  Industries such as finance, meteorology, and energy generate vast amounts of
data daily. Efficiently managing, processing, and displaying this data requires
specialized expertise and is often tedious and repetitive. Leveraging large
language models (LLMs) to develop an automated workflow presents a highly
promising solution. However, LLMs are not adept at handling complex numerical
computations and table manipulations and are also constrained by a limited
context budget. Based on this, we propose Data-Copilot, a data analysis agent
that autonomously performs querying, processing, and visualization of massive
data tailored to diverse human requests. The advancements are twofold: First,
it is a code-centric agent that receives human requests and generates code as
an intermediary to handle massive data, which is quite flexible for large-scale
data processing tasks. Second, Data-Copilot involves a data exploration phase
in advance, which explores how to design more universal and error-free
interfaces for real-time response. Specifically, it actively explores data
sources, discovers numerous common requests, and abstracts them into many
universal interfaces for daily invocation. When deployed in real-time requests,
Data-Copilot only needs to invoke these pre-designed interfaces, transforming
raw data into visualized outputs (e.g., charts, tables) that best match the
user's intent. Compared to generating code from scratch, invoking these
pre-designed and compiler-validated interfaces can significantly reduce errors
during real-time requests. Additionally, interface workflows are more efficient
and offer greater interpretability than code. We open-sourced Data-Copilot with
massive Chinese financial data, such as stocks, funds, and news, demonstrating
promising application prospects.
