Skip to content

A new package that takes unstructured text input from users and returns structured, categorized feedback or suggestions. It uses pattern matching to ensure responses follow a consistent format, such a

Notifications You must be signed in to change notification settings

chigwell/structuredsnip

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

1 Commit
 
 
 
 
 
 

Repository files navigation

StructuredSnip

PyPI version License: MIT Downloads LinkedIn

StructuredSnip is a Python package that takes unstructured text input from users and returns structured, categorized feedback or suggestions. It uses pattern matching to ensure responses follow a consistent format, such as extracting key points, summarizing content, or organizing ideas into predefined sections.

Features

  • Pattern Matching: Ensures responses follow a consistent format.
  • Customizable LLM: Uses ChatLLM7 by default but can be customized with any BaseChatModel from LangChain.
  • Flexible Input: Accepts user input text and processes it into structured output.
  • Error Handling: Provides detailed error messages for failed LLM calls.

Installation

pip install structuredsnip

Usage

Basic Usage

from structuredsnip import structuredsnip

user_input = "Your unstructured text here."
response = structuredsnip(user_input)
print(response)

Using a Custom LLM

You can use any LLM compatible with LangChain's BaseChatModel. Here are examples using different LLMs:

Using OpenAI

from langchain_openai import ChatOpenAI
from structuredsnip import structuredsnip

llm = ChatOpenAI()
response = structuredsnip(user_input, llm=llm)
print(response)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from structuredsnip import structuredsnip

llm = ChatAnthropic()
response = structuredsnip(user_input, llm=llm)
print(response)

Using Google

from langchain_google_genai import ChatGoogleGenerativeAI
from structuredsnip import structuredsnip

llm = ChatGoogleGenerativeAI()
response = structuredsnip(user_input, llm=llm)
print(response)

Using an API Key

You can pass an API key directly or via an environment variable.

Passing API Key Directly

from structuredsnip import structuredsnip

user_input = "Your unstructured text here."
api_key = "your_api_key_here"
response = structuredsnip(user_input, api_key=api_key)
print(response)

Using Environment Variable

export LLM7_API_KEY="your_api_key_here"
from structuredsnip import structuredsnip

user_input = "Your unstructured text here."
response = structuredsnip(user_input)
print(response)

Parameters

  • user_input (str): The user input text to process.
  • llm (Optional[BaseChatModel]): The LangChain LLM instance to use. Defaults to ChatLLM7.
  • api_key (Optional[str]): The API key for LLM7. If not provided, it will use the environment variable LLM7_API_KEY.

Rate Limits

The default rate limits for LLM7's free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own API key via the api_key parameter or the LLM7_API_KEY environment variable. You can get a free API key by registering at LLM7.

Issues

If you encounter any issues, please report them on the GitHub issues page.

Author

About

A new package that takes unstructured text input from users and returns structured, categorized feedback or suggestions. It uses pattern matching to ensure responses follow a consistent format, such a

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages