openai api python examples的进一步展开说明

OpenAI is an industry leader in the field of artificial intelligence. They have released several software products like ChatGPT, DALL-E, GPT⑶, and Whisper, which have had a significant impact on the AI industry. Many businesses and individuals have adopted ChatGPT to improve their efficiency, while those who failed to adapt have faced job losses. The era of AI has arrived, and it is essential for people to embrace AI in order to avoid potential difficulties. In this article, we will explore how you can leverage the power of AI and make your day-to-day tasks easier by using the OpenAI APIs in Python.

What is OpenAI?

OpenAI is a leading company in the field of artificial intelligence. It was established in 2015 as a non-profit organization by Sam Altman and Elon Musk. The company focuses on developing AI-based software products such as ChatGPT, DALL-E, and GPT⑶. These products have incredible capabilities, such as advanced text classification, question-answering, and chatbot applications.

What is OpenAI API?

OpenAI API is a cloud-based platform hosted on Microsoft’s Azure that provides developers with seamless access to state-of-the-art pre-trained AI models. This API allows developers to easily integrate cutting-edge AI capabilities into their applications, regardless of the programming language they choose to work with. By leveraging the OpenAI API, developers can enhance the intelligence and performance of their software solutions.

Generating OpenAI API Key

To use OpenAI’s models in your Python environment, you need to generate an API key. Follow these steps to generate the API key:

1. Create an OpenAI account and log in.
2. Go to the “Personal” section in the top-right menu and choose “View API keys”.
3. Click on the “Create new secret key” button to generate a secret key. Copy and save this key, as it will be required in the next steps.

Installation of OpenAI Package

To install the OpenAI library in your Python environment, follow these steps:

1. Open a text editor or an online notebook like Google Colab or Jupyter Notebook.
2. Run the following command to install the OpenAI library: `!pip install -q openai`
3. Import the OpenAI library in your Python environment and add your API key using the following lines of code:

“`python
import openai
openai.api_key = ‘‘
“`

With these steps, you are ready to use OpenAI in your Python environment. You can now explore the various tutorials and examples to try out OpenAI’s latest models.

Prompt Engineering

Prompt engineering is the process of providing unique instructions to AI models to increase their intelligence and responsiveness. It involves guiding AI models like ChatGPT or GPT⑷ in the right direction by structuring prompts. Prompt engineering helps ensure that AI models provide the best possible answers. The process of prompt engineering is ongoing, and it requires adjusting and experimenting with different approaches. By structuring prompts effectively, you can generate more accurate results with AI.

Some Rules of Prompt Engineering

1. Use the latest AI models for your tasks: It is important to use the latest AI models because they are trained on the most recent data. Using outdated models may result in inaccurate results. For example, ChatGPT trained on data from September 2023 may not provide information beyond that date, while a model like GPT⑷ trained on the latest data can generate up-to-date information.

2. Start your prompt with instructions: Clearly specify your instructions at the beginning of the prompt, and use separators like `”””` or `###` to separate your instructions from the input.

3. Be precise and thorough in describing the context, result, length, format, style, and other desired characteristics.

4. Include examples to illustrate the desired output format.

5. Start with zero-shot learning, then few-shot learning, and if necessary, fine-tuning. Zero-shot learning refers to the ability of the model to perform a task without being explicitly trained for it. Few-shot learning refers to the ability to perform a task after being trained with a limited number of examples. Fine-tuning involves training the model with specific data to improve its performance on particular tasks.

6. Avoid imprecise descriptions in your prompts.

7. Instead of mentioning “what not to do,” focus on “what to do” in your prompts.

8. Use “leading words” to guide the model towards a certain pattern, especially for code generation.

You can use these rules to design prompts and experiment with ChatGPT to see how the results differ from regular prompts.

Text Generation, Text Completion, Translation, Summarization, and Text Conversion

To perform text-specific tasks, you can define a function to generate text using the Completions module from the OpenAI library. This module allows you to generate text based on a given prompt. The important variables for this module are the model, prompt, max_tokens, temperature, and n (number of answers to generate).

Examples of Text Generation:

You can generate text by defining a prompt and using the Completions module. Here are some examples:

“`python
PROMPT = ‘Once upon a time, there lived a Rabbit and a Turtle. Both wanted to be the fastest…’
text_generation_output = generate_text(PROMPT)
print(text_generation_output)
“`

Examples of Text Completion, Translation, Summarization, and Retrieving Factual Information

You can perform text completion, translation, summarization, and retrieval of factual information using the Completions module. Here are some examples:

“`python
PROMPT = ‘Translate the below text to Hindi. Text: “I work at Geeks for Geeks”‘
translation_output = generate_text(PROMPT)
print(translation_output)

PROMPT = ‘Write a short inspiring and funny poem about Geeks for Geeks…’
summarization_output = generate_text(PROMPT)
print(summarization_output)

PROMPT = ‘India is divided up into 28 different states and 8 union territories…’
factual_information_output = generate_text(PROMPT)
print(factual_information_output)
“`

Chat Conversation & Chat Completion

For chat-specific tasks, you can define a function to generate chat responses using the ChatCompletion module from the OpenAI library. This module allows you to have conversations with the ChatGPT model by providing a sequence of messages. The important variables for this module are the model, messages, max_tokens, temperature, and n (number of completions to generate).

