arabic-rag

Semantic search across Quran and Hadith uses AraGemma embeddings and FAISS indexing to retrieve concept-based results, matching meanings rather than exact words. This allows you to find verses and hadith by topic even when different word forms are used.

Exact word matching fails in Arabic text search due to morphology and synonyms. Semantic retrieval overcomes this by applying AraGemma embeddings to match underlying concepts, ensuring relevant verses and hadith are returned despite variations in vocabulary.

To find Quran verses and Hadith related to a concept like patience, use concept-based retrieval. This approach uses AraGemma embeddings to map semantic meaning, returning results that discuss the topic regardless of the specific Arabic words used.

Semantic search supports Arabic linguistic research and religious studies by enabling concept-based retrieval across Arabic documents. It matches semantic meanings across texts, making it suitable for educational contexts requiring deep topical exploration.

FAISS indexing works with AraGemma embeddings by organizing the embedded Arabic texts for rapid similarity lookups. This combination enables efficient semantic retrieval, allowing the system to quickly find and return conceptually matching Quran or Hadith passages.

Arabic semantic search handles morphology and synonyms automatically by using AraGemma embeddings. Instead of relying on surface-level word matching, the embeddings capture the underlying semantic meaning, ensuring that morphological variations do not prevent accurate retrieval.