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Creative acts in documentation (#188)

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Matthew
2023-11-18 14:32:42 -05:00
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@@ -4,10 +4,8 @@ _Structured extraction in Python, powered by OpenAI's function calling api, desi
---
[Star us on Github!](www.github.com/jxnl/instructor).
[![Downloads](https://img.shields.io/pypi/dm/instructor.svg)](https://pypi.python.org/pypi/instructor)
[![GitHub stars](https://img.shields.io/github/stars/jxnl/instructor.svg)](https://github.com/jxnl/instructor/stargazers)
![Star us on Github!](https://img.shields.io/github/stars/jxnl/instructor.svg?style=social)
[![Documentation](https://img.shields.io/badge/docs-available-brightgreen)](https://jxnl.github.io/instructor)
[![GitHub issues](https://img.shields.io/github/issues/jxnl/instructor.svg)](https://github.com/jxnl/instructor/issues)
[![Twitter Follow](https://img.shields.io/twitter/follow/jxnlco?style=social)](https://twitter.com/jxnlco)
@@ -36,10 +34,12 @@ With Instructor, your code becomes more efficient and readable. Here’s a quick
## Usage
```py hl_lines="5 13"
from openai import OpenAI
import instructor
from openai import OpenAI
from pydantic import BaseModel
# Enables `response_model`
# This enables response_model keyword
# from client.chat.completions.create
client = instructor.patch(OpenAI())
class UserDetail(BaseModel):
@@ -103,7 +103,7 @@ assert isinstance(model, UserExtract)
### Step 1: Patch the client
First, import the required libraries and apply the patch function to the OpenAI module. This exposes new functionality with the response_model parameter.
First, import the required libraries and apply the patch function to the OpenAI module. This allows us to parse the raw JSON from our OpenAI completions into Pydantic output.
```python
import instructor
@@ -117,11 +117,9 @@ client = instructor.patch(OpenAI())
### Step 2: Define the Pydantic Model
Create a Pydantic model to define the structure of the data you want to extract. This model will map directly to the information in the prompt.
Create a Pydantic model to define the structure of the data extracted from the OpenAI response. This model will map directly to the information in the prompt.
```python
from pydantic import BaseModel
class UserDetail(BaseModel):
name: str
age: int
@@ -129,8 +127,7 @@ class UserDetail(BaseModel):
### Step 3: Extract
Use the `client.chat.completions.create` method to send a prompt and extract the data into the Pydantic object. The response_model parameter specifies the Pydantic model to use for extraction. Its helpful to annotate the variable with the type of the response model.
which will help your IDE provide autocomplete and spell check.
Use the `client.chat.completions.create` method to generate a completion and extract response data into the Pydantic object. The response_model parameter enables autocomplete and spell check in your IDE.
```python
user: UserDetail = client.chat.completions.create(
@@ -170,7 +167,7 @@ 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.
Note, 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