Documentation IndexFetch the complete documentation index at: /llms.txtUse this file to discover all available pages before exploring further.
Fetch the complete documentation index at: /llms.txt
Use this file to discover all available pages before exploring further.
Combine dense embeddings and sparse keyword vectors in one Milvus search with SearchType.hybrid.
from agno.agent import Agent from agno.knowledge.knowledge import Knowledge from agno.vectordb.milvus import Milvus, SearchType vector_db = Milvus( collection="recipes", uri="/tmp/milvus_hybrid.db", search_type=SearchType.hybrid ) knowledge = Knowledge( vector_db=vector_db, ) knowledge.insert( url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf", ) agent = Agent(knowledge=knowledge) agent.print_response("How to make Tom Kha Gai", markdown=True)
Set up your virtual environment
uv venv --python 3.12 source .venv/bin/activate
uv venv --python 3.12 .venv\Scripts\activate
Install dependencies
uv pip install -U pymilvus pypdf openai agno
Set environment variables
export OPENAI_API_KEY=xxx
Run Agent
python milvus_db_hybrid_search.py
Was this page helpful?