Smarter Retrieval for RAG: Late Chunking with Jina Embeddings v2 and Milvus

What this post added

Introduces and implements a 'Late Chunking' strategy for RAG systems. This involves embedding entire documents first to capture global context, then averaging contiguous token embeddings to form chunk vectors. Provides Python code for `sentence_chunker`, `document_to_token_embeddings`, and `late_chunking` functions. Demonstrates comparison with traditional chunking methods using cosine similarity and shows how to store and query these embeddings in Milvus.