How to Access the OpenAI o1 API?

Introduction

OpenAI’s o1 series models represent a significant leap in large language model (LLM) capabilities, particularly for complex reasoning tasks. These models engage in deep internal thought processes before responding, making them excellent at solving intricate problems in science, mathematics, and programming. This article will guide you through the key features of the OpenAI o1 API, introduce the available models, and provide practical examples of how to use their advanced reasoning and code generation abilities.

Key Features of the o1 Models

The o1 models excel at tackling problems requiring logical, scientific, and mathematical reasoning. They rank in the 89th percentile in competitive programming (Codeforces) and surpass PhD-level accuracy in physics, biology, and chemistry benchmarks (GPQA). They have even placed among the top 500 students in the US in the AIME qualifier for the USA Math Olympiad.

There are two models currently available:

• o1-preview: This model provides an early look at the full capabilities of o1, using broad general knowledge to solve complex problems.
• o1-mini: A faster, more efficient version of the o1 model, optimized for tasks such as coding, math, and scientific reasoning.

With limited features, the o1 models are now in beta testing. Only developers in tier 5 are permitted access, and there are low rate caps (20 RPM).

Also Read: How to Access OpenAI o1?

Pricing and Model Specifications for OpenAI o1-mini and o1-preview

OpenAI has released two variants of the o1 model series, each with different pricing and capabilities tailored to specific use cases:

OpenAI o1-mini

This model is optimized for coding, math, and science tasks, providing a cost-effective solution for developers and researchers. It has a 128K context and utilizes the October 2023 knowledge cutoff.

• Pricing: $3.00 per 1 million tokens.
• Output tokens: $12.00 per 1 million tokens.

OpenAI o1-preview

Designed for more complex tasks that require broad general knowledge, the o1-preview model is positioned for advanced reasoning and problem-solving. It also has 128K context and draws on the October 2023 knowledge cutoff.

• Pricing: $15.00 per 1 million tokens.
• Output tokens: $60.00 per 1 million tokens.

o1-mini vs o1-preview

Also Read: GPT-4o vs OpenAI o1: Is the New OpenAI Model Worth the Hype?

How to Access OpenAI o1 API?

Here is a step-by-step guide on how to access and use the OpenAI o1 API:

Step 1: Get API Access

• Sign up for API Access: If you are not already part of the OpenAI beta program, you’ll need to request access by visiting OpenAI’s API page. Once you sign up, you may need to wait for approval to access the o1 models.
• Generate an API Key: Once you have access, log in to the OpenAI API platform and generate an API key. This key is necessary for making API requests.
  • Go to API Keys and click on “Create New Secret Key”.
  • Copy the key and save it securely, as you’ll need it in the code examples.

Step 2: Install the OpenAI Python SDK

To interact with the o1 API, you will need to install the OpenAI Python SDK. You can do this using the following command:

pip install openai

This package allows you to make API requests to OpenAI from your Python code.

Step 3: Initialize the OpenAI Client

Once you’ve installed the SDK and obtained your API key, you can initialize the client in Python as shown below:

from openai import OpenAI

# Initialize the OpenAI client with your API key
client = OpenAI(api_key="your-api-key")

Replace “your-api-key” with the actual API key you generated earlier.

Using the o1 API for Code Generation

Now that you’ve set up your OpenAI client, let’s look at an example where we use the o1-preview model to generate a Python function that converts temperatures between Fahrenheit and Celsius.

Step 1: Craft the Prompt

In this example, we will ask the model to write a Python function that converts a temperature from Fahrenheit to Celsius and vice versa.

prompt = """
Write a Python function that converts a temperature from Fahrenheit to Celsius and vice versa.
The function should take an input, determine the type (Fahrenheit or Celsius), and return the converted temperature.
"""

Step 2: Make the API Request

We will pass this prompt to the o1 model using the chat.completions.create() method, specifying the model we want to use (o1-preview) and the user message.

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user", 
            "content": prompt
        }
    ]
)

# Output the generated Python code
print(response.choices[0].message.content)

In this example, the o1-preview model intelligently handles the logic for temperature conversion, showing its proficiency in solving simple coding tasks. Depending on the complexity, these requests may take a few seconds or longer.

