ChatGPT Library in Python

OpenAI has released an official Python client library for the ChatGPT API called `openai`. This library provides an easy-to-use interface for interacting with the ChatGPT API and generating text completions.

To use the `openai` library, you first need to install it. You can do this using `pip`:

Once the library is installed, you can use it to generate text completions with ChatGPT. Here's an example of how you might use the library to generate a text completion:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Generate a text completion
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="Once upon a time,",
  max_tokens=50
)

# Get the generated text
generated_text = response['choices'][0]['text']
print(generated_text)

Example input:

Example output:

there lived a young boy named Jack. He was adventurous and always eager to explore new places. One day, while wandering through the forest, he stumbled upon a mysterious cave. Curiosity getting the best of him, Jack decided to venture inside...

Explanation:

In this example, we first set our OpenAI API key, and then use the `openai.Completion.create()` method to generate a text completion. The `engine` parameter specifies the GPT model to use, and the `prompt` parameter specifies the text to complete. The `max_tokens` parameter controls the maximum length of the generated text.

The response from the API is a dictionary that contains the generated text, which we can access using `response['choices'][0]['text']`.

You can find more information and examples of how to use the `openai` library in the official OpenAI API documentation: https://beta.openai.com/docs/

Once the library is installed, you can use it to generate text completions with the ChatGPT API. To do this, you'll need to create an API key from the OpenAI website, which you'll use to authenticate your requests to the API.

The response from the API is a dictionary that contains the generated text, which we can access using `response['choices'][0]['text']`.

You can also specify additional parameters to control the behavior of the text generation, such as the `temperature` parameter which controls the randomness of the generated text, and the `top_p` parameter which controls the probability of including more diverse completions.

In addition to the parameters I mentioned earlier, there are several other parameters that you can use to customize the behavior of the text generation with the ChatGPT API. Here are some of the most commonly used parameters:

`n`: The number of completions to generate. The default is `1`.
`stop`: A list of strings that indicate the end of the generated text. If the generated text contains any of the strings in the `stop` list, the text generation will stop.
`temperature`: A float value that controls the randomness of the generated text. Higher values (e.g., `0.8`) make the output more creative and diverse, while lower values (e.g., `0.2`) make the output more focused and deterministic.
`top_p`: A float value between `0` and `1` that controls the probability of including more diverse completions. Higher values (e.g., `0.9`) make the output more diverse, while lower values (e.g., `0.1`) make the output more focused.
`frequency_penalty`: A float value that controls the penalty for using high-frequency tokens in the generated text. Higher values make the output less likely to include high-frequency tokens.
`presence_penalty`: A float value that controls the penalty for using new tokens in the generated text. Higher values make the output more likely to include new tokens.

Here's an example of how you might use some of these parameters to generate a text completion:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Generate a text completion with additional parameters
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="Once upon a time,",
  max_tokens=50,
  n=2,
  temperature=0.7,
  stop=["."],
)

# Get the generated text
generated_text_1 = response['choices'][0]['text']
generated_text_2 = response['choices'][1]['text']
print(generated_text_1)
print(generated_text_2)

Example input:

Example Output:

1. there was a kingdom ruled by a wise king who had a magical sword that could grant wishes.
2. in a faraway land, there lived a princess who had the power to control the elements.

Explanation:

In this example, we've specified the `n` parameter to generate two completions, the `temperature` parameter to control the randomness of the generated text, and the `stop` parameter to indicate the end of the generated text.

You can experiment with different values for these parameters to see how they affect the behavior of the text generation. Keep in mind that the optimal values for these parameters may vary depending on your specific use case and the prompt you are using.

More on `openai.Completion.create()` method

The `openai.Completion.create()` method is used to generate completions for a given prompt using the ChatGPT model. The method takes several parameters that allow you to customize the behavior of the text generation, as I mentioned in my previous messages.

Using other models

In addition to the `text-davinci-003` model, OpenAI provides several other GPT models that you can use to generate text completions. Each model has its own strengths and trade-offs, and you can choose the one that best fits your specific use case. Some of the available models include:

`text-ada-001`: A smaller and faster model that is well-suited for a wide range of tasks.
`text-babbage-001`: A medium-sized model that provides a good balance between performance and accuracy.
`text-curie-001`: A larger and more accurate model that is well-suited for more complex tasks.

