need multilingual embeddings for support articles

If you need multilingual embeddings for support articles, the best approach is usually to use a single multilingual embedding model so articles in different languages land in the same vector space.

Good model choices

• OpenAI text-embedding-3-small / text-embedding-3-large
  Strong multilingual support, easy to use.
• Cohere Embed Multilingual
• SentenceTransformers multilingual models
  • intfloat/multilingual-e5-base
  • intfloat/multilingual-e5-large
  • sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Recommended setup for support articles

1. Chunk articles into sections or paragraphs, not whole docs.
2. Embed both titles and body text together.
3. Store vectors in a vector database like:
   • Pinecone
   • Weaviate
   • Qdrant
   • FAISS
4. At query time:
   • detect language or let the model handle it
   • embed the query
   • retrieve top matching chunks across all languages
5. Optionally do cross-lingual reranking for better precision.

Best practice for support content

For support articles, multilingual search works best when you also:

• normalize product names and SKUs
• keep key terms untranslated if they are brand-specific
• add language metadata
• consider translating queries to English as a fallback if your content is mostly English

Simple recommendation

If you want the easiest strong default:

• Use text-embedding-3-large for highest quality
• or text-embedding-3-small for lower cost
• store embeddings in a vector DB
• use chunk-level indexing

If you want, I can also give you:

1. a model comparison table,
2. a Python example, or
3. a recommended architecture for multilingual support search.