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Documentation Index

Fetch the complete documentation index at: https://docs-v1.agno.com/llms.txt

Use this file to discover all available pages before exploring further.

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Code

from agno.agent import Agent
from agno.knowledge.csv_url import CSVUrlKnowledgeBase
from agno.vectordb.pgvector import PgVector

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

knowledge_base = CSVUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/csvs/employees.csv"],
    vector_db=PgVector(table_name="csv_documents", db_url=db_url),
)
knowledge_base.load(recreate=False)  # Comment out after first run

agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)

agent.print_response(
    "What is the average salary of employees in the Marketing department?",
    markdown=True,
)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Install libraries

pip install -U sqlalchemy 'psycopg[binary]' 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 \
  agnohq/pgvector:16
4

Run Agent

python cookbook/agent_concepts/knowledge/csv_url_kb.py