embedding-strategies

Select an embedding model for semantic search by comparing OpenAI and Sentence Transformers based on dimensions, token limits, and data type suitability. This ensures accurate vector representations for your specific RAG application.

The best way to chunk text for an embedding pipeline is using strategies like token-based, sentence-based, semantic sections, or recursive splitting. Proper chunking optimizes retrieval quality in vector databases.

Evaluate semantic search retrieval quality by calculating metrics like Precision@K, Recall@K, MRR, and NDCG. These calculations measure how accurately your embedding pipeline retrieves relevant documents from the vector database.

Yes, embedding optimization works for specialized data like legal documents. You can select specific models such as voyage-2, implement sentence-based chunking strategies, and evaluate retrieval performance using targeted legal queries.

To implement chunking strategies you need text preprocessing dependencies like nltk and tiktoken. These libraries support token-based and sentence-based splitting to prepare text data for generating embeddings.