Multi-Vector, Multilingual and Multimodal Embeddings

One space for more than English text

Multilingual embedding models place sentences from many languages in one shared space, so an English query can retrieve a Hindi document and the reverse; test Hindi, English and mixed Hinglish, as quality varies by language and script. Multimodal models (such as CLIP-style models) embed images and text together, enabling "search images by text" and similar-image search. Multi-vector (late-interaction) methods such as ColBERT keep one vector per token and compare sets of vectors, often more accurate but needing more storage and specialised indexes. A parent-child design embeds small chunks for precise matching but returns the larger parent section. Pick the extension that matches your problem; do not add complexity before the single-vector baseline is measured.

Test Hinglish queries

Users type Hindi in Latin letters and mix languages. Include such queries in your golden set.

Quick check: What does a multilingual embedding model enable?

• Removing the need for chunking
• Translating text word for word
• Matching a query in one language to documents in another
• Encrypting text