Usage

MongoDB

Insert a PDF into a MongoDB collection and query it with an agent.

Code

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

mdb_connection_string = "mongodb://localhost:27017"
knowledge = Knowledge(
    vector_db=MongoVectorDb(
        collection_name="recipes",
        db_url=mdb_connection_string,
        search_index_name="recipes",
    ),
)

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

# Create and use the agent
agent = Agent(knowledge=knowledge)
agent.print_response("How to make Thai curry?", markdown=True)

Usage

Set up your virtual environment

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

Install dependencies

uv pip install -U pymongo pypdf openai agno

Run MongoDB

docker run -d \
--rm \
--name mongodb-container \
-p 27017:27017 \
-v ./tmp/mongo-data:/data/db \
mongodb/mongodb-atlas-local:8.0.3

Set environment variables

export OPENAI_API_KEY=xxx

Run Agent

python mongo_db.py

Overview

Use MongoDB as a vector database for your Knowledge Base.

MongoDB Async

Insert knowledge and query it asynchronously with ainsert() and aprint_response() on MongoDB.