Assuming self-hosting, data is processed within the deployment with local deep learning models for embedding, identifying low information documents, etc. A hybrid keyword/vector index is built locally within the deployment as well.
At rest, the data is stored in Postgres and Vespa (the hybrid index), both of which are part of the deployment so it's all local.
The part that typically goes external is the LLM but many teams also host local LLMs to use with Onyx. In either case, the LLM is not being finetuned, the knowledge relevant to the question is passed in as part of the user message.
We built Onyx with data security in mind so we're very proud of the way the data flows within the system. We made the system work well with models that can run without GPUs as well so our users can get good quality results even if deploying on a laptop.
At rest, the data is stored in Postgres and Vespa (the hybrid index), both of which are part of the deployment so it's all local.
The part that typically goes external is the LLM but many teams also host local LLMs to use with Onyx. In either case, the LLM is not being finetuned, the knowledge relevant to the question is passed in as part of the user message.
We built Onyx with data security in mind so we're very proud of the way the data flows within the system. We made the system work well with models that can run without GPUs as well so our users can get good quality results even if deploying on a laptop.