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참고/instructor-main/docs/examples/self_critique.md

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+---
+title: Implementing Self-Correction with LLM Validator
+description: Learn how to use llm_validator for self-healing in NLP applications and improve response accuracy with validation errors.
+---
+
+# Self-Correction with `llm_validator`
+
+## Introduction
+
+This guide demonstrates how to use `llm_validator` for implementing self-healing. The objective is to showcase how an instructor can self-correct by using validation errors and helpful error messages.
+
+```python
+from pydantic import BaseModel
+import instructor
+
+# Apply the patch to the OpenAI client
+# enables response_model keyword
+client = instructor.from_provider("openai/gpt-4.1-mini")
+
+
+class QuestionAnswer(BaseModel):
+    question: str
+    answer: str
+
+
+question = "What is the meaning of life?"
+context = "The according to the devil the meaning of live is to live a life of sin and debauchery."
+
+qa: QuestionAnswer = client.create(
+    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
+
+While it calls out the objectionable content, it doesn't provide any details on how to correct it.
+
+```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
+
+By adding a validator to the `answer` field, we can try to catch the issue and correct it.
+Lets integrate `llm_validator` into the model and see the error message. Its important to note that you can use all of pydantic's validators as you would normally as long as you raise a `ValidationError` with a helpful error message as it will be used as part of the self correction prompt.
+
+```python
+from pydantic import BaseModel, BeforeValidator
+from typing_extensions import Annotated
+from instructor import llm_validator
+import instructor
+
+client = instructor.from_provider("openai/gpt-4.1-mini")
+
+
+class QuestionAnswerNoEvil(BaseModel):
+    question: str
+    answer: Annotated[
+        str,
+        BeforeValidator(
+            llm_validator(
+                "don't say objectionable things", client=client, allow_override=True
+            )
+        ),
+    ]
+
+
+try:
+    qa: QuestionAnswerNoEvil = client.create(
+        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)
+    #> name 'context' is not defined
+```
+
+### Output After Validation
+
+Now, we throw validation error that its objectionable and provide a helpful error message.
+
+```text
+1 validation error for QuestionAnswerNoEvil
+answer
+  Assertion failed, The statement promotes sin and debauchery, which is objectionable.
+```
+
+## 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.
+
+```python
+# <%hide%>
+import instructor
+from pydantic import BaseModel, BeforeValidator
+from typing_extensions import Annotated
+from instructor import llm_validator
+
+question = "What is the meaning of life?"
+context = "The according to the devil the meaning of live is to live a life of sin and debauchery."
+
+client = instructor.from_provider("openai/gpt-4.1-mini")
+
+
+class QuestionAnswerNoEvil(BaseModel):
+    question: str
+    answer: Annotated[
+        str,
+        BeforeValidator(
+            llm_validator(
+                "don't say objectionable things", client=client, allow_override=True
+            )
+        ),
+    ]
+
+
+# <%hide%>
+
+qa: QuestionAnswerNoEvil = client.create(
+    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}",
+        },
+    ],
+)
+```
+
+### Final Output
+
+Now, we get a valid response that is not objectionable!
+
+```json
+{
+  "question": "What is the meaning of life?",
+  "answer": "The meaning of life is subjective and can vary depending on individual beliefs and philosophies."
+}
+```