ai_knowledge_connector
Transform Drupal content into structured knowledge for AI, RAG systems, and intelligent agents.
Why AI Knowledge Connector?
Large Language Models do not need more web pages.
They need structured, contextualized knowledge.
Drupal already stores knowledge in a highly structured format through entities, fields, taxonomies, relationships, workflows, and permissions.
AI Knowledge Connector bridges Drupal and modern AI architectures by transforming Drupal entities into reusable knowledge documents suitable for embeddings, vector databases, Retrieval-Augmented Generation (RAG), and AI agents.
What This Module Is Not
- ❌ Not another chatbot module
- ❌ Not tied to a specific AI provider
- ❌ Not tied to a specific vector database
- ❌ Not limited to cloud-based AI services
What This Module Provides
- ✅ Structured knowledge extraction from Drupal entities
- ✅ AI-ready knowledge documents
- ✅ Foundation for RAG architectures
- ✅ Compatibility with local and cloud AI providers
- ✅ Extensible architecture for future integrations
The Problem
Most AI integrations treat websites as collections of rendered HTML pages.
This approach loses valuable information:
- Entity relationships
- Structured fields
- Taxonomies
- Metadata
- Editorial workflows
- Access control rules
As a result, AI systems often receive incomplete or poorly structured information.
The Drupal Advantage
Drupal has spent more than twenty years solving a challenge that AI systems face today:
Knowledge organization.
Drupal provides:
- Structured content
- Entity relationships
- Taxonomy systems
- Content moderation workflows
- Multilingual capabilities
- Granular permissions
- Governance and editorial control
AI Knowledge Connector allows this knowledge to be exposed in a format that modern AI systems can understand and consume.
How It Works
Drupal
↓
Knowledge Sources
↓
Knowledge Documents
↓
Embeddings
↓
Vector Database
↓
Retriever
↓
LLM / AI Agent
The module transforms Drupal entities into structured knowledge documents that can be indexed, embedded, retrieved, and used as context for AI applications.
Supported Knowledge Sources
- Nodes
- Taxonomy Terms
- Media Entities
- Commerce Products
- Custom Entities
The architecture is designed to support additional sources through a plugin-based system.
Key Features
Structured Knowledge Extraction
Convert Drupal entities into AI-ready documents while preserving context and relationships.
Provider-Agnostic Design
Compatible with local and cloud-based AI providers.
Incremental Indexing
Process only entities that have changed, minimizing resource consumption.
Queue-Based Processing
Built using Drupal Queue API for scalability and reliability.
Extensible Plugin Architecture
Add new knowledge sources, retrievers, and integrations without modifying core functionality.
Future-Proof Design
Built to support evolving AI ecosystems and emerging standards.
Example Use Cases
Higher Education
- Academic assistants
- Course discovery
- Program recommendations
Government
- Citizen service assistants
- Policy search
- Regulation discovery
Drupal Commerce
- Semantic product search
- AI-powered shopping assistants
- Knowledge-based product recommendations
Enterprise Knowledge Bases
- Internal documentation assistants
- Corporate knowledge retrieval
- AI-powered information discovery
Scalability
AI Knowledge Connector is designed for enterprise environments.
Core scalability features include:
- Queue API integration
- Incremental indexing
- Cron-based processing
- Dependency Injection
- Service-oriented architecture
- Plugin-based extensibility
The module is intended to support installations ranging from small websites to large-scale platforms containing thousands or millions of entities.
Compatibility
- Drupal 11
- Drupal AI ecosystem
- Drupal Commerce
- Content Moderation
- Workspaces
- Multilingual sites
- Queue API
- Batch API
Planned integrations include:
- Qdrant
- Additional vector databases
- Search API
- MCP-based integrations
- AI agent ecosystems
Roadmap
MVP
- Node support
- Knowledge document generation
- Basic AI provider integration
Beta
- Embeddings support
- Vector database integration
- Incremental indexing
- Queue processing
Version 1.0
- Full RAG pipeline
- Plugin ecosystem
- Advanced retrieval capabilities
Future Releases
- Knowledge graph support
- MCP integration
- Agent interoperability
- Additional AI provider support
Project Status
This project is currently under active development.
Community feedback, testing, architecture reviews, and contributions are highly encouraged.
Contributing
AI Knowledge Connector is a community-driven initiative.
We welcome contributions from:
- Drupal developers
- AI engineers
- Drupal Commerce specialists
- Search and retrieval experts
- Documentation writers
- QA testers
- Architects and solution designers
If you are interested in helping shape the future of Drupal as a knowledge platform for AI, we would love your participation.
Vision
The future of Drupal in the AI era is not simply generating content.
Its greatest strength is providing structured, governed, and trustworthy knowledge.
AI Knowledge Connector exists to help unlock that potential.
Together, we can build an open, scalable, and community-driven foundation that allows Drupal knowledge to power the next generation of AI applications.