external tools. + +## Practical Example + +To demonstrate how to use this new functionality, let's look at a simple example using the `instructor` library: + +"""python +from openai import OpenAI +from pydantic import BaseModel +import instructor +from instructor.multimodal import Audio +import base64 + +client = instructor.from_openai(OpenAI()) + +class Person(BaseModel): + name: str + age: int + +resp = client.chat.completions.create( + model="gpt-4o-audio-preview", + response_model=Person, + modalities=["text"], + audio={"voice": "alloy", "format": "wav"}, + messages=[ + { + "role": "user", + "content": [ + "Extract the following information from the audio", + Audio.from_path("./output.wav"), + ], + }, + ], +) + +print(resp) +# Expected output: Person(name='Jason', age=20) +""" + +In this example, we're using the `gpt-4o-audio-preview` model to extract information from an audio file. The API processes the audio input and returns structured data (a Person object with name and age) based on the content of the audio. + +## Use Cases + +The addition of audio support to the Chat Completions API enables a wide range of applications: + +1. **Voice-based Personal Assistants**: Create more natural and context-aware voice interfaces for various applications. + +2. **Audio Content Analysis**: Automatically extract information, sentiments, or key points from audio recordings or podcasts. + +3. **Language Learning Tools**: Develop interactive language learning applications that can process and respond to spoken language. + +4. **Accessibility Features**: Improve accessibility in applications by providing audio-based interactions and text-to-speech capabilities. + +## Considerations + +While this new feature is exciting, it's important to note that it's best suited for asynchronous use cases that don't require extremely low latencies. For more dynamic and real-time interactions, OpenAI recommends using their Realtime API. + +As with any AI-powered feature, it's crucial to consider ethical implications and potential biases in audio processing and generation. Always test thoroughly and consider the diversity of your user base when implementing these features. diff --git a/docs/concepts/multimodal.md b/docs/concepts/multimodal.md index abf052d30..baad197ce 100644 --- a/docs/concepts/multimodal.md +++ b/docs/concepts/multimodal.md @@ -1,6 +1,6 @@ --- title: Seamless Multimodal Interactions with Instructor -description: Learn how the Image class in Instructor enables seamless handling of images and text across different AI models. +description: Learn how the Image and Audio class in Instructor enables seamless handling of images, audio and text across different AI models. --- # Multimodal @@ -87,3 +87,43 @@ response = client.chat.completions.create( autodetect_images=True ) ``` + +## `Audio` + +The `Audio` class represents an audio file that can be loaded from a URL or file path. It provides methods to create `Audio` instances but currently only OpenAI supports it. You can create an instance using the `from_path` and `from_url` methods. The `Audio` class will automatically convert it to a base64-encoded image and include it in the API request. + +### Usage + +```python +from openai import OpenAI +from pydantic import BaseModel +import instructor +from instructor.multimodal import Audio +import base64 + +client = instructor.from_openai(OpenAI()) + + +class User(BaseModel): + name: str + age: int + + +with open("./output.wav", "rb") as f: + encoded_string = base64.b64encode(f.read()).decode("utf-8") + +resp = client.chat.completions.create( + model="gpt-4o-audio-preview", + response_model=User, + modalities=["text"], + audio={"voice": "alloy", "format": "wav"}, + "Extract the following information from the audio:", + Audio.from_path("./output.wav"), + ], + }, + ], +) # type: ignore + +print(resp) +# > name='Jason' age=20 +``` diff --git