Add support for generating multiple embeddings from a long document, using Ollama-compatible embedding models like granite-embedding, nomic-embed-text, snowflake-arctic-embed2:568m, etc.

Expected behavior:

• A document (e.g. 65 pages) is automatically split into segments (e.g. per paragraph, page, or fixed-size chunks with overlap)

• Each chunk is processed independently to produce a separate embedding vector

• Output is a list of vectors, one per chunk

• Optionally:

• Export to JSON or CSV

• Show token count and chunk preview

• Use for similarity search or RAG

Do not compute one embedding for the whole document — the goal is to allow semantic lookup from fine-grained vectors, not a single blurred one.