​
Code

cookbook/agent_concepts/vector_dbs/mongodb.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb

mdb_connection_string = "mongodb://ai:ai@localhost:27017/ai?authSource=admin"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=MongoDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        wait_until_index_ready=60,
        wait_after_insert=300,
    ),
)
knowledge_base.load(recreate=True)

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
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.

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

Install libraries

pip install -U pymongo pypdf openai agno
3

Run Agent

python cookbook/agent_concepts/vector_dbs/mongodb.py

​
Code

cookbook/agent_concepts/vector_dbs/mongodb.py
from agno.agent import Agent
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.mongodb import MongoDb

mdb_connection_string = "mongodb://ai:ai@localhost:27017/ai?authSource=admin"

knowledge_base = PDFUrlKnowledgeBase(
    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
    vector_db=MongoDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        wait_until_index_ready=60,
        wait_after_insert=300,
    ),
)
knowledge_base.load(recreate=True)

agent = Agent(knowledge=knowledge_base, show_tool_calls=True)
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.

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

Install libraries

pip install -U pymongo pypdf openai agno
3

Run Agent

python cookbook/agent_concepts/vector_dbs/mongodb.py