Documentation Index
Fetch the complete documentation index at: https://agno-v2-shaloo-ai-support-link.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Recursive chunking is a method of splitting documents into smaller chunks by recursively applying a chunking strategy. This is useful when you want to process large documents in smaller, manageable pieces.
Create a Python file
import asyncio
from agno.agent import Agent
from agno.knowledge.chunking.recursive import RecursiveChunking
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_recursive_chunking", db_url=db_url),
)
asyncio.run(knowledge.ainsert(
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
reader=PDFReader(
name="Recursive Chunking Reader",
chunking_strategy=RecursiveChunking(),
),
))
agent = Agent(
knowledge=knowledge,
search_knowledge=True,
)
agent.print_response("How to make Thai curry?", markdown=True)
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
Install dependencies
uv pip install -U agno sqlalchemy psycopg pgvector
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
Run the script
python recursive_chunking.py
Recursive 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. |