Structured Outputs with Groq AI¶
If you want to try this example using instructor hub
, you can pull it by running
you'll need to sign up for an account and get an API key. You can do that here.
Other Languages
This blog post is written in Python, but the concepts are applicable to other languages as well, as we currently have support for Javascript, Elixir and PHP.
Patching¶
Instructor's patch enhances the openai api it with the following features:
response_model
increate
calls that returns a pydantic modelmax_retries
increate
calls that retries the call if it fails by using a backoff strategy
Learn More
To learn more, please refer to the docs. To understand the benefits of using Pydantic with Instructor, visit the tips and tricks section of the why use Pydantic page.
Groq AI¶
While Groq AI does not support function calling directly, you can still leverage the TOOLS mode for structured outputs.
Getting access
If you want to try this out for yourself check out the docs
import os
import instructor
from groq import Groq
from pydantic import BaseModel
client = Groq(
api_key=os.environ.get("GROQ_API_KEY"),
)
# By default, the patch function will patch the ChatCompletion.create and ChatCompletion.create methods to support the response_model parameter
client = instructor.from_groq(client, mode=instructor.Mode.TOOLS)
# Now, we can use the response_model parameter using only a base model
# rather than having to use the OpenAISchema class
class UserExtract(BaseModel):
name: str
age: int
user: UserExtract = client.chat.completions.create(
model="mixtral-8x7b-32768",
response_model=UserExtract,
messages=[
{"role": "user", "content": "Extract jason is 25 years old"},
],
)
assert isinstance(user, UserExtract), "Should be instance of UserExtract"
assert user.name.lower() == "jason"
assert user.age == 25
print(user.model_dump_json(indent=2))
"""
{
"name": "jason",
"age": 25
}
"""