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Code

cookbook/agent_concepts/knowledge/vector_dbs/qdrant_db/qdrant_db_hybrid_search.py
from typing import Optional

import typer
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.search import SearchType
from agno.vectordb.qdrant import Qdrant
from rich.prompt import Prompt

COLLECTION_NAME = "thai-recipes"

vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333", search_type=SearchType.hybrid)

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=vector_db,
)


def qdrantdb_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__":
    # Comment out after first run
    knowledge_base.load(recreate=True)

    typer.run(qdrantdb_agent)

​
Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set your API key

export OPENAI_API_KEY=xxx
3

Install libraries

pip install -U qdrant-client fastembed tantivy pypdf openai agno
4

Run Agent

python cookbook/agent_concepts/knowledge/vector_dbs/qdrant_db/qdrant_db_hybrid_search.py