Production Multilingual RAG: Hybrid Vector Search for Low-Resource Languages

Retrieval-Augmented Generation (RAG) is now standard for enterprise domain knowledge retrieval. However, deploying RAG systems for non-Latin and low-resource scripts like Bengali reveals severe degradation when relying solely on generic multilingual vector embeddings.

The Semantic Gap in Standard Multilingual Embeddings

Off-the-shelf embedding models frequently cluster specialized Bengali technical terminology into coarse semantic buckets. Searching for "পেমেন্ট গেটওয়ে ইন্টিগ্রেশন" often misses crucial code blocks containing "payment webhook handler" unless the search pipeline combines lexical matching with vector proximity.

Building a Reciprocal Rank Fusion (RRF) Hybrid Pipeline

To achieve benchmark-leading retrieval accuracy, our architecture combines two distinct search paradigms: 1. **Dense Vector Search**: Utilizing pgvector with cosine distance over localized embeddings. 2. **Sparse Lexical Search**: Full-text BM25 search with Bengali compound-word tokenization and normalization.

-- Hybrid RRF query combining vector semantic ranking with full-text scoring
WITH vector_matches AS (
  SELECT id, ROW_NUMBER() OVER (ORDER BY embedding <=> query_embedding) AS v_rank
  FROM kb_documents
  LIMIT 50
),
text_matches AS (
  SELECT id, ROW_NUMBER() OVER (ORDER BY ts_rank(tsv, query_tsv) DESC) AS t_rank
  FROM kb_documents
  WHERE tsv @@ query_tsv
  LIMIT 50
)
SELECT id,
  COALESCE(1.0 / (60 + v_rank), 0.0) + COALESCE(1.0 / (60 + t_rank), 0.0) AS rrf_score
FROM vector_matches
FULL OUTER JOIN text_matches USING (id)
ORDER BY rrf_score DESC
LIMIT 5;

Real-World Results

In our customer benchmarks across 12,000 bilingual legal, logistics, and engineering queries, hybrid RRF elevated top-3 retrieval precision from 71.4% to 94.8%, virtually eliminating hallucinations in end-user streaming responses.