Skip to content

Latest commit

 

History

History
70 lines (44 loc) · 3.89 KB

README.md

File metadata and controls

70 lines (44 loc) · 3.89 KB
Raw

QUERYFOX 🤖🦊

AI-Powered Data Query Interface 🌐

Problem Statement 📝

Organizations often struggle with efficiently accessing and understanding client data stored in internal databases. Traditional methods of data retrieval and analysis can be time-consuming and cumbersome, leading to delays in decision-making and inefficiencies in client management. There is a need for a streamlined and intuitive solution that allows organization members to query and receive timely, accurate insights from the database through a user-friendly interface.

  1. You can view the PPT,

  2. You can view Demo video

Objective 🎯

Develop a chat interface that leverages a Large Language Model (LLM) to read and interpret client data from an internal database. This interface will enable organization members to query the database and receive accurate, contextually relevant responses about the data.

Solution Overview 💡

QueryFox is designed to solve the issues related to inefficient data access, cumbersome data retrieval, and client management inefficiencies.

Screenshots of the application :

image image image

Key Features ✨

  • Streamlined Data Access: Provides an intuitive chat interface that simplifies accessing client data from internal databases. 🗃️💬
  • Efficient Data Retrieval: Leverages an LLM to interpret natural language queries and generate precise SQL queries, eliminating traditional complexity.⚙️🔍
  • Enhanced Client Management: Facilitates immediate and accurate data retrieval, improving client information management and satisfaction. 📈👍
  • User-Friendly Interface: Allows interaction with the database through natural language commands, accessible to all organization members regardless of technical expertise. 🌐🙌

Workflow 🔄

  1. User Input: User enters a natural language request.🗣️
  2. AI Processing: The request is processed by the Generative AI model.🤖
  3. SQL Query Generation: AI generates the corresponding SQL query. 📝
  4. Output Explanation: The generated query is explained in detail. 📖
  5. Formatting and Learning: Users can format their SQL queries and learn from the detailed explanations provided. 🎓

image

Tech Stack 🛠️

image

Efficiency Matrix ( Scalability and Performance) 📈

  • Query Accuracy: 95% accuracy in initial testing. ✅
  • Response Time: Average response time for query generation is 2.3 seconds. ⏱️
  • User Satisfaction: 89% user satisfaction based on surveys. 🎉
  • Error Reduction: 90% reduction in errors compared to manual SQL query generation. ❌➡️✔️
  • Time Savings: 80% reduction in time spent on data retrieval tasks. 🕒💰

Future Scope 🚀

  1. Enhanced AI Models: Improving AI capabilities for more complex queries. 🧠🔧
  2. Database Integration: Expanding support to various database management systems. 🗃️🌐
  3. Advanced Learning Modules: Incorporating interactive SQL tutorials and practice modules. 📚💻
  4. User Feedback: Implementing user feedback mechanisms to continuously improve QueryFox. 📣🔄

Team Members 👥