This example application shows how to use MariaDB, LangChain, FastAPI, and NiceGUI to implement the search functionality for an online store. It uses semantic search storing vectors created by Google Generative AI in MariaDB.

Note: If you are looking for the demo implemented during the webinar Beyond Keywords: AI Vector Search with LangChain and MariaDB Cloud, see the webinar-main.py file.

You'll need:

• A MariaDB server up and running (or spin up a free serverless instance in seconds using MariaDB Cloud)
• Python 3.11 or later
• pip
• A SQL client compatible with MariaDB (for example, DBeaver, the mariadb command-line tool, or an extension for your IDE)

Connect to your MariaDB database and create the following table:

CREATE OR REPLACE TABLE products ( id SERIAL PRIMARY KEY, name VARCHAR(100) NOT NULL, category VARCHAR(100), price DECIMAL(10,2) NOT NULL, description TEXT, created_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP, updated_at DATETIME NOT NULL DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP, FULLTEXT(name), FULLTEXT(description) );

Download this CSV file.

Load the data from that CSV file into the table that you previously created (remember to use the absolute path to your CSV file):

LOAD DATA LOCAL INFILE '/path/to/products.csv' INTO TABLE products FIELDS TERMINATED BY ',' ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 ROWS (@dummy_id, name, category, price, description);

Download or clone this repository and move to the corresponding directory:

git clone https://github.com/mariadb-developers/langchain-fastapi-mariadb-webinar-demo.git cd langchain-fastapi-mariadb-webinar-demo

Define the OS environment variables using the values corresponding to you MariaDB database connection and Google Generative AI API key. For example, on Linux:

export DB_HOST=127.0.0.1 export DB_PORT=3306 export DB_USER=root export DB_PASSWORD=password export DB_NAME=demo export GOOGLE_API_KEY=key1234567 

Alternatively, modify the default values for this variables in the backend.py file.

Create and activate a new virtual environment:

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

Install the required packages:

pip install -r requirements.txt

Run the FastAPI backend:

python backend.py

Go to http://localhost:8000/docs, authorize (use this API key: demo-key-123), and invoke the POST /ingest-products endpoint to calculate and store the vector embeddings. Check the logs in the terminal to confirm 500 products are ingested.

Important! Run the POST /ingest-products endpoint before running using the frontend!

Run the NiceGUI frontend in a separate terminal:

cd langchain-fastapi-mariadb-webinar-demo source venv/bin/activate python frontend.py

This should open your default browser pointing to http://127.0.0.1:8080/.