From Queries to Insights: Unraveling Power of Generative AI Analytics

The integrated application brings together a variety of technologies to provide users with an efficient means of querying data housed within an Oracle Cloud Database. By harnessing the power of natural language processing, users can seamlessly engage with the system using everyday language, with responses delivered in the form of SQL queries, dataframes, plots, and follow-up questions. At its core, the application comprises multiple components, encompassing the Oracle Cloud Database, Vanna AI, a generative AI layer, Flask backend API, and a Node.js/React frontend UI.

This architecture enables users to query and interact with the database in a user-friendly manner, abstracting away complexities and technical jargon typically associated with database management. Through the integration of advanced AI technologies, the application empowers users to pose questions in natural language, with the system intelligently generating appropriate SQL queries to retrieve relevant data. Additionally, the inclusion of a generative AI layer ensures that generated queries are refined to conform to the syntax requirements of the Oracle Cloud environment, enhancing compatibility and accuracy.

The Flask backend API serves as the bridge between user queries and database operations, handling natural language inputs, orchestrating query generation and execution, and delivering query results and follow-up questions to the frontend interface. Meanwhile, the Node.js/React frontend UI provides an intuitive and interactive platform for users to engage with the system, featuring chat-like functionality for seamless communication, as well as tools for managing chat history and interactions.

Together, these components work in tandem to deliver a cohesive and user-centric experience, enabling individuals to effortlessly query and analyze data stored within the Oracle Cloud Database, thereby facilitating informed decision-making and insights generation.

Making Data Analysis Easier with Generative AI

In the world of Gen AI Driven Analytics, generative AI plays a crucial role in making data analysis simpler and more effective. It helps users interact with the system using everyday language, making it easy to ask questions and get answers. By understanding what users are asking for, it can quickly generate the right queries to fetch the needed data from the Oracle Cloud Database. Generative AI can even recommend the best charts or graphs to help users understand the data better. Plus, it’s smart enough to keep track of the conversation and generate follow-up questions. And as it learns from every interaction, it gets better at providing accurate and helpful insights over time. Overall, by using generative AI, data analysis becomes more accessible and efficient for everyone involved.

Diagram

Components

Oracle Cloud Database

• Description: A database instance hosted on Oracle Cloud, providing a managed database service.
• Steps:
  1. Set up an Oracle Cloud account if not already done: If you don’t have an Oracle Cloud account, visit the Oracle Cloud website and sign up for an account. You may need to provide some basic information and set up billing details.
  2. Navigate to the Oracle Cloud console and create a new Database instance: Log in to your Oracle Cloud account and navigate to the Oracle Cloud console. Look for the database services section and click on “Create Database Instance” or a similar option.
  3. Configure the database settings: Provide necessary details such as the name of the database instance, the compartment where you want to create it, the desired CPU count, storage size, and network settings. You may also need to choose the database version and configuration options according to your requirements.
  4. Access the database using appropriate credentials and tools provided by Oracle Cloud: Once the database instance is provisioned successfully, Oracle Cloud will provide you with credentials and connection details to access the database. You can use SQL*Plus, SQL Developer, or any other compatible tool to connect to the database and start managing it.
  5. Define relational tables within the database to store relevant data for the application: After connecting to the database, you can start defining relational tables to store the data needed for your application. Use SQL statements like CREATE TABLE to create tables with appropriate columns and constraints based on your data model.

Integration with Vanna AI

• Description: Vanna AI is integrated with the Oracle Cloud Database to provide natural language query generation capabilities.
• Steps:
  1. Configure Vanna AI to establish a connection with the Database instance : Vanna AI needs to be configured to establish a secure connection with the Oracle Cloud Database instance. This involves providing necessary authentication credentials such as username, password, and connection string. Additionally, network configurations and firewall rules may need to be adjusted to allow Vanna AI to communicate with the Oracle Cloud Database securely.
  2. Train Vanna on Oracle DB’s schema to understand domain-specific natural language queries relevant to the application : Vanna AI must be trained on the schema of the Oracle Database to understand the structure of tables, relationships between entities, and attributes. This training involves parsing the metadata of the database schema and building a knowledge graph that represents the schema in a structured format. Vanna AI can then use this knowledge graph to interpret natural language queries in the context of the database schema.
  3. Ensure Vanna is capable of generating SQL queries based on the questions asked in natural language form : Vanna AI needs to be equipped with natural language processing (NLP) capabilities to understand and interpret queries expressed in natural language. This involves preprocessing the input text, parsing it to extract relevant keywords and entities, and then mapping them to corresponding elements in the database schema. Once the query is understood, Vanna AI must generate the equivalent SQL query that can be executed against the Oracle Cloud Database to retrieve the desired information.

