MongoDB Cosmos vCore

Code

cookbook/knowledge/vector_db/mongo_db/cosmos_mongodb_vcore.py

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.mongodb import MongoVectorDb


mdb_connection_string = "mongodb+srv://<username>:<encoded_password>@cluster0.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000"

knowledge_base = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
        cosmos_compatibility=True,
    ),
)

knowledge_base.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(knowledge=knowledge_base)

agent.print_response("How to make Thai curry?", markdown=True)

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. Install libraries

   pip install -U pymongo pypdf openai agno
   
3. Run Agent

   Mac

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