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Question and Answer Bot with Data Expertise
This video demonstrates how to use the Question and Answer Bot with Data Expertise 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 carparts_db
from carparts_db import create_connection, create_table
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-4-1106-preview")Frequently Asked Questions
How can this chatbot template benefit an automotive parts business?
This chatbot template can significantly enhance customer service and operational efficiency for an automotive parts business. By allowing customers to quickly query the inventory database, they can easily check part availability, pricing, and specifications without needing to contact a representative. This self-service approach can reduce customer service workload and improve customer satisfaction. The bot can handle queries about part numbers, names, prices, stock levels, and manufacturers, providing instant access to crucial information.
Can this template be adapted for other types of inventory management systems?
Absolutely! While this template is pre-loaded with car parts data, its structure is versatile enough to be adapted for various inventory management systems. The core functionality of translating natural language queries into SQL and retrieving data from a database can be applied to any industry. Whether you're dealing with electronics, clothing, groceries, or any other product category, you can modify the database schema and update the prompt to reflect your specific inventory structure.
How does this chatbot template improve data accessibility for non-technical staff?
This chatbot template acts as a bridge between non-technical staff and complex database systems. Employees without SQL knowledge can simply ask questions in natural language, and the bot will generate and execute the appropriate SQL queries. This democratizes data access within an organization, allowing sales representatives, customer service agents, or managers to quickly retrieve accurate information without relying on the IT department or learning SQL themselves.
How can I modify the database schema in this template to include additional fields?
To modify the database schema in this chatbot template, you'll need to update the carparts_db.py file. Here's an example of how you could add a 'category' field to the existing schema:
python
sql_create_carparts_table = """ CREATE TABLE IF NOT EXISTS carparts (
id integer PRIMARY KEY,
part_name text NOT NULL,
part_number text NOT NULL,
manufacturer text,
price real,
in_stock boolean,
category text
); """
You'll also need to update the main() function to include this new field when inserting sample data:
python
car_parts_data = [
(1, 'Steering Wheel', 'SW123', 'Generic Motors', 50.75, True, 'Interior'),
# ... other entries ...
]
Finally, update the prompt in main.py to include the new field in the schema description.
How can I customize the AI model used by this chatbot template?
The chatbot template currently uses the "gpt-4-1106-preview" model, but you can easily change this. In the main.py file, locate the llm_prompt function call and modify the model parameter. For example, to use a different model:
python
response = llm_prompt(prompt, model="gpt-3.5-turbo", temperature=0.7)
You can also adjust the temperature parameter to control the randomness of the AI's responses. A lower temperature (e.g., 0.3) will make responses more focused and deterministic, while a higher temperature (e.g., 0.9) will make them more diverse and creative. Remember to ensure that you have access to the model you specify and that it's capable of handling the task at hand.
Created: | Last Updated:
Introduction to the Chatbot Service Template
Welcome to the Chatbot Service Template on Lazy! This template allows you to create a chatbot that can answer questions about data stored in a SQLite database. The chatbot is designed to generate SQL queries based on user input and return the results in a user-friendly chat interface. This template is perfect for builders who want to create a customer support bot or an interactive data query tool without worrying about the complexities of database management and frontend development.
Getting Started
To begin using this template, simply click on "Start with this Template" on the Lazy platform. This will pre-populate the code in the Lazy Builder interface, so you won't need to copy or paste any code manually.
Initial Setup
There are no environment variables required for this template, so you can skip straight to testing the application.
Test: Deploying the App
Once you have the template loaded, press the "Test" button to begin the deployment of your chatbot application. The Lazy CLI will handle the deployment process, and you won't need to install any libraries or set up your environment manually.
Entering Input
After pressing the "Test" button, if the application requires any user input, the Lazy App's CLI interface will prompt you to provide it. Follow the instructions in the CLI to enter the necessary information.
Using the Chatbot Interface
After deployment, you will be provided with a dedicated server link to interact with your chatbot. The chatbot interface is a simple and intuitive web page where users can type their questions and receive responses. The chat history is displayed on the page, allowing for a seamless conversation experience.
Integrating the Chatbot into External Services
If you wish to integrate the chatbot with external services or tools, you may need to use the server link provided by Lazy. For example, if you want to embed the chatbot into an existing website, you can use an iframe or a web component to include the chatbot interface on your site. Ensure that you adjust the CORS settings on your server if necessary to allow for such integrations.
Here's a sample code snippet to embed the chatbot into an external webpage:
<iframe src="YOUR_SERVER_LINK" width="350" height="500"></iframe>
Replace "YOUR_SERVER_LINK" with the actual link provided by Lazy after deployment.
And that's it! You now have a fully functional chatbot that can answer questions about your car parts database. Enjoy interacting with your chatbot and exploring the capabilities of the Lazy platform.
Template Benefits
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Simplified Data Access: Enables non-technical staff to query complex databases using natural language, improving data accessibility across the organization.
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Improved Customer Service: Can be adapted for customer-facing applications, allowing customers to quickly find information about products or services without navigating complex menus.
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Efficient Inventory Management: Provides a quick and easy way for employees to check stock levels, prices, and product details, streamlining inventory processes.
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Training and Onboarding Tool: Serves as an interactive learning tool for new employees to familiarize themselves with the company's database structure and contents.
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Rapid Prototyping: Offers a foundation for quickly developing and testing data-driven chatbot applications, accelerating the process of creating custom AI solutions for businesses.
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