The Challenge: Deeper Context in RAG
Retrieval-Augmented Generation (RAG) systems depend on retrieving relevant documents to generate accurate answers. While vector embeddings are powerful, they can miss the nuanced, structural meaning embedded in language. How can we model data to capture not just *what* is said, but *how* it's said and *why*? This exploration proposes using linguistic theories—Universal Grammar and Systemic Functional Linguistics—as blueprints for database schemas in an ActiveRecord ORM, creating a richer, more context-aware retrieval layer.
Universal Grammar (UG)
UG posits that all human languages share an innate, underlying structure. By modeling these universal "rules," we can create a database that understands the fundamental grammatical roles and relationships within a sentence, independent of the specific vocabulary used.
Systemic Functional Linguistics (SFL)
SFL views language as a set of choices for making meaning in context. It analyzes language through three "metafunctions": the content (Ideational), the social relationships (Interpersonal), and the textual organization (Textual). This allows us to model the *purpose* and *function* of language.
Proposed Integrated ActiveRecord Schema
This schema integrates concepts from both UG and SFL. The UG-inspired models (`Lexeme`, `Phrase`, `DeepStructure`) capture the syntactic form of the text, while the SFL-inspired models (`ContextOfSituation`, `IdeationalFrame`, etc.) capture its communicative function. The `Clause` model serves as the central pivot, linking form to function.
The Toolchain: A Ruby Gem Pipeline
To populate these database models, a text processing pipeline would use various Ruby gems to extract linguistic features. Each gem plays a specific role in deconstructing the text into structured data suitable for the UG and SFL schemas.
(Parsing)
(Lexical Relations)
(Feature Extraction)
(Database Models)
Interactive Demo
Enter a sentence to see a simplified, simulated analysis based on the proposed integrated schema. This demonstrates how raw text could be mapped to the relational models.