This example shows how to add content to your knowledge base synchronously. While async operations are recommended for better performance, sync operations can be useful in certain scenarios.

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Code

13_sync.py
from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

contents_db = PostgresDb(
    db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
    knowledge_table="knowledge_contents",
)
# Create Knowledge Instance
knowledge = Knowledge(
    name="Basic SDK Knowledge Base",
    description="Agno 2.0 Knowledge Implementation",
    vector_db=PgVector(
        table_name="vectors", db_url="postgresql+psycopg://ai:ai@localhost:5532/ai"
    ),
)

knowledge.add_content(
    name="CV",
    path="cookbook/knowledge/testing_resources/cv_1.pdf",
    metadata={"user_tag": "Engineering Candidates"},
)


agent = Agent(
    name="My Agent",
    description="Agno 2.0 Agent Implementation",
    knowledge=knowledge,
    search_knowledge=True,
    debug_mode=True,
)

agent.print_response(
    "What skills does Jordan Mitchell have?",
    markdown=True,
)

​
Usage

1

Install libraries

pip install -U agno sqlalchemy psycopg pgvector openai
2

Set environment variables

export OPENAI_API_KEY=xxx
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 the example

python cookbook/knowledge/basic_operations/13_sync.py