wuyang6
662 lines
33 KiB
Python
662 lines
33 KiB
Python
from __future__ import annotations
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from collections.abc import Mapping
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from ..agent_tool_core import AgentTool, ToolHandler
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from ._builder import resolve_handler
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def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentTool]:
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"""构建数据 Agent 专用工具声明。
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这里仅保存工具名称、描述和 JSON Schema，具体执行逻辑由调用方传入的 handler 提供。
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这样新增数据 Agent 工具时，可以优先在本文件补充声明，再在实现模块中补充 handler。
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"""
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return [
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AgentTool(
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name='data_agent_prepare_generation_goal',
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description=(
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'Create a pending data-agent generation goal from source evidence or user rules. '
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'Use this before data_agent_prepare_generation_plan; show the returned goal to the user and wait for confirmation.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'dataset_label': {'type': 'string'},
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'goal_summary': {'type': 'string'},
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'target': {
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'type': 'string',
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'description': 'Single full target expression, e.g. Agent(tag="餐饮服务") or a function call. For multi-target boundary tasks, use target_definitions.',
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},
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'target_definitions': {
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'type': 'array',
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'items': {
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'type': 'object',
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'properties': {
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'name': {'type': 'string'},
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'target': {
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'type': 'string',
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'description': 'Full target expression, e.g. Agent(tag="餐饮服务"); do not shorten to a display name.',
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},
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'rule': {'type': 'string'},
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},
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'required': ['target'],
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},
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},
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'plan_hint': {
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'type': 'string',
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'description': (
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'Optional plain-language hint for the later plan, such as '
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'"建议先生成 50 条单轮，输出到 tasks/.../records.jsonl". '
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'Structured total_count, turn_mix, and output_path belong to data_agent_prepare_generation_plan.'
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),
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},
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'coverage': {'type': 'string'},
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'exclusions': {'type': 'string'},
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'open_questions': {
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'type': 'array',
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'items': {'type': 'string'},
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},
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'source_refs': {
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'type': 'array',
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'items': {'type': 'string'},
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},
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'notes': {'type': 'string'},
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},
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'required': ['dataset_label', 'goal_summary', 'coverage', 'exclusions'],
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},
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handler=resolve_handler(handlers, 'data_agent_prepare_generation_goal', 'data-agent'),
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),
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AgentTool(
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name='data_agent_confirm_generation_goal',
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description=(
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'Mark a pending data-agent generation goal as confirmed after the user explicitly approves it. '
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'The returned confirmed_goal_id is required by data_agent_prepare_generation_plan.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'goal_id': {'type': 'string'},
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'confirmation': {
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'type': 'string',
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'description': 'The user approval text, such as 确认, 开始生成, or approve.',
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},
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'reviewed_revision': {
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'type': 'integer',
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'description': 'The goal revision shown to the user before confirmation.',
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},
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},
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'required': ['goal_id', 'confirmation'],
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},
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handler=resolve_handler(handlers, 'data_agent_confirm_generation_goal', 'data-agent'),
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),
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AgentTool(
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name='data_agent_prepare_generation_plan',
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description=(
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'Create a pending data-agent generation plan. Use this before generating dataset draft text; '
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'requires confirmed_goal_id from data_agent_confirm_generation_goal; show the returned plan to the user and wait for confirmation.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'confirmed_goal_id': {
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'type': 'string',
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'description': 'Returned by data_agent_confirm_generation_goal after the user reviews a separate generation goal. Omit only when direct_review is true.',
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},
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'direct_review': {
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'type': 'boolean',
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'description': (
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'Set true for simple hand-written rules where the user already supplied target labels '
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'and wants generation; this creates one combined goal+plan review instead of a separate goal review.'
