Wordpress articles recommender app using Python

An application to recommend articles from a Wordpress blog based on user's query using semantic search

Pre-requisites:

• Adjust the below environment variables before running any of the below steps if required:
  • DATA_LOC: Location where all the application data will be stored. By default, it is stored in a data folder in the current directory. This folder will be created if it does not exist during creation.
  • HuggingFace key (free) to use their inference endpoints to generate embeddings using Sentence Transformer models. Export the key to an environment variable called HF_KEY before creating or querying the index as shown in the steps below.
  • Override the default HuggingFace embeddings model by specifying the name in the environment variable HF_EMBEDDINGS_MODEL_NAME. If not provided, the sentence-transformers/all-MiniLM-l6-v2 model is used to generate embeddings.

How to use the CLI after installing the package from PyPI:

1. Download content: wordpress-recommender download-content "https://learnwoo.com/post-sitemap1.xml"
2. Build index using the downloaded content: wordpress-recommender generate-index "https://learnwoo.com/post-sitemap1.xml"
3. Query index built in previous step: wordpress-recommender query-index "https://learnwoo.com/post-sitemap1.xml" --query "How are the AI regulations different in different parts of the world?"

Release notes:

0.1.1

• Initial release

0.1.2

• Allow client to override the default HuggingFace inference API embeddings model via the environment variable HF_EMBEDDINGS_MODEL_NAME
• Allow client to rebuild index by providing the option --rebuild_index in the CLI
• Breaking change Return final output as a sorted pandas dataframe with all original columns present along with a new column for the score e.g. cosine_distance or cosine_similarity
• Allow client to override the number of results returned by passing an optional --top parameter with an integer value

0.1.3

• Minor updates to docs
• Add tests
• Add GitHub Actions to publish package