Data Analytics Automation Realization Path
Enabling end-to-end analytics processes through toolchain combinations:
- Basic Capability Package::
- SQL Query Tool: Connecting to a Database to Execute Queries
- Data processing tools: pandas/numpy function wrappers
- Visualization tools: generate charts or export files
- process organization: Clarify analysis step requirements in Agent instructions
- Delivery of results: Support for multiple output formats (text/graphs/files)
Example of a typical tool definition:
@function_tool
def sql_query(query: str) -> dict:
"""执行SQL查询返回JSON格式结果"""
with create_connection() as conn:
return pd.read_sql(query, conn).to_dict()
Enhancement Program::
1. metadata awareness: Adddescribe_tables()Tool-assisted statement generation
2. false defense: Implementing SQL Syntax Checking and Query Review
3. Smart Cache: Caching of results for the same query parameters
4. Automation Report: Integration of Jinja2 templates for automatic generation of analysis conclusions
This answer comes from the articleZipAgent: a lightweight Python framework for building exclusive AI assistants in 5 minutesThe
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