textstructify 2025.12.21083245

A new package designed to transform raw text inputs into structured, meaningful outputs using advanced language models. This package leverages the capabilities of llmatch-messages to ensure that the r

textstructify

textstructify is a Python package designed to transform raw text inputs into structured, meaningful outputs using advanced language models. It leverages the llmatch-messages framework to ensure responses are consistent and properly formatted. Ideal for applications requiring extraction of key points, summaries, or specific text formatting.

Installation

Install from PyPI:

pip install textstructify

Usage

Import the package and call the main function as shown:

from textstructify import textstructify

response = textstructify(
    user_input="Your raw text input here",
    api_key="your_llm7_api_key"  # optional if LLM is specified
)
print(response)

Parameters

• user_input (str): The input text string to process.
• llm (Optional[BaseChatModel]): An instance of a language model from langchain. If None, the default ChatLLM7 is used.
• api_key (Optional[str]): API key for LLM7. If not provided, will attempt to read from environment variable LLM7_API_KEY or will use the default.

Using custom LLMs

You can pass your own LLM instances compatible with langchain. For example:

from langchain_openai import ChatOpenAI
from textstructify import textstructify

llm = ChatOpenAI()
response = textstructify(user_input="Your text here", llm=llm)

Similarly, with other supported LLMs like Anthropic or Google Generative AI:

from langchain_anthropic import ChatAnthropic
from textstructify import textstructify

llm = ChatAnthropic()
response = textstructify(user_input="Your text here", llm=llm)

from langchain_google_genai import ChatGoogleGenerativeAI
from textstructify import textstructify

llm = ChatGoogleGenerativeAI()
response = textstructify(user_input="Your text here", llm=llm)

Notes

• The default rate limits for LLM7's free tier are sufficient for most use cases.
• For higher rate limits, supply an API key via environment variable LLM7_API_KEY or directly in the function call.
• Obtain a free API key at https://token.llm7.io/.

References

• llmatch-messages
• langchain
• ChatLLM7

Author

Eugene Evstafev
Email: [e-mail]
GitHub: chigwell

Issue Tracker

Report issues at: https://github.com/chigwell/textstructify/issues