  The Natural Language to SQL (NL2SQL) technology provides non-expert users who
are unfamiliar with databases the opportunity to use SQL for data
analysis.Converting Natural Language to Business Intelligence (NL2BI) is a
popular practical scenario for NL2SQL in actual production systems. Compared to
NL2SQL, NL2BI introduces more challenges.
  In this paper, we propose ChatBI, a comprehensive and efficient technology
for solving the NL2BI task. First, we analyze the interaction mode, an
important module where NL2SQL and NL2BI differ in use, and design a smaller and
cheaper model to match this interaction mode. In BI scenarios, tables contain a
huge number of columns, making it impossible for existing NL2SQL methods that
rely on Large Language Models (LLMs) for schema linking to proceed due to token
limitations. The higher proportion of ambiguous columns in BI scenarios also
makes schema linking difficult. ChatBI combines existing view technology in the
database community to first decompose the schema linking problem into a Single
View Selection problem and then uses a smaller and cheaper machine learning
model to select the single view with a significantly reduced number of columns.
The columns of this single view are then passed as the required columns for
schema linking into the LLM. Finally, ChatBI proposes a phased process flow
different from existing process flows, which allows ChatBI to generate SQL
containing complex semantics and comparison relations more accurately.
  We have deployed ChatBI on Baidu's data platform and integrated it into
multiple product lines for large-scale production task evaluation. The obtained
results highlight its superiority in practicality, versatility, and efficiency.
At the same time, compared with the current mainstream NL2SQL technology under
our real BI scenario data tables and queries, it also achieved the best
results.
