AI Data Agent with Gemini API | Build with Google AI
TLDRThe video introduces an AI-powered tool that enables users to interact with business data through natural language queries, leveraging Google's Gemini AI. The tool, SQL Talk, translates questions into SQL queries or other API calls, retrieves data, and converts it back into understandable language. This simplifies data exploration for non-technical users and developers, allowing real-time insights without extensive coding. The project's extensibility is highlighted, demonstrating its adaptability for various business systems.
Takeaways
- 🤖 The video discusses building AI-powered tools for business data interaction without coding.
- 🚀 Google's Gemini AI is used to translate questions into programming interface calls and data into plain language responses.
- 📊 The demo showcases an application with a chat interface for database interaction of an e-commerce business.
- 🔍 The application uses Gemini's function-calling feature to convert questions into SQL queries.
- 📈 The tool retrieves raw data from the database and translates it into understandable language for users.
- 🛠️ The SQL Talk project is extensible and can be adapted to various business systems with a programming interface.
- 👥 AI technology empowers non-technical users to interact with and extract information from business systems.
- 💡 The project leverages generative AI models to bridge the gap between natural language and API interactions.
- 📝 The developer defines functions and tools within Gemini that correspond to specific database operations.
- 🔧 Extending the project involves adding new function declarations and mapping them to corresponding API calls.
- 🌐 The video encourages developers to explore and extend the SQL Talk project to unlock their organization's data value.
Q & A
What is the main purpose of the AI-powered tool discussed in the video?
-The main purpose of the AI-powered tool is to enable users to interact with business data through natural language queries and receive understandable responses without the need for coding expertise.
Which Google AI technology is used in the project to facilitate natural language queries?
-Google's Gemini AI is used in the project to translate natural language questions into programming interface calls and convert retrieved data into plain language responses.
How does the AI tool demonstrate its functionality in the demo?
-In the demo, the AI tool is shown interacting with a database of an imaginary e-commerce business, taking questions, transforming them into SQL queries, executing them, and providing the answers in plain language.
What is the significance of the Gemini AI's function-calling feature in this application?
-The function-calling feature of Gemini AI is significant as it allows the AI to generate code implementations based on natural language questions and then translate the raw data it retrieves into easily understandable answers.
How can the SQL Talk application be extended to other types of business systems?
-The SQL Talk application can be extended by adding new function call definitions that map to corresponding API calls for different databases, document repositories, or CRM systems, making it adaptable to various business systems.
What is the role of generative AI models like Gemini in the development of applications that allow business users to interact with systems?
-Generative AI models like Gemini unlock the potential for developers to create applications that enable business users to interact with systems using natural language, without the need to understand schemas, API syntax, or other technical details.
How does the SQL Talk project utilize Gemini's structured data output and function-calling capabilities?
-The SQL Talk project uses Gemini's structured data output and function-calling capabilities to define and declare functions at design time, and then at runtime, Gemini picks the appropriate function to answer the user's question, facilitating API calls and translating the responses back into natural language.
What is the developer's approach to extending the SQL Talk application with new functionalities?
-To extend the SQL Talk application, the developer adds new function declarations, maps them to corresponding API calls, and updates the application code to execute these calls and interact with the external systems or databases.
Does the SQL Talk project involve the creation of a new AI model?
-No, the SQL Talk project does not involve creating a new AI model. It utilizes existing models like Gemini for function calling and natural language processing at runtime, without the need for fine-tuning or training a new model.
How does the SQL Talk project empower both developers and end users?
-The SQL Talk project empowers developers by providing a structured API to work with and end users by allowing them to interact with business systems using natural language, without requiring them to be API developers or AI experts.
What additional steps can be taken to enhance the functionality of the SQL Talk application?
-Enhancements to the SQL Talk application can include pulling more data from API responses, creating additional function calls for more detailed information, and integrating with a wider variety of databases and systems to answer a broader range of questions.
Outlines
🤖 Introduction to AI-Powered Data Conversations
This paragraph introduces the concept of building an AI-powered tool that allows users to interact with business data through conversation. It sets the scene for the video by highlighting the common challenges developers face when asked to extract data from business systems to answer questions. The video aims to show how Google's Gemini AI can help non-coding colleagues get answers without the need for developers to write code constantly. A demo of a Google Doc project using Gemini AI is presented, showcasing its ability to translate questions into programming interface calls and data into plain language responses.
🔍 How SQL Talk Application Functions
This section delves into the functionality of the SQL Talk application, explaining how it works by using Gemini AI's capabilities. It describes the process of translating natural language questions into SQL queries and executing them against a database, then translating the raw data back into plain language responses. The conversation with Kris Overholt, the developer of SQL Talk, covers the benefits of using AI as a front end for data access and the potential for extending the application to various business systems beyond SQL queries. The paragraph also touches on the extensibility of the project and how developers can adapt it for different databases or systems.
📈 Extending SQL Talk for Enhanced Data Exploration
The final paragraph focuses on the potential for extending the SQL Talk project. It provides insights into how developers can add new functionalities and connect the application to different types of databases or document repositories. The explanation includes the process of adding function call definitions and mapping them to corresponding API calls. A practical example is given on how to enable the application to answer questions about queries or jobs run against a database. The paragraph concludes with a call to action for viewers to experiment with the project, share their successes, and continue learning to create impactful AI-powered tools.
Mindmap
Keywords
💡AI-powered tool
💡Google AI technology
💡Non-coding colleagues
💡Gemini AI
💡Programming Interface Calls
💡Structured Query Language (SQL)
💡Natural Language Processing (NLP)
💡Data Exploration
💡Extensibility
💡APIs
💡CRM system
Highlights
The video explores building an AI-powered tool for business data interaction, allowing users to ask questions and receive answers.
The AI tool is built using Google AI technology, aiming to provide practical solutions for developers.
Developers can use AI to enable non-coding colleagues to answer their own questions without writing code.
The project uses Google's Gemini AI to translate questions into programming interface calls and data into plain language responses.
A chat interface is provided for interacting with a database of an imaginary e-commerce business.
Gemini AI's function-calling feature is used to convert questions into SQL queries.
The application executes generated queries, retrieves data, and translates it into understandable plain language.
The application is not limited to SQL queries and can be adapted for any business system with a programming interface.
Kris Overholt from Google Cloud developer relations team explains the SQL Talk project's functionality.
Generative AI models like Gemini unlock potential for developers to create systems for business user interaction.
The SQL Talk project is extensible and allows for different types of databases and systems to be queried.
The application code for SQL Talk is less than 200 lines of Python, making it easy to modify and extend.
Developers can use the Gemini API for function calling to translate natural language to API calls.
The end user benefits as they can interact with business systems using natural language without being an API developer.
The project is an AI development project as it utilizes existing models for function calling and does not require training a new model.
Detailed tutorials and code for the SQL Talk project are available for those interested in extending its functionality.
The video encourages developers to build their own AI-powered data exploration tools to unlock the value of organizational data.