📖 Document RAG & chat agent: Google Drive to Qdrant with Mistral OCR

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Description

Knowledge RAG & AI Chat Agent: Google Drive to Qdrant

Description

This workflow transforms a Google Drive folder into an intelligent, searchable knowledge base and provides a chat agent to query it.
It’s composed of two distinct flows:

• An ingestion pipeline to process documents.
• A live chat agent that uses RAG (Retrieval-Augmented Generation) and optional web search to answer user questions.

This system fully automates the creation of a “Chat with your docs” solution and enhances it with external web-searching capabilities.

Quick Implementation Steps

1. Import the workflow JSON into your n8n instance.
2. Set up credentials for Google Drive, Mistral AI, OpenAI, and Qdrant.
3. Open the Web Search node and add your Tavily AI API key to the Authorization header.
4. In the Google Drive (List Files) node, set the Folder ID you want to ingest.
5. Run the workflow manually once to populate your Qdrant database (Flow 1).
6. Activate the workflow to enable the chat trigger (Flow 2).
7. Copy the public webhook URL from the When chat message received node and open it in a new tab to start chatting.

What It Does

The workflow is divided into two primary functions:

1. Knowledge Base Ingestion (Manual Trigger)

This flow populates your vector database.

• Scans Google Drive: Lists all files from a specified folder.
• Processes Files Individually: Downloads each file.
• Extracts Text via OCR: Uses Mistral AI OCR API for text extraction from PDFs, images, etc.
• Generates Smart Metadata: A Mistral LLM assigns metadata like document_type, project, and assigned_to.
• Chunks & Embeds: Text is cleaned, chunked, and embedded via OpenAI’s text-embedding-3-small model.
• Stores in Qdrant: Text chunks, embeddings, and metadata are stored in a Qdrant collection (docaiauto).

2. AI Chat Agent (Chat Trigger)

This flow powers the conversational interface.

• Handles User Queries: Triggered when a user sends a chat message.
• Internal RAG Retrieval: Searches Qdrant Vector Store first for answers.
• Web Search Fallback: If unavailable internally, the agent offers to perform a Tavily AI web search.
• Contextual Responses: Combines internal and external info for comprehensive answers.

Who’s It For

Ideal for:

• Teams building internal AI knowledge bases from Google Drive.
• Developers creating AI-powered support, research, or onboarding bots.
• Organizations implementing RAG pipelines.
• Anyone making unstructured Google Drive documents searchable via chat.

Requirements

• n8n instance (self-hosted or cloud).
• Google Drive Credentials (to list and download files).
• Mistral AI API Key (for OCR & metadata extraction).
• OpenAI API Key (for embeddings and chat LLM).
• Qdrant instance (cloud or self-hosted).
• Tavily AI API Key (for web search).

How It Works

The workflow runs two independent flows in parallel:

Flow 1: Ingestion Pipeline (Manual Trigger)

1. List Files: Fetch files from Google Drive using the Folder ID.
2. Loop & Download: Each file is processed one by one.
3. OCR Processing:
   • Upload file to Mistral
   • Retrieve signed URL
   • Extract text using Mistral DOC OCR
4. Metadata Extraction: Analyze text using a Mistral LLM.
5. Text Cleaning & Chunking: Split into 1000-character chunks.
6. Embeddings Creation: Use OpenAI embeddings.
7. Vector Insertion: Push chunks + metadata into Qdrant.

Flow 2: AI Chat Agent (Chat Trigger)

1. Chat Trigger: Starts when a chat message is received.
2. AI Agent: Uses OpenAI + Simple Memory to process context.
3. RAG Retrieval: Queries Qdrant for related data.
4. Decision Logic:
   • Found → Form answer.
   • Not found → Ask if user wants web search.
5. Web Search: Performs Tavily web lookup.
6. Final Response: Synthesizes internal + external info.

How To Set Up

1. Import the Workflow

Upload the provided JSON into your n8n instance.

2. Configure Credentials

Create and assign:

• Google Drive → Google Drive nodes
• Mistral AI → Upload, Signed URL, DOC OCR, Cloud Chat Model
• OpenAI → Embeddings + Chat Model nodes
• Qdrant → Vector Store nodes

3. Add Tavily API Key

• Open Web Search node → Parameters → Headers
• Add your key under Authorization (e.g., tvly-xxxx).

4. Node Configuration

• Google Drive (List Files): Set Folder ID.
• Qdrant Nodes: Ensure same collection name (docaiauto).

5. Run Ingestion (Flow 1)

Click Test workflow to populate Qdrant with your Drive documents.

6. Activate Chat (Flow 2)

Toggle the workflow ON to enable real-time chat.

7. Test

Open the webhook URL and start chatting!

How To Customize

• Change LLMs: Swap models in OpenAI or Mistral nodes (e.g., GPT-4o, Claude 3).
• Modify Prompts: Edit the system message in ai chat agent to alter tone or logic.
• Chunking Strategy: Adjust chunkSize and chunkOverlap in the Code node.
• Different Sources: Replace Google Drive with AWS S3, Local Folder, etc.
• Automate Updates: Add a Cron node for scheduled ingestion.
• Validation: Add post-processing steps after metadata extraction.
• Expand Tools: Add more functional nodes like Google Calendar or Calculator.

Use Case Examples

• Internal HR Bot: Answer HR-related queries from stored policy docs.
• Tech Support Assistant: Retrieve troubleshooting steps for products.
• Research Assistant: Summarize and compare market reports.
• Project Management Bot: Query document ownership or project status.

Troubleshooting Guide

Need Help or More Workflows?

Want to customize this workflow for your business or integrate it with your existing tools?
Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements.

We can help you set it up for free — from connecting credentials to deploying it live.

Contact: rajeet.nair@digitalbiz.tech
Website: https://www.digitalbiz.tech
LinkedIn: https://www.linkedin.com/company/digital-biz-tech/
You can also DM us on LinkedIn for any help.

🔗 Nodes Used

HTTP Request, Google Drive, AI Agent, Embeddings OpenAI, OpenAI Chat Model, Simple Memory

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup