OpenAI Cookbook – Your Ultimate Guide to Use OpenAI API Key

The OpenAI Cookbook is a comprehensive guide to using the OpenAI API. It includes example code and guides for a variety of tasks, such as generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way. OpenAI Cookbook is a valuable resource for learning how to use the OpenAI API.

What is the OpenAI Cookbook?

The OpenAI Cookbook is a collection of example code and guides for using the OpenAI API. The OpenAI Cookbook is an excellent resource for learning how to utilize the OpenAI API. It includes examples for a variety of tasks, and the code is well-documented.

API usage

When you regularly access the OpenAI API, you may receive error messages such as 429: ‘Too Many Requests’ or RateLimitError. These error messages result from exceeding the rate restrictions of the API.

To Handle rate limits when using the OpenAI API:

  1. Keep an eye out for rate limit headers in API responses.
  2. Before making requests, double-check the remaining rate limit.
  3. For rate limit errors, use retries with exponential backoff.
  4. API requests should be prioritized and optimized.
  5. To reduce redundant calls, cache responses locally.
  6. To stay inside restrictions, monitor and adjust usage.

How to count tokens with tiktoken?

tiktoken is a fast open-source tokenizer by OpenAI.

  1. Run the command “pip install tiktoken” on your terminal or command line to install the tiktoken library.
  2. Insert the line “import tiktoken” into your Python script to import the tiktoken module.
  3. Load the tokenizer model by invoking the “tiktoken.Tokenizer” class and putting it in a variable named “tokenizer”.
  4. Define a text string or file for which you want to count tokens.
  5. Tokenize the text by sending it as a parameter to the “tokenizer.count_tokens” method. Save the result in a variable called “token_count.”
  6. To see the total number of tokens in the text, print or utilize the “token_count” variable.
  7. Optionally, repeat steps 4-6 for each additional text or file.
  8. Remember to handle any errors or exceptions that may occur during the tokenization process.

Using the OpenAI API for GPT

The Cookbook dives deep into OpenAI’s GPT (Generative Pre-trained Transformer), a strong language model. It covers the basics of GPT, its architecture, and shows actual instances of text generation using GPT. You’ll learn how to use GPT’s language generating capabilities to build chatbots, produce code, compose articles, and do a variety of other things.

How to format inputs to ChatGPT models?

ChatGPT is powered by OpenAI’s most advanced models, gpt-3.5-turbo and gpt-4. Using the OpenAI API, you can create your own applications with gpt-3.5-turbo or gpt-4. Chat models receive a series of messages as input and output an AI-written message.

Tips for instructing gpt-3.5-turbo-0301

  1. Make your instructions clear and specific.
  2. System messages can be used to guide the model’s behavior.
  3. The max_tokens argument is used to limit the length of the answer.
  4. Experiment with temperature to change the randomness of the reaction.
  5. Use tokens wisely and keep the 4096 token restriction in mind.
  6. Improve your results by iterating and refining your instructions.
  7. For multiple completions, use the n argument.
  8. When submitting input, keep the context window in mind.
  9. Experiment with various instruction approaches and prompts.
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Counting tokens

Keeping track of the amount of tokens used in a text input is referred to as counting tokens. It ensures that you keep under the OpenAI API’s token limits. Token counting tools, such as OpenAI’s tiktoken library, can help.

To stream completions with the OpenAI API:

  1. Make an initial API call with the openai.ChatCompletion.create() method to start the conversation stream.
  2. To deliver the conversation history as a list of message objects, use the messages option. Each message object should have a role (either “system”, “user”, or “assistant”) as well as content (the message’s text).
  3. Extract the generated message from the API response after receiving it.
  4. To continue the conversation, add a new message object to the existing list of messages with the role “user” and the content as the next user input.
  5. Steps 3 and 4 should be repeated as needed to keep the conversation flowing.
  6. Continue streaming completions by making further API calls with the same conversation ID.

Unit test writing using a multi-step prompt

  1. Define the first step’s first input prompt and the expected output.
  2. Pass the prompt to the model and then obtain the output.
  3. Use the generated output as the following step’s input prompt.
  4. Steps 2 and 3 should be repeated for each succeeding step, chaining the outputs together.
  5. Check that the final output corresponds to the expected result.
  6. To ensure thorough testing, repeat the process for different scenarios and edge cases.
  7. Unit tests should be automated so that they execute rapidly and consistently during development or deployment.

How to work with large language models?

  1. Understand the model’s capabilities and limits.
  2. To fit under the model’s token limit, break tasks down into smaller, more manageable inputs.
  3. Maintain coherence in multi-turn dialogues by using correct context management.
  4. Experiment with different temperature settings to modify the randomness of the reaction.
  5. Control rate limits and optimize API usage for more efficient and cost-effective interactions.
  6. When specialized task performance is required, fine-tune models.
  7. Iterate and fine-tune instructions to achieve the desired results.
  8. Keep prejudice and ethical factors in mind when analyzing model results.
  9. Keep up to date on the most recent research and best practices for maximum utilization.
  10. Use community resources, documentation, and forums to learn from the experiences and insights of others.


