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참고/instructor-main/examples/validators/readme.md

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+# Using `llm_validator` with OpenAI's GPT-3.5 Turbo and Pydantic for Text Validation with Output Examples
+
+## Overview
+
+This document outlines how to use a custom text validation logic (`llm_validator`) with OpenAI's GPT-3.5 Turbo and Pydantic, including the outputs for each operation.
+
+## Code Explanation
+
+### Basic Setup
+
+Import necessary modules and apply patches for compatibility.
+
+```python
+from typing_extensions import Annotated
+from pydantic import (
+    BaseModel,
+    BeforeValidator,
+)
+from instructor import llm_validator, patch
+import openai
+
+patch()
+```
+
+### Defining Response Models
+
+Define a basic Pydantic model named `QuestionAnswer`.
+
+```python
+class QuestionAnswer(BaseModel):
+    question: str
+    answer: str
+```
+
+### Generating a Response
+
+Generate a response from GPT-3.5 Turbo.
+
+```python
+question = "What is the meaning of life?"
+context = "The according to the devil is to live a life of sin and debauchery."
+
+qa: QuestionAnswer = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    response_model=QuestionAnswer,
+    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": f"using the context: {context}\n\nAnswer the following question: {question}",
+        },
+    ],
+)
+```
+
+#### Output
+
+Before validation with `llm_validator`:
+
+```json
+{
+  "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
+
+Add custom validation using `llm_validator`.
+
+```python
+class QuestionAnswerNoEvil(BaseModel):
+    question: str
+    answer: Annotated[
+        str,
+        BeforeValidator(
+            llm_validator("don't say objectionable things", allow_override=True)
+        ),
+    ]
+```
+
+#### Output
+
+```text
+1 validation error for QuestionAnswerNoEvil
+answer
+    Assertion failed, The statement promotes sin and debauchery, which is objectionable.
+```
+
+### Handling Validation Errors
+
+Catch exceptions raised by the validation.
+
+```python
+try:
+    qa: QuestionAnswerNoEvil = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        response_model=QuestionAnswerNoEvil,
+        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": f"using the context: {context}\n\nAnswer the following question: {question}",
+            },
+        ],
+    )
+except Exception as e:
+    print(e)
+```
+
+### Retrying Validation
+
+Allow for retries by setting `max_retries=2`.
+
+```python
+qa: QuestionAnswerNoEvil = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    response_model=QuestionAnswerNoEvil,
+    max_retries=2,
+    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": f"using the context: {context}\n\nAnswer the following question: {question}",
+        },
+    ],
+)
+```
+
+#### Output
+
+After validation with `llm_validator` and `max_retries=2`:
+
+```json
+{
+  "question": "What is the meaning of life?",
+  "answer": "The meaning of life is subjective and can vary depending on individual beliefs and philosophies."
+}
+```
+
+## Summary
+
+This document described how to use `llm_validator` with OpenAI's GPT-3.5 Turbo and Pydantic, including example outputs. This approach allows for controlled and filtered responses.