Read about Parea’s integration with instructor on the instructor blog here.

Instructor makes it easy to reliably get structured data like JSON from LLMs. Parea’s instructor integration provides these features:

  • groups any LLM calls due to retries together under a single trace
  • tracks any field which failed validation with the respective error message
  • visualizes validation error count over time
  • the annotation queue provides a UI to label JSON responses by filling out a form instead of editing JSON objects

​
Quickstart

python
pip install -U parea-ai instructor

First, create a Parea API key as shown here. Then, you will need to wrap the OpenAI client with Parea using p.wrap_openai_client(client, "instructor"). Finally, you can use instructor.patch / instructor.from_openai to patch the OpenAI client with Instructor. In a single code snippet:

Python
from openai import AsyncOpenAI
import instructor
from parea import Parea, trace

client = AsyncOpenAI()

# Initialize Parea and wrap the OpenAI client for automatic tracing
p = Parea(api_key="PAREA_API_KEY") # <--- Replace with your Parea API key
p.wrap_openai_client(client, "instructor")

client = instructor.from_openai(client)

​
Visualizing traces & validation errors

In your Parea logs dashboard, you can visualize your traces and see the detailed steps the LLM took including examining the structured output and the “functions/tools” instructor attached to the LLM call.

To take a look at trace of this execution checkout the screenshot below. Noticeable:

  • left sidebar: all related LLM calls are grouped under a trace called instructor
  • middle section: the root trace visualizes the templated_inputs as inputs and the created Email object as output
  • bottom of right sidebar: any validation errors are captured and tracked as score for the trace which enables visualizing them in dashboards and filtering by them on tables

Tracking & visualizing the validation error count over time.

Here is the Email function schema we passed to OpenAI.

​
Improving LLMs for Structured Output Generation

In order to improve the performance of your function call responses, you can send the requests to an annotation queue. In that annotation queue, non-engineers can easily label the function call responses by filling out a form, and add the corrected responses to a dataset which you can use for fine-tuning.

​
Fully Working Example

Below you can see a fully-working example code which uses Instructor to classify questions into different types.

Python
import os
import re

import instructor
import requests
from dotenv import load_dotenv
from openai import OpenAI
from pydantic import BaseModel, Field, field_validator

from parea import Parea

load_dotenv()

client = OpenAI()

p = Parea(api_key=os.getenv("PAREA_API_KEY"))
p.wrap_openai_client(client, "instructor")

client = instructor.from_openai(client)


class Email(BaseModel):
    subject: str
    body: str = Field(
        ...,
        description="Email body, Should contain links to instructor documentation. ",
    )

    @field_validator("body")
    def check_urls(cls, v):
        urls = re.findall(r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+", v)
        errors = []
        for url in urls:
            if not url.startswith("https://python.useinstructor.com"):
                errors.append(f"URL {url} is not from useinstructor.com, Only include URLs that include use instructor.com. ")
            response = requests.get(url)
            if response.status_code != 200:
                errors.append(f"URL {url} returned status code {response.status_code}. Only include valid URLs that exist.")
            elif "404" in response.text:
                errors.append(f"URL {url} contained '404' in the body. Only include valid URLs that exist.")
        if errors:
            raise ValueError("\n".join(errors))
        return v


def main():
    email = client.messages.create(
        model="gpt-3.5-turbo",
        max_tokens=1024,
        max_retries=3,
        messages=[
            {
                "role": "user",
                "content": "I'm responding to a student's question. Here is the link to the documentation: {{doc_link1}} and {{doc_link2}}",
            }
        ],
        template_inputs={
            "doc_link1": "https://python.useinstructor.com/docs/tutorial/tutorial-1",
            "doc_link2": "https://jxnl.github.io/docs/tutorial/tutorial-2",
        },
        response_model=Email,
    )
    print(email)


if __name__ == "__main__":
    main()
Read about Parea’s integration with instructor on the instructor blog here.

Instructor makes it easy to reliably get structured data like JSON from LLMs. Parea’s instructor integration provides these features:

  • groups any LLM calls due to retries together under a single trace
  • tracks any field which failed validation with the respective error message
  • visualizes validation error count over time
  • the annotation queue provides a UI to label JSON responses by filling out a form instead of editing JSON objects

​
Quickstart

python
pip install -U parea-ai instructor

First, create a Parea API key as shown here. Then, you will need to wrap the OpenAI client with Parea using p.wrap_openai_client(client, "instructor"). Finally, you can use instructor.patch / instructor.from_openai to patch the OpenAI client with Instructor. In a single code snippet:

Python
from openai import AsyncOpenAI
import instructor
from parea import Parea, trace

client = AsyncOpenAI()

# Initialize Parea and wrap the OpenAI client for automatic tracing
p = Parea(api_key="PAREA_API_KEY") # <--- Replace with your Parea API key
p.wrap_openai_client(client, "instructor")

client = instructor.from_openai(client)

​
Visualizing traces & validation errors

In your Parea logs dashboard, you can visualize your traces and see the detailed steps the LLM took including examining the structured output and the “functions/tools” instructor attached to the LLM call.

To take a look at trace of this execution checkout the screenshot below. Noticeable:

  • left sidebar: all related LLM calls are grouped under a trace called instructor
  • middle section: the root trace visualizes the templated_inputs as inputs and the created Email object as output
  • bottom of right sidebar: any validation errors are captured and tracked as score for the trace which enables visualizing them in dashboards and filtering by them on tables

Tracking & visualizing the validation error count over time.

Here is the Email function schema we passed to OpenAI.

​
Improving LLMs for Structured Output Generation

In order to improve the performance of your function call responses, you can send the requests to an annotation queue. In that annotation queue, non-engineers can easily label the function call responses by filling out a form, and add the corrected responses to a dataset which you can use for fine-tuning.

​
Fully Working Example

Below you can see a fully-working example code which uses Instructor to classify questions into different types.

Python
import os
import re

import instructor
import requests
from dotenv import load_dotenv
from openai import OpenAI
from pydantic import BaseModel, Field, field_validator

from parea import Parea

load_dotenv()

client = OpenAI()

p = Parea(api_key=os.getenv("PAREA_API_KEY"))
p.wrap_openai_client(client, "instructor")

client = instructor.from_openai(client)


class Email(BaseModel):
    subject: str
    body: str = Field(
        ...,
        description="Email body, Should contain links to instructor documentation. ",
    )

    @field_validator("body")
    def check_urls(cls, v):
        urls = re.findall(r"https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+", v)
        errors = []
        for url in urls:
            if not url.startswith("https://python.useinstructor.com"):
                errors.append(f"URL {url} is not from useinstructor.com, Only include URLs that include use instructor.com. ")
            response = requests.get(url)
            if response.status_code != 200:
                errors.append(f"URL {url} returned status code {response.status_code}. Only include valid URLs that exist.")
            elif "404" in response.text:
                errors.append(f"URL {url} contained '404' in the body. Only include valid URLs that exist.")
        if errors:
            raise ValueError("\n".join(errors))
        return v


def main():
    email = client.messages.create(
        model="gpt-3.5-turbo",
        max_tokens=1024,
        max_retries=3,
        messages=[
            {
                "role": "user",
                "content": "I'm responding to a student's question. Here is the link to the documentation: {{doc_link1}} and {{doc_link2}}",
            }
        ],
        template_inputs={
            "doc_link1": "https://python.useinstructor.com/docs/tutorial/tutorial-1",
            "doc_link2": "https://jxnl.github.io/docs/tutorial/tutorial-2",
        },
        response_model=Email,
    )
    print(email)


if __name__ == "__main__":
    main()