Rag embedding generation
Skill a5c-ai/babysitter/library/specializations/ai-agents-conversational/skills/rag-embedding-generation
Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free self-orchestration
npx -y skills add a5c-ai/babysitter --skill rag-embedding-generationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Batch embedding generation with caching, rate limiting, and multiple provider support
SKILL.md
1.6 KB, as published. Nobody here has run it
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)