RAG Embedding Generation Skill

Capabilities

• Generate embeddings with multiple providers
• Implement batch processing for large datasets
• Configure caching for embedding reuse
• Handle rate limiting and retries
• Support various embedding models
• Implement embedding quality validation

Target Processes

• rag-pipeline-implementation
• vector-database-setup

Implementation Details

Embedding Providers

1. OpenAI Embeddings: text-embedding-ada-002, text-embedding-3-*
2. HuggingFace: sentence-transformers models
3. Cohere: embed-v3 models
4. Voyage AI: voyage-2 models
5. Local Models: GGUF/ONNX embedding models

Configuration Options

• Model selection and parameters
• Batch size optimization
• Cache backend configuration
• Rate limit settings
• Retry policies
• Dimensionality settings

Best Practices

• Use appropriate model for domain
• Implement caching for cost reduction
• Monitor embedding quality
• Handle API errors gracefully

Dependencies

• langchain-openai / langchain-huggingface
• numpy
• Caching backend (Redis, SQLite)