​
Code

cookbook/knowledge/vector_db/weaviate_db/async_weaviate_db.py

import asyncio

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes_async",
    search_type=SearchType.hybrid,
    vector_index=VectorIndex.HNSW,
    distance=Distance.COSINE,
    local=True,  # Set to False if using Weaviate Cloud and True if using local instance
)
# Create knowledge base
knowledge = Knowledge(
    vector_db=vector_db,
)

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

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",
        )
    )

    # Create and use the agent
    asyncio.run(agent.aprint_response("How to make Tom Kha Gai", 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 weaviate-client pypdf openai agno
3

Set environment variables

export OPENAI_API_KEY=xxx
4

Setup Weaviate

# 1. Create account at https://console.weaviate.cloud/
# 2. Create a cluster and copy the "REST endpoint" and "Admin" API Key
# 3. Set environment variables:
export WCD_URL="your-cluster-url" 
export WCD_API_KEY="your-api-key"
# 4. Set local=False in the code
5

Run Agent

python cookbook/knowledge/vector_db/weaviate_db/async_weaviate_db.py