Search Arabic religious texts by meaning using AraGemma embeddings.
npx skills add https://github.com/Moshe-ship/hurmoz --skill arabic-rag-moshe-ship
Please help me install this Agent Skill. Skill: arabic-rag Source: https://github.com/Moshe-ship/hurmoz/tree/main/arabic-rag Command: npx skills add https://github.com/Moshe-ship/hurmoz --skill arabic-rag-moshe-ship
This skill enables meaning-based search across Quran verses, hadith collections, and Arabic texts, allowing users to retrieve relevant content even when exact words differ, by leveraging AraGemma embeddings.
Run a semantic search to find Arabic content by meaning across Quran, hadith, and Arabic documents using AraGemma embeddings.
High-intent search queries and answers about installing and using this skill.
Semantic search across Quran and hadith texts uses AraGemma embeddings to match meaning rather than exact keywords, retrieving relevant verses and passages even when query words differ. The skill builds a semantic index for fast retrieval across religious texts and user documents.
Finding Arabic text by meaning requires embedding-based retrieval using AraGemma embeddings, which captures semantic similarity to surface related content across Quran, hadith, and Arabic documents without relying on exact keyword matches.
Yes, Arabic semantic search requires Python and the AraGemma embedding model. The skill loads the model and builds a semantic index to enable fast meaning-based retrieval across Quran, hadith, and Arabic documents.
Semantic search can find related verses and hadiths about topics like patience by leveraging AraGemma embeddings to surface content based on semantic similarity across Quran, hadith, and Arabic documents, even when exact words differ.
Embedding-based retrieval supports cross-text queries across Quran, hadith, and user-provided Arabic documents. The skill uses AraGemma embeddings to build a semantic index, enabling meaning-based search across mixed religious and custom document collections.
Semantic search for Arabic religious texts relies on AraGemma embeddings and requires Python to load the model and build an index. Retrieval depends on embedding quality and may surface approximate matches rather than precise textual references.
jackquelinunpredictable827Enable semantic search over Arabic religious texts using AraGemma embeddings and FAISS indexing.
FayazKCompile Quranic verses, hadith, and scholarly opinions into a referenced PDF.
patricio0312revAutomate document embedding pipelines for semantic search.
jackquelinunpredictable827Search hadith collections by book, number, or keyword via curl.
