Provide implementations of advanced data structures
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a5c-ai/babysitter1.9KUpdated Sep 17, 2026
FreePublished on GitHub
rag-embedding-generation
Batch embedding generation with caching, rate limiting, and multiple provider support
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Works withHugging Face
See how to installFiles included (2)
- SKILL.md1.6 KB
- README.md582 B
SKILL.md (original)
Review the instructions the agent will receive before you install.
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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