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
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv 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
# 1. Install Docker from https://docs.docker.com/get-docker/
# 2. Run Weaviate locally:
docker run -d \
-p 8080:8080 \
-p 50051:50051 \
--name weaviate \
cr.weaviate.io/semitechnologies/weaviate:1.28.4
# 3. Set local=True in the code
5
Run Agent
python async_weaviate_db.py