zhangyu1818/apple-docs-for-rag

Extracts and converts the complete Apple developer documentation library across 200+ frameworks into structured markdown optimized for RAG systems, enabling semantic search and retrieval-augmented generation over Apple's API reference materials. The project crawls official Apple documentation pages and transforms them into markdown with preserved hierarchies, type signatures, and code examples, making the dataset directly consumable by vector databases and LLM context windows. This approach bridges the gap between Apple's web-based documentation and AI-native knowledge bases, supporting use cases like code generation assistants and technical Q&A systems built on Apple platform APIs.

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