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

DIGITAL BIZ TECHDIGITAL BIZ TECH
1291 views
2/3/2026
n8n
Official Page

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

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

n8n Website Chatbot with Google Drive Knowledge Base using GPT-4 and Mistral AI

This n8n workflow automates the creation of a sophisticated website chatbot. It leverages a Google Drive folder as its knowledge base, processes documents using text splitting and embeddings, and then uses either OpenAI's GPT-4 or Mistral AI to answer user queries. The workflow is designed to be triggered by incoming chat messages and provides a conversational AI experience.

What it does

This workflow streamlines the process of building and deploying an AI-powered chatbot with the following steps:

  1. Triggers on Chat Message: Initiates the workflow whenever a new chat message is received.
  2. Loads Google Drive Documents: Fetches all documents from a specified Google Drive folder, treating them as the knowledge base for the chatbot.
  3. Splits Documents into Chunks: Divides the loaded documents into smaller, manageable chunks using a Character Text Splitter to optimize for embedding and retrieval.
  4. Generates Embeddings: Converts the text chunks into numerical vector embeddings using the Mistral AI embedding model, preparing them for storage in a vector database.
  5. Stores in Qdrant Vector Store: Stores the generated embeddings in a Qdrant vector database, enabling efficient semantic search for relevant information.
  6. Initializes AI Agent: Sets up an AI Agent that orchestrates the interaction between the user query, the knowledge base, and the language model.
  7. Manages Conversation History: Utilizes a Simple Memory node to maintain the context of the conversation, allowing the chatbot to remember previous interactions.
  8. Selects Language Model: Offers the flexibility to choose between OpenAI's GPT-4 (via the OpenAI Chat Model node) or another language model for generating responses.
  9. Processes User Query: The AI Agent processes the incoming chat message, retrieves relevant information from the Qdrant vector store, and uses the selected language model to formulate a response.
  10. Responds to Chat: Sends the AI-generated answer back to the user via the chat platform.
  11. Optional Manual Trigger: Includes a manual trigger for testing and debugging the workflow.
  12. Utility Nodes: Incorporates Edit Fields (Set), HTTP Request, Loop Over Items (Split in Batches), Code, If, and Wait nodes for data manipulation, external API calls, batch processing, custom logic, conditional routing, and flow control, respectively. These nodes are not directly connected in the provided JSON but are available for potential extensions or debugging.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Drive Account: Access to a Google Drive account containing the documents for your knowledge base. You'll need to configure Google Drive credentials in n8n.
  • Mistral AI API Key: An API key for Mistral AI to generate document embeddings.
  • Qdrant Instance: A running Qdrant vector database instance to store and retrieve document embeddings. You'll need to configure Qdrant credentials and collection details.
  • OpenAI API Key (Optional): An API key for OpenAI if you choose to use the OpenAI Chat Model (e.g., GPT-4) for generating responses.
  • Chat Platform Integration: Integration with a chat platform (e.g., Slack, Telegram, custom frontend) that can send messages to the Chat Trigger webhook and receive responses.

Setup/Usage

  1. Import the Workflow: Download the JSON content and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Drive credentials.
    • Configure your Mistral AI credentials for embeddings.
    • Set up your Qdrant credentials, including the host, API key, and collection name.
    • (Optional) Configure your OpenAI credentials if you plan to use the OpenAI Chat Model.
  3. Specify Google Drive Folder: In the Google Drive node, specify the folder ID where your knowledge base documents are stored.
  4. Activate the Workflow: Once all credentials and configurations are set, activate the workflow.
  5. Integrate Chat Trigger: Copy the webhook URL from the Chat Trigger node and configure your chat platform to send incoming messages to this URL.
  6. Start Chatting: Send a message to your configured chat platform, and the chatbot will process your query using the knowledge base and respond.

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