Fetch API Data, Process, and Store in Supabase

### Set up steps

1. **Fetch File List from Supabase**:
 - Use Supabase to retrieve the stored file list from a specified bucket.
 - Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing.

2. **Compare and Filter Files**:
 - Aggregate the files retrieved from storage and compare them to the existing list in the Supabase `files` table.
 - Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled.

3. **Handle File Downloads**:
 - Download new files using detailed storage configurations for public/private access.
 - Adjust the storage settings and GET requests to match your Supabase setup.

4. **File Type Processing**:
 - Use a Switch node to target specific file types (e.g., PDFs or text files).
 - Employ relevant tools to process the content:
 - For PDFs, extract embedded content.
 - For text files, directly process the text data.

5. **Content Chunking**:
 - Break large text data into smaller chunks using the Text Splitter node.
 - Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks.

6. **Vector Embedding Creation**:
 - Generate vectorized embeddings for the processed content using OpenAI's embedding tools.
 - Ensure metadata, such as file ID, is included for easy data retrieval.

7. **Store Vectorized Data**:
 - Save the vectorized information into a dedicated Supabase vector store.
 - Use the default schema and table provided by Supabase for seamless setup.

8. **AI Chatbot Integration**:
 - Add a chatbot node to handle user input and retrieve relevant document chunks.
 - Use metadata like file ID for targeted queries, especially when multiple documents are involved.