Text-to-Text Generation API Documentation

•    The prompt template serves as the guiding structure for generating the text. It acts as a blueprint, outlining the content and structure of the text to be generated.
•    The template should contain placeholders enclosed in curly braces, indicating where dynamic content will be inserted. For example, "Generate text about '{topic}'" indicates that the word "topic" will be replaced with the actual topic provided during text generation.

• The input list specifies the variables or parameters required by the prompt template.
• Each element in the input list corresponds to a placeholder in the template. It provides a clear indication of the input data needed to generate the text.
• For example, if the template contains placeholders for "topic" and "audience", the input list would include these variables: ["topic", "audience"].

• The map dictionary maps the input variables defined in the input list to their corresponding values.
• It ensures that the correct values are inserted into the template to generate the desired text output.
• Each key-value pair in the map dictionary associates an input variable (key) with its corresponding value (value).
• For example, if the input list includes "topic" and "audience", the map dictionary would specify the values for these variables: ^{{"topic": "machine learning", "audience": "developers"}}.

_```

_{import requests}

 

_{# API endpoint URL}

_{url = "http://51.20.2.174:8000/generate"}

 

_{# Your API key}

_{api_key = "your_api_key_here"  # Replace with the actual API key}

 

_{# Headers including the API key}

_{headers = {}

    _{"Authorization": api_key,}

    _{"Content-Type": "application/json"}

_}

 

_{# Payload for the request}

_{payload = {}

    _{"template": "I am providing you a topic to write a blog on it. Topic='{topic}'",}

    _{"input_list": ["topic"],}

    _{"map_dict": {"topic": "machine learning"}}

_}

 

_{# Sending the POST request}

_{response = requests.post(url, headers=headers, json=payload)}

 

_{# Printing the response}

_{if response.status_code == 200:}

    _{print("Response JSON:", response.json())}

_else:

    _{print("Failed to get a valid response. Status code:", response.status_code)}

    _{print("Response JSON:", response.json())}

_```

_{const axios = require('axios');}

 

_{// API endpoint URL}

_{const url = "http://51.20.2.174:8000/generate";}

 

_{// Your API key}

_{const apiKey = "your_api_key_here";  // Replace with the actual API key}

 

_{// Headers including the API key}

_{const headers = {}

    _{"Authorization": apiKey,}

    _{"Content-Type": "application/json"}

_};

 

_{// Payload for the request}

_{const payload = {}

    _{"template": "Write a persuasive advertisement for '{product}' targeting '{audience}'",}

    _{"input_list": ["product", "audience"],}

    _{"map_dict": {"product": "electric car", "audience": "environmentally conscious consumers"}}

_};

 

_{// Sending the POST request}

_{axios.post(url, payload, { headers: headers })}

    _{.then(response => {}

        _{console.log("Response JSON:", response.data);}

    _})

    _{.catch(error => {}

        _{if (error.response) {}

            _{console.error("Failed to get a valid response. Status code:", error.response.status);}

            _{console.error("Response JSON:", error.response.data);}

        _{} else {}

            _{console.error("Error:", error.message);}

        _}

    _});

_```json

_{

  _{"template": "I am providing you a topic to write a blog on it. Topic='{topic}'",}

  _{"input_list": ["topic"],}

  _{"map_dict": {"topic": "machine learning"}}

_}

_```

_```json

_{

  _{"text": "\n\nMachine learning is a subfield of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It's a method of data analysis that automates analytical model building. It's based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.\n\nMachine learning algorithms are designed to analyze data, learn from it, and then make a determination or prediction about something new based on that data. There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.\n\nSupervised learning is a type of machine learning where the model is trained on a labeled dataset. This means that the data used to train the model has both input data and the correct output data. The model learns to map input data to output data, and once trained, it can make predictions about new data. Examples of supervised learning algorithms include linear regression, logistic regression, and support vector machines.\n\nUnsupervised learning is a type of machine learning where the model is trained on an unlabeled dataset. This means that the data used to train the model does not have any output data. The model learns to identify patterns and relationships in the data on its own. Examples of unsupervised learning algorithms include k-means clustering and principal component analysis.\n\nSemi-supervised learning is a type of machine learning that combines elements of supervised and unsupervised learning. It's used when there is a limited amount of labeled data available, and the model must learn from both labeled and unlabeled data. The model uses the labeled data to learn the correct output, and then uses the unlabeled data to refine its predictions.\n\nReinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize its reward. The agent learns from its mistakes and successes, and adjusts its behavior accordingly. This type of learning is often used in robotics, gaming, and autonomous systems.\n\nMachine learning has a wide range of applications, including image and speech recognition, natural language processing, fraud detection, recommendation systems, and predictive maintenance. It's used in various industries, including healthcare, finance, retail, and manufacturing.\n\nIn conclusion, machine learning is a powerful tool for analyzing data and making predictions or determinations based on that data. It's a subfield of artificial intelligence that provides systems the ability to learn from data and improve from experience. There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Machine learning has a wide range of applications and is used in various industries to improve efficiency, accuracy, and productivity."}

_}

_```

_```json

_{

  _{"template": "Write a persuasive advertisement for '{product}' targeting '{audience}'",}

  _{"input_list": ["product", "audience"],}

  _{"map_dict": {"product": "electric car", "audience": "environmentally conscious consumers"}}

_}

_```

_```josn

_{

  _{"text": "\n\nDiscover the future of driving with our latest electric car, designed specifically for environmentally conscious consumers like you. Experience the perfect blend of innovation, sustainability, and performance. Our electric car not only reduces your carbon footprint but also offers unmatched efficiency and cutting-edge technology.\n\nWith zero emissions, you can drive confidently knowing you're contributing to a cleaner, greener planet. Enjoy the silent, smooth ride and impressive acceleration that electric power provides. Say goodbye to gas stations and hello to the convenience of home charging.\n\nOur electric car is packed with features to enhance your driving experience. From advanced safety systems to state-of-the-art infotainment, every detail is crafted to offer you the ultimate in comfort and convenience. Plus, with a range that takes you further on a single charge, your adventures are limitless.\n\nJoin the revolution in sustainable transportation. Drive the change you want to see in the world. Make the switch to our electric car and lead the way towards a brighter, cleaner future. Visit our website or your nearest dealer to learn more and take a test drive today.\n\nEmbrace the power of electric. Drive green, drive smart."}

_}

_```

• Faster than any other AI API: We provide the fastest API response to your project.
• High-Quality Text Generation: Produce well-structured and contextually accurate text.
• Customization: Tailor text outputs to specific needs using templates and input parameters.
• Scalability: Handle a high volume of requests efficiently.
• Ease of Use: Simple API integration with clear documentation.

1. Content Generation: Generate text for articles, stories, reports, and more.
2. Chatbots: Enhance chatbot responses with dynamically generated text with context.
3. Question Answering: Generate answers to user queries based on context.