Semi-structured Datasource

A semi-structured datasource organizes information without a rigid schema, using tags, metadata, or loosely defined fields to allow flexible handling, categorization, and filtering of diverse data types. This structure is ideal for data that doesn’t fit neatly into tables but still benefits from some organization, enabling a more adaptable approach to data management and querying.

Example and Use Case

Imagine you have a product catalog for a range of cosmetics that includes data fields such as product name, price, ingredients, tags (like "organic" or "fragrance-free"), and conditions for usage (e.g., "sensitive skin," "oily skin"). This catalog can be set up as a semi-structured datasource within LLMate, where each product entry contains flexible fields and tags without a fixed schema.

In practice, this setup allows for dynamic, tag-based querying and filtering, making it perfect for applications like a customer service chatbot. For example, when a customer asks for recommendations for "organic" products suitable for "sensitive skin," the chatbot can query the datasource based on tags and conditions to display relevant products from the catalog. This semi-structured approach supports tailored, responsive data retrieval that adapts to various customer needs, enhancing user experience and simplifying data management.