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Openai非正式Python SDK

OpenAI API的免费且无限的非官方Python SDK，提供了无缝集成和易于使用的方法，可与OpenAI最新功能强大的AI型号进行交互，包括GPT-4O-4O-Audio-audio-Preview＆gpt-4o-4o-4o-4o-Realtime-Preview型号（TTS）免费模型

特征
安装
快速开始
用法示例
- 列表可用型号
- 基本聊天完成
- 与图像输入一起聊天完成
- 使用实时模型流聊天完成
- TTS模型的音频产生
- 使用音频预览模型聊天完成
- 图像生成
- 语音模型的音频语音识别
- 功能调用和工具用法
  - 基本功能调用
贡献
执照

特征

全面的模型支持：与最新的OpenAI模型集成，包括GPT-4，GPT-4O，GPT-3.5 Turbo，Dall·E 3，Whisper，Wishper，Text-to-Speech（TTS）型号以及最新的音频预览和实时模型。
聊天完成：使用各种型号生成类似聊天的响应。
流响应：支持流聊天完成的支持，包括瞬时输出的实时模型。
音频生成：使用TTS模型，通过各种语音选项生成高质量的语音音频。
音频和文本响应：利用诸如gpt-4o-audio-preview之类的模型接收音频和文本响应。
图像生成：使用具有可自定义参数的DALL·e模型创建令人惊叹的图像。
音频转录：使用耳语模型将语音转换为文本。
易于使用：与各种端点交互的简单和直观的方法。
可扩展：旨在为将来的OpenAI模型和端点易于扩展。

安装

通过PIP安装软件包：

pip install -U openai-unofficial

快速开始

 from openai_unofficial import OpenAIUnofficial

# Initialize the client
client = OpenAIUnofficial ()

# Basic chat completion
response = client . chat . completions . create (
    messages = [{ "role" : "user" , "content" : "Say hello!" }],
    model = "gpt-4o"
)
print ( response . choices [ 0 ]. message . content )

用法示例

列表可用型号

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
models = client . list_models ()
print ( "Available Models:" )
for model in models [ 'data' ]:
    print ( f"- { model [ 'id' ] } " )

基本聊天完成

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
response = client . chat . completions . create (
    messages = [{ "role" : "user" , "content" : "Tell me a joke." }],
    model = "gpt-4o"
)
print ( "ChatBot:" , response . choices [ 0 ]. message . content )

与图像输入一起聊天完成

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
response = client . chat . completions . create (
    messages = [{
        "role" : "user" ,
        "content" : [
            { "type" : "text" , "text" : "What's in this image?" },
            {
                "type" : "image_url" ,
                "image_url" : {
                    "url" : "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" ,
                }
            },
        ],
    }],
    model = "gpt-4o-mini-2024-07-18"
)
print ( "Response:" , response . choices [ 0 ]. message . content )

流聊天完成

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
completion_stream = client . chat . completions . create (
    messages = [{ "role" : "user" , "content" : "Write a short story in 3 sentences." }],
    model = "gpt-4o-mini-2024-07-18" ,
    stream = True
)
for chunk in completion_stream :
    content = chunk . choices [ 0 ]. delta . content
    if content :
        print ( content , end = '' , flush = True )

TTS模型的音频产生

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
audio_data = client . audio . create (
    input_text = "This is a test of the TTS capabilities!" ,
    model = "tts-1-hd" ,
    voice = "nova"
)
with open ( "tts_output.mp3" , "wb" ) as f :
    f . write ( audio_data )
print ( "TTS Audio saved as tts_output.mp3" )

使用音频预览模型聊天完成

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
response = client . chat . completions . create (
    messages = [{ "role" : "user" , "content" : "Tell me a fun fact." }],
    model = "gpt-4o-audio-preview" ,
    modalities = [ "text" , "audio" ],
    audio = { "voice" : "fable" , "format" : "wav" }
)

message = response . choices [ 0 ]. message
print ( "Text Response:" , message . content )

if message . audio and 'data' in message . audio :
    from base64 import b64decode
    with open ( "audio_preview.wav" , "wb" ) as f :
        f . write ( b64decode ( message . audio [ 'data' ]))
    print ( "Audio saved as audio_preview.wav" )

图像生成

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
response = client . image . create (
    prompt = "A futuristic cityscape at sunset" ,
    model = "dall-e-3" ,
    size = "1024x1024"
)
print ( "Image URL:" , response . data [ 0 ]. url )

语音模型的音频语音识别

 from openai_unofficial import OpenAIUnofficial

client = OpenAIUnofficial ()
with open ( "speech.mp3" , "rb" ) as audio_file :
    transcription = client . audio . transcribe (
        file = audio_file ,
        model = "whisper-1"
    )
print ( "Transcription:" , transcription . text )

