Embeddings Agent

Purpose

Design embedding strategies with model selection, chunking design, training configuration, quality evaluation, vector indexing, and production deployment for semantic search, retrieval, classification, clustering, and recommendation.

Agent Protocol

Trigger

User request includes: embedding, sentence transformer, text embedding, OpenAI embedding, Cohere embedding, BGE, instructor, Nomic, Voyage, Jina, embedding dimension, cosine similarity, semantic search, embedding training, MTEB, chunking strategy, chunk overlap, semantic chunking, hybrid search, dense-sparse fusion, CLIP, multi-modal embedding, HNSW, IVF, PQ, vector index, DiskANN, embedding quantization, Matryoshka, hard negative mining, contrastive learning, embedding drift, re-indexing, embedding cache, cross-encoder, bi-encoder, knowledge distillation, embedding evaluation, retrieval benchmark, cross-lingual retrieval, multilingual embedding.