Semantica — Graph-Native Context and Provenance for AI Systems

Introduction

Semantica is a Python-based infrastructure platform for context engineering and accountable AI systems. It uses knowledge graphs and ontologies to track data provenance, enable semantic search, and provide explainable AI decision chains. Built for teams that need to understand how their AI systems reach conclusions.

What Semantica Does

• Builds and maintains knowledge graphs from ingested documents and data
• Tracks provenance chains for AI-generated outputs
• Enables semantic search across structured and unstructured data
• Provides context graphs for LLM-based reasoning pipelines
• Supports AI governance through decision traceability

Architecture Overview

Semantica is built around a graph-native data model that connects entities, relationships, and provenance metadata. The Python core handles document ingestion, entity extraction, and graph construction. Query interfaces support both semantic similarity search and structured graph traversal, enabling both RAG and explainability use cases.

Self-Hosting & Configuration

• Install via pip and initialize a local project
• Configure data sources for document ingestion
• Set up graph storage backend for persistence
• Customize ontology definitions for domain-specific use cases
• Integrate with LLM providers for semantic enrichment

Key Features

• Graph-native data model for rich relationship tracking
• Built-in provenance and decision chain tracing
• Semantic search with knowledge graph context
• Support for AI governance and explainability requirements
• Python-native with straightforward API

Comparison with Similar Tools

• LangChain — general LLM application framework; Semantica focuses on context graphs and provenance
• LlamaIndex — data indexing for LLMs; Semantica emphasizes knowledge graphs and accountability
• GraphRAG — Microsoft's graph-enhanced RAG; Semantica adds provenance tracking and governance
• Neo4j — general graph database; Semantica is purpose-built for AI context engineering

FAQ

Q: What is context engineering? A: Context engineering is the practice of structuring and managing the information provided to AI systems to improve their reasoning and accountability.

Q: Does Semantica require a separate graph database? A: Semantica includes built-in graph storage but can also connect to external graph databases.

Q: Can Semantica integrate with existing RAG pipelines? A: Yes, Semantica can serve as the context layer for RAG systems, providing graph-enriched retrieval.

Q: What kind of provenance does Semantica track? A: Semantica tracks data lineage, transformation steps, and decision chains through its graph-native model.

Sources

• https://github.com/semantica-agi/semantica