Google Drive to Supabase RAG Sync
Monitors a Google Drive folder for new or modified files, chunks the text, generates OpenAI embeddings.
What it does
You get instant access to your Google Drive documents by having them automatically indexed as searchable knowledge. The system pulls new or updated files from your folder, breaks them into manageable pieces, creates OpenAI embeddings, and saves the results in Supabase for fast retrieval.
Step by step
- Configure the n8n Google Drive node to trigger on file updates within a specific folder ID
- Use the n8n Code node or built-in text splitter to chunk extracted content into fixed-size segments with overlap
- Send each text chunk to the OpenAI Embeddings API using the text-embedding-3-small model
- Insert the resulting vector and metadata rows into the Supabase table via its REST or PostgREST endpoint
Where the LLM does the work
- Determine the optimal chunk size and overlap percentage based on the typical document structure (e.g., legal vs. markdown)
- Design the prompt for any summarization step if raw text extraction yields noisy headers or footers
- Define the metadata schema keys that will best support semantic filtering during retrieval queries
Watch out for
Large binary files like PDFs may fail silent extraction if you do not explicitly configure a PDF parser tool before the text split step.
Supabase vector search performance degrades without an index, so create a hnsw or ivfflat index on the embedding column after initial load.
Embedding costs can spiral with unbounded file sizes, add a size filter to skip files larger than 10MB before processing.
Tools that fit
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Google Drive to Supabase RAG Sync: Monitors a Google Drive folder for new or modified files, chunks the text, generates OpenAI embeddings. ## Specification - What it does: You get instant access to your Google Drive documents by having them automatically indexed as searchable knowledge. The system pulls new or updated files from your folder, breaks them into manageable pieces, creates OpenAI embeddings, and saves the results in Supabase for fast retrieval. - Trigger: Triggered by an event (Event · on new or modified file in Google Drive) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Google Drive: Trigger and read document files - OpenAI: Generate text embeddings for vector search - Supabase: Store and retrieve contextualized vector data - n8n: Orchestrate the workflow automation pipeline ## Known pitfalls, handle each one explicitly in your implementation 1. Large binary files like PDFs may fail silent extraction if you do not explicitly configure a PDF parser tool before the text split step. 2. Supabase vector search performance degrades without an index, so create a hnsw or ivfflat index on the embedding column after initial load. 3. Embedding costs can spiral with unbounded file sizes, add a size filter to skip files larger than 10MB before processing. ## Reference implementation https://n8n.io/workflows/8200 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/google-drive-to-supabase-rag-sync/ via usecasesforagents.com
Frequently asked questions
Can I use a different Cloud Storage than Google Drive?
Yes. Google Drive is only the example cloud storage in this recipe. The same flow works with Dropbox, Google Cloud Storage, Microsoft OneDrive and Nextcloud. Swap the cloud storage connection and keep the rest of the setup as written.
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Built for developers who want the busywork around code automated, not the code itself.