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integrationEmbeddings Google Gemini node
integrationHTTP Request node

Embeddings Google Gemini and HTTP Request integration

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How to connect Embeddings Google Gemini and HTTP Request

  • 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 HTTP Request integration: Create a new workflow and add the first step

Step 2: Add and configure Embeddings Google Gemini and HTTP Request nodes

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

Embeddings Google Gemini and HTTP Request integration: Add and configure Embeddings Google Gemini and HTTP Request nodes

Step 3: Connect Embeddings Google Gemini and HTTP Request

A connection establishes a link between Embeddings Google Gemini and HTTP Request (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 HTTP Request integration: Connect Embeddings Google Gemini and HTTP Request

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

Embeddings Google Gemini and HTTP Request integration: Customize and extend your Embeddings Google Gemini and HTTP Request integration

Step 5: Test and activate your Embeddings Google Gemini and HTTP Request 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 HTTP Request 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 HTTP Request integration: Test and activate your Embeddings Google Gemini and HTTP Request 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 HTTP Request 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.

Build your own Embeddings Google Gemini and HTTP Request integration

Create custom Embeddings Google Gemini and HTTP Request 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 HTTP Request integration details

Use case

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FAQs

  • Can Embeddings Google Gemini connect with HTTP Request?

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

  • Can I use HTTP Request’s API with n8n?

  • Is n8n secure for integrating Embeddings Google Gemini and HTTP Request?

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

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