lego-museum-structurizer

Simple text-based interface for turning free-form imagination about a secret Lego museum into machine-friendly summary

Overview

PACKAGE gives users a simple way to convert their free-form imagination about a secret Lego museum into a machine-friendly summary

A user writes a brief description of the scene or exhibit, and the PACKAGE orchestrates an LLM to produce a concise, consistently formatted response

Installation

pip install lego_museum_structurizer

Usage

from lego_museum_structurizer import lego_museum_structurizer

response = lego_museum_structurizer(user_input)

so-called "user_input" can be a string, e.g. any of the provided examples (file examples/example-001-Year-Round-Legos-museum.txt, file examples/example-002-Vintage-Spaceship-Carefully-then-down.php etc.)

Parameters

• user_input: the user input text to process
• llm: The langchain llm instance to use
• api_key: the api key for llm7, if not provided the PACKAGE uses default ChatLLM7 from https://pypi.org/project/langchain_llm7/ by default

Examples using different llm instances

You can safely pass your own llm instance (based on https://docs.langchain.com/) if you want to use another LLM via passing it like lego_museum_structurizer(user_input, llm=their_llm_instance), for example to use the openai https://docs.langchain.com/python/openai/ChatOpenAI,

from langchain_openai import ChatOpenAI
from lego_museum_structurizer import lego_museum_structurizer
llm = ChatOpenAI()
response = lego_museum_structurizer(user_input, llm=llm)

or for example to use the anthropic https://docs.langchain.com/python/anthropic/ChatAnthropic,

from langchain_anthropic import ChatAnthropic
from lego_museum_structurizer import lego_museum_structurizer
llm = ChatAnthropic()
response = lego_museum_structurizer(user_input, llm=llm)

or google https://docs.langchain.com/python/google/ChatGoogleGenerativeAI,

from langchain_google_genai import ChatGoogleGenerativeAI
from lego_museum_structurizer import lego_museum_structurizer
llm = ChatGoogleGenerativeAI()
response = lego_museum_structurizer(user_input, llm=llm)

The default rate limits for LLM7 free tier are sufficient for most use cases of this PACKAGE. If you want higher rate limits for LLM7 you can pass your own api_key via environment variable LLM7_API_KEY or via passing it directly like lego_museum_structurizer(user_input, api_key="their_api_key").

The author of this PACKAGE can get a free api key by registering at https://token.llm7.io/

GitHub Issues

GitHub issues located here https://github.com/chigwell/lego-museum-structurizer/issues

Author

Eugene Evstafev

License

This is free software under the MIT license.

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