add reasks and validation

README.md

@@ -73,6 +73,73 @@ assert user.name == "Jason"
 assert user.age == 25
 ```
 
+### LLM-Based Validation
+
+LLM-based validation can also be plugged into the same Pydantic model. Here, if the answer attribute contains content that violates the rule "don't say objectionable things," Pydantic will raise a validation error.
+
+```python hl_lines="9 15"
+from pydantic import BaseModel, ValidationError, BeforeValidator
+from typing_extensions import Annotated
+from instruct import llm_validator
+
+class QuestionAnswer(BaseModel):
+    question: str
+    answer: Annotated[
+        str, 
+        BeforeValidator(llm_validator("don't say objectionable things"))
+    ]
+
+try:
+    qa = QuestionAnswer(
+        question="What is the meaning of life?",
+        answer="The meaning of life is to be evil and steal",
+    )
+except ValidationError as e:
+    print(e)
+```
+
+Its important to not here that the error message is generated by the LLM, not the code, so it'll be helpful for re asking the model.
+
+```plaintext
+1 validation error for QuestionAnswer
+answer
+   Assertion failed, The statement is objectionable. (type=assertion_error)
+```
+
+## Using the Client with Retries
+
+Here, the `UserDetails` model is passed as the `response_model`, and `max_retries` is set to 2.
+
+```python
+import instructor
+from pydantic import BaseModel, field_validator
+
+# Apply the patch to the OpenAI client
+instructor.patch()
+
+class UserDetails(BaseModel):
+    name: str
+    age: int
+
+    @field_validator("name")
+    @classmethod
+    def validate_name(cls, v):
+        if v.upper() != v:
+            raise ValueError("Name must be in uppercase.")
+        return v
+
+model = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    response_model=UserDetails,
+    max_retries=2,
+    messages=[
+        {"role": "user", "content": "Extract jason is 25 years old"},
+    ],
+)
+
+assert model.name == "JASON"
+```
+
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