Thanks.
I suppose I should have mentioned that the final ojbective is to create a chatbot.
My tentative workflow is as follows (noting that this for a MVP - testing a use case with low cost):
| Layer | Technology | Purpose | Implementation Details |
|---|---|---|---|
| Document Preprocessing | Python + Regex + python-docx | Semantic chunking | Custom logic for headings, clauses, structure preservation. Run locally, upload to Supabase |
| Embedding Generation | Hugging Face API from Edge Function | Vector generation | Deno-based edge function calling HF API for embeddings |
| Vector Storage | Supabase pgvector | Semantic search database | Native PostgreSQL with vector similarity (same database) |
| Query Vectorization | Hugging Face API | Query-to-vector conversion | Same API call from edge function |
| Chunk Retrieval | Native SQL in Edge Function | Top-K selection | Direct SQL queries to pgvector within same Supabase instance |
| LLM Completion | OpenAi/Claude/Gemini API | Response generation | API calls from Supabase edge functions |
| Frontend | React/Next.js or WIX | Chat interface | Any frontend calling Supabase edge function endpoints |
| Backend | Supabase Edge Functions | Integrated serverless | TypeScript/Deno functions with direct database access |
Nb: Using Wix because I alread have an account. If I scale up, I’ll find better back and frontend solutions.
Anything glaringly off?