Embeddings are essential to extract semantic meaning from text. You’ll learn how to use OpenAI’s embeddings models to encode text into numerical representations in the OpenAI Cookbook. You can use these embeddings to do sentiment analysis, text classification, and document similarity by encoding phrases, paragraphs, or complete texts. This gives you the ability to gain deeper insights and improve your natural language processing apps.

Embeddings are numerical representations of text that enable various NLP applications. Here are key points about embeddings:

  1. Text Comparison: Embeddings allow you to compare text similarity using distance measures such as cosine similarity.
  2. Obtaining Embeddings: Word2Vec and BERT pre-trained models can create embeddings for words, phrases, or documents.
  3. Question Answering: Embeddings make question answering easier by matching the embedding of the question with the embeddings of the document.
  4. Vector Databases: Vector databases, such as Faiss, provide efficient search based on the proximity of embeddings.
  5. Semantic Search: Semantic search is powered by embeddings, which retrieve related documents or sentences based on their embeddings.
  6. Recommendations: Embeddings help in suggestion generation by locating similar items based on their embeddings.
  7. Clustering: Embeddings allow comparable things to be grouped together using clustering techniques.
  8. Visualization: Embeddings can be viewed in 2D or 3D using approaches such as t-SNE or PCA.
  9. Embedding Long Texts: Techniques such as pooling or hierarchical embeddings can summarize the entire text into a fixed length embedding for long texts.
  10. Playground for Embeddings: Streamlit apps provide interactive interfaces for exploring and experimenting with embeddings.
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The OpenAI Cookbook contains examples and guidance for developing various applications with OpenAI models. Among the subjects highlighted are:

  1. File Q&A: Discover how to create a question-and-answer system that can extract answers from a single document or file. This program is excellent for activities such as document searching, knowledge base retrieval, and FAQ retrieval.
  2. Web Crawl Q&A: Learn how to build a question-and-answer system that can crawl and retrieve data from web sites. This enables you to create a search engine or information retrieval system that can offer web-based replies.
  3. Using ChatGPT and your own data to power your products: Learn how to use ChatGPT models into your own apps and customize them using your own data. This allows you to create personalized conversational experiences and modify the model’s replies to your particular use case.

These examples highlight the adaptability and practicality of OpenAI models in a variety of areas and scenarios. You can use these programs or change them to your own needs by following the directions in the Cookbook.

Fine-tuning GPT-3

GPT-3 text classification fine-tuning involves training the model on a specific classification task using labeled data. Here are some important factors to consider and best practices for fine-tuning GPT-3 for classification:

  1. Preparing a Labeled Dataset: Create a labeled dataset with text samples and their accompanying class labels. Make certain that the dataset is well-balanced, representative, and covers all of the desired classifications.
  2. Task Definition: Clearly state the categorization task as well as the classes that you want the model to predict. Input format and any other requirements, such as maximum sequence length or input encoding, must be specified.
  3. Model Configuration: Based on your work needs and computational resources, select the best GPT-3 variation to utilize. Think about things like model size, token limit, and performance trade-offs.
  4. Tokenization: Tokenize your dataset in order for it to be compatible with the GPT-3 input format. Ensure that tokenization during training and inference is consistent.
  5. Fine-tuning Procedure: To undertake the fine-tuning procedure, go to OpenAI’s fine-tuning handbook and API documentation. This involves building the model with pre-trained weights and then fine-tuning it on your labeled dataset using appropriate optimization approaches.
  6. Hyperparameter Tuning: Experiment with hyperparameter variables such as learning rate, batch size, and number of training steps to get optimal classification results.
  7. Metrics for Evaluation: Define evaluation metrics that are relevant to your classification task, such as accuracy, precision, recall, or F1 score. To evaluate the model’s performance, use a separate validation or test set.
  8. Regularization Techniques: To reduce overfitting and increase generalization, use regularization techniques like as early halting, dropout, or weight decay.
  9. Handling Class Imbalance: If your dataset has unequal class distributions, consider using strategies such as oversampling, under sampling, or class weighting to fix the problem.
  10. Transfer Learning: Transfer learning can help in fine-tuning GPT-3. To use the model’s prior knowledge, you can initialize the model with weights from a pre-trained model and fine-tune on your unique classification task.

By following these best practices and guidelines, you can effectively fine-tune GPT-3 for text classification, improving its capacity to properly and efficiently categorize text samples.


