DerwenAI/strwythura
Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.
Leverages composable open-source SDKs (Senzing for entity resolution, spaCy/GLiNER for NER, LanceDB for vector storage, NetworkX for graph algorithms) rather than monolithic frameworks, enabling air-gapped local execution with optional remote LLM services. Implements a neurosymbolic AI pipeline that unbundles context engineering into discrete stages—entity resolution, ontology mapping, entity linking, and spectral indexing—with human-in-the-loop feedback integrated via Opik for MLOps observability and iterative optimization. Produces a GraphRAG-enhanced QA chatbot packaged as a Streamlit app with declarative LLM integration (DSPy) and interactive graph visualization.
214 stars. Available on PyPI.
Stars
214
Forks
23
Language
Python
License
MIT
Category
Last pushed
Feb 03, 2026
Commits (30d)
0
Dependencies
37
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