> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# SDPM Chunking

> Sentence-level chunking with semantic double-pass merging

SDPM (Semantic Double-Pass Merging) combines sentence-level chunking with semantic analysis for optimal chunk boundaries.

## Quick Start

<CodeGroup>
  ```python Agent with SDPM Chunking theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
  from praisonaiagents import Agent

  agent = Agent(
      instructions="Answer questions from research papers.",
      knowledge={
          "sources": ["papers/"],
          "chunker": {
              "type": "sdpm",
              "chunk_size": 512,
              "embedding_model": "all-MiniLM-L6-v2"
          }
      }
  )

  response = agent.start("Summarize the findings")
  ```

  ```python Direct API theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
  from praisonaiagents.knowledge.chunking import Chunking

  chunker = Chunking(
      chunker_type="sdpm",
      chunk_size=512,
      embedding_model="all-MiniLM-L6-v2"
  )

  chunks = chunker.chunk("Your research paper content...")
  ```
</CodeGroup>

## When to Use

* Research papers with complex structure
* Technical documents with multiple topics
* Content where both sentence flow AND semantic coherence matter

## Parameters

| Parameter         | Type | Default | Description                 |
| ----------------- | ---- | ------- | --------------------------- |
| `chunk_size`      | int  | 512     | Max tokens per chunk        |
| `embedding_model` | str  | auto    | Model for semantic analysis |