567-labs--instructor/docs/blog/posts/anthropic.md

authors, categories, comments, date, description, draft, tags

authors	categories	comments	date	description	draft	tags
jxnl	Anthropic	true	2024-03-20	Learn how to integrate Anthropic's powerful language models into your projects using Instructor, with step-by-step guidance on installation, client setup, and creating structured outputs with Pydantic models.	false	Anthropic API Development Pydantic Python LLM Techniques

Structured Outputs with Anthropic

A special shoutout to Shreya for her contributions to the anthropic support. As of now, all features are operational with the exception of streaming support.

For those eager to experiment, simply patch the client with ANTHROPIC_JSON, which will enable you to leverage the anthropic client for making requests.

pip install instructor[anthropic]

!!! warning "Missing Features"

Just want to acknowledge that we know that we are missing partial streaming and some better re-asking support for XML. We are working on it and will have it soon.

from pydantic import BaseModel
from typing import List
import anthropic
import instructor

# Patching the Anthropics client with the instructor for enhanced capabilities
anthropic_client = instructor.from_openai(
    create=anthropic.Anthropic().messages.create,
    mode=instructor.Mode.JSON
)

class Properties(BaseModel):
    name: str
    value: str

class User(BaseModel):
    name: str
    age: int
    properties: List[Properties]

user_response = anthropic_client(
    model="claude-3-haiku-20240307",
    max_tokens=1024,
    max_retries=0,
    messages=[
        {
            "role": "user",
            "content": "Create a user for a model with a name, age, and properties.",
        }
    ],
    response_model=User,
)  # type: ignore

print(user_response.model_dump_json(indent=2))
"""
{
    "name": "John",
    "age": 25,
    "properties": [
        {
            "key": "favorite_color",
            "value": "blue"
        }
    ]
}

We're encountering challenges with deeply nested types and eagerly invite the community to test, provide feedback, and suggest necessary improvements as we enhance the anthropic client's support.