a/examples/openai-audio/output.wav b/examples/openai-audio/output.wav new file mode 100644 index 000000000..58b1ea711 Binary files /dev/null and b/examples/openai-audio/output.wav differ diff --git a/examples/openai-audio/run.py b/examples/openai-audio/run.py new file mode 100644 index 000000000..06b7642da --- /dev/null +++ b/examples/openai-audio/run.py @@ -0,0 +1,35 @@ +from openai import OpenAI +from pydantic import BaseModel +import instructor +from instructor.multimodal import Audio +import base64 + +client = instructor.from_openai(OpenAI()) + + +class Person(BaseModel): + name: str + age: int + + +with open("./output.wav", "rb") as f: + encoded_string = base64.b64encode(f.read()).decode("utf-8") + +resp = client.chat.completions.create( + model="gpt-4o-audio-preview", + response_model=Person, + modalities=["text"], + audio={"voice": "alloy", "format": "wav"}, + messages=[ + { + "role": "user", + "content": [ + "Extract the following information from the audio", + Audio.from_path("./output.wav"), + ], + }, + ], +) # type: ignore + +print(resp) +# > Person(name='Jason', age=20) diff --git a/instructor/__init__.py b/instructor/__init__.py index 348884f54..e22bd553a 100644 --- a/instructor/__init__.py +++ b/instructor/__init__.py @@ -3,7 +3,7 @@ from .mode import Mode from .process_response import handle_response_model from .distil import FinetuneFormat, Instructions -from .multimodal import Image +from .multimodal import Image, Audio from .dsl import ( CitationMixin, Maybe, @@ -26,6 +26,7 @@ __all__ = [ "Instructor", "Image", + "Audio", "from_openai", "from_litellm", "AsyncInstructor", diff --git a/instructor/batch.py b/instructor/batch.py index afb572bc4..6effd60da 100644 --- a/instructor/batch.py +++ b/instructor/batch.py @@ -141,6 +141,7 @@ def create_from_messages( messages=messages, max_tokens=max_tokens, temperature=temperature, + **kwargs, ), ) else: diff --git a/instructor/multimodal.py b/instructor/multimodal.py index 3be20e936..524f2eced 100644 --- a/instructor/multimodal.py +++ b/instructor/multimodal.py @@ -19,6 +19,7 @@ import mimetypes import requests from pydantic import BaseModel, Field +from .mode import Mode F = TypeVar("F", bound=Callable[..., Any]) K = TypeVar("K", bound=Hashable) @@ -50,11 +51,11 @@ class Image(BaseModel): ) @classmethod - def autodetect(cls, source: str | Path) -> Image: + def autodetect(cls, source: Union[str, Path]) -> Image: # noqa: UP007 """Attempt to autodetect an image from a source string or Path. Args: - source (str | Path): The source string or path. + source (Union[str,path]): The source string or path. Returns: An Image if the source is detected to be a valid image. Raises: @@ -75,11 +76,11 @@ def autodetect(cls, source: str | Path) -> Image: raise ValueError("Unable to determine image type or unsupported image format") @classmethod - def autodetect_safely(cls, source: str | Path) -> Union[Image, str]: # noqa: UP007 + def autodetect_safely(cls, source: Union[str, Path]) -> Union[Image, str]: # noqa: UP007 """Safely attempt to autodetect an image from a source string or path. Args: - source (str | Path): The source string or path. + source (Union[str,path]): The source string or path. Returns: An Image if the source is detected to be a valid image, otherwise the source itself as a string. @@ -146,7 +147,7 @@ def from_url(cls, url: str) -> Image: @classmethod @lru_cache - def from_path(cls, path: str | Path) -> Image: + def from_path(cls, path: Union[str, Path]) -> Image: # noqa: UP007 path = Path(path) if not path.is_file(): raise FileNotFoundError(f"Image file not found: {path}") @@ -204,8 +205,47 @@ def to_openai(self) -> dict[str, Any]: raise ValueError("Image data is missing for base64 encoding.") +class Audio(BaseModel): + """Represents an audio that can be loaded from a URL or file path.""" + + source: Union[str, Path] = Field(..., description="URL or file path of the