HandyLLM 0.5.2

A handy toolkit for using LLM.

HandyLLM

A handy toolkit for using LLM.

Install

pip3 install handyllm

or, install from the Github repo to get latest updates:

pip3 install git+https://github.com/atomiechen/handyllm.git

Examples

Example scripts are placed in tests folder.

OpenAI API Request

Endpoints

Each API request will connect to an endpoint along with some API configurations, which include: api_key, organization, api_base, api_type, api_version and model_engine_map.

An Endpoint object contains these information. An EndpointManager acts like a list and can be used to rotate the next endpoint. See test_endpoint.py.

There are 5 methods for specifying endpoint info:

1. (each API call) Pass these fields as keyword parameters.
2. (each API call) Pass an endpoint keyword parameter to specify an Endpoint.
3. (each API call) Pass an endpoint_manager keyword parameter to specify an EndpointManager.
4. (global) Set class variables: OpenAIAPI.api_base, OpenAIAPI.api_key, OpenAIAPI.organization, OpenAIAPI.api_type, OpenAIAPI.api_version, OpenAIAPI.model_engine_map.
5. (global) Set environment variables: OPENAI_API_KEY, OPENAI_ORGANIZATION, OPENAI_API_BASE, OPENAI_API_TYPE, OPENAI_API_VERSION, MODEL_ENGINE_MAP.

Note: If a field is set to None in the previous method, it will be replaced by the non-None value in the subsequent method, until a default value is used (OpenAI's endpoint information).

Azure OpenAI APIs are supported: Specify api_type='azure', and set api_base and api_key accordingly. Set model_engine_map if you want to use model parameter instead of engine/deployment_id. See test_azure.py. Please refer to Azure OpenAI Service Documentation for details.

Logger

You can pass custom logger and log_marks (a string or a collection of strings) to chat/completions to get input and output logging.

Timeout control

This toolkit supports client-side timeout control:

from handyllm import OpenAIAPI
prompt = [{
    "role": "user",
    "content": "please tell me a joke"
    }]
response = OpenAIAPI.chat(
    model="gpt-3.5-turbo",
    messages=prompt,
    timeout=10
    )
print(response['choices'][0]['message']['content'])

Stream response

Stream response of chat/completions/finetunes_list_events can be achieved using steam parameter:

response = OpenAIAPI.chat(
    model="gpt-3.5-turbo",
    messages=prompt,
    timeout=10,
    stream=True
    )

# you can use this to stream the response text
for text in OpenAIAPI.stream_chat(response):
    print(text, end='')

# or you can use this to get the whole response
# for chunk in response:
#     if 'content' in chunk['choices'][0]['delta']:
#         print(chunk['choices'][0]['delta']['content'], end='')

Supported APIs

• chat
• completions
• edits
• embeddings
• models_list
• models_retrieve
• moderations
• images_generations
• images_edits
• images_variations
• audio_transcriptions
• audtio_translations
• files_list
• files_upload
• files_delete
• files_retrieve
• files_retrieve_content
• finetunes_create
• finetunes_list
• finetunes_retrieve
• finetunes_cancel
• finetunes_list_events
• finetunes_delete_model

Please refer to OpenAI official API reference for details.

Prompt

Prompt Conversion

PromptConverter can convert this text file prompt.txt into a structured prompt for chat API calls:

$system$
You are a helpful assistant.

$user$
Please help me merge the following two JSON documents into one.

$assistant$
Sure, please give me the two JSON documents.

$user$
{
    "item1": "It is really a good day."
}
{
    "item2": "Indeed."
}
%output_format%
%misc1%
%misc2%

from handyllm import PromptConverter
converter = PromptConverter()

# chat can be used as the message parameter for OpenAI API
chat = converter.rawfile2chat('prompt.txt')

# variables wrapped in %s can be replaced at runtime
new_chat = converter.chat_replace_variables(
    chat, 
    {
        r'%misc1%': 'Note1: do not use any bad word.',
        r'%misc2%': 'Note2: be optimistic.',
    }
)

Substitute

PromptConverter can also substitute placeholder variables like %output_format% stored in text files to make multiple prompts modular. A substitute map substitute.txt looks like this:

%output_format%
Please output a SINGLE JSON object that contains all items from the two input JSON objects.

%variable1%
Placeholder text.

%variable2%
Placeholder text.

from handyllm import PromptConverter
converter = PromptConverter()
converter.read_substitute_content('substitute.txt')  # read substitute map
chat = converter.rawfile2chat('prompt.txt')  # variables are substituted already