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),
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},
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'dataset_label': {'type': 'string'},
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'target': {
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'type': 'string',
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'description': 'Single full target expression. For multi-target boundary tasks, omit this and fill target_definitions.',
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},
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'target_definitions': {
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'type': 'array',
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'description': 'Optional target labels for multi-class boundary data.',
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'items': {
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'type': 'object',
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'properties': {
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'name': {'type': 'string'},
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'target': {
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'type': 'string',
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'description': 'Full target expression inherited from the confirmed goal, e.g. Agent(tag="餐饮服务").',
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},
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'rule': {'type': 'string'},
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},
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'required': ['target'],
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},
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},
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'total_count': {'type': 'integer', 'minimum': 1},
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'turn_mix': {'type': 'string', 'description': 'Single-turn and multi-turn count or ratio.'},
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'coverage': {'type': 'string', 'description': 'Query types, intent boundaries, or error types to cover.'},
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'exclusions': {'type': 'string', 'description': 'Negative examples or boundaries to avoid.'},
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'output_path': {
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'type': 'string',
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'description': (
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'Requested records path. The runtime normalizes canonical records to the current '
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'session output/records.jsonl; use output/records.jsonl and do not add dataset '
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'subdirectories or random names.'
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),
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},
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'notes': {'type': 'string'},
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},
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'required': [
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'dataset_label',
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'total_count',
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'turn_mix',
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'coverage',
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'exclusions',
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'output_path',
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],
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},
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handler=resolve_handler(handlers, 'data_agent_prepare_generation_plan', 'data-agent'),
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),
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AgentTool(
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name='data_agent_load_input_sources',
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description=(
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'Load data-agent input files or directories and extract structured paragraphs and table previews '
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'from xlsx, csv, docx, pdf, txt, md, json, or jsonl sources.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'paths': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Workspace-relative files or directories to load.',
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},
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'max_files': {'type': 'integer', 'minimum': 1, 'maximum': 100},
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'max_paragraphs_per_file': {'type': 'integer', 'minimum': 1, 'maximum': 300},
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'max_tables_per_file': {'type': 'integer', 'minimum': 1, 'maximum': 100},
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'max_rows_per_table': {'type': 'integer', 'minimum': 1, 'maximum': 200},
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'max_cell_chars': {'type': 'integer', 'minimum': 20, 'maximum': 2000},
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},
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'required': ['paths'],
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},
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handler=resolve_handler(handlers, 'data_agent_load_input_sources', 'data-agent'),
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),
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AgentTool(
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name='data_agent_extract_case_evidence',
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description=(
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'Extract query/badcase evidence from loaded input sources or source paths. '
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'Profiles table columns first and returns required questions when query or expected label columns are ambiguous.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'paths': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Workspace-relative files or directories. Omit when loaded_sources is provided.',
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},
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'loaded_sources': {
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'type': 'object',
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'description': 'Output from data_agent_load_input_sources.',
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},
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'field_mapping': {
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'type': 'object',
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'description': 'Optional role-to-column mapping, e.g. {"query":"query","expected_label":"预期domain"}.',
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},
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'max_cases': {'type': 'integer', 'minimum': 1, 'maximum': 1000},
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},
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},
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handler=resolve_handler(handlers, 'data_agent_extract_case_evidence', 'data-agent'),
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),
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AgentTool(
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name='data_agent_render_source_context',
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description=(
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'Render loaded data-agent sources into LLM-readable evidence text with source refs. '
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'Use this after data_agent_load_input_sources before asking the model to structure product semantics.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'paths': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Workspace-relative files or directories. Omit when loaded_sources is provided.',
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},