audio") # noqa: UP007 + data: Union[str, None] = Field( # noqa: UP007 + None, description="Base64 encoded audio data", repr=False + ) + + @classmethod + def from_url(cls, url: str) -> Audio: + """Create an Audio instance from a URL.""" + assert url.endswith(".wav"), "Audio must be in WAV format" + + response = requests.get(url) + data = base64.b64encode(response.content).decode("utf-8") + return cls(source=url, data=data) + + @classmethod + def from_path(cls, path: Union[str, Path]) -> Audio: # noqa: UP007 + """Create an Audio instance from a file path.""" + path = Path(path) + assert path.is_file(), f"Audio file not found: {path}" + assert path.suffix.lower() == ".wav", "Audio must be in WAV format" + + data = base64.b64encode(path.read_bytes()).decode("utf-8") + return cls(source=str(path), data=data) + + def to_openai(self) -> dict[str, Any]: + """Convert the Audio instance to OpenAI's API format.""" + return { + "type": "input_audio", + "input_audio": {"data": self.data, "format": "wav"}, + } + + def to_anthropic(self) -> dict[str, Any]: + raise NotImplementedError("Anthropic is not supported yet") + + class ImageWithCacheControl(Image): """Image with Anthropic prompt caching support.""" + cache_control: OptionalCacheControlType = Field( None, description="Optional Anthropic cache control image" ) @@ -232,14 +272,18 @@ def to_anthropic(self) -> dict[str, Any]: def convert_contents( contents: Union[ # noqa: UP007 - list[Union[str, dict[str, Any], Image]], str, dict[str, Any], Image # noqa: UP007 + str, + dict[str, Any], + Image, + Audio, + list[Union[str, dict[str, Any], Image, Audio]], # noqa: UP007 ], mode: Mode, ) -> Union[str, list[dict[str, Any]]]: # noqa: UP007 """Convert content items to the appropriate format based on the specified mode.""" if isinstance(contents, str): return contents - if isinstance(contents, Image) or isinstance(contents, dict): + if isinstance(contents, (Image, Audio)) or isinstance(contents, dict): contents = [contents] converted_contents: list[dict[str, Union[str, Image]]] = [] # noqa: UP007 @@ -248,7 +292,7 @@ def convert_contents( converted_contents.append({"type": "text", "text": content}) elif isinstance(content, dict): converted_contents.append(content) - elif isinstance(content, Image): + elif isinstance(content, (Image, Audio)): if mode in {Mode.ANTHROPIC_JSON, Mode.ANTHROPIC_TOOLS}: converted_contents.append(content.to_anthropic()) elif mode in {Mode.GEMINI_JSON, Mode.GEMINI_TOOLS}: @@ -264,9 +308,15 @@ def convert_messages( messages: list[ dict[ str, - Union[list[Union[str, dict[str, Any], Image]], str, dict[str, Any], Image], # noqa: UP007 + Union[ # noqa: UP007 + str, + dict[str, Any], + Image, + Audio, + list[Union[str, dict[str, Any], Image, Audio]], # noqa: UP007 + ], ] - ], # noqa: UP007 + ], mode: Mode, autodetect_images: bool = False, ) -> list[dict[str, Any]]: @@ -277,11 +327,16 @@ def is_image_params(x: Any) -> bool: return isinstance(x, dict) and x.get("type") == "image" and "source" in x # type: ignore for message in messages: + if "type" in message: + if message["type"] in {"audio", "image"}: + converted_messages.append(message) # type: ignore + else: + raise ValueError(f"Unsupported message type: {message['type']}") role = message["role"] content = message["content"] if autodetect_images: if isinstance(content, list): - new_content: list[Union[str, dict[str, Any], Image]] = [] # noqa: UP007 + new_content: list[Union[str, dict[str, Any], Image, Audio]] = [] # noqa: UP007 for item in content: if isinstance(item, str): new_content.append(Image.autodetect_safely(item)) diff --git a/mkdocs.yml b/mkdocs.yml index 8635e71bc..ae4817ab2 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -164,6 +164,7 @@ nav: - "blog/index.md" - Concepts: - Models: 'concepts/models.md' + - Multimodal : 'concepts/multimodal.md' - Retrying: 'concepts/retrying.md' - Patching: 'concepts/patching.md' - Hooks: 