Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

AI Powered Website Chatbot with Google Drive Knowledge Base Overview This workflow combines website chatbot intelligence with automated document ingestion and vectorization — enabling live Q&A from...

Best for

Support Chatbot automation workflows
AI Chatbot automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.lmchatopenai, @n8n/n8n-nodes-langchain.memorybufferwindow, n8n-nodes-base.splitinbatches, n8n-nodes-base.wait, @n8n/n8n-nodes-langchain.documentdefaultdataloader, @n8n/n8n-nodes-langchain.textsplittercharactertextsplitter, n8n-nodes-base.if

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by DIGITAL BIZ TECH.

Original n8n.io source

1.1 Workflow description

Title: Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI
Workflow name: Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI

AI-Powered Website Chatbot with Google Drive Knowledge Base

Overview

This workflow combines website chatbot intelligence with automated document ingestion and vectorization — enabling live Q&A from both chat input and processed Google Drive files.
It uses Mistral AI for OCR + embeddings, and Qdrant for vector search.

Chatbot Flow

Trigger: When chat message received or webhook based upon deployed chatbot
Model: OpenAI gpt-4.1-mini
Memory: Simple Memory (Buffer Window)
Vector Search Tool: Qdrant Vector Store
Embeddings: Mistral Cloud
Agent: website chat agent
- Responds based on chatdbtai Supabase content
- Enforces brand tone and informative documents.
- Integratration with both:
  - Embedded chat UI
  - Webhook

Document → Knowledge Base Pipeline

Triggered manually to keep vector store up-to-date.

Steps

Google Drive (brand folder)
→ Fetch files from folder Website kb (ID: 1o3DK9Ceka5Lqb8irvFSfEeB8SVGG_OL7)
Loop Over Items
→ For each file:
- Set metadata
- Download file
- Upload to Mistral for OCR
- Get Signed URL
- Run OCR extraction (mistral-ocr-latest)
If OCR success
→ Pass to chunking pipeline
Else → skip and continue
Chunking Logic (Code node)
- Splits document into 1,000-character JSON chunks
- Adds metadata (source, char positions, file ID)
Default Data Loader + Text Splitter
→ Prepares chunks for embedding
Embeddings (Mistral Cloud)
→ Generates embeddings for text chunks
Qdrant Vector Store (Insert mode)
→ Saves embeddings into docragtestkb collection
Wait
→ Optional delay between batches

Integrations Used

Service	Purpose	Credential
Google Drive	File source	`Google Drive account 6 rn dbt`
Mistral Cloud	OCR + embeddings	`Mistral Cloud account 2 dbt rn`
Qdrant	Vector storage	`QdrantApi account`
OpenAI	Chat model	`OpenAi account 8 dbt digi`

Agent System Prompt Summary

> “You are the official AI assistant for this website.
Use chatdbtai only as your knowledge source.
Respond conversationally, list offerings clearly, link blogs, and say
‘I couldn’t find that on this site’ if no match.”

Key Features

✅ Automated OCR + chunking → vectorization
✅ Persistent memory for chat sessions
✅ Multi-channel (Webhook + Embedded Chat)
✅ Fully brand-guided, structured responses
✅ Live data retrieval from Qdrant vector store

Summary

> A unified workflow that turns brand files + web content into a knowledge base that powers a intelligent chatbot — capable of responding to visitors in real time, powered by Mistral, OpenAI, and Qdrant.

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: [email protected]
Website: https://www.digitalbiz.tech
LinkedIn: https://www.linkedin.com/company/digital-biz-tech/
You can also DM us on LinkedIn for any help.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - When chat message received

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 2 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 3 - Simple Memory

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 4 - Loop Over Items1

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 5 - Wait

Type / Role: n8n-nodes-base.wait - wait
Config choices: Version 1.1

Block 6 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 7 - Character Text Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - textSplitterCharacterTextSplitter
Config choices: Version 1

Block 8 - If2

Type / Role: n8n-nodes-base.if - if
Config choices: Version 2.2

Block 9 - Qdrant Vector Store1

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices: Version 1.1

Block 10 - Embeddings Mistral Cloud

Type / Role: @n8n/n8n-nodes-langchain.embeddingsMistralCloud - embeddingsMistralCloud
Config choices: Version 1

Block 11 - Qdrant Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices: Version 1.3

Block 12 - Embeddings Mistral Cloud1

Type / Role: @n8n/n8n-nodes-langchain.embeddingsMistralCloud - embeddingsMistralCloud
Config choices: Version 1

Block 13 - When clicking ‘Execute workflow’

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 14 - Set metadata

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 15 - Mistral Upload

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 16 - Mistral Signed URL

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 17 - Mistral DOC OCR

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 18 - Sticky Note4

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 19 - Google Drive(brand related data for chatbot)

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 20 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 21 - Google Drive(load file)

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 22 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 23 - Code(convert to chunks for loading into vector db)

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 24 - prepare for chunking

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Showing the first 24 of 32 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI
Complexity	advanced
Nodes	32
Categories	Support Chatbot, AI Chatbot
Author	DIGITAL BIZ TECH
Published	25 Oct 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/10142/10142.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Support Chatbot workflows AI Chatbot workflows

Frequently asked questions

What does Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Support Chatbot, AI Chatbot use case.

DIGITAL BIZ TECH

12 workflows

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.lmchatopenai @n8n/n8n-nodes-langchain.memorybufferwindow n8n-nodes-base.splitinbatches n8n-nodes-base.wait @n8n/n8n-nodes-langchain.documentdefaultdataloader @n8n/n8n-nodes-langchain.textsplittercharactertextsplitter n8n-nodes-base.if

Complexity

advanced

Published 25 Oct 2025

Likes 0

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Install path: /data/workflows/10142/10142.json

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Website chatbot with Google Drive knowledge base using GPT-4 and Mistral AI

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

AI-Powered Website Chatbot with Google Drive Knowledge Base

Overview

Chatbot Flow

Document → Knowledge Base Pipeline

Steps

Integrations Used

Agent System Prompt Summary

Key Features

Summary

Need Help or More Workflows?

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - When chat message received

Block 2 - OpenAI Chat Model

Block 3 - Simple Memory

Block 4 - Loop Over Items1

Block 5 - Wait

Block 6 - Default Data Loader

Block 7 - Character Text Splitter

Block 8 - If2

Block 9 - Qdrant Vector Store1

Block 10 - Embeddings Mistral Cloud

Block 11 - Qdrant Vector Store

Block 12 - Embeddings Mistral Cloud1

Block 13 - When clicking ‘Execute workflow’

Block 14 - Set metadata

Block 15 - Mistral Upload

Block 16 - Mistral Signed URL

Block 17 - Mistral DOC OCR

Block 18 - Sticky Note4

Block 19 - Google Drive(brand related data for chatbot)

Block 20 - Sticky Note

Block 21 - Google Drive(load file)

Block 22 - Sticky Note1

Block 23 - Code(convert to chunks for loading into vector db)

Block 24 - prepare for chunking

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions