n8n community sub-node for Google Vertex AI Embeddings with output dimensions and configurable batch size support - resolves LangChain compatibility issues
npm install n8n-nodes-google-vertex-embeddings-extendedThis 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.
- Support for any Google Vertex AI embedding model (specify by name)
- Output dimensions configuration (for supported models like gemini-embedding-001)
- Batch size control to support models that only allow one document per request
- 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
1. In n8n, go to Settings > Community Nodes
2. Search for n8n-nodes-google-vertex-embeddings-extended
3. Click Install
``bash`
npm install n8n-nodes-google-vertex-embeddings-extended
1. A Google Cloud Platform account
2. A project with Vertex AI API enabled
3. Google API credentials configured in n8n
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
This is a sub-node that provides embeddings functionality to other n8n AI nodes.
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
`
[Document Loader] → [Vector Store] ← [Embeddings Google Vertex Extended]
↓
[AI Agent/Chain]
#### 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
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#### 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 dimensions256
- Set to a specific number (e.g., , 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
- 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
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
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
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
Contributions are welcome! Please feel free to submit a Pull Request.
MIT
For issues and feature requests, please use the GitHub issue tracker.