text2structured 2025.12.20175536

A new package designed to transform unstructured text inputs into structured, actionable insights using advanced language models. This package leverages the capabilities of llmatch-messages to ensure

text2structured

Transform unstructured text inputs into structured, actionable insights using advanced language models.

Overview

This package is designed to leverage the capabilities of langchain's messages to transform text inputs into structured, actionable insights. It uses the ChatLLM7 model from langchain_llm7 by default, but allows users to pass their own langchain llm instance for custom LLM usage.

Installation

pip install text2structured

Usage

from text2structured import text2structured

user_input = "How Hurricanes Became a Hot Investment"
response = text2structured(user_input)

Parameters

• user_input: the user input text to process (str)
• llm: the langchain llm instance to use, if not provided the default ChatLLM7 will be used (Optional[BaseChatModel])
• api_key: the api key for llm7, if not provided the default rate limits for LLM7 free tier will be used (Optional[str])

Custom LLM usage

You can safely pass your own langchain llm instance to use a custom LLM. For example to use the openai model:

from langchain_openai import ChatOpenAI
from text2structured import text2structured

llm = ChatOpenAI()
response = text2structured(user_input, llm=llm)

Similarly you can use anthropic or google models with their respective packages:

# with anthropic model
from langchain_anthropic import ChatAnthropic
from text2structured import text2structured

llm = ChatAnthropic()
response = text2structured(user_input, llm=llm)

# with google model
from langchain_google_genai import ChatGoogleGenerativeAI
from text2structured import text2structured

llm = ChatGoogleGenerativeAI()
response = text2structured(user_input, llm=llm)

Default rate limits

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits you can get a free api key by registering at https://token.llm7.io/. You can pass your api key via environment variable LLM7_API_KEY or directly to the text2structured function:

text2structured(user_input, api_key="your_api_key")

Documentation

For more information about the(ChatLLM7) model and its API, please refer to the langchain_llm7 documentation and the langchain documentation.

GitHub Issues

Open issues on GitHub.

Author

Eugene Evstafev ([e-mail]).

Social media

License