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)
)
Create a virtual environment
Terminal
and create a python virtual environment.python3 -m venv .venv
source .venv/bin/activate
Install libraries
pip install -U pinecone-client pypdf openai agno
Set environment variables
export PINECONE_API_KEY="your-pinecone-api-key"
export OPENAI_API_KEY=xxx
Run Agent
python cookbook/knowledge/vector_db/pinecone_db/pinecone_db.py