Propagation of SQL by Automatic Speech Recognition (ASR) Using NLP

Business People may not be aware of the complex SQL writing skills. To convert the business requirements into SQL queries, which can be timeconsuming and error-prone. So, we decided to resolve this problem through developing a software to convert a text or voice to SQL query using Natural Language Processing. To form an SQL query from natural language processing, Parse the natural language input to extract the relevant information. This might involve tokenizing the input, identifying the parts of speech, and extracting nouns, verbs, and other important elements. • Map the extracted information to the appropriate SQL keywords and clauses. For example, a verb like “select” might map to the SQL SELECTkeyword, and a noun like “employees” might map to a table name. Use the extracted information to construct an SQL query. This might involve building the query string piece by piece, using the mapped keywords and clauses as well as any additional conditions or filters that were specified in the natural language input. • Test and refine the query to ensure that it is correct and returns the expected results. This might involve trying the query out on a test database or adding additional processing steps to handle edge cases or ambiguities in the input. • NLP models can be difficult to design and implement: Building an NLP system that can accurately understand and generate SQL queries canbe a challenging task, requiring specialized knowledge and expertise.