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'loaded_sources': {
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'type': 'object',
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'description': 'Output from data_agent_load_input_sources.',
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},
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'max_chars': {'type': 'integer', 'minimum': 1000, 'maximum': 200000},
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'max_tables_per_source': {'type': 'integer', 'minimum': 1, 'maximum': 100},
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'max_rows_per_table': {'type': 'integer', 'minimum': 1, 'maximum': 200},
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'focus_keywords': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Optional keywords used to keep only matching paragraphs/table rows.',
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},
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},
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},
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handler=resolve_handler(handlers, 'data_agent_render_source_context', 'data-agent'),
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),
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AgentTool(
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name='data_agent_profile_router_sessions',
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description=(
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'Profile local router_session_parquet date partitions before mining online sessions. '
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'Returns schema, sampled row count, distributions, and example sessions.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'dates': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Date partitions such as ["20260428"]. Resolved under router_session_parquet/date=YYYYMMDD.',
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},
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'paths': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Parquet files or date directories. Relative paths resolve from workspace root; absolute paths are allowed for external online data.',
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},
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'max_files': {'type': 'integer', 'minimum': 1, 'maximum': 200},
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'max_rows_per_file': {'type': 'integer', 'minimum': 1, 'maximum': 100000},
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'examples_limit': {'type': 'integer', 'minimum': 0, 'maximum': 20},
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},
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},
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handler=resolve_handler(handlers, 'data_agent_profile_router_sessions', 'data-agent'),
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),
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AgentTool(
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name='data_agent_search_router_sessions',
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description=(
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'Search local router_session_parquet data with reviewable filters such as device, domain, intent, func, '
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'query keywords, regex, turn count, and match scope. Returns turn-level candidates with prev turns.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'dates': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Date partitions such as ["20260428"]. Resolved under router_session_parquet/date=YYYYMMDD.',
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},
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'paths': {
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'type': 'array',
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'items': {'type': 'string'},
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'description': 'Parquet files or date directories. Relative paths resolve from workspace root; absolute paths are allowed for external online data.',
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},
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'devices': {'type': 'array', 'items': {'type': 'string'}},
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'domains': {'type': 'array', 'items': {'type': 'string'}},
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'intents': {'type': 'array', 'items': {'type': 'string'}},
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'funcs': {'type': 'array', 'items': {'type': 'string'}},
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'query_keywords': {'type': 'array', 'items': {'type': 'string'}},
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'keyword_match_mode': {'type': 'string', 'enum': ['any', 'all']},
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'query_regex': {'type': 'string'},
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'match_scope': {'type': 'string', 'enum': ['any_turn', 'last_turn']},
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'turn_count_min': {'type': 'integer', 'minimum': 1},
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'turn_count_max': {'type': 'integer', 'minimum': 1},
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'max_files': {'type': 'integer', 'minimum': 1, 'maximum': 200},
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'max_rows_per_file': {'type': 'integer', 'minimum': 1, 'maximum': 100000},
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'max_candidates': {'type': 'integer', 'minimum': 1, 'maximum': 5000},
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},
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},
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handler=resolve_handler(handlers, 'data_agent_search_router_sessions', 'data-agent'),
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),
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AgentTool(
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name='data_agent_sample_router_candidates',
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description=(
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'Sample router session candidates for human review and return candidate-level summary statistics. '
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'Use after data_agent_search_router_sessions.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'candidates': {
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'oneOf': [
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{'type': 'array'},
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{'type': 'object'},
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{'type': 'string'},
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],
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'description': 'Candidates array, search result object, or JSON string containing candidates.',
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},
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'sample_size': {'type': 'integer', 'minimum': 1, 'maximum': 1000},
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'strategy': {'type': 'string', 'enum': ['random', 'first', 'stride']},
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'seed': {'type': 'integer'},
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},
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'required': ['candidates'],
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},
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handler=resolve_handler(handlers, 'data_agent_sample_router_candidates', 'data-agent'),
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),
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AgentTool(
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name='data_agent_convert_router_candidates_to_records',
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description=(
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'Convert reviewed online router session candidates directly into canonical data-agent records. '
