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"""
PgVector Database
=================

Demonstrates PgVector-backed knowledge with sync, async, and async-batching flows.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pgvector import PgVector

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


# ---------------------------------------------------------------------------
# Create Knowledge Base
# ---------------------------------------------------------------------------
def create_sync_knowledge() -> tuple[Knowledge, PgVector]:
    vector_db = PgVector(table_name="vectors", db_url=db_url)
    knowledge = Knowledge(
        name="My PG Vector Knowledge Base",
        description="This is a knowledge base that uses a PG Vector DB",
        vector_db=vector_db,
    )
    return knowledge, vector_db


def create_async_knowledge(enable_batch: bool = False) -> tuple[Knowledge, PgVector]:
    if enable_batch:
        vector_db = PgVector(
            table_name="recipes",
            db_url=db_url,
            embedder=OpenAIEmbedder(enable_batch=True),
        )
    else:
        vector_db = PgVector(table_name="recipes", db_url=db_url)
    knowledge = Knowledge(vector_db=vector_db)
    return knowledge, vector_db


# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
def create_sync_agent(knowledge: Knowledge) -> Agent:
    return Agent(
        knowledge=knowledge,
        search_knowledge=True,
        read_chat_history=True,
    )


def create_async_agent(knowledge: Knowledge) -> Agent:
    return Agent(knowledge=knowledge)


# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
def run_sync() -> None:
    knowledge, vector_db = create_sync_knowledge()
    knowledge.insert(
        name="Recipes",
        url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
        metadata={"doc_type": "recipe_book"},
    )

    agent = create_sync_agent(knowledge)
    agent.print_response("How do I make pad thai?", markdown=True)

    vector_db.delete_by_name("Recipes")
    vector_db.delete_by_metadata({"doc_type": "recipe_book"})


async def run_async(enable_batch: bool = False) -> None:
    knowledge, _ = create_async_knowledge(enable_batch=enable_batch)
    agent = create_async_agent(knowledge)

    await knowledge.ainsert(url="https://docs.agno.com/basics/agents/overview.md")
    await agent.aprint_response("What is the purpose of an Agno Agent?", markdown=True)


if __name__ == "__main__":
    run_sync()
    asyncio.run(run_async(enable_batch=False))
    asyncio.run(run_async(enable_batch=True))

​
Run the Example

# Clone and setup repo
git clone https://github.com/agno-agi/agno.git
cd agno/cookbook/07_knowledge/vector_db/pgvector

# Create and activate virtual environment
./scripts/demo_setup.sh
source .venvs/demo/bin/activate

# Optiona: Run PgVector (needs docker)
./cookbook/scripts/run_pgvector.sh

python pgvector_db.py