voyageai

What This Skill Does

The Voyage AI Skill is a powerful CLI-driven integration for OpenClaw that connects directly to the Voyage AI ecosystem and MongoDB Atlas Vector Search. It provides a seamless interface for developers to handle the entire lifecycle of vector operations without requiring Python. By leveraging the voyageai-cli, this skill allows users to generate high-quality text embeddings, perform advanced reranking of search results, and manage Atlas Vector Search indexes directly from their terminal. Whether you are building RAG (Retrieval-Augmented Generation) pipelines, performing semantic similarity analysis, or managing large-scale vector datasets, this tool simplifies complex AI workflows into easy-to-use commands.

Installation

To integrate this skill into your environment, use the OpenClaw skill installer:

clawhub install openclaw/skills/skills/mrlynn/voyageai-skill

Additionally, ensure the underlying CLI is installed globally on your system to enable command execution:

npm install -g voyageai-cli

Use Cases

• Semantic Search Systems: Build intelligent search bars that understand context rather than just keyword matching.
• Content Recommendation Engines: Use the rerank functionality to surface the most relevant documents based on user intent.
• Database Enrichment: Automatically embed raw text data and store it alongside metadata in MongoDB Atlas for instant retrieval.
• AI Education: Use the explain commands to quickly understand complex machine learning concepts like cosine similarity or RAG architecture.
• Data Pipeline Automation: Bulk ingest text files into vector databases using CLI piping, perfect for CI/CD environments.

Example Prompts

1. "Voyage AI, generate an embedding for the text 'How does MongoDB Atlas vector search work?' and output it as an array."
2. "Rerank these documents for the query 'performance tuning' using the voyage-rerank-2 model: [list of documents]."
3. "Create a new vector search index on the 'knowledgebase' collection with 1024 dimensions and cosine similarity."

Tips & Limitations

• Environment Security: Always use environment variables (VOYAGE_API_KEY and MONGODB_URI) rather than hardcoding credentials into your scripts.
• Model Selection: Use vai models to identify the most cost-effective model for your specific latency vs. accuracy requirements.
• Resource Management: Vector searches can be resource-intensive; ensure your Atlas cluster tier is configured to support the required vector index memory overhead.
• Connectivity: Always run vai ping after configuring your API keys to verify connectivity before attempting large batch ingestion jobs.