{"repo":"DerwenAI/strwythura","free":true,"listed":false,"github":"https://github.com/DerwenAI/strwythura","clone":"git clone https://github.com/DerwenAI/strwythura.git","description":"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.","language":"Python","stars":231,"topics":["textgraphs","unstructured-data","vector-database","entity-linking","entity-resolution","machine-learning","named-entity-recognition","semantic-expansion","graphrag","ontology"],"license":"MIT","category":"machine-learning","readme_excerpt":"Strwythura: Put the Context in Context Engineering This tutorial explains how to 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. The process gets enriched by entity embeddings and graph algorithms used in an enhanced GraphRAG approach, which implements a question/answer chat bot about a particular domain. The material here provides hands-on experience with advanced techniques as well as working code you can use elsewhere. An article on Medium.com plus some other resources available online serve as \"companions\" for working with this repo. Please read along while running through each of the steps in this tutorial: purpose URL ------- --- words tubes slides codes quiz wiki DOI Overview What this tutorial includes and how to use it.   Downstream there can be multiple patterns of usage, such as graph analytics, dashboards, GraphRAG for question/answer chat bots, agents, memory, tools, planners, and so on. We will emphasize how to curate and leverage the domain-specific semantics , and optimize for the downstream AI application outcomes. Think of this as an interactive exploration of neurosymbolic AI in practice. Code in the tutorial shows how to integrate popular Python libraries following a maxim that OSFA and relying on monolithic frameworks doesn't work well; using composable SDKs works mu","default_branch":null,"files":null,"tree":[],"storefront":"/r/DerwenAI","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/DerwenAI/strwythura/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}