Output:

```python
def convert_temperature(temp_input):
    """
    Converts a temperature from Fahrenheit to Celsius or vice versa.

    Parameters:
    temp_input (str): A temperature input string, e.g., '100F' or '37C'.

    Returns:
    str: The converted temperature with the unit.
    """
    import re  # Importing inside the function to keep the scope local

    # Remove leading and trailing whitespaces
    temp_input = temp_input.strip()

    # Regular expression to parse the input string
    match = re.match(r'^([+-]?[0-9]*\.?[0-9]+)\s*([cCfF])

Beta Limitations

During the beta phase, certain features of the o1 API are not yet fully supported. Key limitations include:

• Modalities: Text only, no image support.
• Message Types: Only user and assistant messages, no system messages.
• Streaming: Not supported.
• Tools and Functions: Not yet available, including response format parameters and function calling.
• Temperature and Penalties: Fixed values for temperature, top_p, and penalties.

Coding and Reasoning with o1 Models

The o1 models excel at handling algorithmic tasks and reasoning. Here’s an updated example where the o1-mini model is tasked with finding the sum of all prime numbers below 100:

Create the Prompt

Write a clear prompt that describes the task you want the model to perform. In this case, the task is to write a Python function that calculates the sum of all prime numbers below 100:

prompt = """
Write a Python function that calculates the sum of all prime numbers below 100. 
The function should first determine whether a number is prime, and then sum up 
all the prime numbers below 100.
"""

Make the API Call

Use the chat.completions.create method to send the prompt to the o1-mini model. Here’s the complete code:

response = client.chat.completions.create(
    model="o1-mini",
    messages=[
        {
            "role": "user",
            "content": prompt
        }
    ]
)

print(response.choices[0].message.content)

This example shows how the o1-mini model is capable of writing efficient code for tasks like identifying prime numbers and performing basic mathematical operations.

Output

Certainly! Below is a Python function that calculates the sum of all prime numbers below 100. The function includes a helper function `is_prime` to determine if a number is prime. After identifying all prime numbers below 100, it sums them up and returns the total.

```python
def is_prime(n):
    """
    Check if a number is a prime number.

    Parameters:
    n (int): The number to check for primality.

    Returns:
    bool: True if n is prime, False otherwise.
    """
    if n < 2:
        return False
    # Only need to check up to the square root of n
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

def sum_primes_below_100():
    """
    Calculate the sum of all prime numbers below 100.

    Returns:
    int: The sum of prime numbers below 100.
    """
    prime_sum = 0
    for number in range(2, 100):
        if is_prime(number):
            prime_sum += number
    return prime_sum

# Call the function and print the result
total = sum_primes_below_100()
print(f"The sum of all prime numbers below 100 is: {total}")
```

### Explanation:

1. **Helper Function (`is_prime`)**:
    - **Purpose**: Determines whether a given number `n` is a prime number.
    - **Logic**:
        - Numbers less than 2 are not prime.
        - For numbers 2 and above, check divisibility from 2 up to the square root of `n`. If `n` is divisible by any of these numbers, it's not prime.
        - If no divisors are found, the number is prime.

2. **Main Function (`sum_primes_below_100`)**:
    - **Purpose**: Iterates through all numbers from 2 up to (but not including) 100.
    - **Logic**:
        - For each number in the range, it uses the `is_prime` function to check if it's prime.
        - If the number is prime, it's added to the cumulative `prime_sum`.
    - **Return**: The total sum of all prime numbers below 100.

3. **Execution**:
    - The function `sum_primes_below_100` is called, and the result is stored in the variable `total`.
    - The sum is then printed to the console.

### Output

When you run the above code, it will output:

