n8n community sub-node for Google Gemini Embeddings with extended features like output dimensions support
npm install n8n-nodes-google-gemini-embeddings-extendedThis is an n8n community sub-node that provides Google Gemini Embeddings with extended features, including support for task types, titles, and optimized handling for different Google embedding models.
- Support for any Google Gemini embedding model (dynamically loaded from Google's API)
- Task type specification for optimized embeddings (RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, etc.)
- Title support for retrieval documents (improves embedding quality)
- Optimized API handling using official @google/genai library
- Uses standard Google API credentials (same as other Google AI nodes)
- Works as a sub-node with vector stores and other AI nodes
- Clean, production-ready implementation
1. In n8n, go to Settings > Community Nodes
2. Search for n8n-nodes-google-gemini-embeddings-extended
3. Click Install
``bash`
npm install n8n-nodes-google-gemini-embeddings-extended
1. A Google AI Studio account
2. A Gemini API key
This node uses the standard Google PaLM/Gemini API credentials:
1. Get your API key from Google AI Studio
2. In n8n, create Google PaLM API credentials
3. Enter your API key
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 Gemini Extended node to the embeddings input of the vector store
3. Configure your Google PaLM API credentials
4. Enter your model name (e.g., text-embedding-004, gemini-embedding-001)
5. Configure additional options as needed
6. The vector store will use these embeddings to process your documents
`
[Document Loader] → [Vector Store] ← [Embeddings Google Gemini Extended]
↓
[AI Agent/Chain]
#### Model Name
Select any valid Google Gemini embedding model from the dropdown (dynamically loaded from Google's API). Examples:
- text-embedding-004 (Latest model, 768 default dimensions)gemini-embedding-001
- (Advanced model, 3072 default dimensions)embedding-001
- (Legacy model, 768 default dimensions)
#### 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
- Question Answering: For Q&A systems
- Fact Verification: For fact-checking applications
- Code Retrieval Query: For code search
#### Additional Options
- Title: Add a title to documents (only for RETRIEVAL_DOCUMENT task type)
- Strip New Lines: Remove line breaks from input text (enabled by default)
- Semantic Search: Generate embeddings for documents and queries in vector stores
- RAG Applications: Build retrieval-augmented generation systems
- Document Similarity: Find similar documents in your vector database
- Multi-language Support: Use models that support multiple languages
- Code Search: Use CODE_RETRIEVAL_QUERY for searching code repositories
- Advanced model with 3072 default dimensions
- High-quality embeddings for complex use cases
- Optimized for semantic similarity and retrieval tasks
- Supports batch processing
- Default dimensions: 768
- Good balance of performance and quality
This community node extends the official Google Gemini Embeddings node with:
1. Extended Task Types: More task type options for embedding optimization
2. Title Support: Add titles to documents for better retrieval quality
3. Official Library: Uses @google/genai library for better compatibility
4. Model Flexibility: Dynamic model loading from Google's available models
5. Production Ready: Clean implementation with optional debug logging
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 PaLM API key is valid
- Check that the API is enabled in your Google Cloud project
- Verify you have sufficient quota
2. Model Errors
- Verify the model name is spelled correctly
- Check Google's documentation for valid model names
3. Rate Limit Errors
- Add delays between requests if processing large datasets
- Check your Google API quota and rate limits
4. Bad Request Errors
- Ensure text inputs are within token limits
- Verify model names are valid and available
Contributions are welcome! Please feel free to submit a Pull Request.
MIT
For issues and feature requests, please use the GitHub issue tracker.
from 0.0.23 to 0.2.10
- Updated n8n-workflow` peer dependency to match current version (1.82.0)