TextForgePy

Transform unstructured text inputs into standardized, machine-readable outputs using natural language processing (NLP) and reinforcement learning.

Overview

TextForgePy is a Python package designed to convert free-form text inputs into structured data, perfect for domains where consistency and formatting are crucial. By leveraging LLM7, it reduces ambiguity and enhances reliability.

Installation

pip install textforgepy

Example Usage

from textforgepy import textforgepy

response = textforgepy(user_input="Your user input text here")
print(response)  # response is a list of processed strings

Input Parameters

• user_input: The user input text to process (string)
• llm: The langchain LLM instance to use; defaults to ChatLLM7 from langchain_llm7 (optional)
• api_key: The API key for LLM7; if not provided, uses the LLM7_API_KEY environment variable or defaults to "None" (optional)

Note: You can safely pass your own LLM instance by using a different langchain library, e.g.:

from langchain_openai import ChatOpenAI
from textforgepy import textforgepy

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

or:

from langchain_anthropic import ChatAnthropic
from textforgepy import textforgepy

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

or even:

from langchain_google_genai import ChatGoogleGenerativeAI
from textforgepy import textforgepy

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

Rate Limits

The default rate limits for LLM7's free tier are sufficient for most use cases of TextForgePy. If you need higher rate limits, you can pass your own API key via environment variable LLM7_API_KEY or directly like textforgepy(user_input, api_key="your_api_key").

Get a free API key at https://token.llm7.io/

Contributing

Please report issues at https://github.com/chigwell/textforgepy

Author: Eugene Evstafev Email: hi@euegne.plus GitHub: https://github.com/chigwell

License

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