```
The sum of all prime numbers below 100 is: 1060
```

This means that the sum of all prime numbers below 100 is **1060**.

More Complex Use Cases

The o1-preview model can handle reasoning-heavy tasks, such as outlining a plan for developing an inventory management system. Below is an example where the model provides a high-level structure for the project:

Create Prompt

prompt = """
I want to develop an inventory management system that tracks products, quantities, 
and locations. It should notify the user when stock is low. Create a plan for the 
directory structure and provide code snippets for the key components.
"""

Make API Call

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user",
            "content": prompt
        }
    ]
)

print(response.choices[0].message.content)

In this example, the o1-preview model intelligently plans out the system’s structure and provides relevant code snippets, showcasing its problem-solving abilities.

Output

Certainly! Developing an inventory management system involves several components, including database models,
 user interfaces, and business logic for tracking inventory levels and notifying users when stock is low. 
 Below is a plan for the directory structure of your project, along with code snippets for key components 
 using Python and the Flask web framework.

---

## Directory Structure

Here's a suggested directory structure for your Flask-based inventory management system:

```
inventory_management/
├── app.py
├── requirements.txt
├── config.py
├── run.py
├── instance/
│   └── config.py
├── models/
│   ├── __init__.py
│   └── product.py
├── routes/
│   ├── __init__.py
│   └── product_routes.py
├── services/
│   ├── __init__.py
│   └── notification_service.py
├── templates/
│   ├── base.html
│   ├── index.html
│   └── product_detail.html
├── static/
│   ├── css/
│   └── js/
└── db/
    └── inventory.db
```

- **app.py**: Initializes the Flask app and the database.
- **config.py**: Contains configuration variables.
- **run.py**: The entry point to run the application.
- **models/**: Contains database models.
- **routes/**: Contains route handlers for URL endpoints.
- **services/**: Contains service layers like notification services.
- **templates/**: Contains HTML templates for rendering web pages.
- **static/**: Contains static files like CSS and JavaScript.
- **db/**: Contains the SQLite database file.

---

## Key Components Code Snippets

### 1. `app.py`: Initialize Flask App and Database

```python
# app.py
from flask import Flask
from flask_sqlalchemy import SQLAlchemy
from config import Config

db = SQLAlchemy()

def create_app():
    app = Flask(__name__)
    app.config.from_object(Config)

    db.init_app(app)

    with app.app_context():
        from models import product
        db.create_all()

        from routes.product_routes import product_bp
        app.register_blueprint(product_bp)

    return app
```

### 2. `config.py`: Configuration Settings

```python
# config.py
import os

class Config:
    SECRET_KEY = os.environ.get('SECRET_KEY', 'your_secret_key_here')
    SQLALCHEMY_DATABASE_URI = 'sqlite:///db/inventory.db'
    SQLALCHEMY_TRACK_MODIFICATIONS = False
    LOW_STOCK_THRESHOLD = 10  # Quantity at which to notify for low stock
```

### 3. `models/product.py`: Product Model

```python
# models/product.py
from app import db

class Product(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(100), nullable=False)
    quantity = db.Column(db.Integer, nullable=False, default=0)
    location = db.Column(db.String(100), nullable=False)

    def __repr__(self):
        return f'<Product {self.name}>'
```

### 4. `routes/product_routes.py`: Route Handlers

```python
# routes/product_routes.py
from flask import Blueprint, render_template, request, redirect, url_for, flash
from app import db
from models.product import Product
from services.notification_service import check_and_notify_low_stock

product_bp = Blueprint('product_bp', __name__)

@product_bp.route('/')
def index():
    products = Product.query.all()
    return render_template('index.html', products=products)

@product_bp.route('/product/<int:product_id>')
def product_detail(product_id):
    product = Product.query.get_or_404(product_id)
    return render_template('product_detail.html', product=product)

@product_bp.route('/add_product', methods=['GET', 'POST'])
def add_product():
    if request.method == 'POST':
        name = request.form['name']
        quantity = int(request.form['quantity'])
        location = request.form['location']

        new_product = Product(name=name, quantity=quantity, location=location)
        db.session.add(new_product)
        db.session.commit()

        check_and_notify_low_stock(new_product)
        flash('Product added successfully!', 'success')
        return redirect(url_for('product_bp.index'))

    return render_template('add_product.html')

