Unified Multilingual Vector Index vs Per-Language Index Architecture

UNIFIED MULTILINGUAL INDEX
Single index containing vectors from all languages. Multilingual embedding model maps semantically similar content close together regardless of source language.
Advantages: Simpler architecture. Single index to maintain. Cross-lingual retrieval automatic—query in English, retrieve Spanish documents that are semantically relevant.
Disadvantages: Multilingual embeddings are less precise than monolingual. Cross-lingual retrieval recall typically 10-20% lower than same-language. Index size includes all languages.
Best for: systems where cross-lingual retrieval is valuable and slight quality degradation is acceptable.
PER-LANGUAGE INDEXES
Separate index for each language. Language-specific embedding models optimized for each language.
Advantages: Higher retrieval quality per language. Index size per language is smaller. Can use best-in-class models for each language.
Disadvantages: More indexes to maintain. No automatic cross-lingual retrieval—need explicit translation or separate cross-lingual queries. Routing logic required.
Best for: high-quality requirements where each language market is important and cross-lingual retrieval is not needed.
HYBRID ARCHITECTURE
Unified index for cross-lingual discovery plus per-language indexes for high-quality same-language retrieval. Query both, merge results.
Implementation: Use unified index for initial retrieval (cast wide net). Use per-language index for re-ranking (precision). Combine scores with tunable weights.
Complexity vs quality trade-off: this approach gets best of both but requires maintaining multiple index types and merge logic.
SCALING CONSIDERATIONS
Per-language indexes scale horizontally by language—add new language, add new index. Unified index grows with total corpus. Choose based on expected language growth and corpus size.
When To Use: Unified for simplicity and cross-lingual needs. Per-language for quality-critical markets. Hybrid when you need both cross-lingual discovery and high same-language precision.

💡 Key Takeaways

✓Unified index: simpler, automatic cross-lingual retrieval; 10-20% recall penalty vs monolingual

✓Per-language indexes: higher quality, smaller per-language; no automatic cross-lingual, routing complexity

✓Hybrid: unified for discovery + per-language for precision; best quality but highest complexity

📌 Interview Tips

1Interview Tip: Compare unified vs per-language: simplicity/cross-lingual vs quality/complexity.

2Interview Tip: Describe hybrid query flow: unified retrieval → per-language re-ranking → merge.

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