rag-embedding-generation

# 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)