Can mixed language documents be used in multiple indexes for training?

Mixed language documents can indeed be utilized in multiple indexes for training, and it is advisable to keep them separate to ensure clearer data processing and analysis. This approach helps maintain the integrity of the models being trained, as each language may have different structures, semantics, and contexts that need to be considered individually. Keeping mixed language documents separate allows for a more focused and effective training of models, as it reduces the potential confusion that could arise from mixing languages within a single index.

Additionally, processing documents in their respective languages can lead to improved results and enhanced understanding of each language's unique patterns and usages. In contrast, combining languages without careful attention can lead to a diluted model that struggles to accurately interpret or generate context-specific responses, diminishing the overall effectiveness of the training process.