'concepts/hooks.md' diff --git a/poetry.lock b/poetry.lock index 12643bf1d..5241de453 100644 --- a/poetry.lock +++ b/poetry.lock @@ -150,13 +150,13 @@ files = [ [[package]] name = "anthropic" -version = "0.34.2" +version = "0.36.2" description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.7" files = [ - {file = "anthropic-0.34.2-py3-none-any.whl", hash = "sha256:f50a628eb71e2c76858b106c8cbea278c45c6bd2077cb3aff716a112abddc9fc"}, - {file = "anthropic-0.34.2.tar.gz", hash = "sha256:808ea19276f26646bfde9ee535669735519376e4eeb301a2974fc69892be1d6e"}, + {file = "anthropic-0.36.2-py3-none-any.whl", hash = "sha256:308ddc6c538de03c081552e456bc0b387b6f7c7d1dea0c20122cc11c7cdbaf6a"}, + {file = "anthropic-0.36.2.tar.gz", hash = "sha256:d5a3fa56d1c82a51944f9dc7b0dc72048deb89f8df5ebfd09e2d1b59c62eb8eb"}, ] [package.dependencies] @@ -353,47 +353,6 @@ webencodings = "*" [package.extras] css = ["tinycss2 (>=1.1.0,<1.3)"] -[[package]] -name = "boto3" -version = "1.35.42" -description = "The AWS SDK for Python" -optional = false -python-versions = ">=3.8" -files = [ - {file = "boto3-1.35.42-py3-none-any.whl", hash = "sha256:e1f36f8be453505cebcc3da178ea081b2a06c0e5e1cdee774f1067599b8d9c3e"}, - {file = "boto3-1.35.42.tar.gz", hash = "sha256:a5b00f8b82dce62870759f04861747944da834d64a64355970120c475efdafc0"}, -] - -[package.dependencies] -botocore = ">=1.35.42,<1.36.0" -jmespath = ">=0.7.1,<2.0.0" -s3transfer = ">=0.10.0,<0.11.0" - -[package.extras] -crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] - -[[package]] -name = "botocore" -version = "1.35.42" -description = "Low-level, data-driven core of boto 3." -optional = false -python-versions = ">=3.8" -files = [ - {file = "botocore-1.35.42-py3-none-any.whl", hash = "sha256:05af0bb8b9cea7ce7bc589c332348d338a21b784e9d088a588fd10ec145007ff"}, - {file = "botocore-1.35.42.tar.gz", hash = "sha256:af348636f73dc24b7e2dc760a34d08c8f2f94366e9b4c78d877307b128abecef"}, -] - -[package.dependencies] -jmespath = ">=0.7.1,<2.0.0" -python-dateutil = ">=2.1,<3.0.0" -urllib3 = [ - {version = ">=1.25.4,<1.27", markers = "python_version < \"3.10\""}, - {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}, -] - -[package.extras] -crt = ["awscrt (==0.22.0)"] - [[package]] name = "cachecontrol" version = "0.14.0" @@ -706,17 +665,16 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} [[package]] name = "cohere" -version = "5.10.0" +version = "5.11.1" description = "" optional = false python-versions = "<4.0,>=3.8" files = [ - {file = "cohere-5.10.0-py3-none-any.whl", hash = "sha256:46e50e3e8514a99cf77b4c022c8077a6205fba948051c33087ddeb66ec706f0a"}, - {file = "cohere-5.10.0.tar.gz", hash = "sha256:21020a7ae4c30f72991ef91566a926a9d7d1485d7abeed7bfa2bd6f35ea34783"}, + {file = "cohere-5.11.1-py3-none-any.whl", hash = "sha256:117c718bfbc7637cf22c1025e8e2bf820ebeef51f7fbb2b9d74f3e9c0a9c6c25"}, + {file = "cohere-5.11.1.tar.gz", hash = "sha256:821e20593def7796d314be9bcba87e9ecf69dc6ef17172f842447275f8679d0f"}, ] [package.dependencies] -boto3 = ">=1.34.0,<2.0.0" fastavro = ">=1.9.4,<2.0.0" httpx = ">=0.21.2" httpx-sse = "0.4.0" @@ -728,6 +686,9 @@ tokenizers = ">=0.15,<1" types-requests = ">=2.0.0,<3.0.0" typing_extensions = ">=4.0.0" +[package.extras] +aws = ["boto3 (>=1.34.0,<2.0.0)", "sagemaker (>=2.232.1,<3.0.0)"] + [[package]] name = "colorama" version = "0.4.6" @@ -1400,12 +1361,12 @@ files = [ google-auth = ">=2.14.1,<3.0.dev0" googleapis-common-protos = ">=1.56.2,<2.0.dev0" grpcio = [ - {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, ] grpcio-status = [ - {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""}, + {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""}, ] proto-plus = ">=1.22.3,<2.0.0dev" protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0.dev0" @@ -1812,13 +1773,13 @@ test = ["objgraph", "psutil"] [[package]] name = "griffe" -version = "1.4.1" +version = "1.5.1" description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." optional = false python-versions = ">=3.9" files = [ - {file = "griffe-1.4.1-py3-none-any.whl", hash = "sha256:84295ee0b27743bd880aea75632830ef02ded65d16124025e4c263bb826ab645"}, - {file = "griffe-1.4.1.tar.gz", hash = "sha256:911a201b01dc92e08c0e84c38a301e9da5ec067f00e7d9f2e39bc24dbfa3c176"}, + {file = "griffe-1.5.1-py3-none-any.whl", hash = "sha256:ad6a7980f8c424c9102160aafa3bcdf799df0e75f7829d75af9ee5aef656f860"}, + {file = "griffe-1.5.1.tar.gz", hash = "sha256:72964f93e08c553257706d6cd2c42d1c172213feb48b2be386f243380b405d4b"}, ] [package.dependencies] @@ -2076,13 +2037,13 @@ wsproto = "*" [[package]] name = "huggingface-hub" -version = "0.25.2" +version = "0.26.0" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" optional = false python-versions = ">=3.8.0" files = [ - {file = "huggingface_hub-0.25.2-py3-none-any.whl", hash = "sha256:1897caf88ce7f97fe0110603d8f66ac264e3ba6accdf30cd66cc0fed5282ad25"}, - {file = "huggingface_hub-0.25.2.tar.gz", hash = "sha256:a1014ea111a5f40ccd23f7f7ba8ac46e20fa3b658ced1f86a00c75c06ec6423c"}, + {file = "huggingface_hub-0.26.0-py3-none-any.whl", hash = "sha256:e43b8f36042b2103b48dea822535e08f5f089c4aa7013a067fca7b4ebf7f85a3"}, + {file = "huggingface_hub-0.26.0.tar.gz", hash = "sha256:524fe9281b015b76aa73ff1a83bf1cbe8cab851c9ac5ae5fcd2a25d5173ce629"}, ] [package.dependencies] @@ -2095,16 +2056,16 @@ tqdm = ">=4.42.1" typing-extensions = ">=3.7.4.3" [package.extras] -all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", 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= "^0.9.0", optional = true } pydantic_extra_types = { version = "^2.6.0", optional = true } litellm = { version = "^1.35.31", optional = true } -anthropic = { version = "^0.34.0", optional = true } +anthropic = { version = "^0.36.2", optional = true } xmltodict = { version = "^0.13.0", optional = true } groq = { version = "^0.4.2", optional = true } cohere = { version = "^5.1.8", optional = true } @@ -86,7 +86,7 @@ mkdocs-minify-plugin = "^0.8.0" mkdocs-redirects = "^1.2.1" [tool.poetry.group.anthropic.dependencies] -anthropic = "^0.34.0" +anthropic = "^0.36.2" [tool.poetry.group.test-docs.dependencies] fastapi = "^0.109.2" @@ -96,7 +96,7 @@ pandas = "^2.2.0" tabulate = "^0.9.0" pydantic_extra_types = "^2.6.0" litellm = "^1.35.31" -anthropic = "^0.34.0" +anthropic = "^0.36.2" xmltodict = "^0.13.0" groq = "^0.4.2" phonenumbers = "^8.13.33" diff --git a/tests/llm/test_openai/test_multimodal.py b/tests/llm/test_openai/test_multimodal.py index 2790e62b9..62785c97c 100644 --- a/tests/llm/test_openai/test_multimodal.py +++ b/tests/llm/test_openai/test_multimodal.py @@ -1,9 +1,51 @@ import pytest -from instructor.multimodal import Image +from instructor.multimodal import Image, Audio import instructor from pydantic import Field, BaseModel from itertools import product from .util import models, modes +import requests +from pathlib import Path + + +audio_url = "https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg.wav" + + +def gettysburg_audio(): + audio_file = Path("gettysburg.wav") + if not audio_file.exists(): + response = requests.get(audio_url) + response.raise_for_status() + with open(audio_file, "wb") as f: + f.write(response.content) + return audio_file + + +@pytest.mark.parametrize( + "audio_file", + [Audio.from_url(audio_url), Audio.from_path(gettysburg_audio())], +) +def test_multimodal_audio_description(audio_file, client): + client = instructor.from_openai(client) + + class AudioDescription(BaseModel): + source: str + + response = client.chat.completions.create( + model="gpt-4o-audio-preview", + response_model=AudioDescription, + modalities=["text"], + messages=[ + { + "role": "user", + "content": [ + "Where's this excerpt from?", + audio_file, + ], + }, + ], + audio={"voice": "alloy", "format": "wav"}, + ) class ImageDescription(BaseModel):