Self-Correction with `LLMValidator`¶

Introduction¶

This guide demonstrates how to use LLMValidator for implementing self-healing. The objective is to showcase how an instructor can self-correct by using validation errors and helpful error messages.

Setup¶

Import required modules to create a zod model

import { z } from "zod"

Defining Models¶

Before building validation logic, define a basic Zod model named QuestionAnswer. We'll use this model to generate a response without validation to see the output.

const QuestionAnswer = z.object({
  question: z.string(),
  answer: z.string()
})

Generating a Response¶

Here we coerce the model to generate a response that is objectionable.

import { LLMValidator } from "@/dsl/validator"
import Instructor from "@/instructor"
import OpenAI from "openai"

const openAi = new OpenAI({ apiKey: process.env.OPENAI_API_KEY ?? "" })

const instructor = Instructor({
  client: openAi,
  mode: "TOOLS"
})

const question = "What is the meaning of life?"
const context = "According to the devil the meaning of live is to live a life of sin and debauchery."

await instructor.chat.completions.create({
    model: "gpt-4o",
    max_retries: 0,
    response_model: { schema: QuestionAnswer, name: "Question and Answer" },
    messages: [
      {
        role: "system",
        content:
          "You are a system that answers questions based on the context. answer exactly what the question asks using the context."
      },
      {
        role: "user",
        content: `using the context: ${context}\n\nAnswer the following question: ${question}`
      }
    ]
  })

Output Before Validation¶

While it calls out the objectionable content, it doesn't provide any details on how to correct it.

{
  "question": "What is the meaning of life?",
  "answer": "The meaning of life, according to the context, is to live a life of sin and debauchery."
}

Adding Custom Validation¶

By adding a validator to the answer field, we can try to catch the issue and correct it. Lets integrate LLMValidator into the model and see the error message. Its important to note that you can use all of Zod's validators as you would normally which raise a ZodError with a helpful error message as it will be used as part of the self correction prompt.

const QuestionAnswer = z.object({
  question: z.string(),
  answer: z.string().superRefine(
    LLMValidator(instructor, statement, {
      model: "gpt-4o"
    })
  )
})

try {
  await instructor.chat.completions.create({
    model: "gpt-4o",
    max_retries: 0,
    response_model: { schema: QuestionAnswer, name: "Question and Answer" },
    messages: [
      {
        role: "system",
        content:
          "You are a system that answers questions based on the context. answer exactly what the question asks using the context."
      },
      {
        role: "user",
        content: `using the context: ${context}\n\nAnswer the following question: ${question}`
      }
    ]
  })
} catch (e as ZodError[]) {
  console.error(e[0].message)
}

Output After Validation¶

Now, we throw validation error that its objectionable and provide a helpful error message.

[
  {
    "code": "custom",
    "message": "The value is promoting a negative lifestyle with sin and debauchery, which is questionable.",
    "path": [
      "answer"
    ]
  }
]

Retrying with Corrections¶

By adding the max_retries parameter, we can retry the request with corrections and use the error message to correct the output.

try {
  await instructor.chat.completions.create({
    model: "gpt-4o",
    max_retries: 2,
    response_model: { schema: QuestionAnswer, name: "Question and Answer" },
    messages: [
      {
        role: "system",
        content:
          "You are a system that answers questions based on the context. answer exactly what the question asks using the context."
      },
      {
        role: "user",
        content: `using the context: ${context}\n\nAnswer the following question: ${question}`
      }
    ]
  })
} catch (e as ZodError[]) {
  console.error(e[0].message)
}

Final Output¶

Now, we get a valid response that is not objectionable!

{
  "question": "What is the meaning of life?",
  "answer": "The meaning of life is a subjective and complex question, often explored in religious, philosophical, and moral contexts. Different individuals and cultures have different beliefs and interpretations regarding the purpose and meaning of life.",
}

Self-Correction with LLMValidator¶