Multimodal¶
Instructor supports multimodal interactions by providing helper classes that are automatically converted to the correct format for different providers, allowing you to work with both text and images in your prompts and responses. This functionality is implemented in the multimodal.py module and provides a seamless way to handle images alongside text for various AI models.
Image¶
The core of multimodal support in Instructor is the Image class. This class represents an image that can be loaded from a URL or file path. It provides methods to create Image instances and convert them to formats compatible with different AI providers.
It's important to note that Anthropic and OpenAI have different formats for handling images in their API requests. The Image class in Instructor abstracts away these differences, allowing you to work with a unified interface.
Usage¶
You can create an Image instance from a URL or file path using the from_url or from_path methods. The Image class will automatically convert the image to a base64-encoded string and include it in the API request.
import instructor
import openai
from pydantic import BaseModel
class ImageAnalyzer(BaseModel):
description: str
image1 = instructor.Image.from_url(url)
image2 = instructor.Image.from_path("muffin.jpg")
client = instructor.from_openai(openai.OpenAI())
response = client.chat.completions.create(
model="gpt-4o-mini",
response_model=ImageAnalyzer,
messages=[
{"role": "user", "content": ["What is in this two images?", image1, image2]}
],
)
print(response.model_dump_json())
"""
{"description":"A tray of freshly baked blueberry muffins. The muffins have a golden-brown top, are placed in paper liners, and some have blueberries peeking out. In the background, more muffins are visible, along with a single blueberry on the tray."}
"""
The Image class takes care of the necessary conversions and formatting, ensuring that your code remains clean and provider-agnostic. This flexibility is particularly valuable when you're experimenting with different models or when you need to switch providers based on specific project requirements.
By leveraging Instructor's multimodal capabilities, you can focus on building your application logic without worrying about the intricacies of each provider's image handling format. This not only saves development time but also makes your code more maintainable and adaptable to future changes in AI provider APIs.
Alternatively, by passing autodetect_images=True to client.chat.completions.create, you can pass file paths, URLs, or base64 encoded content directly as strings.
import instructor
import openai
client = instructor.from_openai(openai.OpenAI())
response = client.chat.completions.create(
model="gpt-4o-mini",
response_model=ImageAnalyzer,
messages=[
{
"role": "user",
"content": [
"What is in this two images?",
"https://example.com/image.jpg",
"path/to/image.jpg",
],
}
],
autodetect_images=True,
)
Anthropic Prompt Caching¶
Instructor supports Anthropic prompt caching with images. To activate prompt caching, you can pass image content as a dictionary of the form
and setautodetect_images=True, or flag it within a constructor such as instructor.Image.from_path("path/to/image.jpg", cache_control=True). For example: import instructor
from anthropic import Anthropic
client = instructor.from_anthropic(Anthropic(), enable_prompt_caching=True)
cache_control = {"type": "ephemeral"}
response = client.chat.completions.create(
model="claude-3-haiku-20240307",
response_model=ImageAnalyzer, # This can be set to `None` to return an Anthropic prompt caching message
messages=[
{
"role": "user",
"content": [
"What is in this two images?",
{
"type": "image",
"source": "https://example.com/image.jpg",
"cache_control": cache_control,
},
{
"type": "image",
"source": "path/to/image.jpg",
"cache_control": cache_control,
},
],
}
],
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¶
from openai import OpenAI
from pydantic import BaseModel
import instructor
from instructor.multimodal import Audio
client = instructor.from_openai(OpenAI())
class User(BaseModel):
name: str
age: int
resp = client.chat.completions.create(
model="gpt-4o-audio-preview",
response_model=User,
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)
#> name='Jason' age=20