# semantic

Group semantically coherent passages for embeddings and LLMs.

`semantic` groups coherent passages so boundaries follow meaning rather than
markup. It's the recommended mode when the chunks feed an embedding model or an
LLM, because each chunk is a self-contained idea.

```python
from py_chunks import get_chunks

chunks = get_chunks("research.pdf", mode="semantic")
```

## content_type

Semantic chunks carry the `content_type` `semantic`. The grouping is driven by
multiple signals (heading structure, paragraph proximity, length, and content
similarity), so a chunk may span several source paragraphs that belong together.

## When to use it

- Feeding an embedding model — coherent chunks embed better.
- LLM context where you want complete thoughts, not fragments.
- General-purpose RAG when you're not sure which mode to pick.

  Start with `semantic` for LLM/embedding pipelines and `section` for search
  indexes.