Implement more efficient chunking strategies

# Motivation

Currently the chunking is possible using Layout, Page, Fixed-size and Paragraph strategies with possible overlap. I would suggest an additional strategy focused solely on quality: LLM chunking. A LLM is called to have coherent and relevant chunks, each chunk expressing the same idea or concept or thought.

In all chunking strategies, a LLM can be used to generate additional metadata to improve the reranking by Azure AI Search when semantic search is True. The LLM would populate for each chunk:
- "Title" by summarizing the chunk into one short sentence
- "Keyword" by extracting the main keywords of the chunk

Please note that in order to make the reranking useful, [ticket 2093](https://github.com/Azure-Samples/chat-with-your-data-solution-accelerator/issues/2093) needs to be implemented.

# Tasks

_To be filled in by the engineer picking up the issue_

- [ ] Task 1
- [ ] Task 2
- [ ] ...

Start by reviewing the existing Layout, Page, Fixed-size, and Paragraph chunking strategies and the Azure AI Search semantic-search path. Define the LLM chunking and per-chunk Title/Keyword metadata requirements, then check how ticket 2093 affects reranking. Done should include concrete tasks and an agreed implementation scope.

Written by the indexing model from the issue text.