rag-embedding-generation
Batch embedding generation with caching, rate limiting, and multiple provider support
npx skills add a5c-ai/babysitter --skill rag-embedding-generation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)
- Capabilities
- Target Processes
- Implementation Details
- Embedding Providers
- Configuration Options
- Best Practices
- Dependencies
What does the rag-embedding-generation skill do?
Batch embedding generation with caching, rate limiting, and multiple provider support
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill rag-embedding-generation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.