Building a cognitive search solution for searching in courses databases and articles within the organization.
- Users can search the course data by searchable description.
- Users can search the course data for course rating and duration.
- Users can facet course data by source, skills, and role.
- Users can search the curated library for papers by searchable content (document cracking using OCR).
- Users can search the curated library for papers by authors' name, institution name, publication name, publisher, and publication date.
- Users should be able to search all text fields for keywords.
- Users should be able to filter on course ratings, duration, title, type, source, level, technology, rating, instructor, or role.
- Users should be able to filter and facet on source, skills, or role.
- Users should be able to sort by relevant fields, such as title, type, technology, role.
- Users should be able to search and filter by keywords and phrases, authors, and institutions based on the contents of a PDF as inferred from an AI enrichment of the Document Extraction.
- Users can search the course data by keywords and phrases from descriptions and other fields.
- Users can filter course data results based on relevant criteria such as course ratings or duration.
- Users can see results from searches of course data for the course instructors for their own internal training.
- Users can facet course data by level, product, or role.
- Users can sort course data search results by relevant fields.
- Users can search the curated library for papers by keywords and phrases from the Journal name, authors, and their associated institutions.
- Users can search the curated library for papers by publication name, publisher, and publication date.
- Users can see results from searches of the curated library for papers with DOI (Digital Object Identifier) URL for the file.
Both data sources are stored in a blob storage. The course data is stored as tabulated data while the pdf articles are stored in a container blob.