You can specify the model to use by passing its name to the `engine` parameter of the `openai.Completion.create()` method, like this:

Python

response = openai.Completion.create(
  engine="text-ada-001",
  prompt="Once upon a time,",
  max_tokens=50
)

Handling errors

When making a request to the ChatGPT API, it's possible that an error may occur. For example, the API may return an error if your API key is invalid, or if you've exceeded your usage limits. You can handle errors by catching the `openai.error.OpenAIError` exception, like this:

Python

import openai

try:
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Once upon a time,",
        max_tokens=50
    )
except openai.error.OpenAIError as e:
    print(f"An error occurred: {e}")

In this example, we catch the `OpenAIError` exception and print an error message to the console.

Batch Requests

You can make batch requests to the ChatGPT API by passing a list of prompts to the `prompts` parameter of the `openai.Completion.create()` method. This allows you to generate completions for multiple prompts in a single API call. Here's an example:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Generate completions for multiple prompts
response = openai.Completion.create(
  engine="text-davinci-003",
  prompts=["Once upon a time,", "The quick brown fox"],
  max_tokens=50
)

# Get the generated text for each prompt
generated_text_1 = response['choices'][0]['text']
generated_text_2 = response['choices'][1]['text']
print(generated_text_1)
print(generated_text_2)

Example input:

Example Output:

1. there was a young girl named Alice who lived in a small village.
2. jumps over the lazy dog. The quick brown fox jumps over the lazy dog's back.

Explanation:

In this example, we pass a list of two prompts to the `prompts` parameter, and the API returns a list of completions, one for each prompt.

Streaming

The `openai` library also supports streaming, which allows you to receive completions in real-time as they are generated by the model. You can enable streaming by passing the `stream=True` parameter to the `openai.Completion.create()` method, like this:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Generate a text completion with streaming enabled
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="Once upon a time,",
  max_tokens=50,
  stream=True
)

# Get the generated text as it is received
for message in response:
    if message['message']['role'] == 'system':
        continue
    generated_text = message['message']['content']
    print(generated_text)

Example input:

Example Output:

there lived a beautiful princess named Cinderella. She had a kind heart and was loved by all who knew her.

Explanation:

In this example, we enable streaming by passing the `stream=True` parameter, and the API returns a generator object that we can iterate over to get the generated text as it is received.

Language Support

The ChatGPT model supports text generation in multiple languages. While the model is primarily trained on English text, it can still generate text in other languages to some extent. You can specify the language by including it in the prompt, like this:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Generate a text completion in Spanish
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="Había una vez,",
  max_tokens=50
)

# Get the generated text
generated_text = response['choices'][0]['text']
print(generated_text)

Example input:

Example Output:

una princesa llamada Aurora que vivía en un castillo encantado en lo alto de una colina.

Explanation:

In this example, we specify a prompt in Spanish, and the API generates a text completion in Spanish.

Here are some more topics and tips that you might find useful when using the `openai` Python client library:

Prompt Engineering

The prompt you use can have a big impact on the quality and relevance of the completions generated by the ChatGPT model. Prompt engineering is the process of designing and fine-tuning prompts to get the desired output from the model. Some tips for prompt engineering include:

Make your prompt as specific and clear as possible to guide the model towards generating the desired output.
Include context or background information in the prompt to help the model understand the task.
Experiment with different prompt formulations and styles to see what works best for your use case.

Rate Limits

The ChatGPT API has rate limits that restrict the number of requests you can make within a certain time period. The rate limits vary depending on your subscription plan and can be found in the OpenAI API documentation. It's important to keep track of your usage and handle rate limit errors appropriately.

Cost

The cost of using the ChatGPT API is based on the number of tokens processed by the model. Both the input and output tokens count towards the total cost. You can calculate the number of tokens in a text string using the `openai.Util.count_tokens()` method, like this:

Python

import openai

# Count the number of tokens in a text string
text = "Once upon a time,"
num_tokens = openai.Util.count_tokens(text)
print(f"The text contains {num_tokens} tokens")

Example input:

Example Output:

The text contains 4 tokens

Explanation:

In this example, we calculate the number of tokens in the input text and print the result to the console.

