Instructor

python3 -m venv .venv
source .venv/bin/activate
python --version
pip install instructor

export SAMBANOVA_API_KEY="your-sambanova-api-key"

from openai import OpenAI
import instructor
import os
from pydantic import BaseModel

client = instructor.from_openai(
    OpenAI(
        base_url="https://api.sambanova.ai/v1",
        api_key=os.environ["SAMBANOVA_API_KEY"]
    )
)

class User(BaseModel):
    name: str
    age: int

user = client.chat.completions.create(
    model="Meta-Llama-3.3-70B-Instruct",
    messages=[
        {"role": "user", "content": "Ivan is 28"},
    ],
    response_model=User,
)
print(user)
# > User(name='Ivan', age=28)

import instructor
from instructor import Mode
from openai import OpenAI
import os

client = instructor.from_openai(
    OpenAI(
        base_url="https://api.sambanova.ai/v1",
        api_key=os.environ["SAMBANOVA_API_KEY"]
    ),
    mode=Mode.JSON,
)

from openai import AsyncOpenAI
import instructor
import asyncio
import os
from pydantic import BaseModel

client = instructor.from_openai(
    AsyncOpenAI(
        base_url="https://api.sambanova.ai/v1",
        api_key=os.environ["SAMBANOVA_API_KEY"]
    )
)

class User(BaseModel):
    name: str
    age: int

async def get_user():
    user = await client.chat.completions.create(
        model="Meta-Llama-3.3-70B-Instruct",
        messages=[
            {"role": "user", "content": "Ivan is 28"},
        ],
        response_model=User,
    )
    return user

user = asyncio.run(get_user())
print(user)
# > User(name='Ivan', age=28)

import instructor
import os
from enum import Enum
from openai import OpenAI
from pydantic import BaseModel, Field, field_validator
from typing import Optional

client = instructor.from_openai(
    OpenAI(
        base_url="https://api.sambanova.ai/v1",
        api_key=os.environ["SAMBANOVA_API_KEY"]
    )
)

class Priority(str, Enum):
    low = "low"
    medium = "medium"
    high = "high"

class Customer(BaseModel):
    name: str = Field(description="Full name of the person who wrote the message.")
    email: Optional[str] = Field(
        default=None, description="Email address, or null if not stated."
    )

class SupportTicket(BaseModel):
    customer: Customer
    priority: Priority = Field(description="How urgent the request is.")
    summary: str = Field(description="One sentence describing the problem.")
    tags: list[str] = Field(
        default_factory=list, description="Up to three short topic tags."
    )

    @field_validator("tags")
    @classmethod
    def at_most_three_tags(cls, value: list[str]) -> list[str]:
        if len(value) > 3:
            raise ValueError("Return at most three tags.")
        return value

email = """
From: Dana Whitfield <dana.w@example.com>
Our production dashboard has been down since 3am and the whole team is blocked.
"""

ticket = client.chat.completions.create(
    model="Meta-Llama-3.3-70B-Instruct",
    messages=[{"role": "user", "content": email}],
    response_model=SupportTicket,
    max_retries=3,
)
print(ticket.model_dump_json(indent=2))

from instructor.core import InstructorRetryException

try:
    ticket = client.chat.completions.create(
        model="Meta-Llama-3.3-70B-Instruct",
        messages=[{"role": "user", "content": email}],
        response_model=SupportTicket,
        max_retries=3,
    )
except InstructorRetryException as exc:
    print(f"Gave up after {exc.n_attempts} attempts")
    print(f"Total token usage: {exc.total_usage}")
    for attempt in exc.failed_attempts:
        print(f"Attempt {attempt.attempt_number} failed: {attempt.exception}")
    print(f"Last raw completion: {exc.last_completion}")