Add training and planning eval exports

src/agent_tool_specs/data_agent.py

@@ -547,4 +547,102 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
             },
             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. 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. newPrompt uses the same prompt body as training instruction.'
+            ),
+            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"].',
+                    },
+                    'complex_default': {
+                        'type': 'boolean',
+                        'description': 'Default complex column value. Defaults to false.',
+                    },
+                    'require_validation_ok': {'type': 'boolean'},
+                    'overwrite': {'type': 'boolean'},
+                },
+            },
+            handler=resolve_handler(handlers, 'data_agent_export_planning_eval_csv', 'data-agent'),
+        ),
     ]

src/agent_tools.py

@@ -34,6 +34,8 @@ from .data_agent_records import (
     confirm_generation_goal,
     confirm_generation_plan,
     export_dataset_records,
+    export_planning_eval_csv,
+    export_training_jsonl,
     get_generation_plan,
     normalize_dataset_draft,
     prepare_generation_goal,
@@ -1049,6 +1051,8 @@ def default_tool_registry() -> dict[str, AgentTool]:
             'data_agent_normalize_dataset_draft': _normalize_dataset_draft_tool,
             'data_agent_validate_dataset_records': _validate_dataset_records_tool,
             'data_agent_export_dataset_records': _export_dataset_records_tool,
+            'data_agent_export_training_jsonl': _export_training_jsonl_tool,
+            'data_agent_export_planning_eval_csv': _export_planning_eval_csv_tool,
         }),
     ]
     return {tool.name: tool for tool in tools}
@@ -1550,6 +1554,70 @@ def _export_dataset_records_tool(arguments: dict[str, Any], context: ToolExecuti
     return json.dumps(payload, ensure_ascii=False, indent=2)
 
 
+def _export_training_jsonl_tool(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
+    require_validation_ok = arguments.get('require_validation_ok', True)
+    if not isinstance(require_validation_ok, bool):
+        raise ToolExecutionError('require_validation_ok must be a boolean')
+    overwrite = arguments.get('overwrite', True)
+    if not isinstance(overwrite, bool):
+        raise ToolExecutionError('overwrite must be a boolean')
+    system_prompt = arguments.get('system_prompt', '你是小爱同学，中文智能语音助手。')
+    if not isinstance(system_prompt, str):
+        raise ToolExecutionError('system_prompt must be a string')
+    try:
+        records = _records_from_arguments(arguments, context)
+        payload = export_training_jsonl(
+            records,
+            root=str(context.root),
+            output_path=_data_agent_output_path(
+                _optional_string(arguments, 'output_path') or 'output/training.jsonl',
+                context,
+                canonical_filename='training.jsonl',
+            ),
+            session_num=_coerce_int(arguments, 'session_num', 5),
+            session_time_minutes=_coerce_int(arguments, 'session_time_minutes', 5),
+            context_fields=_optional_string_list(arguments, 'context_fields'),
+            system_prompt=system_prompt,
+            require_validation_ok=require_validation_ok,
+            overwrite=overwrite,
+        )
+    except (DataRecordError, json.JSONDecodeError) as exc:
+        raise ToolExecutionError(str(exc)) from exc
+    return json.dumps(payload, ensure_ascii=False, indent=2)
+
+
+def _export_planning_eval_csv_tool(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
+    require_validation_ok = arguments.get('require_validation_ok', True)
+    if not isinstance(require_validation_ok, bool):
+        raise ToolExecutionError('require_validation_ok must be a boolean')
+    overwrite = arguments.get('overwrite', True)
+    if not isinstance(overwrite, bool):
+        raise ToolExecutionError('overwrite must be a boolean')
+    complex_default = arguments.get('complex_default', False)
+    if not isinstance(complex_default, bool):
+        raise ToolExecutionError('complex_default must be a boolean')
+    try:
+        records = _records_from_arguments(arguments, context)
+        payload = export_planning_eval_csv(
+            records,
+            root=str(context.root),
+            output_path=_data_agent_output_path(
+                _optional_string(arguments, 'output_path') or 'output/eval_planning.csv',
+                context,
+                canonical_filename='eval_planning.csv',
+            ),
+            session_num=_coerce_int(arguments, 'session_num', 5),
+            session_time_minutes=_coerce_int(arguments, 'session_time_minutes', 5),
+            context_fields=_optional_string_list(arguments, 'context_fields'),
+            complex_default=complex_default,
+            require_validation_ok=require_validation_ok,
+            overwrite=overwrite,
+        )
+    except (DataRecordError, json.JSONDecodeError) as exc:
+        raise ToolExecutionError(str(exc)) from exc
+    return json.dumps(payload, ensure_ascii=False, indent=2)
+
+
 def _draft_text_from_arguments(arguments: dict[str, Any], context: ToolExecutionContext) -> str:
     draft_text = arguments.get('draft_text')
     draft_path = arguments.get('draft_path')

src/data_agent_records.py

@@ -18,6 +18,8 @@ from typing import Any
 
 DEFAULT_REQUEST_ID = 'aabbccdd'
 DEFAULT_TIMESTAMP_STEP_MS = 60_000
+DEFAULT_SYSTEM_PROMPT = '你是小爱同学，中文智能语音助手。'
+DEFAULT_CONTEXT_FIELDS = ('location', 'rag')
 DEFAULT_PLAN_STATE_FILE = '.port_sessions/data_agent_generation_plans.json'
 DEFAULT_GOAL_STATE_FILE = '.port_sessions/data_agent_generation_goals.json'
 
