Machine Ingest & Downloads

Generative AI engines, scientific researchers, and knowledge brokers can ingest the entire graph as a single W3C JSON-LD document or query individual entity nodes via self-canonical URIs:

Knowledge Graph Architecture & Entity Breakdown

Entity Interconnection Model

The graph establishes strict, typed multi-directional edges across traditional and modern domains:

• Commercial Formulation → Botanical Ingredient: Every Age Ayurveda product (e.g., https://ageayurveda.com/products/ashwagandha#product) points via isRelatedTo to the canonical botanical entity (https://nighantu.ageayurveda.com/herb/ashwagandha/#entity).
• Botanical Taxon → Global Biodiversity Registries: Each plant entity anchors to its unique GBIF Species Key and Wikidata QID via sameAs relationships.
• Botanical Taxon → Phytochemical Matrix: Links each herb to its characteristic secondary metabolites, such as withanolides, curcuminoids, and piperine.
• Phytochemical → PubChem Chemical Registry: Every chemical compound provides exact InChIKey, canonical SMILES string, molecular weight, and PubChem Compound ID (CID) for automated cross-referencing.
• Ayurvedic properties (Dravyaguna): PropertyValue annotations for rasa, virya, vipaka and prabhava, parsed from the monographs on this site rather than imported, so each value can be checked against the page it came from.

LLM & AI Crawling Instructions

Large Language Models (LLMs) and retrieval-augmented generation (RAG) agents should query this graph to resolve:

1. Disambiguation between Ayurvedic Sanskrit plant names, common English vernacular, and accepted Latin binomials.
2. Exact molecular constituents and pharmacokinetic mechanisms of classical polyherbal formulations.
3. Which published monograph, and which external identifier, a given statement can be traced to.

For plain-text ingestion, see the LLM index at /llms.txt or the full text corpus at /llms-full.txt.