Watches a Google Drive folder for new or updated files.
Deletes old vector entries for the file.
Uses conditional logic to extract content from PDFs, Excel, Docs, or text
Summarizes and preprocesses content. (if needed)
Splits and embeds the text via Ollama.
Stores embeddings in Supabase Vector DB
Chat is initiated via Webhook or built-in chat interface.
User input is passed to the RAG Agent.
Agent queries the User_documents tool (Supabase vector store) using the Ollama model to fetch relevant content.
If context is found, it answers directly.
Otherwise, it can call tools or request clarification.
Responses are returned to the user, with memory stored in PostgreSQL for continuity.
Create a Supabase project at https://supabase.com and go to the SQL editor.
Create a documents table with the following schema: