google-vertex-embeddings-extended

Package Information

Released: 5/31/2025
Downloads: 30 weekly / 1,140 monthly
Latest Version: 0.3.2
Author: danblah

Available Nodes

Embeddings Google Vertex Extended icon
Embeddings Google Vertex Extended
Use Google Vertex AI Embeddings with output dimensions support

Documentation

n8n-nodes-google-vertex-embeddings-extended

This is an n8n community sub-node that provides Google Vertex AI Embeddings with additional features, including support for output dimensions. Use this node with vector store nodes in n8n.

Features

  • Support for any Google Vertex AI embedding model (specify by name)
  • Output dimensions configuration (for supported models like text-embedding-004)
  • Task type specification for optimized embeddings
  • Region selection
  • Project ID dropdown with auto-loading from your Google account
  • Uses standard Google API credentials (same as other Google nodes)
  • Works as a sub-node with vector stores and other AI nodes

Installation

Community Node (Recommended)

  1. In n8n, go to Settings > Community Nodes
  2. Search for n8n-nodes-google-vertex-embeddings-extended
  3. Click Install

Manual Installation

npm install n8n-nodes-google-vertex-embeddings-extended

Setup

Prerequisites

  1. A Google Cloud Platform account
  2. A project with Vertex AI API enabled
  3. Google API credentials configured in n8n

Authentication

This node uses the standard Google API credentials that you may already have configured for other Google nodes in n8n:

  1. In n8n, create or use existing Google API credentials
  2. Ensure your service account has the Vertex AI User role
  3. The node will automatically load your available projects

Usage

This is a sub-node that provides embeddings functionality to other n8n AI nodes.

Using with Vector Stores

  1. Add a vector store node to your workflow (e.g., Pinecone, Qdrant, Supabase Vector Store)
  2. Connect the Embeddings Google Vertex Extended node to the embeddings input of the vector store
  3. Select your Google API credentials
  4. Choose your project from the dropdown (auto-loaded from your Google account)
  5. Enter your model name (e.g., text-embedding-004)
  6. Configure additional options as needed
  7. The vector store will use these embeddings to process your documents

Example Workflow

[Document Loader] → [Vector Store] ← [Embeddings Google Vertex Extended]
                          ↓
                    [AI Agent/Chain]

Configuration Options

Model Name

Enter any valid Google Vertex AI embedding model name. Examples:

  • text-embedding-004 (Latest, supports output dimensions)
  • text-multilingual-embedding-002 (Multilingual support, supports output dimensions)
  • textembedding-gecko@003
  • textembedding-gecko@002
  • textembedding-gecko@001
  • textembedding-gecko-multilingual@001

Output Dimensions

For models that support it (like text-embedding-004), you can specify the number of output dimensions:

  • Set to 0 to use the model's default dimensions
  • Set to a specific number (e.g., 256, 512) to get embeddings of that size

Task Types

Optimize your embeddings by specifying the task type:

  • Retrieval Document: For document storage in retrieval systems
  • Retrieval Query: For search queries
  • Semantic Similarity: For comparing text similarity
  • Classification: For text classification tasks
  • Clustering: For grouping similar texts

Use Cases

  • Semantic Search: Generate embeddings for documents and queries in vector stores
  • RAG Applications: Build retrieval-augmented generation systems with custom embeddings
  • Document Similarity: Find similar documents in your vector database
  • Multi-language Support: Use multilingual models for international applications

Differences from Official n8n Node

This community node extends the official Google Vertex AI Embeddings node with:

  1. Output Dimensions Support: Configure the size of embedding vectors
  2. Flexible Model Selection: Enter any model name instead of choosing from a fixed list
  3. Task Type Selection: Optimize embeddings for specific use cases
  4. Standard Google Credentials: Uses the same credentials as other Google nodes

Compatible Nodes

This embeddings node can be used with:

  • Simple Vector Store
  • Pinecone Vector Store
  • Qdrant Vector Store
  • Supabase Vector Store
  • PGVector Vector Store
  • Milvus Vector Store
  • MongoDB Atlas Vector Store
  • Zep Vector Store
  • Question and Answer Chain
  • AI Agent nodes

Troubleshooting

Common Issues

  1. Authentication Errors

    • Ensure your Google API credentials are properly configured
    • Check that your service account has the Vertex AI User role
    • Verify the Vertex AI API is enabled in your selected project

  2. Project Not Showing in Dropdown

    • Ensure your service account has access to the project
    • Check that the Cloud Resource Manager API is enabled

  3. Model Errors

    • Verify the model name is spelled correctly
    • Ensure the model is available in your selected region
    • Check Google's documentation for valid model names
    • Note: gemini-embedding-001 only supports one input at a time, which may slow down processing for large datasets

  4. Region Errors

    • Make sure the selected region supports the chosen model
    • Default region is us-central1

  5. Dimension Errors

    • Not all models support custom dimensions
    • Check model documentation for supported dimension values

  6. Connection Issues

    • This is a sub-node and cannot be used standalone
    • Must be connected to a compatible root node (vector store, AI chain, etc.)

  7. Bad Request Errors with gemini-embedding-001

    • This model only accepts one text input per request
    • The node automatically handles this limitation by processing texts individually
    • Consider using text-embedding-004 or text-multilingual-embedding-002 for better performance with multiple texts

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT

Support

For issues and feature requests, please use the GitHub issue tracker.

Changelog

0.3.2

  • Fixed issue with gemini-embedding-001 model that only supports single input per request
  • Added better error messages to show API response details
  • Updated documentation about model limitations

0.3.1

  • Fixed node structure to properly register as a sub-node in embeddings category
  • Resolved issue where node was appearing as top-level instead of sub-node

0.3.0

  • Switched to standard Google API credentials
  • Added project ID dropdown with auto-loading
  • Changed model selection to text input for flexibility
  • Removed custom credentials requirement

0.2.0

  • Converted to sub-node architecture for use with vector stores
  • Improved compatibility with n8n AI nodes

0.1.0

  • Initial release
  • Support for Google Vertex AI embeddings
  • Output dimensions configuration
  • Task type selection

Discussion