Building RAG Pipelines for Arabic Enterprise Search

When we first started building our retrieval-augmented generation pipeline, the biggest challenge was not the LLM integration itself. It was making the system work reliably with Arabic text, where tokenization, stemming, and embedding models all behave differently than English.

The Challenge

Arabic presents unique challenges for RAG systems. Standard English-optimized tokenizers split Arabic words incorrectly. Diacritics change meaning but are often omitted in user queries. And most embedding models have significantly lower quality on Arabic text compared to English.

Our Approach

We built a three-stage pipeline:

1. Document ingestion with Arabic-aware preprocessing
2. Multilingual embedding using models optimized for Arabic
3. Retrieval and generation with prompt templates designed for Arabic context

Key Insights

The most important lesson was that Arabic text needs different chunk sizes. Arabic sentences tend to be longer, and semantic meaning is distributed across longer spans than in English. We found that 512-token chunks worked better than the 256-token default.

Results

The pipeline now handles thousands of queries daily with sub-second retrieval times, serving knowledge base search across multiple enterprise clients.