In today’s data-driven business environment, organizations deal with loads of data stored, handled, and processed in complex databases. However, accessing this valuable data typically requires technical expertise in writing SQL queries.
So, we created an intuitive NLP-to-SQL system that simplifies database interaction by:
price for processing one query
in saving time and resources
took the system to process a query
Data access obstacles and delays
Data retrieval bottlenecks
Dependency on technical staff
NLP, SQL, AI
4 weeks
Team composition:
Service provided:
Every business manages vast amounts of data within their database, but not everyone can extract this valuable data using SQL queries. This presents a barrier for non-technical users who need to retrieve specific information quickly and efficiently. Also, this gap between technical capabilities and business needs often leads to inefficiencies and delays in decision-making processes.
The idea for the NLP-to-SQL system emerged from this need to streamline data access. By enabling users to input queries in everyday language and receive accurate SQL queries and results in return, the system aims to empower non-technical users to retrieve valuable insights from their data seamlessly. This project represents a significant step towards making advanced data tools more accessible to a broader audience within an organization, ultimately enhancing productivity and decision-making capabilities.
Leveraging the latest advancements in natural language processing and machine learning, particularly GPT-4о, we developed a system that not only generates accurate SQL queries but also provides a user-friendly interface through Swagger UI. Key system features and capabilities include:
When a query is made, the system performs a cosine similarity search within this vector database to identify the most relevant tables. These tables, along with the user’s query, are then used to generate a prompt for the NLP-to-SQL conversion. The prompt is processed by GPT-4о, which generates the corresponding SQL query in JSON format. Finally, this SQL query is returned to the user, ready for execution against the database.
Let’s take one example in practice:
The system begins with users inputting natural language queries, which are then processed to retrieve the relevant database schema via a direct connection. This schema is embedded using the text-embedding-ada-002 model, and the resulting vector representations are stored in a PostgreSQL Vector DB.
“I need the first names, last names, and email addresses of all contacts who have the role Project Manager.”
Result:
{ “query”: “/* Retrieve first names, last names, and email addresses of all contacts who have the role Project Manager */\nSELECT first_name, last_name, true__e_mail__phantombuster \nFROM contacts \nWHERE role = ‘Project Manager’;”}
The system allows non-technical users to access data using natural language without knowing SQL.
Queries are processed within 5-7 seconds, significantly speeding up data retrieval and analysis.
Rapid access to accurate data empowers users to make more informed business decisions.
The tool frees up technical and other staff to focus on higher-value tasks, improving productivity.
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