This project provides an AI-powered agent for grocery shopping, leveraging MongoDB for data storage and Google Vertex AI for semantic search and embeddings.

• Semantic product search using MongoDB Atlas Vector Search and Vertex AI embeddings
• Add products to user carts in MongoDB

• Python 3.10+
• Access to Google Cloud Vertex AI
• Access to a MongoDB Atlas cluster (instructions below)
• Required Python packages (instructions below)
• Google ADK CLI installed (instructions below)

1. Create a free MongoDB Atlas cluster

• Go to MongoDB Atlas and sign up for a free account.
• Click "Build a Database" and choose the free tier (Shared, M0).
• Select your preferred cloud provider and region, then click "Create".
• Create a database user with a username and password.
• Add your IP address to the IP Access List (or allow access from anywhere for development).
• Once the cluster is created, click "Connect" and choose "Connect your application" to get your connection string. Use this string for the CONNECTION_STRING environment variable in the next steps.

2. Load the Dataset into MongoDB

Import the provided dataset into your MongoDB database using the following command (replace placeholders as needed):

mongoimport --uri "$CONNECTION_STRING" --db "$DATABASE_NAME" --collection "$COLLECTION_NAME" --type csv --headerline --file mongodb-groceries-agent/dataset.csv

3. Generate Embeddings for the Inventory

After loading the data, you need to generate vector embeddings for each product. Run the following script:

python mongodb-groceries-agent/create-embeddings.py

This will process all products in the collection and add/update the embedding field required for semantic search.

1. Clone the repository

git clone <your-repo-url>
cd MongoDB-VertexAI-ADK

2. Install dependencies

pip install -r requirements.txt

3. Install the ADK CLI

Follow the official ADK installation instructions or run:

pip install google-adk

4. Set environment variables

Set the following environment variables (e.g., in your shell or a .env file):

• GOOGLE_CLOUD_PROJECT: Your Google Cloud project ID
• GOOGLE_CLOUD_LOCATION: Your Google Cloud region (e.g., us-central1)
• DATABASE_NAME: The MongoDB database name (e.g., grocery_store)
• COLLECTION_NAME: The MongoDB collection name (e.g., inventory)
• CONNECTION_STRING: Your MongoDB Atlas connection string

Example (for bash/zsh):

export GOOGLE_CLOUD_PROJECT=your-gcp-project
export GOOGLE_CLOUD_LOCATION=us-central1
export DATABASE_NAME=grocery_store
export COLLECTION_NAME=inventory
export CONNECTION_STRING="mongodb+srv://<user>:<password>@<cluster-url>/"

5. Run the agent using ADK

Navigate to the mongodb-groceries-agent directory and run:

adk web

• The agent will start and be ready to handle product search and cart operations.
• You can extend the agent with new tools or integrate it into a larger application.

• mongodb-groceries-agent/agent.py: Main agent logic
• mongodb-groceries-agent/create-embeddings.py: Utility for creating embeddings
• mongodb-groceries-agent/dataset.csv: Example dataset

• Ensure your Google Cloud and MongoDB credentials are valid and have the necessary permissions.
• For local development, you may want to use a virtual environment.
• The ADK CLI is required for running and managing agents.

See LICENSE for details.