Document Chunking

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

   Mac

   uv venv --python 3.12
   source .venv/bin/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 document_chunking.py
   

Document 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.