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.18:T4fe,I built a CLI tool to catch slow imports and sneaky side effects before they ship to production.

The problem: Python imports can quietly become a performance trap. Your CLI takes 2 seconds to show --help because importing your package pulls in pandas. Or worse, importing a module makes network calls or writes files as a side effect.

Importguard uses Python's -X importtime flag to measure exactly how long each import takes, then lets you:

- Set time budgets: `importguard check mypkg --max-ms 200` - Ban heavy imports: `importguard check mypkg.cli --ban pandas --ban torch` - Get reliable timing: `--repeat 5` runs multiple times and reports median - Configure per-module rules in `.importguard.toml`

It runs in an isolated subprocess so it doesn't pollute your interpreter, and outputs JSON for CI integration.

Use cases: - Keep CLI startup snappy - Prevent serverless cold start regressions (Lambda/Cloud Funct

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