Show HN: Semantica – Open-source semantic layer and GraphRAG framework

Semantica 2026: The Open‑Source Semantic Layer Shaping Enterprise RAG Semantica 2026: The Open‑Source Semantic Layer Shaping Enterprise RAG Published 2026‑01‑08 | Last modified 2026‑01‑08 Large language models such as GPT‑4o, Claude 3.5 Sonnet, Gemini 1.5, and the o1‑preview are now production staples. Yet data still lags: PDFs, legacy databases, and siloed APIs refuse to speak a common language. Semantica , launched in late 2025, promises to close this “semantic gap” with a unified ingestion pipeline, automated ontology generation, and built‑in quality assurance—all wrapped in a GraphRAG‑ready architecture. Executive Summary: Why Semantica Matters in 2026 Core promise: A MIT‑licensed stack that ingests over ten data formats, auto‑generates ontologies, and exposes a Cypher API for GraphRAG workflows. Competitive edge: Zero vendor lock‑in, rapid onboarding (days vs. months), and compliance‑ready QA suitable for regulated sectors. Key gaps: No performance benchmarks, limited multimodal support, sparse LLM connectors, and absent scaling guidance. Strategic move: Pilot Semantica in a low‑risk domain (e.g., internal knowledge base) to validate ingestion speed and graph quality before scaling enterprise‑wide RAG pipelines. Understanding the Semantic Gap: Why It Matters Today In 2026, LLMs still rely on retrieval mechanisms to surface context. When data is scattered across PDFs, spreadsheets, APIs, and legacy databases, you feed a bag of words with no connective tissue—leading to hallucinations, compliance risks, and sub‑optimal answers. Semantica’s thesis is to build a semantic layer : an abstraction that transforms raw data into a graph of entities, relationships, and attributes. This enables consistent schema discovery, conflict detection, and graph‑based retrieval for GraphRAG engines. Three‑Layer Architecture: From Raw Input to Knowledge Graph The stack’s architecture—Input → Semantic → Knowledge—mirrors ETL but adds semantic enrichment. Key layers: Input Layer: Auto‑