Urdu and Bangla Embedding Models and Vector Databases: A Retrieval Benchmark

The problem: English benchmarks, Urdu users

Hundreds of millions of people read Urdu or Bangla, but retrieval benchmarks are dominated by English. A developer building a RAG assistant for a Pakistani or Bangladeshi audience has no clear answer to two questions: which embedding model finds the right passage for an Urdu or Roman Urdu query, and which vector database keeps recall high and latency low on a modest server?

Small details matter. Urdu has several Unicode forms for the same letter, Roman Urdu has no standard spelling, and Bangla text from different sources mixes encodings. A model that looks good on paper can fail on real queries.

What already exists and the gap

• MTEB and its massively multilingual extension rank embedding models on many tasks and languages.
• ANN-Benchmarks compares approximate nearest neighbour libraries on standard datasets.
• Vector databases such as pgvector, Qdrant, Chroma, Milvus and Weaviate each publish their own benchmarks.

The gap is a single, reproducible study that tests both layers together on Urdu, Roman Urdu and Bangla, including normalisation and filtered search, which most RAG apps need.

Inspiration

ANN-Benchmarks showed the value of running every library on the same hardware with the same data. Your project applies that discipline to a language community underserved by existing benchmarks.

What you build

• A judged test set from public text.
• Normalisation with measured impact.
• Two benchmark layers: embedding quality and vector store performance.
• A results page and a decision guide.

The technically interesting part

• Relevance judgements. Build them carefully with two judges and measure agreement. This is the most valuable artefact you leave behind.
• Fair vector store tuning. HNSW parameters change recall and speed. Compare at matched recall, not default settings.
• Filtered search. Many stores lose recall when you add a metadata filter. Test it, because real apps filter by language, date or category.

How you will evaluate it

The benchmark is the evaluation. Report recall@10, nDCG@10, p95 latency, memory and build time, with confidence intervals and every setting recorded.

Path to a product

Results like these are directly useful to companies building local-language search, and they support consulting on RAG for South Asian markets. A maintained public benchmark also builds a strong reputation.

Scope warning

Use only public text with licences that allow it. Four models and three stores are enough. Do not train your own embedding model in the main scope.

Learn what you need on Sefism

• Choosing databases roadmap: how to compare databases, including vector stores, on real workloads.
• Choosing LLMs roadmap: picking models, including embedding models, with evidence.
• GenAI app architecture cheat sheet: where embeddings and vector search fit in a RAG app.
• pgvector: vector search inside PostgreSQL.
• Hugging Face Hub: finding and downloading embedding models.
• RAG techniques: hybrid search and reranking ideas for stretch goals.
• Do I need a vector database for my AI project?: when a dedicated vector database is worth it at all.

Get help with this idea

The risk is a benchmark that is not reproducible or not fair. Tauseef can help you design the test set, the tuning protocol and a clear results page. If you are new here, create a free account first.

• Discuss this idea with a mentor
• Send a query to Tauseef
• Book a 1:1 session