AI Agents

Automated document analysis and vector indexing system

13 nodes 329 149 Scheduled trigger
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Workflow Description

Scheduled automation that periodically retrieves files from Google Drive, extracts their content, converts text to embeddings using OpenAI, stores vectors in Pinecone, logs metadata in Postgres, and sends real-time notifications via Telegram throughout the pipeline.

How it works

  1. 1.Execute workflow at configured time intervals
  2. 2.Fetch and read documents from Google Drive
  3. 3.Generate vector embeddings using OpenAI's embedding model
  4. 4.Store vectors in Pinecone and metadata in Postgres database
  5. 5.Send processing summary to Telegram channel

Use cases

  • Create a searchable vector database from your Google Drive documents
  • Build semantic search capabilities over unstructured content automatically
  • Monitor document processing pipeline with real-time status notifications

Requirements

  • Google Drive API credentials and account access
  • OpenAI API key with embeddings model access
  • Active Postgres database instance
  • Pinecone account with initialized vector index
  • Telegram Bot token for notifications

Service Value

Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.

Apps Used

Google Drive Vector Store Postgres Telegram OpenAI

Details

Trigger Scheduled trigger
Nodes 13
Apps 5
Views 329
Downloads 149

How to Use

  1. 1.Click "Download Template"
  2. 2.Open your n8n dashboard
  3. 3.Go to Workflows > Import from File
  4. 4.Select downloaded file and configure credentials

Nodes Used (13)

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Is New or Updated?

If

#1

Download Workflow File

Google Drive

#2

Analyze Workflow Structure

Code

#3

Prepare Vector Data

Code

#4

Store in Pinecone

Vector Store Pinecone

#5

Update Processing Record

Postgres

#6

Send Success Notification

Set

#7

Edit Fields

Set

#8

Embeddings OpenAI

OpenAI

#9

Merge

Merge

#10

Search files and folders

Google Drive

#11

Schedule Trigger

Schedule Trigger

#12

Execute a SQL query

Postgres

#13