Weaviate Async

Code

cookbook/08_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.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

   Mac

   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

   Weaviate Cloud

   # 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 cookbook/08_knowledge/vector_db/weaviate_db/async_weaviate_db.py