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Document chunking is a method of splitting documents into smaller chunks based on document structure like paragraphs and sections. It analyzes natural document boundaries rather than splitting at fixed character counts. This is useful when you want to process large documents while preserving semantic meaning and context.
1

Create a Python file

document_chunking.py
import asyncio
from agno.agent import Agent
from agno.knowledge.chunking.document import DocumentChunking
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge = Knowledge(
    vector_db=PgVector(table_name="recipes_document_chunking", db_url=db_url),
)

asyncio.run(knowledge.ainsert(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        name="Document Chunking Reader",
        chunking_strategy=DocumentChunking(),
    ),
))

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

agent.print_response("How to make Thai curry?", markdown=True)
2

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

uv venv --python 3.12
.venv\Scripts\activate
3

Install dependencies

uv pip install -U agno sqlalchemy psycopg pgvector pypdf openai
4
Set OpenAI Key
5
Set your OPENAI_API_KEY as an environment variable. You can get one from OpenAI.
6
Mac
export OPENAI_API_KEY=sk-***
Windows
setx OPENAI_API_KEY sk-***
7

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql \
  -v pgvolume:/var/lib/postgresql \
  -p 5532:5432 \
  --name pgvector \
  agnohq/pgvector:18
8

Run the script

python document_chunking.py

​
Document Chunking Params

ParameterTypeDefaultDescription
chunk_sizeint5000The maximum size of each chunk.
overlapint0The number of characters to overlap between chunks.