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Embeddings Google Gemini and Google Drive integration

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How to connect Embeddings Google Gemini and Google Drive

  • 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.

Embeddings Google Gemini and Google Drive integration: Create a new workflow and add the first step

Step 2: Add and configure Embeddings Google Gemini and Google Drive nodes

You can find Embeddings Google Gemini and Google Drive 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 Embeddings Google Gemini and Google Drive nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Embeddings Google Gemini and Google Drive integration: Add and configure Embeddings Google Gemini and Google Drive nodes

Step 3: Connect Embeddings Google Gemini and Google Drive

A connection establishes a link between Embeddings Google Gemini and Google Drive (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.

Embeddings Google Gemini and Google Drive integration: Connect Embeddings Google Gemini and Google Drive

Step 4: Customize and extend your Embeddings Google Gemini and Google Drive 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 Embeddings Google Gemini and Google Drive with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Embeddings Google Gemini and Google Drive integration: Customize and extend your Embeddings Google Gemini and Google Drive integration

Step 5: Test and activate your Embeddings Google Gemini and Google Drive workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Embeddings Google Gemini to Google Drive 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.

Embeddings Google Gemini and Google Drive integration: Test and activate your Embeddings Google Gemini and Google Drive workflow

RAG Chatbot for Company Documents using Google Drive and Gemini

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.
Automated Indexing: When a new or updated document is detected
The Google Drive node downloads the file.
The Default Data Loader node loads the document content.
The Recursive Character Text Splitter node breaks the document into smaller text chunks.
The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model.
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.
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.
The AI Agent sends the retrieved information and the user's question to the Google Gemini Chat Model (gemini-pro).
The Google Gemini Chat Model generates a comprehensive and informative answer based on the retrieved documents.
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

Google Cloud Project and 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:
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.
Google Drive:
Create a dedicated folder in your Google Drive where company documents will be stored.
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)
Import the Workflow:
Import this workflow into your n8n instance.
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.

Nodes used in this workflow

Popular Embeddings Google Gemini and Google Drive workflows

+4

RAG Chatbot for Company Documents using Google Drive and Gemini

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. Automated Indexing: When a new or updated document is detected The Google Drive node downloads the file. The Default Data Loader node loads the document content. The Recursive Character Text Splitter node breaks the document into smaller text chunks. The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model. 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. 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. The AI Agent sends the retrieved information and the user's question to the Google Gemini Chat Model (gemini-pro). The Google Gemini Chat Model generates a comprehensive and informative answer based on the retrieved documents. 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 Google Cloud Project and 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: 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. Google Drive: Create a dedicated folder in your Google Drive where company documents will be stored. 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) Import the Workflow: Import this workflow into your n8n instance. 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.
+2

RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini

Workflow based on the following article. https://www.anthropic.com/news/contextual-retrieval This n8n automation is designed to extract, process, and store content from documents into a Pinecone vector store using context-based chunking. The workflow enhances retrieval accuracy in RAG (Retrieval-Augmented Generation) setups by ensuring each chunk retains meaningful context. Workflow Breakdown: 🔹 Google Drive - Retrieve Document: The automation starts by fetching a source document from Google Drive. This document contains structured content, with predefined boundary markers for easy segmentation. 🔹 Extract Text Content - Once retrieved, the document’s text is extracted for processing. Special section boundary markers are used to divide the text into logical sections. 🔹 Code Node - Create Context-Based Chunks: A custom code node processes the extracted text, identifying section boundaries and splitting the document into meaningful chunks. Each chunk is structured to retain its context within the entire document. 🔹 Loop Node - Process Each Chunk: The workflow loops through each chunk, ensuring they are processed individually while maintaining a connection to the overall document context. 🔹 Agent Node - Generate Context for Each Chunk: We use an Agent node powered by OpenAI’s GPT-4.0-mini via OpenRouter to generate contextual metadata for each chunk, ensuring better retrieval accuracy. 🔹 Prepend Context to Chunks & Create Embeddings - The generated context is prepended to the original chunk, creating context-rich embeddings that improve searchability. 🔹 Google Gemini - Text Embeddings: The processed text is passed through Google Gemini text-embedding-004, which converts the text into semantic vector representations. 🔹 Pinecone Vector Store - Store Embeddings: The final embeddings, along with the enriched chunk content and metadata, are stored in Pinecone, making them easily retrievable for RAG-based AI applications. Use Case: This automation enhances RAG retrieval by ensuring each chunk is contextually aware of the entire document, leading to more accurate AI responses. It’s perfect for applications that require semantic search, AI-powered knowledge management, or intelligent document retrieval. By implementing context-based chunking, this workflow ensures that LLMs retrieve the most relevant data, improving response quality and accuracy in AI-driven applications.
+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.
+6

API Schema Extractor

This workflow automates the process of discovering and extracting APIs from various services, followed by generating custom schemas. It works in three distinct stages: research, extraction, and schema generation, with each stage tracking progress in a Google Sheet. 🙏 Jim Le deserves major kudos for helping to build this sophisticated three-stage workflow that cleverly automates API documentation processing using a smart combination of web scraping, vector search, and LLM technologies. How it works Stage 1 - Research: Fetches pending services from a Google Sheet Uses Google search to find API documentation Employs Apify for web scraping to filter relevant pages Stores webpage contents and metadata in Qdrant (vector database) Updates progress status in Google Sheet (pending, ok, or error) Stage 2 - Extraction: Processes services that completed research successfully Queries vector store to identify products and offerings Further queries for relevant API documentation Uses Gemini (LLM) to extract API operations Records extracted operations in Google Sheet Updates progress status (pending, ok, or error) Stage 3 - Generation: Takes services with successful extraction Retrieves all API operations from the database Combines and groups operations into a custom schema Uploads final schema to Google Drive Updates final status in sheet with file location Ideal for: Development teams needing to catalog multiple APIs API documentation initiatives Creating standardized API schema collections Automating API discovery and documentation Accounts required: Google account (for Sheets and Drive access) Apify account (for web scraping) Qdrant database Gemini API access Set up instructions: Prepare your Google Sheets document with the services information. Here's an example of a Google Sheet – you can copy it and change or remove the values under the columns. Also, make sure to update Google Sheets nodes with the correct Google Sheet ID. Configure Google Sheets OAuth2 credentials, required third-party services (Apify, Qdrant) and Gemini. Ensure proper permissions for Google Drive access.

Build your own Embeddings Google Gemini and Google Drive integration

Create custom Embeddings Google Gemini and Google Drive 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 Drive supported actions

Copy
Create a copy of an existing file
Create From Text
Create a file from a provided text
Delete
Permanently delete a file
Download
Download a file
Move
Move a file to another folder
Share
Add sharing permissions to a file
Update
Update a file
Upload
Upload an existing file to Google Drive
Search
Search or list files and folders
Create
Create a folder
Delete
Permanently delete a folder
Share
Add sharing permissions to a folder
Create
Create a shared drive
Delete
Permanently delete a shared drive
Get
Get a shared drive
Get Many
Get the list of shared drives
Update
Update a shared drive

FAQs

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  • Can I use Embeddings Google Gemini’s API with n8n?

  • Can I use Google Drive’s API with n8n?

  • Is n8n secure for integrating Embeddings Google Gemini and Google Drive?

  • How to get started with Embeddings Google Gemini and Google Drive integration in n8n.io?

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