Qwen3

The Multi-Stage Text-to-SQL Architecture uses Qwen3 for complex query handling, table selection, and answer synthesis. TableReranker uses the Qwen3 model to re-rank candidate tables and select the most relevant ones for query answering. AnswerSynthesizer leverages Qwen3 to synthesize human-readable answers in Danish or English from SQL query results. The Multi-Stage Architecture uses the Qwen3 model for schema selection and answer synthesis phases. The Data Discovery feature architecture uses Qwen3 with large context for table selection and Arctic with smaller context for SQL generation. The Data Discovery feature architecture uses Qwen3 with large context for table selection and Arctic with smaller context for SQL generation. The Data Discovery System requires the Qwen3 table selection capability to identify relevant tables before SQL generation. SQL extraction regex fix addresses the problem in Qwen3 response format where the SQL query was not properly captured due to missing newline before closing backticks. Multi-Stage Text-to-SQL Architecture uses Qwen3 for schema comprehension and complex SQL tasks. The Discovery Service uses Qwen3 LLM for table selection in the intelligent table discovery process. The Data Discovery Architecture uses Qwen3 for table selection with a large context window. The Qwen3 response format required the SQL extraction regex to be fixed to handle SQL code blocks without a newline before closing backticks. The Live Backend processes Qwen3 multi-block XML responses for SQL extraction.