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'Use this when the user wants to keep mined online data as samples; do not use generation-plan tools for this path.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'candidates': {
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'oneOf': [
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{'type': 'array'},
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{'type': 'object'},
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{'type': 'string'},
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],
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'description': 'Candidates array, search/sample result object, or JSON string containing candidates.',
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},
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'dataset_label': {'type': 'string'},
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'default_target': {
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'type': 'string',
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'description': 'Target label used for included candidates unless review_decisions provides a target.',
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},
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'default_complex': {
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'type': 'boolean',
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'description': 'Default complex dimension for included candidates unless review_decisions provides complex.',
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},
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'review_decisions': {
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'type': 'array',
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'description': 'Optional include/exclude/uncertain decisions keyed by semantic_session_id or req_id.',
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'items': {
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'type': 'object',
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'properties': {
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'semantic_session_id': {'type': 'string'},
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'req_id': {'type': 'string'},
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'matched_turn_index': {'type': 'integer'},
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'decision': {'type': 'string', 'enum': ['include', 'exclude', 'uncertain']},
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'target': {'type': 'string'},
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'complex': {'type': 'boolean'},
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'notes': {'type': 'string'},
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},
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},
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},
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'batch_id': {'type': 'string'},
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'include_uncertain': {'type': 'boolean'},
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},
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'required': ['candidates', 'dataset_label'],
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},
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handler=resolve_handler(handlers, 'data_agent_convert_router_candidates_to_records', 'data-agent'),
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),
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AgentTool(
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name='data_agent_show_generation_plan',
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description='Show a data-agent generation plan for human review.',
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parameters={
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'type': 'object',
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'properties': {
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'plan_id': {'type': 'string'},
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},
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'required': ['plan_id'],
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},
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handler=resolve_handler(handlers, 'data_agent_show_generation_plan', 'data-agent'),
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),
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AgentTool(
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name='data_agent_update_generation_plan',
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description=(
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'Update a pending data-agent generation plan from human review feedback, then show it again for review.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'plan_id': {'type': 'string'},
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'review_feedback': {
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'type': 'string',
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'description': 'Human review feedback that explains why the plan is changing.',
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},
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'updates': {
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'type': 'object',
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'description': 'Fields to update on the plan.',
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},
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},
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'required': ['plan_id', 'review_feedback', 'updates'],
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},
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handler=resolve_handler(handlers, 'data_agent_update_generation_plan', 'data-agent'),
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),
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AgentTool(
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name='data_agent_confirm_generation_plan',
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description=(
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'Mark a pending data-agent generation plan as confirmed after the user explicitly approves it.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'plan_id': {'type': 'string'},
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'confirmation': {
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'type': 'string',
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'description': 'The user approval text, such as 确认, 开始生成, or approve.',
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},
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'reviewed_revision': {
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'type': 'integer',
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'description': 'The plan revision shown to the user before confirmation.',
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},
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},
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'required': ['plan_id', 'confirmation'],
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},
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handler=resolve_handler(handlers, 'data_agent_confirm_generation_plan', 'data-agent'),
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),
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AgentTool(
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name='data_agent_normalize_dataset_draft',
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description=(
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'Parse dataset draft text written with 用户/小爱/target blocks and build canonical '
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'data-agent records with generated record IDs, timestamps, source metadata, context, labels, '
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'and dimensions.complex. '
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'Generated data requires a confirmed_plan_id from data_agent_confirm_generation_plan. '
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'Use draft_path instead of draft_text when the draft is large or contains many quoted targets.'
