SCORE Decoupled Drupal
SCORE Drupal Content RAG
An advanced, editor-controlled QA generation and vector embedding interface bridging Drupal CMS content with Agentic AI Flows and Vector Databases.
SCORE Drupal Content RAG integrates the CMS directly with AI-driven retrieval and vector processing pipelines. Built to connect Drupal content nodes with Agentic AI Flows and Vector Databases, STD.DEV enables editors to trigger synthetic QA generation, manage high-dimensional vector embeddings, and dynamically update prompts directly from the administration dashboard.
Dynamic Prompt Overrides
Providing full control over AI behavior, the Admin interface enables editors to write and edit prompt templates directly within the CMS. Updates are instantly pushed via webhook APIs to the Agentic AI service, bypassing code redeployments and allowing real-time optimization of AI generation logic.
Synthetic QA Generation
High-quality training and retrieval data is built automatically. With a single click, text extraction is triggered from any Drupal node, sending it through Agentic AI Flows to generate structured QA pairs. Each pair is classified by intent (mapped to the marketing funnel) and difficulty grade, and contains the exact ground truth text used to evaluate the quality of chatbot answers.
Vector Embedding Management
High-dimensional embeddings are recalculated with ease. The administration dashboard provides direct operations to recalculate vector embeddings for the entire database. Generated vectors are pushed to downstream services and mapped directly back to Drupal's companion QA files, keeping the search index perfectly in sync.
3D Space Visualization
The semantic topology of content can be visualized by triggering spatial projection calculations directly from the administration dashboard. The system streams the resulting 3D coordinate data in real time, projecting complex, high-dimensional vector spaces into an interactive visual model for index health verification.
KNN Similarity & Graph Sync
Optimizing search retrieval routes, administrators can trigger K-Nearest Neighbors (KNN) similarity processes to automatically establish content associations. A dedicated graph sync operation pushes node hierarchies and semantic connections straight to the Vector Databases, establishing robust retrieval routes.
Intent & Funnel Mapping
User questions are aligned with marketing strategies. The dashboard includes a dedicated subtab to configure RAG User Intents. Editors can manage and edit intent schemas and map them directly to standard Agentic AI intent schemas, ensuring chatbot queries are directed appropriately based on the user's funnel stage.
SCORE Drupal Content RAG provides editors with unprecedented control over agentic AI assets, enabling seamless dynamic prompt overrides, automatic QA generation, and dashboard-level management of high-dimensional vector database indexing.
How does the SCORE Drupal Content RAG module allow for real-time optimization of AI generation logic?
The Admin interface enables editors to write and edit prompt templates directly within the CMS, and these updates are instantly pushed via webhook APIs to the Agentic AI service, bypassing code redeployments and allowing real-time optimization of AI generation logic.
What is the purpose of the synthetic QA generation feature in the SCORE Drupal Content RAG module?
How does the SCORE Drupal Content RAG module ensure the search index remains synchronized with the vector database?
What functionality does the 3D Space Visualization feature offer within the SCORE Drupal Content RAG module?
How does the SCORE Drupal Content RAG module help align user questions with marketing strategies?
What is the business value of the dynamic prompt overrides and synthetic QA generation features?
How does the module facilitate the management of high-dimensional vector embeddings?
What is the process for establishing content associations and robust retrieval routes within the Vector Databases?
How does the SCORE Drupal Content RAG module integrate Drupal content with AI capabilities?
What is the philosophy behind the automatic generation of structured QA pairs?