139 lines
5.6 KiB
Python
139 lines
5.6 KiB
Python
"""数据文件的本地工具：describe_data 查看列概况，query_data 执行只读 SQL；问答和 screen agent 共用
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apply_actions 里的 $query 也通过 DataContext.query 执行，见 tools.py
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工具只能看到 DataContext 里的表，也就是当前用户在本会话里未过期的文件，查询引擎也只为这些表建视图
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工具通过 runtime.context.data 读取 DataContext，用到它们的 agent 上下文都要有 data 字段；
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上下文类型因 agent 而异，所以写 ToolRuntime[Any]（不写类型参数时按 None 处理，序列化会告警）
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"""
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Annotated, Any
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from langchain.tools import ToolRuntime, tool
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from langchain_core.messages import ToolMessage
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from pydantic import Field
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from app.config import settings
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from app.datafiles.query import MAX_ROWS, QueryError, QueryResult, run_query
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from app.schemas.data_file import RequestDataFile, RequestDataTable
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# 列超过这么多时，不指定 columns 只返回列名和类型，避免一次塞进太多概况
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BRIEF_COLUMNS = 50
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@dataclass(frozen=True)
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class DataContext:
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files: tuple[RequestDataFile, ...] = ()
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def find(self, name: str) -> tuple[RequestDataFile, RequestDataTable] | None:
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for file in self.files:
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for table in file.tables:
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if table.table == name:
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return file, table
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return None
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def paths(self) -> dict[str, Path]:
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return {
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table.table: Path(table.path)
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for file in self.files
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for table in file.tables
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}
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def query(self, sql: str, *, max_rows: int = MAX_ROWS) -> QueryResult:
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"""执行一条只读 SQL；失败时抛 QueryError，原因可以直接返回给模型修正"""
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paths = self.paths()
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# 任务执行期间被删除或清理的文件跳过，不影响查询其他表
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existing = {name: path for name, path in paths.items() if path.exists()}
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try:
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return run_query(
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sql, existing, timeout=settings.data_query_timeout, max_rows=max_rows
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)
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except QueryError as error:
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if gone := [name for name in paths if name not in existing]:
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raise QueryError(
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f"{error}；{'、'.join(gone)} 的文件已删除或过期，不能再查询"
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) from None
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raise
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def _error(runtime: ToolRuntime, message: str) -> ToolMessage:
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# 返回给模型修正，不中断 agent
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return ToolMessage(message, tool_call_id=runtime.tool_call_id, status="error")
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def _table_list(data: DataContext) -> str:
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return "、".join(data.paths()) or "无"
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@tool
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def describe_data(
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table: str,
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runtime: ToolRuntime[Any],
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columns: Annotated[
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list[str] | None,
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Field(description="只看这些列；不传时看全部列"),
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] = None,
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) -> dict[str, Any] | ToolMessage:
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"""查看一张数据表的列概况（类型、空值率、去重值个数、数值范围和四分位、常见值、样例）、随机样例行，以及解析时做的调整
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分析或取数前先看要用的表；表名见请求 data_files 里的 table。统计值来自全表，样例只是其中几行
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列很多时只返回列名和类型，再用 columns 指定要看的列
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"""
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data: DataContext = runtime.context.data
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found = data.find(table)
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if found is None:
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return _error(runtime, f"没有表 {table}，可用的表：{_table_list(data)}")
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file, item = found
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profiled: list[dict[str, Any]] = item.profile["columns"]
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result: dict[str, Any] = {
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"table": item.table,
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"label": item.label,
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"rows": item.rows,
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}
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if columns is None and len(profiled) > BRIEF_COLUMNS:
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result["columns"] = [
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{key: column[key] for key in ("name", "type", "format") if key in column}
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for column in profiled
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]
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result["note"] = (
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f"共 {len(profiled)} 列，只列出列名和类型；用 columns 指定列查看详细概况和样例"
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)
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else:
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index = {column["name"]: position for position, column in enumerate(profiled)}
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names = list(index) if columns is None else list(dict.fromkeys(columns))
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positions = [index[name] for name in names if name in index]
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result["columns"] = [profiled[position] for position in positions]
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# 样例行的值和 columns 一一对应
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result["sample_rows"] = [
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[row[position] for position in positions] for row in item.profile["sample"]
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]
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if missing := [name for name in names if name not in index]:
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result["missing_columns"] = missing
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# 针对这张表的警告，以及针对整个文件的警告（例如跳过的坏行）
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result["warnings"] = [
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warning.message
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for warning in file.warnings
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if warning.table in (item.table, None)
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]
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return result
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@tool
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def query_data(sql: str, runtime: ToolRuntime[Any]) -> dict[str, Any] | ToolMessage:
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"""对上传的数据表执行一条只读 SQL（DuckDB 语法），表名是请求 data_files 里的 table（t1、t2……）
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中文或带符号的列名用双引号括起来；宽表转长表用 UNPIVOT，PIVOT 要用 IN 写明取值
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最多返回 200 行，truncated 为 true 时结果不完整，改用聚合或 LIMIT
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回答和图表里的数字以查询结果为准，不要自己心算
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"""
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data: DataContext = runtime.context.data
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try:
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return data.query(sql).to_dict()
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except QueryError as error:
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return _error(runtime, str(error))
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DATA_TOOLS = [describe_data, query_data]
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