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Babysitter rag-embedding-generation

Batch embedding generation with caching, rate limiting, and multiple provider support

install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/ai-agents-conversational/skills/rag-embedding-generation" ~/.claude/skills/a5c-ai-babysitter-rag-embedding-generation && rm -rf "$T"
manifest: library/specializations/ai-agents-conversational/skills/rag-embedding-generation/SKILL.md
tags
#embedding-generation#rag-pipeline#vector-database#model-configuration#batch-processing#caching
source content

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)