one-api-tool 0.3.2

Use only one line of code to call multiple model APIs similar to ChatGPT. Currently supported: Azure OpenAI Resource endpoint API, OpenAI Official API, and Anthropic Claude series model API.

OneAPI

Easily access multiple ChatGPT or similar APIs with just one line of code/command.

Save a significant amount of ☕️ time by avoiding the need to read multiple API documents and test them individually.

The currently supported APIs include:

• OpenAI Official API.
• Microsoft Azure OpenAI Resource endpoint API.
• Anthropic Claude series model API.

Installation

pip install -U one-api-tool

Usage

1. Using python.

OpenAI config:

{
    "api_key": "YOUR_API_KEY",
    "api_base": "https://api.openai.com/v1",
    "api_type": "open_ai"
}

Azure OpenAI config:

{
    "api_key": "YOUR_API_KEY",
    "api_base": "Replace with your Azure OpenAI resource's endpoint value.",
    "api_type": "azure"
}

Anthropic config:

{
    "api_key": "YOUR_API_KEY",
    "api_base": "https://api.anthropic.com",
    "api_type": "claude"
}

api_key: Obtain OpenAI API key from the OpenAI website and Claude API key from the Anthropic website.

api_base: This is the base API that is used to send requests. You can also specify a proxy URL, such as "https://your_proxy_domain/v1". For example, you can use Cloudflare workers to proxy the OpenAI site.

If you are using Azure APIs, you can find relevant information on the Azure resource dashboard. The API format typically follows this pattern: https://{your_organization}.openai.azure.com/.

api_type: Currently supported values are "open_ai", "azure", or "claude".

Here is simple example:

from oneapi import OneAPITool
# Two ways to initialize the OneAPITool object  
# tool = OneAPITool.from_config(api_key, api_base, api_type)
tool = OneAPITool.from_config_file("your_config_file.json")
# Say hello to ChatGPT/Claude/GPT-4
res = tool.simple_chat("Hello AI!")
print(res)
# Get embeddings of some sentences for further usage, e.g., clustering
embeddings = tool.get_embeddings(["Hello AI!", "Hello world!"])
print(len(embeddings)))
# Count the number of tokens
print(tool.count_tokens(["Hello AI!", "Hello world!"]))

Note: Currently, count_tokens and get_embeddings only support OpenAI or Microsoft Azure API.

2. Using command line

open-api --config_file CHANGE_TO_YOUR_CONFIG_PATH \
--model gpt-3.5-turbo \
--prompt "1+1=?" 

Output detail

-------------------- prompt detail 🚀  --------------------

1+1=?

-------------------- prompt end --------------------

-------------------- gpt-3.5-turbo response ⭐️ --------------------

2

-------------------- response end --------------------

Arguments detail:

--config_file string ${\color{orange}\text{Required}}$
A local configuration file containing API key information.

--prompt string ${\color{orange}\text{Required}}$
The question that would be predicted by LLMs, e.g., A math question would be like: "1+1=?".

--model string ${\color{grey}\text{Optional}}$ Defaults to GPT-3.5-turbo or Claude-v1.3 depends on api_type
Which model to use, e.g., gpt-4.

--temperature number ${\color{grey}\text{Optional}}$ Defaults to 1
What sampling temperature to use. Higher values like 0.9 will make the output more random, while lower values like 0.1 will make it more focused and deterministic.

--max_new_tokens integer ${\color{grey}\text{Optional}}$ Defaults to 2048
The maximum number of tokens to generate in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length.

ToDo

• Batch requests.
• Token number counting.
• Custom token budget.