DALLE is an OpenAI AI model that generates visuals from textual descriptions. It combines the power of words and image generation to produce unique and inventive graphics. Here’s an introduction of how to construct dynamic masks with DALL·E and Segment Anything, as well as how to generate and edit photos with DALL·E.

Generating Images with DALL·E:

  • Describe an image: Textually describe the image you wish to create, including specific objects, scenes, or thoughts.
  • Encode the text: Convert the text description into a number that DALLE can understand and process.
  • Decode the representation: From DALLE, use the encoded text to generate an image output.
  • Iterate and refine: Experiment with various text inputs to generate image variations until you obtain the desired result.
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Editing Images with DALL·E:

  • Start with an existing image: Start with an image that you want to edit or change.
  • Generate textual description: Create a textual description of the requested alterations or modifications to the image.
  • Encode the text: Convert a text description into numerical form.
  • Combine with the original image: Use the encoded text as input to DALLE along with the original image.
  • Obtain altered image output: Generate an image output that includes the changes described in the text.

Creating Dynamic Masks with DALL·E and Segment Anything:

  • Define the mask parameters: Choose the specific attributes or criteria for the mask, such as color, shape, or object category.
  • Create a textual description: Write a description of the desired mask attributes or properties.
  • Encode the text: Convert a text description into numerical form.
  • Apply the mask: As inputs to DALLE, use the encoded text and the original image to generate an image with the dynamic mask applied.

You can use DALLE’s ability to generate and edit images based on textual descriptions, as well as construct dynamic masks for image segmentation jobs, by following these steps. This allows you to investigate AI’s creative potential in the visual domain.

Azure OpenAI (alternative API from Microsoft Azure)

Azure OpenAI Service is a new Azure product offering that gives you access to OpenAI’s powerful large language models including GPT-4, GPT-3, Codex, and DALLE. Natural language processing (NLP) and computer vision solutions can use these models to interpret, converse, and generate content. The service is accessible through REST APIs, SDKs, and Azure OpenAI Studio. ChatGPT, a conversational model based on GPT-3, is one of the models accessible in Azure OpenAI Service. ChatGPT may respond to user inputs in a natural and engaging manner.

How to use ChatGPT with Azure OpenAI?

To use ChatGPT with Azure OpenAI Service, you need to follow these steps:

  • Apply for Azure OpenAI Service access and add a resource to your Azure Subscription.
  • Use the Azure portal or Azure OpenAI Studio to obtain your API key and endpoint.
  • Choose the ChatGPT model and engine size that best meets your requirements. The engine sizes available are gpt-35-turbo-small, gpt-35-turbo-medium, and gpt-35-turbo-large.
  • Send a POST request to the endpoint, passing as parameters your API key, the model’s name, and the user input. To control the generation, you can also define parameters such as temperature, top_p, frequency_penalty, presence_penalty, stop_sequence, and so on.
  • Receive the model’s response as a JSON object with a “choices” field containing an array of possible completions. Embeddings is another model provided in Azure OpenAI Service that may generate high-dimensional vector representations of text or code snippets. Embeddings can help with activities like semantic search, clustering, and similarity analysis.

How to get embeddings from Azure OpenAI?

To use Embeddings with Azure OpenAI Service, you need to follow these steps:

  • Apply for Azure OpenAI Service access and add a resource to your Azure Subscription.
  • Use the Azure portal or Azure OpenAI Studio to obtain your API key and endpoint.
  • Select the Embeddings model and engine size that best meets your requirements. The engine sizes that are offered are embeddings-v1-small, embeddings-v1-medium, embeddings-v1-large, and embeddings-v1-xlarge.
  • As parameters, send a POST request to the endpoint with your API key, the model’s name, and the text or code snippet. Other parameters, such as max_length and logprobs, can be used to influence the generation.
  • Receive the model’s response as a JSON object with a “embeddings” field containing an array of floating-point values representing the vector embedding. DALLE, a model that can produce images from text descriptions using a variational autoencoder (VAE), is a third model accessible in Azure OpenAI Service. DALLE can generate unique and different visuals that correspond to the given text.

How to generate images with DALL·E from Azure OpenAI?

To use DALL·E with Azure OpenAI Service, you need to follow these steps:

  • Apply for Azure OpenAI Service access and add a resource to your Azure Subscription.
  • Use the Azure portal or Azure OpenAI Studio to obtain your API key and endpoint.
  • Select the DALLE model and engine size that best meets your requirements. The engine sizes available are dalle-v0-small and dalle-v0-large.

Also Read: How to Get a ChatGPT API Key for Free

In conclusion, the OpenAI Cookbook is a helpful guide for people who want to use OpenAI’s models and tools. It covers a wide range of topics, including how to use the API, work with different models like GPT and DALL·E, and build applications. Please share your thoughts and feedback in the comment section below.