Support Multilingual Docs

Khoj uses an embedding model to understand documents. Multilingual embedding models improve the search quality for documents not in English. This affects both search and chat with docs experiences across Khoj.

To improve search and chat quality for non-english documents you can use a multilingual model.

For example, the paraphrase-multilingual-MiniLM-L12-v2 supports 50+ languages, has decent search quality and speed for a consumer machine. To use it:

1. Open the search config on your server's admin settings page. Either create a new search model, if none exists, or update the existing one. For example,
   • Set the bi_encoder field to sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
   • Set the cross_encoder field to mixedbread-ai/mxbai-rerank-xsmall-v1
2. Regenerate your content index from all the relevant clients. This step is very important, as you'll need to re-encode all your content with the new model.

:::info[Note] Modern search/embedding model like mixedbread-ai/mxbai-embed-large-v1 expect a prefix to the query (or docs) string to improve encoding. Update the bi_encoder_query_encode_config field of your embedding model with {prompt: <prefix-prompt>} to improve the search quality of these models.

E.g. {prompt: "Represent this query for searching documents"}. You can pass any valid JSON object that the SentenceTransformer encode function accepts

:::