MicroCore: A Minimalistic Foundation for Large Language Model Applications

This package is rooted in a design philosophy that promotes following guiding principles:

• Keeping it simple
• Keep implementation details away from business logic
• Emphasis on working conveniently with complex prompt templates
• Laconic code

By adhering to these principles, MicroCore aims to deliver a range of benefits that address the specific needs of the rapidly evolving field of AI application development:

• Rapid prototyping / RnD / experimentation
• Smooth sub-systems interchangeability without need of massive codebase changes
• Code designed to be well accepted by AI agents with introspective code analysis and generation
• Clean and easy-to-follow examples to aid learning and development

Architectural approaches

• Python functions as tools for AI agents
• Python packages as AI Modules
• Templating with inheritance and blocks for prompt composition
• Business-logic layers with shared interfaces and smoothly interchangeable implementations:
  • LLM (OpenAI, BLOOM, Koala, etc)
  • Embedding databases (Chroma, Pinecone, Milvus, etc)
  • Communication (HTTP, WebSockets, Console, etc)

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
llm(dict(role='user', content='1+2='))

# Or use specific classes
from microcore.openai_chat import UserMsg, SysMsg

llm(SysMsg('1+2='))

# The returned value is a string
out = llm([SysMsg('1+2=3'), UserMsg('3+4=')]).upper()

# 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.