Yes, embed-multilingual-v3.0 can handle code-switching (mixed languages in the same text) to a useful degree, but you should treat it as something to validate and tune rather than assume it is perfect. Code-switching is common in real data: users mix English product names into Japanese sentences, include Spanish phrasing with English technical terms, or switch languages mid-ticket. A multilingual embedding model is generally better positioned to encode such inputs than an English-only model, because it has been trained to represent multiple languages in a shared space.
In practice, how well code-switching works depends on where the “semantic load” is. If the key meaning is carried by shared technical terms (API names, error codes, product features) and the surrounding natural language provides intent, embeddings often still cluster correctly. Where it gets harder is when meaning depends on subtle grammar and the text blends languages with different scripts and tokenization behavior. From a system design perspective, you can improve results by keeping preprocessing minimal (don’t strip out important tokens), preserving original casing for identifiers, and chunking long mixed-language documents into coherent passages rather than large blocks.
When you store embeddings for mixed-language content, include metadata that helps retrieval behave predictably. For example, you can store languages_detected=["en","ja"] or a primary_language label, and use that in filtering or ranking. In a vector database such as Milvus or Zilliz Cloud, you can run the same-language-first strategy using primary_language, but still allow mixed-language chunks to appear when they are highly similar. The best way to confirm code-switch handling is to build a small evaluation set from real user messages that contain code-switching and test retrieval outcomes. If failures are consistent, you can add a lightweight normalization step (like duplicating key terms into a side field) without overcomplicating the pipeline.
For more resources, click here: https://zilliz.com/ai-models/embed-multilingual-v3.0