Instructor - Portkey Docs

Instructor is a framework for extracting structured outputs from LLMs, available in Python & JS.

Integrating Portkey with Instructor

Instructor Python - OpenAI
Instructor JS - OpenAI

import instructor
from pydantic import BaseModel
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

portkey = OpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        virtual_key="OPENAI_VIRTUAL_KEY",
        api_key="PORTKEY_API_KEY"
    )
)

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

client = instructor.from_openai(portkey)

user_info = client.chat.completions.create(
    model="gpt-4-turbo",
    max_tokens=1024,
    response_model=User,
    messages=[{"role": "user", "content": "John Doe is 30 years old."}],
)

print(user_info.name)
print(user_info.age)

import Instructor from "@instructor-ai/instructor";
import OpenAI from "openai";
import { z } from "zod";
import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

const portkey = new OpenAI({
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY",
    virtualKey: "OPENAI_API_KEY",
  }),
});

const client = Instructor({
  client: portkey,
  mode: "TOOLS",
});

const UserSchema = z.object({
  age: z.number().describe("The age of the user"),
  name: z.string(),
});

const user = await client.chat.completions.create({
  messages: [{ role: "user", content: "Jason Liu is 30 years old" }],
  model: "claude-3-sonnet-20240229",
  //  model: "gpt-4",
  max_tokens: 512,
  response_model: {
    schema: UserSchema,
    name: "User",
  },
});

console.log(user);

Caching Your Requests

Let’s now bring down the cost of running your Instructor pipeline with Portkey caching. You can just create a Config object where you define your cache setting:

{
  "cache": {
    "mode": "simple"
  }
}

You can write it raw, or use Portkey’s Config builder and get a corresponding config id. Then, just pass it while instantiating your OpenAI client:

Instructor Python - OpenAI
Instructor JS - OpenAI

import instructor
from pydantic import BaseModel
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

cache_config = {
  "cache": {
    "mode": "simple"
  }
}

portkey = OpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        virtual_key="OPENAI_VIRTUAL_KEY",
        api_key="PORTKEY_API_KEY",
        config=cache_config # Or pass your Config ID saved from Portkey app
    )
)

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

client = instructor.from_openai(portkey)

user_info = client.chat.completions.create(
    model="gpt-4-turbo",
    max_tokens=1024,
    response_model=User,
    messages=[{"role": "user", "content": "John Doe is 30 years old."}],
)

print(user_info.name)
print(user_info.age)

import Instructor from "@instructor-ai/instructor";
import OpenAI from "openai";
import { z } from "zod";
import { PORTKEY_GATEWAY_URL, createHeaders } from "portkey-ai";

const cache_config = {
  "cache": {
    "mode": "simple"
  }
}

const portkey = new OpenAI({
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY",
    virtualKey: "OPENAI_API_KEY",
    config: cache_config // Or pass your Config ID saved from Portkey app
  }),
});

const client = Instructor({
  client: portkey,
  mode: "TOOLS",
});

const UserSchema = z.object({
  age: z.number().describe("The age of the user"),
  name: z.string(),
});

const user = await client.chat.completions.create({
  messages: [{ role: "user", content: "Jason Liu is 30 years old" }],
  model: "claude-3-sonnet-20240229",
  //  model: "gpt-4",
  max_tokens: 512,
  response_model: {
    schema: UserSchema,
    name: "User",
  },
});

console.log(user);

Similarly, you can add Fallback, Loadbalancing, Timeout, or Retry settings to your Configs and make your Instructor requests robust & reliable.

​Integrating Portkey with Instructor

​Caching Your Requests

Integrating Portkey with Instructor

Caching Your Requests