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.

Code

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

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

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

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U sqlalchemy psycopg pgvector agno
   
3. 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
   
4. Run Agent

   Mac

   python cookbook/knowledge/chunking/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.