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Code

cookbook/agent_concepts/vector_dbs/weaviate_db.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.weaviate import Distance, VectorIndex, Weaviate

vector_db = Weaviate(
    collection="recipes",
    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_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)
knowledge_base.load(recreate=False)  # Comment out after first run

# Create and use the agent
agent = Agent(
    knowledge=knowledge_base,
    search_knowledge=True,
    show_tool_calls=True,
)
agent.print_response("How to make Thai curry?", markdown=True)

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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

Run Agent

python cookbook/agent_concepts/vector_dbs/weaviate_db.py