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The CSV Reader with asynchronous processing allows you to handle CSV files and integrate them with knowledge bases efficiently.

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Code

examples/concepts/knowledge/readers/csv_reader_async.py
import asyncio
from pathlib import Path

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

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

knowledge = Knowledge(
    vector_db=PgVector(
        table_name="csv_documents",
        db_url=db_url,
    ),
    max_results=5,  # Number of results to return on search
)

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

if __name__ == "__main__":
    # Comment out after first run
    asyncio.run(knowledge.add_content_async(path=Path("data/csv")))

    # Create and use the agent
    asyncio.run(agent.aprint_response("What is the csv file about", markdown=True))

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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 pandas sqlalchemy psycopg 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 \
  agno/pgvector:16
4

Run Agent

python examples/concepts/knowledge/readers/csv_reader_async.py

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Params

ParameterTypeDefaultDescription
fileUnion[Path, IO[Any]]RequiredPath to CSV file or file-like object
delimiterstr","Character used to separate fields in the CSV
quotecharstr'"'Character used to quote fields in the CSV