openai-api-call 0.6.0

A short wrapper of the OpenAI api call.

中文文档移步这里。

Openai API call

A simple wrapper for OpenAI API, which can be used to send requests and get responses.

Installation

pip install openai-api-call --upgrade

Usage

Set API Key

import openai_api_call as apicall
apicall.api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Or set OPENAI_API_KEY in ~/.bashrc to avoid setting the API key every time:

# Add the following code to ~/.bashrc
export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Also, you might set different api_key for each Chat object:

from openai_api_call import Chat
chat = Chat("hello")
chat.api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

Set Proxy (Optional)

from openai_api_call import proxy_on, proxy_off, proxy_status
# Check the current proxy
proxy_status()

# Set proxy(example)
proxy_on(http="127.0.0.1:7890", https="127.0.0.1:7890")

# Check the updated proxy
proxy_status()

# Turn off proxy
proxy_off() 

Alternatively, you can use a proxy URL to send requests from restricted network, as shown below:

from openai_api_call import request

# set request url
request.base_url = "https://api.example.com"

You can set OPENAI_BASE_URL in ~/.bashrc as well.

Basic Usage

Example 1, send prompt and return response:

from openai_api_call import Chat, show_apikey

# Check if API key is set
show_apikey()

# Check if proxy is enabled
proxy_status()

# Send prompt and return response
chat = Chat("Hello, GPT-3.5!")
resp = chat.getresponse(update=False) # Not update the chat history, default to True

Example 2, customize the message template and return the information and the number of consumed tokens:

import openai_api_call as apicall

# Customize the sending template
apicall.default_prompt = lambda msg: [
    {"role": "system", "content": "帮我翻译这段文字"},
    {"role": "user", "content": msg}
]
chat = Chat("Hello!")
# Set the number of retries to Inf
# The timeout for each request is 10 seconds
response = chat.getresponse(temperature=0.5, max_requests=-1, timeout=10)
print("Number of consumed tokens: ", response.total_tokens)
print("Returned content: ", response.content)

# Reset the default template
apicall.default_prompt = None

Example 3, continue chatting based on the last response:

# first call
chat = Chat("Hello, GPT-3.5!")
resp = chat.getresponse() # update chat history, default is True
print(resp.content)

# continue chatting
chat.user("How are you?")
next_resp = chat.getresponse()
print(next_resp.content)

# fake response
chat.user("What's your name?")
chat.assistant("My name is GPT-3.5.")

# get the last result
print(chat[-1])

# save chat history
chat.save("chat_history.log", mode="w") # default to "a"

# print chat history
chat.print_log()

Moreover, you can check the usage status of the API key:

# show usage status of the default API key
chat = Chat()
chat.show_usage_status()

# show usage status of the specified API key
chat.api_key = "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
chat.show_usage_status()

Advance usage

Save the chat history to a file:

checkpoint = "tmp.log"
# chat 1
chat = Chat()
chat.save(checkpoint, mode="w") # default to "a"
# chat 2
chat = Chat("hello!")
chat.save(checkpoint)
# chat 3
chat.assistant("你好, how can I assist you today?")
chat.save(checkpoint)

Load the chat history from a file:

# load chats(default)
chats = load_chats(checkpoint)
assert chats == [Chat(log) for log in chat_logs]
# load chat log only
chat_logs = load_chats(checkpoint, chat_log_only=True)
assert chat_logs == [[], [{'role': 'user', 'content': 'hello!'}],
                      [{'role': 'user', 'content': 'hello!'}, 
                       {'role': 'assistant', 'content': '你好, how can I assist you today?'}]]
# load the last message only
chat_msgs = load_chats(checkpoint, last_message_only=True)
assert chat_msgs == ["", "hello!", "你好, how can I assist you today?"]

In general, one can create a function msg2chat and use process_chats to process the data:

def msg2chat(msg):
    chat = Chat(api_key=api_key)
    chat.system("You are a helpful translator for numbers.")
    chat.user(f"Please translate the digit to Roman numerals: {msg}")
    chat.getresponse()

checkpath = "tmp.log"
# first part of the data
msgs = ["1", "2", "3"]
chats = process_chats(msgs, msg2chat, checkpath, clearfile=True)
assert len(chats) == 3
assert all([len(chat) == 3 for chat in chats])
# continue the process
msgs = msgs + ["4", "5", "6"]
continue_chats = process_chats(msgs, msg2chat, checkpath)

License

This package is licensed under the MIT license. See the LICENSE file for more details.

update log

• Since version 0.2.0, Chat type is used to handle data
• Since version 0.3.0, you can use different API Key to send requests.
• Since version 0.4.0, this package is mantained by cubenlp.
• Since version 0.5.0, one can use process_chats to process the data, with a customized msg2chat function and a checkpoint file.
• Since version 0.6.0, the feature function call is added.