SQLBot ensures SQL generation quality through a combination of the following technologies:
- RAG (Retrieval Augmented Generation) technology
- Automatic retrieval of database metadata (table structure, field comments, etc.) during the problem processing phase
- Provide relevant structural information to LLM as a context
- Effectively solving the problem of insufficient understanding of specific database structures by large models
- Multi-model support mechanism
- Access to large models from different vendors such as GPT, Wenxin Yiyin, etc.
- Open source models supporting local deployment (through tools such as Ollama)
- Allows selection of the optimal model based on the scenario
- Query Verification Process
- The system provides data source connection test function
- Support for previewing SQL statements before execution
- Results are automatically formatted when returned
This technical architecture is particularly well suited to on-premises database environments and significantly reduces the risk of erroneous queries due to natural language ambiguities.
This answer comes from the articleSQLBot: The Intelligent Bot That Converts Natural Language to SQL QueriesThe
May not be reproduced without permission:AI productivity tools " Why does SQLBot guarantee the accuracy of generated SQL queries? What key technologies are used?
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