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

# Custom Chunking: Implementing Your Own Strategy

> When built-in strategies don't fit your content, implement a custom one.

```python custom_chunking.py theme={null}
"""
Custom Chunking: Implementing Your Own Strategy
=================================================
When built-in strategies don't fit your content, implement a custom one.

A chunking strategy is a class that takes a Document and returns a list
of Document chunks. You control how content is split.

Use cases:
- Domain-specific splitting (legal clauses, medical records)
- Structured data (tables, forms)
- Content with custom delimiters

See also: ../02_building_blocks/01_chunking_strategies.py for built-in strategies.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.chunking.strategy import ChunkingStrategy
from agno.knowledge.document import Document
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Custom Chunking Strategy
# ---------------------------------------------------------------------------


class ParagraphChunking(ChunkingStrategy):
    """Splits documents on double newlines (paragraphs).

    Each paragraph becomes its own chunk. Simple but effective
    for well-structured prose content.
    """

    def chunk(self, document: Document) -> list[Document]:
        chunks = []
        if not document.content:
            return chunks

        paragraphs = document.content.split("\n\n")
        for i, paragraph in enumerate(paragraphs):
            paragraph = paragraph.strip()
            if paragraph:
                chunks.append(
                    Document(
                        name="%s_chunk_%d" % (document.name, i),
                        content=paragraph,
                        meta_data={
                            **(document.meta_data or {}),
                            "chunk_index": i,
                            "chunking_strategy": "paragraph",
                        },
                    )
                )
        return chunks


# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

qdrant_url = "http://localhost:6333"

knowledge = Knowledge(
    vector_db=Qdrant(
        collection="custom_chunking",
        url=qdrant_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

# Use the custom chunking strategy with a PDF reader
reader = PDFReader(chunking_strategy=ParagraphChunking())

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        await knowledge.ainsert(
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
            reader=reader,
        )

        print("\n" + "=" * 60)
        print("Custom paragraph-based chunking")
        print("=" * 60 + "\n")

        agent.print_response("What Thai recipes do you know about?", stream=True)

    asyncio.run(main())
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno fastembed openai pypdf qdrant-client rapidocr-onnxruntime
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run Qdrant">
    ```bash theme={null}
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

  <Step title="Run the example">
    Save the code above as `custom_chunking.py`, then run:

    ```bash theme={null}
    python custom_chunking.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/04\_advanced/02\_custom\_chunking.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/04_advanced/02_custom_chunking.py)