I’m sharing Semantica, an MIT-licensed open-source framework for building semantic layers and knowledge engineering systems for AI.

Many RAG and agent systems fail not due to model quality, but due to the semantic gap — unstructured, inconsistent data without explicit entities, rules, or relationships. Vector-only approaches often hallucinate or fail silently under real-world data.

Semantica focuses on transforming messy data into reasoning-ready semantic knowledge.

Core capabilities: - Universal ingestion (PDF, DOCX, HTML, JSON, CSV, databases, APIs) - Automated entity and relationship extraction - Knowledge graph construction with entity resolution - Automated ontology generation and validation - GraphRAG (hybrid vector + graph retrieval, multi-hop reasoning) - Persistent semantic memory for AI agents - Conflict detection, deduplication, and provenance tracking

Project links: Docs: https://hawksight-ai.github.io/semantica/ GitHub: https://github.com/Hawksight-AI/semantica

I’d appreciate feedback from people working on knowledge graphs, GraphRAG, agent memory, or production RAG reliability.

Happy to discuss design trade-offs or answer technical questions.