Parse Google Drive documents to RAG-ready Markdown with Landing.ai and Supabase cache

Make your unstructured large documents LLM ready markdown using LandingAI Document Parsing.

• Automatically watches a Google Drive folder, submits new documents to Landing.ai for parsing, caches processed files in - Supabase to avoid reprocessing, and reliably polls results with retry and timeout handling.

Use Cases

• Automated document ingestion for RAG pipelines
• Invoice, contract, or report parsing
• AI-powered document analysis workflows
• Knowledge base ingestion from Google Drive
• Preventing duplicate document processing in ETL pipelines

External services:

• Google Drive
• Landing.ai
• Supabase

Credentials Required

Required

• Google Drive OAuth2
• Landing.ai API (HTTP Bearer Token)
• Supabase API

How it works

Once the pdf land in google drive location it trigger and it convert pdf (even more then 200 pages to LLM ready markdown).
It also check in database if the parsing is already done or not, this help to avoid any unnecessary landingAI api call.

Setup Instructions

Step 1: Google Drive

1. Create or select a folder in Google Drive
2. Copy the folder ID
3. Update the Google Drive Trigger node with this folder ID

Step 2: Landing.ai

1. Create a Landing.ai account
2. Generate an API key
3. Add it in n8n as an HTTP Bearer Auth credential
4. Update the organization-id header if required

Step 3: Supabase

1. Create a Supabase project
2. Create a table named landing_parse_cache
3. Add fields such as:
   • file_id
   • document_name
   • mime_type
   • file_size_bytes
   • job_id
   • job_status
   • markdown
   • uploaded_at
   • workflow_run_id
4. Connect Supabase credentials in n8n

Expected Input

• A document uploaded into the configured Google Drive folder
  (PDF, DOCX, or other supported formats)

Expected Output

• Parsed markdown content stored in Supabase
• Metadata including:
  • File ID
  • File name
  • MIME type
  • File size
  • Job ID
  • Processing status
• Early exit if the document already exists in cache

Error Handling & Edge Cases

• Cache check to prevent duplicate processing
• Retry-based polling for async job completion
• Timeout detection for stuck jobs
• Large file output URL handling
• Detailed logging for debugging and audits

Customization Ideas

• Push parsed output to a vector database
• Trigger Slack or email notifications
• Store results in cloud storage (S3, GCS)
• Extend into a RAG or AI agent pipeline

Categories

• Document Processing
• AI & LLM
• Knowledge Management
• Automation

Difficulty Level

Advanced

Happy Automating - from Alok