Handling multilingual product catalogs in vector databases requires careful planning around embedding models, data structure, and query handling. The core challenge is ensuring text in different languages maps to comparable vectors while maintaining search accuracy. Here’s a practical approach:
Use Multilingual Embedding Models Start by selecting an embedding model trained to handle multiple languages. Models like multilingual BERT, LASER, or Sentence-BERT’s paraphrase-multilingual variants encode text from different languages into a shared vector space. For example, a product named “shoe” in English and “chaussure” in French would produce similar vectors if their semantic meanings align. These models normalize linguistic differences, allowing queries in one language (e.g., Spanish) to match products stored in another (e.g., German). When testing models, verify their performance on your target languages—some may handle European languages better than Asian or low-resource ones.
Structure Data for Language Flexibility
Store product metadata in a way that preserves language-specific details while enabling cross-lingual search. For instance, a product document might include fields like title_en, title_es, and description_fr, alongside a combined embedding field generated from all available language versions. Alternatively, create separate embeddings per language if your use case requires language-specific ranking (e.g., prioritizing Japanese results for Japanese queries). When indexing, decide whether to store one “fusion” vector (combining all languages) or multiple language-specific vectors. For example, an e-commerce platform could concatenate English and Spanish product descriptions into a single text block before embedding, ensuring the vector captures cross-lingual context.
Optimize Query Handling
Translate or map user queries to the vector space effectively. If using a multilingual model, embed the query directly without translation. For language-specific models, translate the query to the catalog’s primary language first. For hybrid systems, run parallel searches: use a multilingual vector for semantic matches and filter results using language tags (e.g., lang:de). Tools like CLIP for image-text pairs or hybrid keyword/vector search can further refine results. For example, a user searching for “bücherregal” (German: bookshelf) would have their query embedded using the same multilingual model as the catalog, returning English “bookshelf” and German “bücherregal” products with similar vectors. Always benchmark latency and accuracy when adding languages, as some models scale better than others.