## Summary There is a new module, [AI Knowledge Connector](https://www.drupal.org/project/ai_knowledge_connector) that we should assess to see how it might work with CCC. From the project page: ### Overview 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. 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. This module leverages that structure to feed AI systems with high-quality, organized information. ### 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. ### 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 ### Key Features Structured Knowledge Extraction Convert Drupal entities into AI-ready documents while preserving context and relationships. The module respects entity structures, field relationships, and taxonomy hierarchies. #### Provider-Agnostic Design Compatible with any AI provider supported by the Drupal AI module, including local (Ollama) and cloud-based (OpenAI, Anthropic, Mistral) services. Also works with any vector database provider (Milvus, Pinecone, Qdrant, etc.). #### Incremental Indexing Process only entities that have changed, minimizing resource consumption. The module uses content hashing to detect changes and only re-indexes when necessary. #### Queue-Based Processing Built using Drupal Queue API for scalability and reliability, ensuring large sites can index thousands of entities without performance degradation. #### Extensible Plugin Architecture Add new knowledge sources, retrievers, and integrations without modifying core functionality. Developers can create custom plugins for specific entity types or data sources. #### AI Search Tracker Integration The AI Search tracker ensures Search API can generate vector embeddings at scale, providing efficient tracking of indexed content. #### 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 ## Tasks - [x] Review the module's code - [x] Assess how it might work with CCC ## AI usage - [x] AI assisted issue - [x] AI generated review