Tutorial: Reviews by Sentiment

Visual Interface Track

Open /app/ and configure credentials.
Upload 1000_movie_reviews.csv.
Run the SQL query below.

samtSQL settings panel with connection configuration fields

Step 1: configure credentials and database settings.

samtSQL data tab with file chooser and upload button

Step 2: choose 1000_movie_reviews.csv and upload it.

samtSQL query tab with SQL editor and run button

Step 3: run the sentiment filter query in the SQL editor.

SELECT text, tag
FROM movie_reviews
WHERE AIFILTER(text, 'reviews with positive sentiment')
LIMIT 20;

Python + REST Track

import json
import requests

API_BASE = "https://www.samtsql.com/api"
API_TOKEN = "YOUR_API_TOKEN"  # create one under "API Tokens" in the web app
HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}

db = {
    "system": "postgres",
    "connection": {
        "host": "db.example.com",
        "port": 5432,
        "database": "analytics",
        "user": "app_user",
        "password": "secret"
    }
}

SCHEMA = "public"  # PostgreSQL schema for the uploaded table and result

with open("1000_movie_reviews.csv", "rb") as f:
    files = {"file": ("1000_movie_reviews.csv", f, "text/csv")}
    data = {"db_config": json.dumps(db), "target_schema": SCHEMA}
    upload = requests.post(f"{API_BASE}/upload", headers=HEADERS, files=files, data=data, timeout=120)
    upload.raise_for_status()
    table = upload.json()["table_name"]

query_payload = {
    "db": db,
    "sql": f"SELECT text, tag FROM {table} WHERE AIFILTER(text, 'reviews with positive sentiment') LIMIT 20",
    "format": "json",
    "result_schema": SCHEMA,
}
result = requests.post(f"{API_BASE}/query", headers=HEADERS, json=query_payload, timeout=300)
result.raise_for_status()
print(result.json())

Tutorial: Retrieve Reviews by Sentiment

Visual Interface Track