ai-microcore 0.4.1

Minimalistic core for large language model applications

AI MicroCore: A Minimalistic Foundation for AI Applications

This package is a collection of wrappers around Large Language Models and Semantic Search APIs allowing to communicate with these services convenient way, make it easily switchable and separate business logic from implementation details.

It defines interfaces for features typically used in AI applications, that allows you to keep your application as simple as possible and try various models & services without need to change your application code.

You even can switch between text completion and chat completion models only using configuration.

The basic example of usage is as follows:

from microcore import llm
while user_msg := input('Enter message: '):
    print('AI: ' + llm(user_msg))

Installation

pip install ai-microcore

Core Functions

llm(prompt: str, **kwargs) → str

Performs a request to a large language model (LLM)

from microcore import *

# Will print all requests and responses to console
use_logging()

# Basic usage
ai_response = llm('What is your model name?')

# You also may pass a list of strings as prompt
# - For chat completion models elements are treated as separate messages
# - For completion LLMs elements are treated as text lines
llm(['1+2', '='])
llm('1+2=', model='gpt-4')

# To specify a message role, you can use dictionary or classes
llm(dict(role='system', content='1+2='))
# equivalent
llm(SysMsg('1+2='))

# The returned value is a string
assert '7' == llm([
 SysMsg('You are a calculator'),
 UserMsg('1+2='),
 AssistantMsg('3'),
 UserMsg('3+4=')]
).strip()

# But it contains all fields of the LLM response in additional attributes
for i in llm('1+2=?', n=3, temperature=2).choices:
    print('RESPONSE:', i.message.content)

# To use response streaming you may specify the callback function:
llm('Hi there', callback=lambda x: print(x, end=''))

# Or multiple callbacks:
output = []
llm('Hi there', callbacks=[
    lambda x: print(x, end=''),
    lambda x: output.append(x),
])

tpl(file_path, **params) → str

Renders prompt template with params.

Full-featured Jinja2 templates are used by default.

Related configuration options:

from microcore import configure
configure(
    # 'tpl' folder in current working directory by default
    PROMPT_TEMPLATES_PATH = 'my_templates_folder'
)

*store(collection: str | None, **kwargs)

Stores data in embeddings database of your choice

@TODO

*search(collection: str | None, **kwargs)

Performs semantic / similarity search over embeddings database

@TODO

API providers and models support

LLM Microcore supports all models & API providers having OpenAI API.

List of API providers and models tested with LLM Microcore:

API Provider	Models
OpenAI	All GPT-4 and GTP-3.5-Turbo models all text completion models (davinci, gpt-3.5-turbo-instruct, etc)
Microsoft Azure	All OpenAI models
deepinfra.com	deepinfra/airoboros-70b jondurbin/airoboros-l2-70b-gpt4-1.4.1 meta-llama/Llama-2-70b-chat-hf and other models having OpenAI API
Anyscale	meta-llama/Llama-2-70b-chat-hf meta-llama/Llama-2-13b-chat-hf meta-llama/Llama-7b-chat-hf

Examples

code-review-tool example

Performs code review by LLM for changes in git .patch files in any programming languages.

Other examples

Python functions as AI tools

@TODO

AI Modules

This is experimental feature.

Tweaks the Python import system to provide automatic setup of MicroCore environment based on metadata in module docstrings.

Usage:

import microcore.ai_modules

Features:

• Automatically registers template folders of AI modules in Jinja2 environment

License

© 2023—∞ Vitalii Stepanenko

Licensed under the MIT License.