@product_bp.route('/update_product/<int:product_id>', methods=['GET', 'POST'])
def update_product(product_id):
    product = Product.query.get_or_404(product_id)
    if request.method == 'POST':
        product.name = request.form['name']
        product.quantity = int(request.form['quantity'])
        product.location = request.form['location']

        db.session.commit()

        check_and_notify_low_stock(product)
        flash('Product updated successfully!', 'success')
        return redirect(url_for('product_bp.product_detail', product_id=product.id))

    return render_template('update_product.html', product=product)
```

### 5. `services/notification_service.py`: Notification Service

```python
# services/notification_service.py
from flask import current_app, flash

def check_and_notify_low_stock(product):
    if product.quantity <= current_app.config['LOW_STOCK_THRESHOLD']:
        message = f'Stock for "{product.name}" is low (Quantity: {product.quantity})!'
        # Implement actual notification logic here (e.g., send email)
        flash(message, 'warning')
```

### 6. `templates/index.html`: Display Products

```html
<!-- templates/index.html -->
{% extends 'base.html' %}

{% block content %}
<h1>Inventory Management</h1>
<a href="{{ url_for('product_bp.add_product') }}">Add New Product</a>
<table>
    <tr>
        <th>Name</th>
        <th>Quantity</th>
        <th>Location</th>
        <th>Actions</th>
    </tr>
    {% for product in products %}
    <tr>
        <td>{{ product.name }}</td>
        <td>{{ product.quantity }}</td>
        <td>{{ product.location }}</td>
        <td>
            <a href="{{ url_for('product_bp.product_detail', product_id=product.id) }}">View</a>
            <a href="{{ url_for('product_bp.update_product', product_id=product.id) }}">Edit</a>
        </td>
    </tr>
    {% endfor %}
</table>
{% endblock %}
```

### 7. `run.py`: Run the Application

```python
# run.py
from app import create_app

app = create_app()

if __name__ == '__main__':
    app.run(debug=True)
```

---

## Explanation of Key Components

- **Product Model**: Represents the product entity with fields for
 `name`, `quantity`, and `location`. It interacts with the database using SQLAlchemy.
- **Routes**: Define endpoints for viewing, adding, and updating products. 
They handle HTTP requests and render templates.
- **Notification Service**: 
Contains logic to check if the product's quantity is below the threshold defined in the configuration. 
It currently uses Flask's `flash` system for notifications,
 but you can extend it to send emails or push notifications.
- **Templates**: HTML files that structure the presentation layer. 
The `index.html` template displays the list of products in a table format.
- **Configuration**: Global settings like the database URI and the low stock threshold are defined in `config.py`.

---

## Next Steps

- **Database Migration**: Consider using Flask-Migrate for handling database migrations as your models evolve.
- **User Authentication**: Implement user login functionality if needed.
- **Enhanced Notifications**: Integrate an email server or a messaging API for sending out low stock alerts.
- **Error Handling**: Add proper error handling for robustness.
- **Front-End Enhancements**: Improve the user interface with CSS frameworks like Bootstrap or Materialize.

---

## Requirements Installation

To set up the project, create a virtual environment and install the required packages:

```bash
python3 -m venv venv
source venv/bin/activate
pip install flask flask_sqlalchemy
```

Add these dependencies to your `requirements.txt`:

```
Flask
Flask_SQLAlchemy
```

---

## Running the Application

After setting up, you can run the application using:

