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Jason Liu
90 lines
2.3 KiB
Markdown
90 lines
2.3 KiB
Markdown
# Patching
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Instructor enhances client functionality with three new keywords for backwards compatibility. This allows use of the enhanced client as usual, with structured output benefits.
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- `response_model`: Defines the response type for `chat.completions.create`.
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- `max_retries`: Determines retry attempts for failed `chat.completions.create` validations.
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- `validation_context`: Provides extra context to the validation process.
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There are three methods for structured output:
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1. **Function Calling**: The primary method. Use this for stability and testing.
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2. **Tool Calling**: Useful in specific scenarios; lacks the reasking feature of OpenAI's tool calling API.
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3. **JSON Mode**: Offers closer adherence to JSON but with more potential validation errors. Suitable for specific non-function calling clients.
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## Function Calling
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```python
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import instructor
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from openai import OpenAI
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client = instructor.patch(OpenAI(), mode=instructor.Mode.FUNCTIONS)
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```
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## Tool Calling
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```python
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import instructor
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from openai import OpenAI
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client = instructor.patch(OpenAI(), mode=instructor.Mode.TOOLS)
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```
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## JSON Mode
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```python
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import instructor
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from instructor import Mode
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from openai import OpenAI
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client = instructor.patch(OpenAI(), mode=Mode.JSON)
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```
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## Markdown JSON Mode
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!!! warning "Experimental"
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This is not recommended, and may not be supported in the future, this is just left to support vision models.
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```python
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import instructor
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from openai import OpenAI
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client = instructor.patch(OpenAI(), mode=instructor.Mode.MD_JSON)
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```
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### Schema Integration
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In JSON Mode, the schema is part of the system message:
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```python
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import instructor
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from openai import OpenAI
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client = instructor.patch(OpenAI())
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class UserExtract(instructor.OpenAISchema):
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name: str
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age: int
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response = client.chat.completions.create(
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model="gpt-3.5-turbo-1106",
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response_format={"type": "json_object"},
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messages=[
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{
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"role": "system",
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"content": f"Match your response to this json_schema: \n{UserExtract.model_json_schema()['properties']}",
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},
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{
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"role": "user",
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"content": "Extract jason is 25 years old",
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},
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],
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)
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user = UserExtract.from_response(response, mode=instructor.Mode.JSON)
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print(user)
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#> name='Jason' age=25
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```
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