feat: add optional embed_model to SemanticDoubleMergingSplitterNodeParser

[MkDev11]

Description

Adds optional embedding-model support to SemanticDoubleMergingSplitterNodeParser so users can chunk text in any language (e.g. via Hugging Face / sentence-transformers) without depending on Spacy. When embed_model is set, similarity is computed with BaseEmbedding.get_text_embedding_batch and similarity() instead of Spacy; when unset, existing Spacy + LanguageConfig behavior is unchanged. No new dependencies in llama-index-core; users supply an embedding (e.g. llama-index-embeddings-huggingface) if they want HF.

Closes #15041

New Package?

• No

Version Bump?

• No

Type of Change

• New feature (non-breaking change which adds functionality)

How Has This Been Tested?

• I added new unit tests to cover this change

Tests added: test_embed_model_path_returns_nodes, test_embed_model_similarity_in_range, test_embed_model_single_sentence_document in tests/node_parser/test_semantic_double_merging_splitter.py (use MockEmbedding; no Spacy required).

Run: cd llama-index-core && python3 -m pytest tests/node_parser/test_semantic_double_merging_splitter.py -v

Suggested Checklist:

• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• I have added Google Colab support for the newly added notebooks.
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• I ran uv run make format; uv run make lint to appease the lint gods

[MkDev11]

@AstraBert can you please review the PR and let me know your feedback?