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Code

cookbook/knowledge/vector_db/pinecone_db/pinecone_db.py
import asyncio
from os import getenv

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.pineconedb import PineconeDb

api_key = getenv("PINECONE_API_KEY")
index_name = "thai-recipe-index"

vector_db = PineconeDb(
    name=index_name,
    dimension=1536,
    metric="cosine",
    spec={"serverless": {"cloud": "aws", "region": "us-east-1"}},
    api_key=api_key,
)

knowledge = Knowledge(
    name="My Pinecone Knowledge Base",
    description="This is a knowledge base that uses a Pinecone Vector DB",
    vector_db=vector_db,
)

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

if __name__ == "__main__":
    asyncio.run(
        knowledge.add_content_async(
            name="Recipes",
            url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
            metadata={"doc_type": "recipe_book"},
        )
    )

    asyncio.run(
        agent.aprint_response("How do I make pad thai?", 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 pinecone-client pypdf openai agno
3

Set environment variables

export PINECONE_API_KEY="your-pinecone-api-key"
export OPENAI_API_KEY=xxx
4

Run Agent

python cookbook/knowledge/vector_db/pinecone_db/pinecone_db.py