Example Request Format

import requests
import json

# Define the endpoint URL
url = 'https://cloud.dataoorts.com/api/v1'

# Define the headers Auth
headers = {
    'Content-Type': 'application/json',
    'email': '<Email>',
    'api': '<API>'
}

# Define the Model Name and Data Payload
payload = {
    "model_name": "meta/llama-3.2-1b-instruct",
    "data": {
        "messages": [
            {"role": "system", "content": "You are a expert deep learning engineer"},
            {"role": "user", "content": "What is Deep Learning"}
        ]
    }
}

# Make the POST request
response = requests.post(url, headers=headers, data=json.dumps(payload))
response_json = response.json()
print(json.dumps(response_json, indent=4))

Example Response Format

{
    "errors": [],
    "messages": [],
    "result": {
        "response": "Deep Learning! It's a subfield of Artificial Intelligence (AI) that focuses on the development of algorithms and statistical models that can learn and discover patterns in data, similar to how humans do. Introduced in the early 2000s, Deep Learning has revolutionized various fields such as Computer Vision, Natural Language Processing (NLP), Robotics, and many others.\n\n**What is Deep Learning?**\n\nDeep Learning is a type of machine learning that uses multiple layers (hence the name) of artificial neural networks to learn and improve performance on real-world problems. Each layer is designed to convert input data into a meaningful output, and the network of layers processes and transforms the input data in a hierarchical manner.\n\n**Key Concepts:**\n\n1. **Artificial Neural Networks (ANNs)**: Inspired by the structure and function of the human brain, ANNs consist of layers of interconnected nodes (neurons) that process and transmit information.\n2. **Deep Learning**: A subfield of Machine Learning that uses ANN's to analyze complex and high-dimensional data.\n3. **Convolutional Neural Networks (CNNs)**: A type of neural network designed for image and image-related tasks, such as image classification, object detection, and segmentation.\n4. **Recurrent Neural Networks (RNNs)**: Designed for sequential data, such as speech, time series, or text data.\n\n**Types of Deep Learning:**\n\n1. **Feedforward Networks**: As the name suggests, this is a straightforward feed-forward network that looks to input data and produces output directly.\n2. **Autoencoders**: A type of neural network that learns to compress and reconstruct data.\n3. **Generative Models**: Such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which generate new data samples that mimic existing data.\n\n**Deep Learning Applications:**\n\n1. **Computer Vision**: Image captioning, object detection, facial recognition, self-driving cars, and surveillance systems.\n2. **Natural Language Processing (NLP)**: Text classification, sentiment analysis, language translation, and speech recognition.\n3. **Robotics**: Autonomous systems, object recognition, and motion planning.\n4. **Healthcare**: Medical diagnosis, disease detection, and personalized medicine.\n5. **Finance**: Anomaly detection, credit risk assessment, and portfolio optimization.\n\n**The Importance of Deep Learning:**\n\n1. **Improved Accuracy**: Deep Learning algorithms have been shown to be more accurate than traditional machine learning models in many applications.\n2. **Real"
    },
    "success": true
}