Embedding Strategies

About

This skill provides a comprehensive framework for selecting and implementing embedding models across various use cases, from RAG systems to specialized code search. It guides developers through the entire embedding pipeline, including document preprocessing, sophisticated chunking methods (token-based, semantic, and recursive), and the use of both proprietary APIs like OpenAI and local open-source models via Sentence Transformers. Additionally, it offers implementation templates for reducing embedding dimensions and evaluating retrieval quality using metrics like MRR and nDCG, ensuring high-performance semantic search implementations.

Key Features

• Comparison of leading proprietary and open-source embedding models
• Implementation templates for OpenAI, Voyage, and local Sentence Transformer models
• Embedding dimension reduction techniques using Matryoshka embeddings
• 23,139 GitHub stars
• Retrieval quality evaluation metrics like MRR, nDCG, and precision@k
• Advanced chunking strategies including recursive character and semantic sectioning

Use Cases

• Implementing domain-specific semantic search for code or legal documents
• Building or optimizing Retrieval-Augmented Generation (RAG) pipelines
• Reducing vector storage costs by optimizing embedding dimensions and batching