```bash
python run.py
```

Navigate to `http://localhost:5000` in your web browser to interact with the inventory management system.

---

By following this structure and using these code snippets as a starting point, 
you can develop a functional inventory management system that tracks products, their quantities, and locations, 
and notifies users when stock levels are low.

Scientific Reasoning and Beyond

The o1-preview model is also excellent for scientific research, where advanced reasoning is required. Here’s an example where the model is asked to explore the potential of CRISPR technology for treating genetic disorders:

Create the Prompt

prompt = """
Explain how CRISPR technology can be used to treat genetic disorders. What are the 
main challenges, and what future advancements might be necessary to make it widely 
available?
"""

Make the API Call

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user", 
            "content": prompt
        }
    ]
)

print(response.choices[0].message.content)

This example highlights how the o1-preview model can reason through complex scientific challenges, making it an excellent tool for researchers and scientists.

Output

**Introduction to CRISPR Technology**

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology is a groundbreaking gene-editing tool that allows scientists to 
alter DNA sequences and modify gene function with unprecedented precision and efficiency. 
Derived from a natural defense mechanism found in bacteria and archaea, CRISPR-Cas systems protect these microorganisms from viral infections. 
The most commonly used system in gene editing is CRISPR-Cas9, where the Cas9 enzyme acts as molecular scissors to cut DNA at a specific location 
guided by a customizable RNA sequence.

**Using CRISPR to Treat Genetic Disorders**

Genetic disorders are often caused by mutations or alterations in an individual's DNA that disrupt normal gene function. CRISPR technology can 
potentially correct these mutations at the genetic level, offering the prospect of curing diseases rather than just managing symptoms. 
The general steps involved in using CRISPR for treating genetic disorders include:

1. **Identification of the Target Gene:** Determining the exact genetic mutation responsible for the disorder.
   
2. **Designing the Guide RNA (gRNA):** Crafting a sequence of RNA that matches the DNA sequence at the mutation site.

3. **Delivery into Target Cells:** Introducing the CRISPR-Cas9 components into the patient's cells, either ex vivo (outside the body) or in vivo (inside the body).

4. **Gene Editing Process:** Once inside the cells, the Cas9 enzyme, guided by the gRNA, binds to the target DNA sequence and introduces a cut. 
The cell's natural repair mechanisms then take over to fix the cut, ideally correcting the mutation.

5. **Restoration of Normal Function:** If successful, the gene is corrected, and normal protein production and cellular functions are restored, 
alleviating or eliminating disease symptoms.

**Main Challenges in Using CRISPR for Treating Genetic Disorders**

While CRISPR holds immense potential, several significant challenges need to be addressed before it can become a routine treatment for genetic disorders:

1. **Delivery to Target Cells and Tissues:**

   - **Efficient Delivery Methods:** Getting the CRISPR components into the right cells in the body is a major hurdle. Delivery methods must be efficient, 
   specific to the target cells, and safe.
   - **Viral Vectors Limitations:** Common delivery methods use viral vectors (like adenoviruses or adeno-associated viruses), 
   which can provoke immune responses or have size limitations for the genetic payload.
   - **Non-Viral Delivery Systems:** Alternatives like lipid nanoparticles are being explored, but optimizing their efficiency and specificity remains challenging.

2. **Off-Target Effects and Unintended Mutations:**

   - **Precision Concerns:** Cas9 might cut at unintended sites in the genome, leading to off-target effects that could cause harmful mutations or disrupt other genes.
   - **Genome Integrity:** Ensuring that gene editing does not inadvertently increase the risk of cancer or other genetic diseases is crucial.

3. **Immune Responses:**

   - **Immunogenicity of CRISPR Components:** The Cas9 protein is of bacterial origin and may be recognized as foreign by the human immune system,
    leading to immune reactions that can reduce treatment efficacy or cause adverse effects.

4. **Ethical and Regulatory Issues:**

   - **Germline Editing Concerns:** Editing genes in germline cells (sperm, eggs, or embryos) raises ethical questions
    because changes are heritable and can affect future generations.
   - **Regulatory Approval:** Robust regulatory frameworks are needed to oversee clinical applications, ensure patient safety, and address bioethical considerations.

5. **Long-Term Effects and Safety:**

   - **Durability of Gene Correction:** It is necessary to understand how long the effects of gene editing last and whether repeated treatments are needed.
