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Code
cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db_hybrid_search.py
import typer
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
from rich.prompt import Prompt
vector_db = Weaviate(
collection = "recipes" ,
search_type = SearchType.hybrid,
vector_index = VectorIndex. HNSW ,
distance = Distance. COSINE ,
local = False , # Set to True if using Weaviate Cloud and False if using local instance
# Adjust alpha for hybrid search (0.0-1.0, default is 0.5), where 0 is pure keyword search, 1 is pure vector search
hybrid_search_alpha = 0.6 ,
)
knowledge_base = Knowledge(
name = "Weaviate Hybrid Search" ,
description = "A knowledge base for Weaviate hybrid search" ,
vector_db = vector_db,
)
knowledge_base.insert(
url = "https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf" ,
)
def weaviate_agent ( user : str = "user" ):
agent = Agent(
user_id = user,
knowledge = knowledge_base,
search_knowledge = True ,
)
while True :
message = Prompt.ask( f "[bold] :sunglasses: { user } [/bold]" )
if message in ( "exit" , "bye" ):
break
agent.print_response(message)
if __name__ == "__main__" :
typer.run(weaviate_agent)
Usage
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
Install dependencies
uv pip install -U weaviate-client typer rich pypdf openai agno
Setup Weaviate
Weaviate Cloud
Local Development
# 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
Set environment variables
export OPENAI_API_KEY = xxx
Run Agent
python cookbook/08_knowledge/vector_db/weaviate_db/weaviate_db_hybrid_search.py