AI Agents

Automate Google Drive document processing with AI

14 nodes 331 146 Automatic trigger
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Workflow Description

Advanced automation that monitors Google Drive, extracts file content automatically, and processes it through an AI model (Gemini) for analysis and Vector Store indexing. Users can then query the processed data through a conversational chat interface powered by language models.

How it works

  1. 1.Monitor Google Drive folder and detect new file uploads
  2. 2.Extract text content from uploaded documents
  3. 3.Split text and generate embeddings using Gemini AI
  4. 4.Store processed data and embeddings in Vector Store (Pinecone)
  5. 5.Receive user queries through chat interface
  6. 6.Return intelligent answers based on indexed document content

Use cases

  • Automatically process large document collections for fast semantic search
  • Build a knowledge base chatbot answering employee questions about company policies
  • Extract and index data from reports and contracts uploaded in real-time

Requirements

  • Active Google Drive account with folder access permissions
  • Gemini API key and Pinecone account for embeddings storage
  • OpenRouter account for accessing advanced language models

Service Value

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

Apps Used

AI Agent Google Drive Vector Store Gemini Document Default Data Loader Text Splitter Chat Trigger Lm Chat Open Router

Details

Trigger Automatic trigger
Nodes 14
Apps 8
Views 331
Downloads 146

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 (14)

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AI Agent

Agent

#1

Monitor Google Drive for New Files

Google Drive Trigger

#2

Download File from Google Drive

Google Drive

#3

Extract PDF Content

Extract From File

#4

Insert Document into Pinecone Vector Store

Vector Store Pinecone

#5

Generate Document Embeddings (Google Gemini)

Gemini Model

#6

Clean and Normalize PDF Text

Code

#7

Load Document Data for Processing

Document Default Data Loader

#8

Split Document Text into Chunks

Text Splitter Recursive Character Text Splitter

#9

Chat Message Trigger

Chat Trigger

#10

Retrieve Relevant Documents from Pinecone

Vector Store Pinecone

#11

Generate Query Embeddings (Google Gemini)

Gemini Model

#12

Generate Chat Prompt with Context

Code

#13

OpenRouter Chat Model Interface

Lm Chat Open Router

#14

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