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integrationEmbeddings Google Gemini node
integrationGoogle Gemini Chat Model node

Embeddings Google Gemini and Google Gemini Chat Model integration

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

How to connect Embeddings Google Gemini 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.

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

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

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

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

Step 3: Connect Embeddings Google Gemini and Google Gemini Chat Model

A connection establishes a link between Embeddings Google Gemini 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.

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

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

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

Step 5: Test and activate your Embeddings Google Gemini 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 Embeddings Google Gemini 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.

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

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.

Nodes used in this workflow

Popular Embeddings Google Gemini and Google Gemini Chat Model workflows

+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.
+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 Embeddings Google Gemini and Google Gemini Chat Model integration

Create custom Embeddings Google Gemini 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.

Embeddings Google Gemini and Google Gemini Chat Model integration details

FAQs

  • Can Embeddings Google Gemini connect with Google Gemini Chat Model?

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

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

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

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