Agentic Chunking

Agentic chunking is an intelligent method of splitting documents into smaller chunks by using a model to determine natural breakpoints in the text. Rather than splitting text at fixed character counts, it analyzes the content to find semantically meaningful boundaries like paragraph breaks and topic transitions.

1. Create a Python file

   agentic_chunking.py

   import asyncio
   from agno.agent import Agent
   from agno.knowledge.chunking.agentic import AgenticChunking
   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_agentic_chunking", db_url=db_url),
   )
   
   asyncio.run(knowledge.ainsert(
       url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
       reader=PDFReader(
           name="Agentic Chunking Reader",
           chunking_strategy=AgenticChunking(),
       ),
   ))
   
   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 agentic_chunking.py
   

Agentic Chunking Params

Parameter	Type	Default	Description
model	Model	OpenAIChat	The model to use for chunking.
max_chunk_size	int	5000	The maximum size of each chunk.