Fixed Size Chunking

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

   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.ainsert(
       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)
   
2. Set up your virtual environment

   Mac

   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
   
4. 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
   
5. Run the script

   python fixed_size_chunking.py
   

Fixed Size Chunking Params

Parameter	Type	Default	Description
chunk_size	int	5000	The maximum size of each chunk.
overlap	int	0	The number of characters to overlap between chunks.