   - **Monitoring for Adverse Outcomes:** Long-term studies are required to monitor for delayed adverse effects, such as tumor development.

6. **Technical Limitations:**

   - **Gene Delivery Size Constraints:** Some genes are too large to be efficiently delivered using current vectors.
   - **Control Over Gene Repair Mechanisms:** The cell's natural DNA repair pathways may not always result in the
    desired gene correction after Cas9-induced cuts.

**Future Advancements Needed for Widespread Availability**

To overcome these challenges and make CRISPR-based therapies widely available, several advancements and developments are necessary:

1. **Enhanced Delivery Systems:**

   - **Targeted Delivery Mechanisms:** Developing safe, efficient, and precise methods to deliver CRISPR 
   components specifically to affected cells or tissues.
   - **Non-Viral Vectors:** Advancing non-viral delivery methods like nanoparticles, engineered exosomes, 
   or physical methods (e.g., electroporation) to reduce immunogenicity and increase payload capacity.

2. **Improving Specificity and Reducing Off-Target Effects:**

   - **High-Fidelity Cas9 Variants:** Engineering Cas9 enzymes with higher specificity to minimize unintended cuts.
   - **Alternative Nucleases:** Exploring other enzymes (e.g., Cas12a) or base editors that can make 
   precise changes without introducing double-strand breaks.

3. **Safety Enhancements:**

   - **Transient Expression Systems:** Designing CRISPR components that are active only temporarily to 
   reduce the window for off-target activity.
   - **Immunomodulation Strategies:** Developing approaches to mitigate immune responses against CRISPR components.

4. **Ethical Guidelines and Regulatory Frameworks:**

   - **International Consensus:** Establishing global ethical standards and regulations for permissible 
   uses of gene editing, particularly concerning germline modifications.
   - **Public Engagement:** Educating the public and stakeholders to build trust and informed consent regarding gene-editing therapies.

5. **Clinical Trials and Long-Term Studies:**

   - **Robust Clinical Data:** Conducting extensive clinical trials to demonstrate safety and efficacy 
   across diverse populations and genetic disorders.
   - **Monitoring and Reporting Systems:** Implementing long-term monitoring for patients who 
   undergo CRISPR-based treatments to track outcomes and side effects.

6. **Scalability and Cost Reduction:**

   - **Manufacturing Advances:** Improving the production processes for CRISPR components to make therapies more affordable.
   - **Healthcare Infrastructure:** Enhancing healthcare systems to support the delivery and follow-up care required for gene-editing treatments.

7. **Personalized Medicine Integration:**

   - **Genetic Screening and Diagnostics:** Incorporating comprehensive genetic 
   testing to identify patients who can benefit from CRISPR therapies.
   - **Customized Treatments:** Developing protocols for tailoring CRISPR therapies to individual genetic profiles.

8. **Regulatory Approval Pathways:**

   - **Streamlined Processes:** Working with regulatory bodies to establish clear pathways 
   for the approval of gene-editing therapies while ensuring patient safety.
   - **Collaborative Efforts:** Encouraging collaboration between academia, 
   industry, and regulatory agencies to accelerate development and approval.

**Conclusion**

CRISPR technology holds transformative potential for treating genetic disorders by providing a 
means to correct disease-causing mutations at their source. 
However, significant scientific, technical, ethical, and regulatory challenges must 
be addressed to realize this potential fully. Advancements in delivery methods, specificity, safety, 
ethical guidelines, and regulatory frameworks are crucial. 
With continued research, collaboration, and responsible innovation, CRISPR-based therapies may 
become a widely available reality, offering hope to individuals affected by genetic diseases.

Also Read: 3 Hands-On Experiments with OpenAI’s o1 You Need to See

Conclusion

The OpenAI o1 models are a breakthrough in reasoning-based AI, capable of handling complex programming, mathematical, and scientific tasks. Whether you’re working on algorithms, refactoring code, or planning applications, the o1 API offers a powerful toolset for creating solutions that require deep thought and advanced logic.

While still in beta, these models promise even more capabilities in the future, such as multimodality and tool usage. If your projects demand advanced reasoning and don’t require rapid response times, the o1 models could be your go-to option.

This version uses different prompts to demonstrate the flexibility and reasoning abilities of the OpenAI o1 models across various tasks.

Stay tuned to Analytics Vidhya blog to know more about the uses of o1!

Frequently Asked Questions

Janvi Kumari

Hi, I am Janvi, a passionate data science enthusiast currently working at Analytics Vidhya. My journey into the world of data began with a deep curiosity about how we can extract meaningful insights from complex datasets.