Generative AI Layer for Correcting SQL Queries for Oracle Cloud

• Description: The addition of a generative AI layer aims to enhance Vanna’s capabilities by ensuring the generated SQL queries are compatible with Oracle Cloud’s syntax. This layer acts as a refinement mechanism, analyzing the queries produced by Vanna and making necessary adjustments to ensure adherence to Oracle Cloud’s SQL standards.
• Steps:
  1. Utilization of Generative AI Layer: Implement a generative AI model specifically trained to analyze and refine SQL queries. This model should be proficient in understanding both Vanna’s generated queries and the syntax requirements of Oracle Cloud.
  2. Analysis and Refinement: Upon receiving a SQL query from Vanna, the generative AI layer will thoroughly analyze its structure and syntax. It identifies any discrepancies or incompatible elements with Oracle Cloud’s SQL requirements.
  3. Syntax Correction: The AI layer then applies corrections to the query to ensure it conforms to Oracle Cloud’s SQL syntax. This may involve adjusting keywords, rearranging clauses, or modifying expressions within the query.
  4. Prompt Engineering for Query Generation: Engage in prompt engineering techniques to consistently generate syntactically correct SQL queries tailored for Oracle Cloud. This involves crafting prompts and training methodologies that prioritize Oracle Cloud’s SQL syntax rules during query generation.
  5. Integration with Vanna’s Process: Seamlessly integrate the generative AI layer with Vanna’s existing query generation process. This ensures that every SQL query generated by Vanna undergoes refinement by the AI layer before being finalized for execution on Oracle Cloud.

Flask Backend API

• Description: The Flask application serves as the backend API responsible for handling natural language queries and interacting with the Oracle Cloud database.
• Steps:
  1. Creating Flask Project and Defining Routes: Start by setting up a Flask project. Define necessary routes to handle incoming requests. These routes will include endpoints for receiving natural language questions, managing chat creation and deletion, and any other required functionalities.
  2. Creating POST API for Natural Language Questions: Implement a POST API endpoint to receive natural language questions as input. Upon receiving a question, the API should pass it to Vanna, a component responsible for generating SQL queries based on the provided natural language input.
  3. Utilizing Generative AI for Query Refinement: After generating SQL queries using Vanna, incorporate a generative AI layer to refine the generated SQL queries before execution. This step enhances the accuracy and relevance of the SQL queries, ensuring better retrieval of information from the database.
  4. Executing SQL Queries on Oracle Cloud Database: Once the SQL queries are refined, execute them on the Oracle Cloud Database. This involves establishing a connection to the database and executing the queries to retrieve the relevant data based on the user’s input.
  5. Formatting and Returning Query Results: Format the query results, which could include generated query, data frames, plots, or follow-up questions, into JSON responses. This ensures that the data is structured and easy to interpret for the frontend or any other consuming applications.
  6. Logging Queries, Responses, and Timestamps into MongoDB: Implement logging functionality to track and analyze queries, responses, and timestamps. Store this information in MongoDB or any other suitable database for future reference, analysis, and performance monitoring.
  7. Creating APIs for Chat Creation and Deletion: In addition to handling natural language queries, create APIs for chat creation and deletion. These APIs enable users to initiate new chat sessions and end existing ones, providing a seamless communication experience.

Frontend UI with Node.js/React

• Description: A user-friendly frontend interface built using Node.js and React to interact with the Flask backend API. This interface facilitates seamless communication between users and the backend server, allowing for intuitive query processing and result display.
• Steps:
  1. Create a new Node.js/React project structure:Begin by setting up a new Node.js project using npm or yarn.Utilize create-react-app or similar tools to scaffold a React project structure.Organize directories and files for clear separation of concerns, following best practices.
  2. Design and implement a chat-like UI interface using React components for user interaction:Design UI components for chat messages, input field, buttons, etc., using React.Implement a chat-like interface where users can input queries and receive responses.Style the UI to enhance user experience and visual appeal, considering usability principles.
  3. Incorporate features such as chat creation, deletion, and history management for seamless user experience:Implement functionalities for creating and deleting chats.Manage chat history to allow users to review previous conversations.Utilize React state management or libraries like Redux for efficient state handling.
  4. Integrate API calls to communicate with the Flask backend for query processing:Implement functions to make HTTP requests to the Flask backend API endpoints.Handle asynchronous operations using async/await or promises.Ensure proper error handling and feedback to users in case of API communication failures.
  5. Handle the display of query results, plots, and follow-up questions within the UI in a clear and intuitive manner:Design components to display query results and follow-up questions dynamically.Render plots or visualizations returned by the backend API in an intuitive manner.Ensure clarity in presenting information to users, providing context where necessary.

Conclusion

the integration of advanced AI technologies within Gen AI Driven Analytics marks a significant leap forward in simplifying data analysis. By seamlessly combining natural language processing, generative AI, and database management, the application empowers users to effortlessly query and interact with Oracle Cloud Database. The cohesive architecture, comprising Flask backend API and Node.js/React frontend UI, ensures a user-centric experience, abstracting away complexities and fostering informed decision-making. With continuous learning and refinement, Gen AI Driven Analytics not only makes data analysis accessible but also paves the way for deeper insights and enhanced productivity in diverse domains.