Cleanup and Formatting

The generated text returned by the ChatGPT API may include leading and trailing spaces, newlines, or other formatting characters. You can clean up and format the generated text as needed using standard Python string manipulation functions.

Asynchronous Requests

If you need to make multiple requests to the ChatGPT API in parallel, you can use the `openai.AsyncCompletion.create()` method to make asynchronous requests. This allows you to generate completions more efficiently by sending multiple requests at once without waiting for each one to complete. Here's an example:

Python

import openai
import asyncio

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Define a list of prompts
prompts = [
    "Once upon a time,",
    "The quick brown fox",
    "In a galaxy far, far away,"
]

# Define a function to generate completions
async def generate_completion(prompt):
    response = await openai.AsyncCompletion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=50
    )
    return response['choices'][0]['text']

# Create a list of tasks to generate completions for each prompt
tasks = [generate_completion(prompt) for prompt in prompts]

# Run the tasks in parallel and wait for them to complete
results = asyncio.run(asyncio.gather(*tasks))

# Print the generated text for each prompt
for result in results:
    print(result)

Example input:

prompts = [
    "Once upon a time,",
    "The quick brown fox",
    "In a galaxy far, far away,"
]

Example Output:

1. there was a young girl named Alice who lived in a small village.
2. jumps over the lazy dog. The quick brown fox jumps over the lazy dog's back.
3. there was a rebellion against the tyrannical empire led by a group of brave rebels.

Explanation:

In this example, we define a list of prompts and a function to generate completions for each prompt. We then create a list of tasks to generate completions for each prompt, and use `asyncio.gather()` to run the tasks in parallel. Finally, we print the generated text for each prompt.

Error Handling

It's important to handle errors gracefully when making requests to the ChatGPT API. In addition to the `OpenAIError` exception I mentioned earlier, there are several other exceptions you might encounter, such as `openai.error.RequestError`, `openai.error.RateLimitError`, and `openai.error.OpenAIAPIError`. You can catch these exceptions individually or catch the base `openai.error.OpenAIError` exception to handle all errors.

Model Fine-Tuning

In addition to using the pre-trained GPT models provided by OpenAI, you can also fine-tune your own models using the OpenAI fine-tuning API. Fine-tuning allows you to customize the behavior of the model by training it on your own data. You can find more information on fine-tuning in the OpenAI fine-tuning guide: https://beta.openai.com/docs/guides/fine-tuning

Uploading Files

The OpenAI API also allows you to upload files, which can be useful for tasks like fine-tuning your own models or performing file-based search. You can use the `openai.File.create()` method to upload a file, like this:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Upload a file
response = openai.File.create(
    file=open("myfile.txt", "rb"),
    purpose="fine-tune"
)

# Get the file ID
file_id = response['id']
print(f"File uploaded successfully with ID: {file_id}")

Example input:

Let's assume you have a file named "myfile.txt" containing some text or data that you want to upload.

Example Output:

File uploaded successfully with ID: your-file-id

Explanation:

In this output, "your-file-id" would be replaced with the actual ID assigned to the uploaded file by the OpenAI API. This ID can be used later for tasks such as fine-tuning models with the content of this file.

In this example, we upload a file named `myfile.txt` with the purpose of `fine-tune`. The `purpose` parameter specifies the intended use of the file.

Working with Models

The `openai` library provides a `Model` class that allows you to retrieve information about the available models and their capabilities. You can use the `openai.Model.retrieve()` method to get information about a specific model, like this:

Python

import openai

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Get information about a specific model
model = openai.Model.retrieve("text-davinci-003")

# Print the model information
print(model)

Example input:

Let's assume you want to retrieve information about the model named "text-davinci-003".

Example Output:

{
  "id": "text-davinci-003",
  "created_at": 1621232514,
  "owner": "openai",
  "permissions": null,
  "name": "Davinci Codex",
  "description": "The davinci-codex engine is a successor to the davinci model, trained on a large-scale dataset sourced from the internet that includes a mixture of licensed data, data created by human trainers, and publicly available data. The model has been trained to work with the codex completion model class.",
  "is_real_time": false,
  "is_owner": true,
  "nlp_version": "1.0.0",
  "display_name": "Davinci Codex",
  "training_time": "2018-05-01T00:00:00Z",
  "fine_tunable": false,
  "credentials_needed": ["api_key"]
}

In this example, the code retrieves information about the "text-davinci-003" model from the OpenAI API and prints out the model information, including its ID, name, description, creation date, and other relevant details.In this example, we retrieve information about the `text-davinci-003` model and print the model information to the console.

