MongoDB Hybrid Search

Code

cookbook/08_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.insert(
    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. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv 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/08_knowledge/vector_db/mongo_db/mongo_db_hybrid_search.py