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Updated last month

Hi all I’m attempting to build a tool

HHABBYMAN
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Hi all, I’m attempting to build a tool that allows users to upload various documents to an S3 bucket, and then an API and front end that can allow a user to query those documents after they have been stored and processed.

My understanding of AI / LlamaIndex is limited, I’m coming from a backend Golang discipline and trying to learn the ropes. My proposed architecture is this:

API Upload
  • Upload documents to a backend server - forward on to upload in S3.
Upload Processing
  • An AWS S3 event triggers a python script (example below) to process the documents and store the nodes + indexes. This point is where my lack of knowledge comes in, how can I make this storage process happen in a way that users do not need to re-index these documents, and to speed everything up?
Process Completed Notifications
  • Alert users that their documents are now queryable
Front-end
  • Query documents
Firstly, is my understanding of the LlamaIndex project accurate?
Secondly, is my application of these technologies correct?
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1 comment
VVaylonn
From as far as I understood this tool, you understand what lamaindex is used for and you want to create what i'm trying to create.

So yes, the understanding is accurate. But, I don't know if the tool you want to use are compatible with LlamaIndex.

And the application is correct, i guess that's what it was created for.


For the speed, I guess it's everyone's main issue rn (after the fact everyone try to understand how all of this works, and make it work, even in the more basic way), so i'm not able to help you on that unfortunately 😦
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