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.
Project description
OneAPI
LLM calling tool for researchers, which can interact with the language model based on UI or code.
Engage in multi-turn conversations with ChatGPT or Other LLMs APIs and automatically save them in a training-specific data format.
Step 1: Installation (requires Python environment and python >= 3.11): pip install one-api-tool
Step 2: Start the command: one-api
Step 3: Select the API type and set the key or other information following the guide.
Step 4: Initiate the chat dialogue and start a conversation:
Step 5: Edit the conversation without restart:
: + clear
to clear the conversation history: + d_clear
to clear the conversation history and system prompt: + undo
to remove the latest message: + save
to save the current session history (the program also saves automatically upon exit): + load
to load the last conversation from cache file: + system
to set the system prompt
The currently supported APIs include:
-
OpenAI Official API.
- ChatGPT: GPT-3.5-turbo/GPT-4.
- Token number counting.
- Embedding generation.
-
Microsoft Azure OpenAI Resource endpoint API.
- ChatGPT: GPT-3.5-turbo/GPT-4.
- Token number counting.
- Embedding generation.
-
Anthropic Claude series model API.
- Claude-v1.3-100k, etc.
- Token number counting.
-
Huggingface LLMs.
- Huggingface_hub
- Local deployed Inference Endpoint.
-
VLLM deployed Inference Endpoint
Installation
Requirements Python >=3.11
pip install -U one-api-tool
Usage
1. With 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",
"api_version": "2023-03-15-preview"
}
Anthropic config:
{
"api_key": "YOUR_API_KEY",
"api_base": "https://api.anthropic.com",
"api_type": "claude"
}
Huggingface config:
{
"api_key": "",
"api_base": "http://ip:port",
"api_type": "huggingface",
"chat_template": "your_jinja2_template"
}
VLLM config:
{
"api_key": "",
"api_base": "http://ip:port/generate",
"api_type": "vllm",
"chat_template": "your_jinja2_template"
}
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".
api_version
: This field is optional. Azure provides several versions of APIs, such as "2023-03-15-preview". However, the OpenAI SDK always has a default value set for this field. Therefore, you should only specify a specific value if you want to use that particular version of APIs.
chat_template
: This field is optional. When using local endpoint server, pass a JinJa2 template designed specifically for that model. The template render function would takes prompt
and system
as parameters template.render(prompt=prompt, system=system)
. The default template is {{system + prompt}}
.
Chat example:
There are three acceptable types of inputs for function chat()
:
- list of dicts
- string
- list of string
from oneapi import OneAPITool
import asyncio
# 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")
# There are three acceptable types of inputs.
conversations_openai_style = [{"role": "user", "content": "hello"}, {"role": "assistant", "content": "Hello, how can i assistant you today?"}, {"role": "user", "content": "I want to know the weather of tomorrow"}]
conversation_with_system_msg = [{"role": "system", "content": "Now you are a weather forecast assistant."},{"role": "user", "content": "hello"}, {"role": "assistant", "content": "Hello, how can i assistant you today?"}, {"role": "user", "content": "I want to know the weather of tomorrow"}]
string_message = "Hello AI!"
list_of_string_messages = ["Hello AI!", "Hello, how can i assistant you today?", "I want to know the weather of tomorrow"]
for msg in [conversations_sharegpt_style, conversations_openai_style, conversation_with_system_msg, string_message, list_of_string_messages]:
res = tool.chat(msg)
print(res)
# Pass system message independently
res = tool.chat("Hello AI!", system="Now you are a helpful assistant.")
print(res)
#Set `vebose=True` to print the detail of args passing to LLMs
res = tool.chat("Hello AI!", verbose=True)
# Async chat
res = asyncio.run(tool.achat("How\'s the weather today?", model="gpt-4", stream=False))
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, get_embeddings
only support OpenAI or Microsoft Azure API.
Batch request with asyncio
from oneapi.one_api import batch_chat
import asyncio
claude_config = "anthropic_config.json"
openai_config = "openapi_config.json"
azure_config = "openapi_azure_config.json"
# The coccurent number of requests would be 3, which is the same as the length of the configs list.
configs = [claude_config, openai_config, azure_config]
prompts = ["How\'s the weather today?", "How\'s the weather today?", "How\'s the weather today?"]
res = asyncio.run(batch_chat(configs, prompts, stream=False))
print(res)
Output detail
On July 10th, 2022, the weather in New York is expected to be sunny. The temperature will be around 30 degrees Celsius (86 degrees Fahrenheit). The air quality is expected to be good.
2. Using command line
Interactive
one-api
Non-interactive
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=?".
--system
string ${\color{grey}\text{Optional}}$ Defaults to null
System message to instruct chatGPT, e.g., You are a helpful assistant.
--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
int ${\color{grey}\text{Optional}}$ 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
int ${\color{grey}\text{Optional}}$ 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.
--save_to_file
bool ${\color{grey}\text{Optional}}$ Defaults to True
Save the prompt and response to local file at directory "~/.cache/history_cache_{date_of_month}" with the format style of shareGPT.
ToDo
- Batch requests.
- Token number counting.
- Async requests.
- Custom LLMs.
- Custom token budget.
- Using tools.
Architecture
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