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),
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parameters={
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'type': 'object',
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'properties': {
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'draft_text': {
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'type': 'string',
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'description': 'Dataset draft text in dataset draft text v1 format. Each case should include complex: true/false. Provide exactly one of draft_text or draft_path.',
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},
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'draft_path': {
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'type': 'string',
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'description': 'Path to a UTF-8 dataset draft text file. Prefer this for large drafts. Provide exactly one of draft_text or draft_path.',
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},
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'batch_id': {
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'type': 'string',
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'description': 'Batch identifier used in generated record_id values.',
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},
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'source_type': {
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'type': 'string',
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'enum': ['generated', 'online', 'manual', 'mixed'],
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},
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'base_timestamp': {
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'type': 'integer',
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'description': 'Optional millisecond timestamp for deterministic generated records.',
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},
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'timestamp_step_ms': {
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'type': 'integer',
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'minimum': 1,
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'description': 'Millisecond gap between adjacent turns. Defaults to 60000.',
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},
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'default_request_id': {
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'type': 'string',
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'description': 'Request ID to use when a case does not provide a real request_id.',
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},
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'confirmed_plan_id': {
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'type': 'string',
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'description': 'Required for generated data; returned by data_agent_confirm_generation_plan.',
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},
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},
|
|
},
|
|
handler=resolve_handler(handlers, 'data_agent_normalize_dataset_draft', 'data-agent'),
|
|
),
|
|
AgentTool(
|
|
name='data_agent_validate_dataset_records',
|
|
description=(
|
|
'Validate canonical data-agent records for required fields, labels, source metadata, '
|
|
'prev_session structure, dimensions.complex, timestamp order, and 5-minute prompt stitching constraints.'
|
|
),
|
|
parameters={
|
|
'type': 'object',
|
|
'properties': {
|
|
'records': {
|
|
'oneOf': [
|
|
{'type': 'array'},
|
|
{'type': 'string'},
|
|
],
|
|
'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
|
|
},
|
|
'records_path': {
|
|
'type': 'string',
|
|
'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
|
|
},
|
|
},
|
|
},
|
|
handler=resolve_handler(handlers, 'data_agent_validate_dataset_records', 'data-agent'),
|
|
),
|
|
AgentTool(
|
|
name='data_agent_export_dataset_records',
|
|
description=(
|
|
'Validate canonical data-agent records and write them to a workspace file. '
|
|
'Defaults to compact JSONL, one canonical record per line, and also writes records.csv '
|
|
'in the same output directory for human sharing. The table function column combines '
|
|
'complex=true/false and label.target.'
|
|
),
|
|
parameters={
|
|
'type': 'object',
|
|
'properties': {
|
|
'records': {
|
|
'oneOf': [
|
|
{'type': 'array'},
|
|
{'type': 'string'},
|
|
],
|
|
'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
|
|
},
|
|
'records_path': {
|
|
'type': 'string',
|
|
'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
|
|
},
|
|
'output_path': {
|
|
'type': 'string',
|
|
'description': (
|
|
'Requested workspace-relative path. For canonical records, the runtime normalizes this '
|
|
'to the current session output/records.jsonl or output/records.json; do not encode '
|
|
'dataset names or timestamps in the filename.'
|
|
),
|
|
},
|
|
'output_format': {
|
|
'type': 'string',
|
|
'enum': ['jsonl', 'json'],
|
|
'description': 'Defaults to jsonl. json writes a compact JSON array.',
|
|
},
|
|
'require_validation_ok': {
|
|
'type': 'boolean',
|
|
'description': 'When true, refuse to write records with validation errors.',
|
|
},
|
|
'overwrite': {
|
|
'type': 'boolean',
|
|
'description': 'When false, refuse to overwrite an existing file.',
|
|
},
|
|
'export_table': {
|
|
'type': 'boolean',
|
|
'description': 'Defaults to true. Also write records.csv with the shared spreadsheet columns.',
|
|
},
|
|
},
|
|
'required': ['output_path'],
|
|
},
|
|
handler=resolve_handler(handlers, 'data_agent_export_dataset_records', 'data-agent'),
|
|
),
|
|
AgentTool(
|
|
name='data_agent_export_training_jsonl',
|
|
description=(
|
|
'Convert canonical data-agent records into training JSONL. Each line has system, instruction, '
|
|
'and output. Output combines dimensions.complex and label.target as two lines. '
|
|
'The instruction contains 知识注入, 系统状态, 对话历史, 当前query, and function sections.'
