LLM Embedding Strategies

소개

This skill provides a comprehensive framework for building high-performance semantic search and Retrieval-Augmented Generation (RAG) systems. It guides developers through the critical process of selecting appropriate embedding models—ranging from OpenAI and Voyage AI to local open-source models like BGE—while providing robust templates for advanced chunking strategies. By implementing these patterns, developers can improve retrieval accuracy, manage vector database costs through dimension reduction, and handle complex data types including code, legal documents, and multilingual content.

주요 기능

• Advanced chunking (Recursive, Semantic, and Token-based splitting)
• Multi-model comparison and selection (OpenAI, Voyage, BGE, E5)
• Local embedding implementation with Sentence Transformers
• Matryoshka dimension reduction for cost-effective storage
• 1 GitHub stars
• Retrieval quality evaluation metrics (Precision/Recall at K)

사용 사례

• Optimizing search relevance for specialized domains like code or legal text
• Migrating from cloud-based embedding APIs to local open-source models
• Building high-accuracy Retrieval-Augmented Generation (RAG) pipelines