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πŸ€– AI powered RAG chatbot for your docs + Google Drive + Gemini + Qdrant

Joseph LePageJoseph LePage
82407 views
2/3/2026
File ManagementBackupAmazon S3SynchronizationGoogle Drive
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πŸ€– AI-Powered RAG Chatbot with Google Drive Integration

This workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI.

How It Works

Document Processing & Storage πŸ“š

  • Retrieves documents from a specified Google Drive folder
  • Processes and splits documents into manageable chunks
  • Extracts metadata using AI for enhanced search capabilities
  • Stores document vectors in Qdrant for efficient retrieval

Intelligent Chat Interface πŸ’¬

  • Provides a conversational interface powered by Google Gemini
  • Uses RAG to retrieve relevant context from stored documents
  • Maintains chat history in Google Docs for reference
  • Delivers accurate, context-aware responses

Vector Store Management πŸ—„οΈ

  • Features secure delete operations with human verification
  • Includes Telegram notifications for important operations
  • Maintains data integrity with proper version control
  • Supports batch processing of documents

Setup Steps

  1. Configure API Credentials:

    • Set up Google Drive & Docs access
    • Configure Gemini AI API
    • Set up Qdrant vector store connection
    • Add Telegram bot for notifications
    • Add OpenAI Api Key to the 'Delete Qdrant Points by File ID' node

  2. Configure Document Sources:

    • Set Google Drive folder ID
    • Define Qdrant collection name
    • Set up document processing parameters

  3. Test and Deploy:

    • Verify document processing
    • Test chat functionality
    • Confirm vector store operations
    • Check notification system

This workflow is ideal for organizations needing to create intelligent chatbots that can access and understand large document repositories while maintaining context and providing accurate responses through RAG technology.

AI-Powered RAG Chatbot for Your Docs (Google Drive, Gemini, Qdrant)

This n8n workflow creates an AI-powered Retrieval-Augmented Generation (RAG) chatbot that can answer questions based on documents stored in your Google Drive. It leverages Google Gemini for conversational AI, OpenAI for embeddings, and Qdrant as a vector store to efficiently retrieve relevant information from your documents.

What it does

This workflow automates the following steps:

  1. Triggers on Chat Message: Initiates when a chat message is received, likely from a connected chat platform (e.g., Telegram, though not explicitly configured in the provided JSON).
  2. Initial Response: Sends a "Thinking..." message to acknowledge the user's query.
  3. Fetches Documents from Google Drive: Retrieves files from a specified Google Drive folder.
  4. Extracts Text from Files: Uses a "Default Data Loader" to extract textual content from the retrieved documents.
  5. Splits Text into Chunks: Breaks down the extracted document text into smaller, manageable chunks using a "Token Splitter" for efficient processing by the AI.
  6. Generates Embeddings: Creates numerical representations (embeddings) of these text chunks using OpenAI's embedding model.
  7. Stores Embeddings in Qdrant: Indexes the generated embeddings along with their corresponding text chunks in a Qdrant vector store. This allows for fast semantic search.
  8. Retrieves Relevant Documents: When a user asks a question, it queries the Qdrant vector store to find the most semantically similar document chunks.
  9. Generates AI Response: Feeds the user's question and the retrieved document chunks to a Google Gemini Chat Model.
  10. Maintains Conversation History: Uses a "Simple Memory" node to keep track of the conversation context, allowing for more coherent and continuous interactions.
  11. Sends AI Response: Delivers the AI-generated answer back to the user via Telegram.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • Google Drive Account: With documents you wish to use for the RAG system.
  • Google Gemini API Key: For the conversational AI model.
  • OpenAI API Key: For generating text embeddings.
  • Qdrant Instance: A running Qdrant vector database (self-hosted or cloud-managed).
  • Telegram Bot Token: If you intend to use Telegram as the chat interface (as indicated by the Telegram node).
  • n8n LangChain Nodes: Ensure the @n8n/n8n-nodes-langchain package is installed in your n8n instance.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • Google Drive: Set up a Google OAuth2 credential for Google Drive to allow n8n to access your files.
    • Qdrant: Configure your Qdrant credential with the host and API key for your Qdrant instance.
    • OpenAI: Add your OpenAI API key credential.
    • Google Gemini: Add your Google Gemini API key credential.
    • Telegram: If using Telegram, set up a Telegram Bot API credential with your bot token.
  3. Configure Nodes:
    • Google Drive Node (ID: 58): Specify the folder ID in Google Drive containing the documents you want the chatbot to reference.
    • Qdrant Vector Store Node (ID: 1248):
      • Provide a unique Collection Name for your Qdrant collection.
      • Ensure the Embeddings field is correctly linked to the output of the "Embeddings OpenAI" node.
      • The Document field should point to the output of the "Token Splitter" node.
    • Google Gemini Chat Model Node (ID: 1262): Ensure the model is configured as desired (e.g., gemini-pro).
    • Telegram Node (ID: 49): Configure the Chat ID where the bot should send messages.
    • Chat Trigger Node (ID: 1247): This node will automatically generate a webhook URL once activated. Configure your chat platform (e.g., Telegram webhook) to send messages to this URL.
  4. Activate the Workflow: Once all credentials and nodes are configured, activate the workflow.

Your AI-powered RAG chatbot will now be ready to answer questions based on your Google Drive documents via the configured chat interface.

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