ReddRadar
Agent skill
rag-embedding-generation
Batch embedding generation with caching, rate limiting, and multiple provider support
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/ai-agents-conversational/skills/rag-embedding-generation
SKILL.md
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
- OpenAI Embeddings: text-embedding-ada-002, text-embedding-3-*
- HuggingFace: sentence-transformers models
- Cohere: embed-v3 models
- Voyage AI: voyage-2 models
- 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)
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