hashtag-utils 0.5.6

Simple LLM-powered hashtag utilities.

Hashtag Utilities 🏷️

An LLM-powered multi-purpose hashtag utilities library written in Python.

Hashtags are used on almost every content platform today. This makes it a vital tool for creating & analyzing content. This utilities library makes it easy to do things with hashtags. Ideally, this module is intended to be used within a larger system where more specific problems are solved.

Feel free to suggest features, ideas and improvements! If you want to contribute, feel free to fork and send a Pull Request. I'm actively working on this project.

Features

• Hashtag Generator: Generates hashtags for a content piece.
• Hashtag Relevance: Returns the percentage relevance of a hashtag with respect to a content piece.
• Similar Hashtags: Generates hash tages similar to the provided ones.
• Hashtag Distance: Computes how close two hashtags are.
• Hashtag Definitions: Get hashtag definitions.

Usage: HashtagUtils API

Make sure you add your OPENAI_API_KEY to a .env file from the location where you're running the script.

from hashtag_utils import HashtagUtils

hg = HashtagUtils()

text = "A new study shows that eating chocolate can help you lose weight."

hashtags = hg.get_hashtags(text)

# check other methods below

Methods

get_hashtags(text: str, temperature: float = 1.0, num_tags: int = 5) -> List[str]

Generates a list of hashtags based on the given text.

• text: The input text to generate hashtags from.
• temperature: Controls the randomness of the hashtag generation. Lower values make the output more deterministic. Default is 1.0.
• num_tags: The number of hashtags to generate. Default is 5.

get_similar_hashtags(hashtags: List[str], temperature: float = 1.0, num_tags: int = 5) -> List[str]

Generates a list of hashtags that are similar to the given hashtags.

• hashtags: The input hashtags to find similar hashtags for.
• temperature: Controls the randomness of the hashtag generation. Lower values make the output more deterministic. Default is 1.0.
• num_tags: The number of hashtags to generate. Default is 5.

get_hashtag_definition(hashtag: str) -> str

Returns the definition of the given hashtag.

• hashtag: The hashtag to get the definition for.

get_hashtags_relevance(hashtags: List[str], text: str) -> Dict[str, float]

Returns a dictionary mapping each hashtag to its relevance to the given text.

• hashtags: The hashtags to check the relevance of.
• text: The text to check the relevance against.

get_hashtag_distance(hashtag1: str, hashtag2: str) -> float

Returns the semantic distance between two hashtags.

• hashtag1: The first hashtag.
• hashtag2: The second hashtag.

Run Examples

• Add OPENAI_API_KEY to the .env file.
• Run examples: python example.py

Contributors

• Aditya Patange (AdiPat)