Examples of Chat Conversation & Chat Completion:

You can have chat conversations and receive responses from the model using the ChatCompletion module. Here are some examples:

“`python
MSGS = [
{‘role’: ‘system’, ‘content’: ‘You are a helpful assistant.’},
{‘role’: ‘user’, ‘content’: ‘Who won the world cup in 2019?’},
{‘role’: ‘assistant’, ‘content’: ‘The 2019 Cricket World Cup was won by England.’}
]

chat_completion_output = chat(MSGS)
print(chat_completion_output)
“`

Image Generation & Image Editing

You can generate and edit images using the DALL-E model from OpenAI. To generate images, you need to use the Image module and provide a text prompt. To edit images, you need to use the create_edit endpoint of the DALL-E API and provide an image file and a mask indicating the area to edit.

Examples of Image Generation:

You can generate images by providing a text prompt and using the DALL-E API. Here are some examples:

“`python
text = ‘batman art in red and blue color’
image_generation_output = generate_image(text)
display_image(image_generation_output)

text = ‘a scenic view of moon shining light on a yacht’
image_generation_output = generate_image(text)
display_image(image_generation_output)
“`

Examples of Image Editing:

You can edit images by providing an image file, a mask file, and a text prompt. Here are some examples:

“`python
image_file = ‘img.png’
mask_file = ‘mask.png’
text_prompt = ‘gotham city skyline behind batman’

image_editing_output = edit_image(image_file, mask_file, text_prompt)
display_image(image_editing_output)
“`

Speech-to-Text

You can transcribe and translate audio files using the Whisper module from OpenAI. The Whisper module provides two functions: transcribe and translate. You need to provide an audio file and specify the model for speech-to-text conversion.

Examples of Audio Transcription:

You can transcribe audio files by providing the audio file and model information. Here is an example:

“`python
audio_file = ‘sample_audio.mp3’
transcription_output = transcribe_audio(audio_file)
print(transcription_output)
“`

Examples of Audio Translation:

You can translate audio files by providing the audio file and specifying the model. Here is an example:

“`python
audio_file = ‘sample_audio.mp3’
translation_output = translate_audio(audio_file)
print(translation_output)
“`

Embeddings

You can obtain vector representations of text using the embeddings module from OpenAI. This module allows you to generate vector embeddings of a given input. The important variables for this module are the text prompt and the specific model to use.

Examples of Embeddings:

You can generate embeddings by providing a text prompt and the model to use. Here is an example:

“`python
text_prompt = ‘This is a sample text for generating embeddings.’
embedding_output = generate_embedding(text_prompt)
print(embedding_output)
“`

Fine-Tuning

To fine-tune a model, you need to prepare your dataset in JSONL format and upload it to OpenAI. Once your dataset is prepared, you can fine-tune a model by providing the training and validation files, model name, number of epochs, batch size, and learning rate multiplier.

Example of Fine-Tuning:

You can fine-tune a model by following the steps mentioned in the article. Here is an example:

“`python
training_file = ‘spam_prepared_train.jsonl’
validation_file = ‘spam_prepared_valid.jsonl’
model_name = ‘davinci’
n_epochs = 10
batch_size = 4
learning_rate_multiplier = 0.133

fine_tuning_output = fine_tune_model(training_file, validation_file, model_name, n_epochs, batch_size, learning_rate_multiplier)
print(fine_tuning_output)
“`

API Error Codes

If you encounter any errors while using the OpenAI API, you may see different error codes. Here are some common error codes you may encounter:

– 401: Invalid Authentication or Incorrect API key provided.
– 429: Rate limit reached for requests or You exceeded your current quota.
– 500: The server had an error while processing your request.

If you encounter any errors, refer to the specific error code section in the article for further guidance.

Conclusion

In this article, we have explored the various features provided by OpenAI API and demonstrated how to use them effectively in Python. By leveraging OpenAI’s models and APIs, you can enhance the capabilities and efficiency of your applications and tasks. Whether it is text generation, chat conversations, image generation, speech-to-text, or fine-tuning models, OpenAI offers a wide range of possibilities for developers and researchers.

openai api python examples的常见问答Q&A

import openai

# 设置 API 密钥
openai.api_key = 'YOUR_API_KEY'

# 创建 OpenAI API 客户端
api_client = openai.ApiClient()

# 调用相应的 API 方法
response = api_client.call_something()

# 处理 API 响应数据
data = response.data