feat(ai): Implement Document Chat & Summarization (RAG) - #89
Merged
MistryVishwa merged 5 commits intoJul 10, 2026
Conversation
|
@rach-kanc is attempting to deploy a commit to the vishwamistrylearning-1037's projects Team on Vercel. A member of the Team first needs to authorize it. |
Contributor
Author
|
@MistryVishwa Check the PR |
MistryVishwa
requested changes
Jul 10, 2026
MistryVishwa
left a comment
Owner
There was a problem hiding this comment.
@rach-kanc Please resolve the branch conflicts.
Contributor
Author
|
@MistryVishwa fixed the conflicts 👍🏻 |
MistryVishwa
approved these changes
Jul 10, 2026
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Description
This Pull Request introduces a native Document Chat feature using Retrieval-Augmented Generation (RAG).
Motivation: Students needed a way to extract insights and generate study materials from long PDFs without leaving the platform or hitting context-window limits in the standard AI Tutor.
Context: We enabled
pgvectoron Supabase to store document embeddings. PDFs are now parsed server-side usingpdf-parse, chunked, and embedded using Gemini'stext-embedding-004model. A new split-screen UI was built so students can view the document and chat with the AI simultaneously.Related Issue: Closes #78
Type of Change
How Has This Been Tested?
Note: This project does not currently have an automated test suite. The following manual verification was performed:
document_chunkstable without breaking the max duration limit.match_document_chunksPostgres function to ensure relevant context is fetched for user queries.Checklist:
mainTags
hard,ssoc26