Skip to content

Latest commit

 

History

History
41 lines (33 loc) · 1.77 KB

README.md

File metadata and controls

41 lines (33 loc) · 1.77 KB

pgvector to Prod in 2 hours

Workshop: pgvector to Prod in 2 hours

Create a production-ready MVP for securely chatting with your documents.

☑️ Learning from 👉 HERE

☑️ I kept my learning 📝notes in different branches, just follow the GR great tutorial and you will learn everything.

☑️ From this tutorial you will learn:

  • Supabase
  • Database migration
  • The structure of how to use retrival augmented generation (RAG) to process your files, and query smaller meaningful document sections.
  • You will learn pgvector, how to index your text, and create edge functions to process the markdown files into document_sections table.
  • You will learn Embedding from GR ❤️, highly recommend his Embedding teaching.
  • The interesting step is generating the ueser message embedding in the frontend. Then determine the similarity with markdown files embedding.

📑Notes files:

The first time following the learning using git branches, I like this way, time efficient and more focus on the knowledge and code logic. The code edits in the following Branches:

  • Step-2
    • Readme
    • app/files/pages.tsx
    • supabase/functions/process/index.js
    • supbase/seed.sql
    • supabase/migrations/documents.sql
  • Step-3
    • Readme
    • supabase/migrations/embed.sql
  • Step-4
    • Readme
    • next.config.js
    • app/chat/pages.tsx
    • supabase/migrations/match.sql
    • supabase/functions/chat/index.tsx
  • Step-5
    • Generate DB schema typescript type, add <Database> generic and type error check.