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integrationGoogle Gemini Chat Model node

Google Docs and Google Gemini Chat Model integration

Save yourself the work of writing custom integrations for Google Docs and Google Gemini Chat Model and use n8n instead. Build adaptable and scalable Miscellaneous, AI, and Langchain workflows that work with your technology stack. All within a building experience you will love.

How to connect Google Docs and Google Gemini Chat Model

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Google Docs and Google Gemini Chat Model integration: Create a new workflow and add the first step

Step 2: Add and configure Google Docs and Google Gemini Chat Model nodes

You can find Google Docs and Google Gemini Chat Model in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Google Docs and Google Gemini Chat Model nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Google Docs and Google Gemini Chat Model integration: Add and configure Google Docs and Google Gemini Chat Model nodes

Step 3: Connect Google Docs and Google Gemini Chat Model

A connection establishes a link between Google Docs and Google Gemini Chat Model (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Google Docs and Google Gemini Chat Model integration: Connect Google Docs and Google Gemini Chat Model

Step 4: Customize and extend your Google Docs and Google Gemini Chat Model integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Google Docs and Google Gemini Chat Model with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Google Docs and Google Gemini Chat Model integration: Customize and extend your Google Docs and Google Gemini Chat Model integration

Step 5: Test and activate your Google Docs and Google Gemini Chat Model workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Google Docs to Google Gemini Chat Model or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Google Docs and Google Gemini Chat Model integration: Test and activate your Google Docs and Google Gemini Chat Model workflow

🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant

🤖 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 📚
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 💬
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 🗄️
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

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

Configure Document Sources:
Set Google Drive folder ID
Define Qdrant collection name
Set up document processing parameters

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.

Nodes used in this workflow

Popular Google Docs and Google Gemini Chat Model workflows

+8

🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant

🤖 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 📚 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 💬 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 🗄️ 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 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 Configure Document Sources: Set Google Drive folder ID Define Qdrant collection name Set up document processing parameters 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.
+6

AI-Powered RAG Workflow For Stock Earnings Report Analysis

This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc. How it works: Data loading and indexing Fetches links to PDF earnings document from a Google Sheet containing a list of file links. Downloads the PDFs from Google Drive. Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model). Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search. Report generation with AI agent Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process. The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database. Report delivery Saves the generated report as a Google Doc in a specified Google Drive location. Set up steps Google Cloud Project & Vertex AI API: Create a Google Cloud project. Enable the Vertex AI API for your project. Google AI API key: Obtain a Google AI API key from Google AI Studio. Pinecone account and API key: Create a free account on the Pinecone website. Obtain your API key from your Pinecone dashboard. Create an index named company-earnings in your Pinecone project. Google Drive - download and save financial documents: Go to a company you want to analize and download their quarterly earnings PDFs Save the PDFs in Google Drive Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive Configure credentials in your n8n environment for: Google Sheets OAuth2 Google Drive OAuth2 Google Docs OAuth2 Google Gemini(PaLM) Api (using your Google AI API key) Pinecone API (using your Pinecone API key) Import and configure the workflow: Import this workflow into your n8n instance. Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet. Update the Download File From Google Drive to point to the column where the file URLs are Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

Build your own Google Docs and Google Gemini Chat Model integration

Create custom Google Docs and Google Gemini Chat Model workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Google Docs supported actions

Create
Get
Update

FAQs

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  • How to get started with Google Docs and Google Gemini Chat Model integration in n8n.io?

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