Code
Usage
1
Create a virtual environment
Open the
Terminal and create a python virtual environment.2
Install libraries
3
Run PgVector
4
Run Agent
Documentation Index
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
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.add_content_async(
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)
Create a virtual environment
Terminal and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
python3 -m venv .venv
.venv/scripts/activate
Install libraries
pip install -U sqlalchemy psycopg pgvector agno
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 Agent
python cookbook/knowledge/chunking/recursive_chunking.py
python cookbook/knowledge/chunking/recursive_chunking.py
| 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. |