​
Code

cookbook/knowledge/vector_db/upstash_db/upstash_db.py
import asyncio
import os

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.upstashdb import UpstashVectorDb

# How to connect to an Upstash Vector index
# - Create a new index in Upstash Console with the correct dimension
# - Fetch the URL and token from Upstash Console
# - Replace the values below or use environment variables

vector_db = UpstashVectorDb(
    url=os.getenv("UPSTASH_VECTOR_REST_URL"),
    token=os.getenv("UPSTASH_VECTOR_REST_TOKEN"),
)

# Initialize Upstash DB
knowledge = Knowledge(
    name="Basic SDK Knowledge Base",
    description="Agno 2.0 Knowledge Implementation with Upstash Vector DB",
    vector_db=vector_db,
)

# Create and use the agent
agent = Agent(knowledge=knowledge)

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

    # Create and use the agent
    asyncio.run(
        agent.aprint_response("How to 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 upstash-vector pypdf openai agno
3

Set environment variables

export UPSTASH_VECTOR_REST_URL="your-upstash-vector-rest-url"
export UPSTASH_VECTOR_REST_TOKEN="your-upstash-vector-rest-token"
export OPENAI_API_KEY=xxx
4

Run Agent

python cookbook/knowledge/vector_db/upstash_db/upstash_db.py