Google Gemini Embeddings
Generates vector embeddings from text using Google Gemini, enabling semantic search, similarity analysis, and powerful AI applications.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add jackspace-google-gemini-embeddings npx -- -y @trustedskills/jackspace-google-gemini-embeddings
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"jackspace-google-gemini-embeddings": {
"command": "npx",
"args": [
"-y",
"@trustedskills/jackspace-google-gemini-embeddings"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
This skill allows AI agents to generate embeddings using Google's Gemini models. Embeddings are numerical representations of text that capture semantic meaning, enabling tasks like similarity search and clustering. It leverages the power of Gemini for high-quality vector representations suitable for various NLP applications.
When to use it
- Semantic Search: Find documents or passages similar in meaning to a given query.
- Clustering: Group related text documents together based on their content.
- Recommendation Systems: Recommend items (e.g., articles, products) based on the similarity of their descriptions.
- Anomaly Detection: Identify unusual or outlier text entries that deviate from typical patterns.
Key capabilities
- Generates embeddings using Google Gemini models.
- Provides numerical vector representations of text.
- Supports various input text lengths.
Example prompts
- "Generate an embedding for the following sentence: 'The quick brown fox jumps over the lazy dog.'"
- "Create a vector representation of this product description: 'A comfortable and stylish running shoe with excellent cushioning.'"
- "What is the embedding for this customer review?"
Tips & gotchas
- Ensure your AI agent has access to Google Gemini API keys.
- Longer input texts may incur higher costs depending on the specific Gemini model used.
Tags
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