custom_chunking.py
"""
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
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno fastembed openai pypdf qdrant-client rapidocr-onnxruntime
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run Qdrant
docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
5
Run the example
Save the code above as
custom_chunking.py, then run:python custom_chunking.py