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Fixed size chunking is a method of splitting documents into smaller chunks of a specified size, with optional overlap between chunks. This is useful when you want to process large documents in smaller, manageable pieces.
1

Create a Python file

touch fixed_size_chunking.py
2

Add the following code to your Python file

fixed_size_chunking.py
import asyncio
from agno.agent import Agent
from agno.knowledge.chunking.fixed import FixedSizeChunking
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_fixed_size_chunking", db_url=db_url),
)

asyncio.run(knowledge.add_content_async(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    reader=PDFReader(
        name="Fixed Size Chunking Reader",
        chunking_strategy=FixedSizeChunking(),
    ),
))
agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)

agent.print_response("How to make Thai curry?", markdown=True)
3

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
4

Install libraries

pip install -U sqlalchemy psycopg pgvector agno
5

Run PgVector

docker run -d \
  -e POSTGRES_DB=ai \
  -e POSTGRES_USER=ai \
  -e POSTGRES_PASSWORD=ai \
  -e PGDATA=/var/lib/postgresql/data/pgdata \
  -v pgvolume:/var/lib/postgresql/data \
  -p 5532:5432 \
  --name pgvector \
  agno/pgvector:16
6

Run the script

python fixed_size_chunking.py

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Fixed Size Chunking Params

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