Resources and Learning

OpenAI Playground: The OpenAI Playground is an interactive web-based tool that allows you to experiment with the ChatGPT model and generate text completions using a graphical interface. It's a great way to get a feel for how the model works and explore different prompt formulations.
OpenAI Blog: The OpenAI Blog is a great resource for staying up-to-date with the latest developments and research from OpenAI. It includes articles, tutorials, and case studies on a wide range of topics related to machine learning and artificial intelligence.
OpenAI GitHub Repository: The OpenAI GitHub repository contains the source code for the `openai` Python client library, as well as other tools and resources developed by OpenAI. It's a great resource for developers who want to contribute to the library or learn more about its inner workings.

Handling Long Documents

If you need to generate completions for long documents, it's important to be aware of token limitations. The total number of tokens (both input and output) in a single API call must be below the model's maximum token limit, which is typically around 4096 tokens for the base GPT-3 models. If your content exceeds this limit, you'll need to split it into smaller chunks and make multiple API calls. You can use the `openai.Completion.create()` method's `max_tokens` parameter to control the length of the response.

Custom Prompting

You can experiment with different prompt styles to get the desired output. For example, you can use instructions like "Translate the following English text to French:" or "Write a Python code snippet to do the following:". The way you phrase your prompt can influence the model's behavior.

Context Management

You can maintain a conversation or context with the model by including the conversation history in the prompt. This allows you to build on previous responses and have more interactive and dynamic interactions with the model. For example:

Python

conversation = [
    "User: Tell me a joke.",
    "AI: Why did the chicken cross the road?",
    "User: I don't know, why?",
    "AI: To get to the other side."
]

response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=conversation,
    max_tokens=50
)

Multi-Document Summarization

You can use the ChatGPT model to generate summaries for multiple documents by providing the documents in the prompt and instructing the model to summarize them. For example:

Python

documents = [
    "Document 1: This is the content of the first document...",
    "Document 2: Here's the content of the second document...",
]

prompt = f"Summarize the following documents:\n\n{documents}"

response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=prompt,
    max_tokens=150  # Adjust max_tokens as needed
)

Post-Processing

After generating text completions, you can perform post-processing to clean up and format the output as needed. This can include removing unnecessary white spaces, correcting grammar, or reformatting the text.

Experiment and Fine-Tune

Don't hesitate to experiment with different prompts, parameters, and approaches to fine-tune the model's behavior for your specific use case. Fine-tuning your prompts and parameters can help you get the most accurate and relevant results.

Versioning

The `openai` library has multiple versions, and it's important to specify the version of the library that you are using in your code. You can do this by passing the `version` parameter to the `openai.api_version` attribute, like this:

Python

import openai

# Set your OpenAI API key and library version
openai.api_key = 'your-api-key'
openai.api_version = 'v1'

This ensures that your code is using the correct version of the library and that you are getting the expected behavior from the API.

Caching

If you are making repeated requests to the ChatGPT API with the same prompt, you can use caching to store the results and avoid making unnecessary API calls. Caching can help improve the performance of your application and reduce your API usage. You can use a caching library such as `diskcache` or `cachetools` to implement caching in your code.

Rate Limit Handling

To handle rate limits effectively, you can use the `ratelimit` library in combination with the `backoff` library to implement exponential backoff and retry logic. This allows you to automatically retry failed requests after a certain delay, and gradually increase the delay between retries to avoid hitting the rate limits.

Security

When using the `openai` library, it's important to ensure that your code and your API key are secure. Avoid hardcoding your API key in your code, and use environment variables or a configuration file to store your API key securely. Additionally, make sure to follow best practices for securing your code and infrastructure, such as using HTTPS, securing your server, and keeping your dependencies up to date.

Documentation

The `openai` library is well-documented, and the official OpenAI API documentation provides detailed information on how to use the library and the ChatGPT API. Make sure to refer to the documentation for guidance on best practices, usage examples, and troubleshooting tips.

Next TopicCheck if any two intervals intersect among a given set of intervals using python

← prev next →