Sync Operations

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.

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

   Mac

   python cookbook/knowledge/basic_operations/13_sync.py