Hey, this is really cool! Thanks for the article and the tool itself.
One question - in the RAG projects we've done, most of the source data was scattered in various source systems, but wasn't necessarily imported into a single DB or Data Lake. For example, building an internal Q&A tool for a company that has knowledge stored in services like Zendesk, Google Drive, an internal company Wiki, etc.
In those cases, it made sense to not import the source documents, or only import metadata about them, and keep the embeddings in a dedicated Vector DB. This seems to me to be a fairly common use case - most enterprises have this kind of data scattered across various systems.
How do you envision this kind of use case working with this tool? I may have missed it, but you mention things like working with images, etc, is your assumption that everyone is storing all of that data in Postgres?
One question - in the RAG projects we've done, most of the source data was scattered in various source systems, but wasn't necessarily imported into a single DB or Data Lake. For example, building an internal Q&A tool for a company that has knowledge stored in services like Zendesk, Google Drive, an internal company Wiki, etc.
In those cases, it made sense to not import the source documents, or only import metadata about them, and keep the embeddings in a dedicated Vector DB. This seems to me to be a fairly common use case - most enterprises have this kind of data scattered across various systems.
How do you envision this kind of use case working with this tool? I may have missed it, but you mention things like working with images, etc, is your assumption that everyone is storing all of that data in Postgres?