Use this skill when users ask about RAG (Retrieval-Augmented Generation), semantic search, document indexing, embeddings, vector databases, or chunking strategies. Trigger phrases include "how does rag-research work", "improve search results", "RAG optimization", "embedding model", "chunking strategy", "vector database".
Provides best practices for RAG, semantic search, and document indexing. Use when users ask about embeddings, chunking strategies, or vector databases to optimize retrieval quality.
/plugin marketplace add docutray/docutray-claude-code-plugins/plugin install rag-research@docutray-pluginsThis skill inherits all available tools. When active, it can use any tool Claude has access to.
references/configuration.mdreferences/examples.mdUse this skill when users ask about RAG (Retrieval-Augmented Generation), semantic search, document indexing, embeddings, vector databases, or chunking strategies. This skill provides best practices for working with the rag-research plugin and optimizing document retrieval.
Trigger this skill when users:
The plugin uses FastEmbed with ONNX Runtime for efficient CPU inference:
| Model | Dimensions | Speed | Quality | Use Case |
|---|---|---|---|---|
BAAI/bge-small-en-v1.5 | 384 | Fast | Good | Default, general use |
BAAI/bge-base-en-v1.5 | 768 | Medium | Better | Higher accuracy needs |
BAAI/bge-large-en-v1.5 | 1024 | Slow | Best | Maximum quality |
Chunk size affects retrieval quality:
Recommend: Start with defaults (512/50), adjust based on results.
/rag-research:list to verify documents are indexed--limit 20 for comprehensive research/rag-research:list --filter "keyword"CHUNK_SIZE in .envMISTRAL_API_KEY in .env for scanned PDFs--no-ocr flag for text-based PDFsrm -rf ~/.rag-research and re-indexuv run rag-research statsSee references/configuration.md for detailed settings documentation.
See references/examples.md for common usage patterns.
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