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

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Code

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

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

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

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

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

if __name__ == "__main__":
    asyncio.run(
        knowledge.add_content_async(
            path=Path("README.md"),
        )
    )

    asyncio.run(
        agent.aprint_response(
            "What can you tell me about Agno?",
            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 markdown sqlalchemy psycopg pgvector agno openai
3

Set environment variables

export OPENAI_API_KEY=xxx
4

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
5

Run Agent

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