Blog Post Categorisation with Embeddings & LLMs

In the realm of marketing and SEO, categorizing blog posts is a crucial yet arduous task that streamlines content organization and enhances discoverability for readers. Automation of this process can be achieved through large language models (LLMs), embeddings, and tools like LangChain, with initial steps involving the installation of open-source embeddings such as Sentence Transformers and importing essential packages including LangChain sets, transformers, pydantic, and numpy.

The categorization process necessitates setting up various tags, including "tagged articles" with text and categories, "untagged articles" with only text, and "tags" encompassing a list of category names, followed by preparing the data accordingly.

Utility functions play a pivotal role in this automation, where "get_category_embeddings" generates category embeddings using Sentence Transformers to eliminate duplicates, and "find_similar_category" employs cosine similarity to match new categories with existing ones, returning matches above a certain threshold.

Additionally, an LLM model predicts categories for untagged articles based on content, with functions like "encode_articles" and "find_similar_articles" leveraging Sentence Transformers to encode text into embeddings and find category matches, respectively. The culmination of this process through "process_untagged_articles" involves encoding tagged articles, initializing categories, and assigning relevant categories to untagged articles based on similarity, significantly streamlining content categorization.