# How to get structured outputs from Openai every time.

# Get Structured Outputs from OpenAI Using Pydantic

Are you struggling to get structured outputs from OpenAI? Tired of inconsistent formats and hallucinations? 🤯

![Structured Outputs](https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExcDhuaWJlaG51bjl5bXJvdXVuajMweWhrb24yZG0zaWR6a2g0a2l6MSZlcD12MV9naWZzX3NlYXJjaCZjdD1n/l1KVbUh3NprfY26R2/giphy.gif)

Well, worry no more! OpenAI has launched **structured outputs** in its beta version. Here's how you can take advantage of it.

## Using Pydantic Models for Structured Outputs

First, you need to define your **Pydantic models**, which serve as a schema for the values you want to extract or generate.

### Step 1: Define Your Schema

Let's define a schema to extract **novel details** from OpenAI. Create a file called `schema.py` and add the following:

```python
from pydantic import BaseModel, Field
from typing import List
from datetime import date

class NovelDetails(BaseModel):
    novel_name: str = Field(..., description="The name of the novel")
    writer_name: str = Field(..., description="The author's name")
    year_published: date = Field(..., description="The publication year of the novel")

class Novel(BaseModel):
    novels: List[NovelDetails] = Field(..., description="List of novels with their details")
```

### Step 2: Use OpenAI's Structured Output API

Now, let's use OpenAI's **beta structured output API** to extract these fields.

```python
import openai
from schema import Novel

openai.api_key = "xxxxxxxxxxxxxxxxxxxxx"

response = openai.beta.chat.completions.create(
    model="gpt-4-turbo",
    messages=[
        {"role": "system", "content": "You are a book expert with vast knowledge about books. Answer user questions accurately."},
        {"role": "user", "content": "Give me a list of 10 thriller novels."}
    ],
    response_format={"type": "json_schema", "schema": Novel.model_json_schema()}
)

parsed_data = Novel(**response.choices[0].message.parsed)
print(parsed_data)
```

### What’s Happening Here?
1. **Define the schema**: The `Novel` class ensures that the API response follows a strict format.
2. **Send a request**: Using `openai.beta.chat.completions.create`, we request OpenAI to return structured data.
3. **Parse the response**: The `Novel` model ensures that the extracted data is well-structured and correctly formatted.

### Output Example

```json
{
  "novels": [
    {
      "novel_name": "Gone Girl",
      "writer_name": "Gillian Flynn",
      "year_published": "2012-05-24"
    },
    {
      "novel_name": "The Girl with the Dragon Tattoo",
      "writer_name": "Stieg Larsson",
      "year_published": "2005-08-23"
    }
  ]
}
```