Weaviate Async

Insert knowledge and run an agent with Agno's async methods and a Weaviate backend.

Code

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.ainsert(
            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

Set up your virtual environment

uv venv --python 3.12
source .venv/bin/activate

Install dependencies

uv pip install -U weaviate-client pypdf openai agno

Set environment variables

export OPENAI_API_KEY=xxx

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

Run Agent

python async_weaviate_db.py