Text2SQL

• Natural language understanding: Use advanced natural language processing technology, including semantic analysis, entity recognition, and relationship extraction, to ensure that the system accurately captures the user's query intent. Users do not need to master SQL syntax to query data, which greatly reduces the technical threshold for data access.

• Query generation and optimization: Convert query intent processed by natural language understanding into valid SQL query statements, and optimize them to improve execution efficiency. Ensure the accuracy of query results, while improving query performance and reducing resource consumption through optimization.

• Feedback and iteration: By collecting user feedback information in real time, the system can quickly adjust the query logic to ensure that the final result meets user expectations. Enhance user experience, ensure the relevance and accuracy of query results, and promote continuous learning and improvement of the system.

1. Create a new Query console

2. Press '/' to invoke AI

See how to create AI datasets (opens in a new tab).

3. Generate SQL

4. AI Copilot

Similarly, you can use the AI Copilot on the right side to describe the data you want to query in natural language.

1. Select a data source (choose directly from the database or select an AI dataset)
2. Enter your query in natural language
3. Choose a large model and then click send
4. Obtain the SQL, and automatically generate the most suitable visualization chart based on the queried data.