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This video demonstrates how to use the Chat To Your POSTGres Database With AI template.
import logging
import os
from flask import Flask, render_template, session, request, jsonify
from flask_session import Session
from gunicorn.app.base import BaseApplication
import psycopg2
from psycopg2.extras import DictCursor
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
# Configuring server-side session
app.config["SESSION_PERMANENT"] = False
app.config["SESSION_TYPE"] = "filesystem"
Session(app)
from abilities import llm_prompt
@app.route("/")
def root_route():
return render_template("template.html", model_name="gpt-4o-mini")Frequently Asked Questions
How can this Chat To Your POSTGres Database With AI template benefit my business?
The Chat To Your POSTGres Database With AI template can significantly enhance your business operations by providing quick, natural language access to your database information. Instead of writing complex SQL queries, your team can simply ask questions in plain English about sales trends, inventory status, or customer activity. This can lead to faster decision-making, improved data accessibility for non-technical staff, and more efficient use of your business data.
What types of businesses would find this template most useful?
This template is versatile and can benefit a wide range of businesses, particularly those with large databases and frequent need for data analysis. It's especially useful for: - Retail businesses managing inventory and sales data - Manufacturing companies tracking parts and production - Customer service departments needing quick access to customer information - Financial services firms analyzing transaction data - Any business that relies on frequent database queries for decision-making
Can I customize the Chat To Your POSTGres Database With AI template for my specific industry?
Absolutely! While the template comes pre-loaded with sample car parts data, it's designed to be adaptable to any PostgreSQL database schema. You can easily modify the schema to match your industry-specific data structure. For example, if you're in the healthcare industry, you could adapt it to query patient records, treatment outcomes, or medication inventory.
How do I connect the Chat To Your POSTGres Database With AI template to my own PostgreSQL database?
To connect the template to your own PostgreSQL database, you'll need to modify the connection details in the main.py file. Look for the following code block and replace the environment variables with your database credentials:
python
conn = psycopg2.connect(
dbname=os.environ['DB_NAME'],
user=os.environ['DB_USER'],
password=os.environ['DB_PASSWORD'],
host=os.environ['DB_HOST'],
port=os.environ['DB_PORT']
)
Make sure to set these environment variables in your deployment environment or use a secure method to store and retrieve these credentials.
Can I extend the Chat To Your POSTGres Database With AI template to include data visualization?
Yes, you can enhance the template to include data visualization. One way to do this is by integrating a charting library like Chart.js or D3.js. Here's a basic example of how you might modify the send_message route in main.py to return data suitable for a chart:
```python from flask import jsonify import json
@app.route("/send_message", methods=['POST']) def send_message(): # ... existing code ...
try:
# ... database connection and query execution ...
# Convert results to a format suitable for charting
chart_data = {
"labels": [row['category'] for row in rows],
"values": [row['value'] for row in rows]
}
response_message = f"SQL Query Executed: {sql_query}\\n"
response_message += f"Results: {json.dumps(chart_data)}"
except Exception as e:
# ... error handling ...
return jsonify({"message": response_message, "chart_data": chart_data})
```
You would then need to modify the front-end JavaScript to interpret this data and render it as a chart using your chosen visualization library.
Created: | Last Updated:
Here's a step-by-step guide for using the "Chat To Your POSTGres Database With AI" template:
Introduction
The "Chat To Your POSTGres Database With AI" template allows you to create a chatbot that connects to a PostgreSQL database, generates queries based on the schema, and runs those queries to provide answers about your business data. This chatbot comes with a nicely styled chat UI, making it easy for users to interact with their database using natural language.
Getting Started
- Click "Start with this Template" to begin using this template in the Lazy Builder interface.
Setting Up Environment Variables
Before running the app, you need to set up the following environment variables in the Environment Secrets tab within the Lazy Builder:
- DB_NAME: Your PostgreSQL database name
- DB_USER: Your PostgreSQL username
- DB_PASSWORD: Your PostgreSQL password
- DB_HOST: Your PostgreSQL host address
- DB_PORT: Your PostgreSQL port number
- DB_SCHEMA: Your PostgreSQL database schema
To get these values:
- Access your PostgreSQL database management tool or console.
- Retrieve the connection details for your database.
- Copy these details into the corresponding environment variables in the Lazy Builder.
Testing the App
- Click the "Test" button in the Lazy Builder interface to start the deployment process.
- Wait for the deployment to complete. The Lazy CLI will provide you with a dedicated server link to access your chatbot.
Using the Chat Interface
- Open the provided server link in your web browser to access the chat interface.
- You'll see a default message from Databot explaining how to use the chatbot.
- Type your questions about the data in natural language. For example:
- "What are the trends in our sales data?"
- "Show me customer activity from last month"
- "What's our current inventory status?"
- Press "Ask" or hit Enter to send your question.
- The chatbot will generate an SQL query based on your question, execute it on the connected PostgreSQL database, and display the results in the chat window.
Sample Data
The template comes pre-loaded with sample data related to car parts. You can query the following columns:
- part_number
- part_name
- price
- in_stock (units left in stock)
- manufacturer
Feel free to ask questions about these data points to test the chatbot's functionality.
Integrating the App
This chatbot is designed to be used as a standalone application. There's no need for additional integration steps. Simply use the provided server link to access and interact with your database through the chat interface.
By following these steps, you'll have a fully functional AI-powered chatbot that can query your PostgreSQL database and provide insights based on natural language questions.
Here are 5 key business benefits for this template:
Template Benefits
-
Instant Data Insights: Enables non-technical users to quickly gain insights from complex databases without needing SQL knowledge, improving decision-making speed and efficiency.
-
Improved Data Accessibility: Democratizes data access across an organization, allowing employees at all levels to query and understand business data through natural language interactions.
-
Reduced IT Workload: Decreases the burden on IT and data teams by empowering other departments to self-serve their data needs, freeing up technical resources for more complex tasks.
-
Enhanced Customer Service: Can be adapted for customer-facing applications, allowing clients to quickly access account information or product details, improving customer satisfaction and reducing support calls.
-
Flexible Data Exploration: Facilitates ad-hoc data exploration and analysis, encouraging a data-driven culture and potentially uncovering valuable business insights that might otherwise be missed.
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