Skip to main content

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.

​
Code

from pathlib import Path

from agno.agent import Agent
from agno.knowledge.csv import CSVKnowledgeBase
from agno.vectordb.pgvector import PgVector

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

knowledge_base = CSVKnowledgeBase(
    path=Path("data/csvs"),
    vector_db=PgVector(
        table_name="csv_documents",
        db_url=db_url,
    ),
    num_documents=5,  # Number of documents to return on search
)
# Load the knowledge base
knowledge_base.load(recreate=False)

# Initialize the Agent with the knowledge_base
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
)

# Use the agent
agent.print_response("Ask me about something from the knowledge base", 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_kb.py