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RAG chatbot for company documents using Google Drive and Gemini

Mihai FarcasMihai Farcas
245944 views
2/3/2026
LinearGoogle SheetsUser ManagementAutomationTeam Management
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This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated documents in a Pinecone vector database, allowing the chatbot to provide accurate and up-to-date information. The workflow uses Google's Gemini AI for both embeddings and response generation.

How it works

The workflow uses two Google Drive Trigger nodes: one for detecting new files added to a specified Google Drive folder, and another for detecting file updates in that same folder.

  1. Automated Indexing: When a new or updated document is detected
  2. The Google Drive node downloads the file.
  3. The Default Data Loader node loads the document content.
  4. The Recursive Character Text Splitter node breaks the document into smaller text chunks.
  5. The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model.
  6. The Pinecone Vector Store node indexes the text chunks and their embeddings in a specified Pinecone index. 7.The Chat Trigger node receives user questions through a chat interface. The user's question is passed to an AI Agent node.
  7. The AI Agent node uses a Vector Store Tool node, linked to a Pinecone Vector Store node in query mode, to retrieve relevant text chunks from Pinecone based on the user's question.
  8. The AI Agent sends the retrieved information and the user's question to the Google Gemini Chat Model (gemini-pro).
  9. The Google Gemini Chat Model generates a comprehensive and informative answer based on the retrieved documents.
  10. A Window Buffer Memory node connected to the AI Agent provides short-term memory, allowing for more natural and context-aware conversations.

Set up steps

  1. Google Cloud Project and Vertex AI API:
  • Create a Google Cloud project.
  • Enable the Vertex AI API for your project.
  1. Google AI API Key:
  • Obtain a Google AI API key from Google AI Studio.
  1. Pinecone Account:
  • Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard.
  • Create an index named company-files in your Pinecone project.
  1. Google Drive:
  • Create a dedicated folder in your Google Drive where company documents will be stored.
  1. Credentials in n8n: Configure credentials in your n8n environment for:
  • Google Drive OAuth2
  • Google Gemini(PaLM) Api (using your Google AI API key)
  • Pinecone API (using your Pinecone API key)
  1. Import the Workflow:
  • Import this workflow into your n8n instance.
  1. Configure the Workflow:
  • Update both Google Drive Trigger nodes to watch the specific folder you created in your Google Drive.
  • Configure the Pinecone Vector Store nodes to use your company-files index.

RAG Chatbot for Company Documents using Google Drive and Gemini

This n8n workflow simplifies the process of creating a Retrieval Augmented Generation (RAG) chatbot. It automates the ingestion of documents from Google Drive into a Pinecone vector store, and then uses a Google Gemini-powered AI agent to answer questions based on those documents.

What it does

This workflow consists of two main parts:

Part 1: Document Ingestion (Manual Trigger)

  1. Google Drive: Manually triggered to fetch documents from a specified Google Drive folder.
  2. Default Data Loader: Loads the content of the fetched documents.
  3. Recursive Character Text Splitter: Splits the document content into smaller chunks to prepare them for embedding.
  4. Embeddings Google Gemini: Converts the text chunks into numerical embeddings using Google Gemini's embedding model.
  5. Pinecone Vector Store: Stores these embeddings in a Pinecone vector database, making them searchable for the chatbot.

Part 2: Chatbot Interaction (Chat Trigger)

  1. When chat message received: Listens for incoming chat messages (e.g., from a user asking a question).
  2. Simple Memory: Maintains a conversational memory for the AI agent, allowing it to remember previous turns in the conversation.
  3. Google Gemini Chat Model: Serves as the large language model (LLM) for the AI agent, powered by Google Gemini.
  4. Answer questions with a vector store: Configures a tool for the AI agent to query the Pinecone vector store to retrieve relevant document chunks based on the user's question.
  5. AI Agent: Orchestrates the entire process, using the Google Gemini Chat Model and the Vector Store Question Answer Tool to understand the user's query, retrieve relevant information from the documents stored in Pinecone, and generate a coherent response.

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 chatbot.
  • Google Cloud Project: To access the Google Gemini API for embeddings and the chat model. You'll need credentials for this.
  • Pinecone Account: A Pinecone API key and environment to store and retrieve vector embeddings.

Setup/Usage

  1. Import the workflow: Download the JSON file and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Google Drive credentials.
    • Set up your Google Gemini credentials for both the "Embeddings Google Gemini" and "Google Gemini Chat Model" nodes.
    • Set up your Pinecone credentials for the "Pinecone Vector Store" node.
  3. Configure Google Drive Node (Node ID 58): Specify the folder ID in Google Drive containing the documents you want to ingest.
  4. Configure Pinecone Vector Store (Node ID 1230): Ensure the index name matches your Pinecone setup.
  5. Manually Trigger Ingestion: Run the "Google Drive" node (Node ID 58) manually to ingest your documents into Pinecone. This step only needs to be done when documents are added or updated.
  6. Activate the Workflow: Once all configurations are complete and documents are ingested, activate the workflow.
  7. Start Chatting: The "When chat message received" trigger (Node ID 1247) will now listen for incoming messages, and the AI Agent will respond using your company documents.

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