功能调用和工具用法

SDK支持OpenAI的函数调用功能，使您可以在对话中定义和使用工具/功能。以下是函数调用和工具用法的示例：

基本功能调用

配x重要说明：在当前版本（0.1.2）中，尚未完全支持复杂或多个功能调用。 SDK当前支持基本功能调用功能。在即将发布的版本中将添加对多个功能调用和更复杂的工具使用模式的支持。

 from openai_unofficial import OpenAIUnofficial
import json

client = OpenAIUnofficial ()

# Define your functions as tools
tools = [
    {
        "type" : "function" ,
        "function" : {
            "name" : "get_current_weather" ,
            "description" : "Get the current weather in a given location" ,
            "parameters" : {
                "type" : "object" ,
                "properties" : {
                    "location" : {
                        "type" : "string" ,
                        "description" : "The city and state, e.g., San Francisco, CA"
                    },
                    "unit" : {
                        "type" : "string" ,
                        "enum" : [ "celsius" , "fahrenheit" ],
                        "description" : "The temperature unit"
                    }
                },
                "required" : [ "location" ]
            }
        }
    }
]

# Function to actually get weather data
def get_current_weather ( location : str , unit : str = "celsius" ) -> str :
    # This is a mock function - replace with actual weather API call
    return f"The current weather in { location } is 22° { unit [ 0 ]. upper () } "

# Initial conversation message
messages = [
    { "role" : "user" , "content" : "What's the weather like in London?" }
]

# First API call to get function calling response
response = client . chat . completions . create (
    model = "gpt-4o-mini-2024-07-18" ,
    messages = messages ,
    tools = tools ,
    tool_choice = "auto"
)

# Get the assistant's message
assistant_message = response . choices [ 0 ]. message
messages . append ( assistant_message . to_dict ())

# Check if the model wants to call a function
if assistant_message . tool_calls :
    # Process each tool call
    for tool_call in assistant_message . tool_calls :
        function_name = tool_call . function . name
        function_args = json . loads ( tool_call . function . arguments )
        
        # Call the function and get the result
        function_response = get_current_weather ( ** function_args )
        
        # Append the function response to messages
        messages . append ({
            "role" : "tool" ,
            "tool_call_id" : tool_call . id ,
            "name" : function_name ,
            "content" : function_response
        })
    
    # Get the final response from the model
    final_response = client . chat . completions . create (
        model = "gpt-4o-mini-2024-07-18" ,
        messages = messages
    )
    
    print ( "Final Response:" , final_response . choices [ 0 ]. message . content )

贡献

欢迎捐款！请按照以下步骤操作：

分叉存储库。
创建一个新的分支： git checkout -b feature/my-feature 。
提交您的更改： git commit -am 'Add new feature' 。
推入分支： git push origin feature/my-feature 。
打开拉动请求。

请确保您的代码遵守项目的编码标准并通过所有测试。

执照

该项目是根据MIT许可证获得许可的 - 有关详细信息，请参见许可证文件。

注意：此SDK是非正式的，与OpenAI不隶属。

如果您遇到任何问题或有建议，请在Github上打开一个问题。

支持的模型

这是SDK当前支持的部分模型列表。有关完整列表，请查看/models端点：

聊天模型：
- gpt-4
- gpt-4-turbo
- gpt-4o
- gpt-4o-mini
- gpt-3.5-turbo
- gpt-3.5-turbo-16k
- gpt-3.5-turbo-instruct
- gpt-4o-realtime-preview
- gpt-4o-audio-preview
图像生成模型：
- dall-e-2
- dall-e-3
文本到语音（TTS）模型：
- tts-1
- tts-1-hd
- tts-1-1106
- tts-1-hd-1106
音频模型：
- whisper-1
嵌入模型：
- text-embedding-ada-002
- text-embedding-3-small
- text-embedding-3-large

展开

openai unofficial

Openai非正式Python SDK

目录

特征

安装

快速开始

用法示例

列表可用型号

基本聊天完成

与图像输入一起聊天完成

流聊天完成

TTS模型的音频产生

使用音频预览模型聊天完成

图像生成

语音模型的音频语音识别

功能调用和工具用法

基本功能调用

贡献

执照

支持的模型

Cats Blender Plugin Unofficial

openai realtime console

GitHub sgrebnov/cordova plugin background download

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开放性

OpenAI国内版

chat.petals.dev

GPT Prompt Templates

GPTyped

Google Dorks

shepherd

mongo express

Google Dorks

shepherd

mongo express