|
|
),
|
|
parameters={
|
|
'type': 'object',
|
|
'properties': {
|
|
'records': {
|
|
'oneOf': [
|
|
{'type': 'array'},
|
|
{'type': 'string'},
|
|
],
|
|
'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
|
|
},
|
|
'records_path': {
|
|
'type': 'string',
|
|
'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
|
|
},
|
|
'output_path': {
|
|
'type': 'string',
|
|
'description': 'Optional requested path. Runtime normalizes to current session output/training.jsonl.',
|
|
},
|
|
'session_num': {
|
|
'type': 'integer',
|
|
'minimum': 1,
|
|
'description': 'Max history queries to include. Defaults to 5.',
|
|
},
|
|
'session_time_minutes': {
|
|
'type': 'integer',
|
|
'minimum': 1,
|
|
'description': 'Max adjacent history gap in minutes. Defaults to 5.',
|
|
},
|
|
'context_fields': {
|
|
'type': 'array',
|
|
'items': {'type': 'string'},
|
|
'description': 'Context fields to inject. Defaults to ["location", "rag"].',
|
|
},
|
|
'system_prompt': {
|
|
'type': 'string',
|
|
'description': 'Defaults to 你是小爱同学，中文智能语音助手。',
|
|
},
|
|
'require_validation_ok': {'type': 'boolean'},
|
|
'overwrite': {'type': 'boolean'},
|
|
},
|
|
},
|
|
handler=resolve_handler(handlers, 'data_agent_export_training_jsonl', 'data-agent'),
|
|
),
|
|
AgentTool(
|
|
name='data_agent_export_planning_eval_csv',
|
|
description=(
|
|
'Convert canonical data-agent records into evaluation CSV with columns: request_id, newPrompt, '
|
|
'query, 类别真实标签, code标签, complex. The complex column is read from dimensions.complex. '
|
|
'newPrompt wraps the prompt body with <|im_start|>system/user/assistant chat-template tags.'
|
|
),
|
|
parameters={
|
|
'type': 'object',
|
|
'properties': {
|
|
'records': {
|
|
'oneOf': [
|
|
{'type': 'array'},
|
|
{'type': 'string'},
|
|
],
|
|
'description': 'Canonical records as an array, or a JSON string containing the array. Provide exactly one of records or records_path.',
|
|
},
|
|
'records_path': {
|
|
'type': 'string',
|
|
'description': 'Path to records JSON/JSONL. Prefer this when records are large. Provide exactly one of records or records_path.',
|
|
},
|
|
'output_path': {
|
|
'type': 'string',
|
|
'description': 'Optional requested path. Runtime normalizes to current session output/eval_planning.csv.',
|
|
},
|
|
'session_num': {
|
|
'type': 'integer',
|
|
'minimum': 1,
|
|
'description': 'Max history queries to include. Defaults to 5.',
|
|
},
|
|
'session_time_minutes': {
|
|
'type': 'integer',
|
|
'minimum': 1,
|
|
'description': 'Max adjacent history gap in minutes. Defaults to 5.',
|
|
},
|
|
'context_fields': {
|
|
'type': 'array',
|
|
'items': {'type': 'string'},
|
|
'description': 'Context fields to inject. Defaults to ["location", "rag"].',
|
|
},
|
|
'system_prompt': {
|
|
'type': 'string',
|
|
'description': 'System prompt wrapped into newPrompt chat template. Defaults to 你是小爱同学，中文智能语音助手。',
|
|
},
|
|
'complex_default': {
|
|
'type': 'boolean',
|
|
'description': 'Fallback complex value only for legacy records missing dimensions.complex. Defaults to false.',
|
|
},
|
|
'require_validation_ok': {'type': 'boolean'},
|
|
'overwrite': {'type': 'boolean'},
|
|
},
|
|
},
|
|
handler=resolve_handler(handlers, 'data_agent_export_planning_eval_csv', 'data-agent'),
|
|
),
|
|
]
|