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Code

cookbook/knowledge/vector_db/mongo_db/mongo_db_hybrid_search.py
import typer
from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb
from agno.vectordb.search import SearchType
from rich.prompt import Prompt

mdb_connection_string = "mongodb://localhost:27017"

vector_db = MongoVectorDb(
    collection_name="recipes",
    db_url=mdb_connection_string,
    search_index_name="recipes",
    search_type=SearchType.hybrid,
)

knowledge_base = Knowledge(
    vector_db=vector_db,
)

knowledge_base.add_content(
    name="Recipes",
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
    metadata={"doc_type": "recipe_book"},
)


def mongodb_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(mongodb_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

Install libraries

pip install -U pymongo typer rich pypdf openai agno
3

Run MongoDB

docker run -d \
--name local-mongo \
-p 27017:27017 \
-e MONGO_INITDB_ROOT_USERNAME=mongoadmin \
-e MONGO_INITDB_ROOT_PASSWORD=secret \
mongo
4

Run Agent

python cookbook/knowledge/vector_db/mongo_db/mongo_db_hybrid_search.py