@@ -520,6 +522,100 @@ def export_dataset_records(
     return payload
 
 
+def export_training_jsonl(
+    records: list[dict[str, Any]],
+    *,
+    root: str,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    system_prompt: str = DEFAULT_SYSTEM_PROMPT,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    """导出训练集 JSONL：system / instruction / output。"""
+
+    if session_num <= 0:
+        raise DataRecordError('session_num must be greater than 0')
+    if session_time_minutes <= 0:
+        raise DataRecordError('session_time_minutes must be greater than 0')
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation['ok']:
+        raise DataRecordError(
+            f'records failed validation with {validation["error_count"]} errors'
+        )
+    path = _resolve_output_path(root, output_path)
+    if path.exists() and not overwrite:
+        raise DataRecordError(f'output_path already exists: {output_path}')
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = _normalize_context_fields(context_fields)
+    content = ''.join(
+        _training_jsonl_line(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=fields,
+            system_prompt=system_prompt,
+        )
+        + '\n'
+        for record in records
+    )
+    path.write_text(content, encoding='utf-8')
+    return {
+        'output_path': str(path.relative_to(Path(root).resolve())),
+        'output_format': 'jsonl',
+        'record_count': len(records),
+        'bytes_written': len(content.encode('utf-8')),
+        'validation': validation,
+    }
+
+
+def export_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    root: str,
+    output_path: str,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+    require_validation_ok: bool = True,
+    overwrite: bool = True,
+) -> dict[str, Any]:
+    """导出评测 CSV：request_id / newPrompt / query / 类别真实标签 / code标签 / complex。"""
+
+    if session_num <= 0:
+        raise DataRecordError('session_num must be greater than 0')
+    if session_time_minutes <= 0:
+        raise DataRecordError('session_time_minutes must be greater than 0')
+    validation = validate_dataset_records(records)
+    if require_validation_ok and not validation['ok']:
+        raise DataRecordError(
+            f'records failed validation with {validation["error_count"]} errors'
+        )
+    path = _resolve_output_path(root, output_path)
+    if path.exists() and not overwrite:
+        raise DataRecordError(f'output_path already exists: {output_path}')
+    path.parent.mkdir(parents=True, exist_ok=True)
+    fields = _normalize_context_fields(context_fields)
+    content = render_planning_eval_csv(
+        records,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+        context_fields=fields,
+        complex_default=complex_default,
+    )
+    path.write_text(content, encoding='utf-8-sig')
+    return {
+        'output_path': str(path.relative_to(Path(root).resolve())),
+        'output_format': 'csv',
+        'record_count': len(records),
+        'bytes_written': len(content.encode('utf-8-sig')),
+        'validation': validation,
+    }
+
+
 def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     """把 canonical records 转成同事流转用的表格 CSV。"""
 
@@ -531,6 +627,185 @@ def render_dataset_records_table_csv(records: list[dict[str, Any]]) -> str:
     return output.getvalue()
 
 
+def render_planning_eval_csv(
+    records: list[dict[str, Any]],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+    complex_default: bool = False,
+) -> str:
+    """把 canonical records 转成含 newPrompt 的评测 CSV。"""
+
+    fields = _normalize_context_fields(context_fields)
+    output = StringIO()
+    writer = csv.DictWriter(
+        output,
+        fieldnames=['request_id', 'newPrompt', 'query', '类别真实标签', 'code标签', 'complex'],
+        lineterminator='\n',
+    )
+    writer.writeheader()
+    for record in records:
+        source = record.get('source') if isinstance(record.get('source'), dict) else {}
+        turn = record.get('turn') if isinstance(record.get('turn'), dict) else {}
+        label = record.get('label') if isinstance(record.get('label'), dict) else {}
+        target = str(label.get('target') or '')
+        writer.writerow(
+            {
+                'request_id': str(source.get('request_id') or ''),
+                'newPrompt': build_training_instruction(
+                    record,
+                    session_num=session_num,
+                    session_time_minutes=session_time_minutes,
+                    context_fields=fields,
+                ),
+                'query': str(turn.get('query') or ''),
+                '类别真实标签': _category_label_from_target(target),
+                'code标签': target,
+                'complex': 'true' if complex_default else 'false',
+            }
+        )
+    return output.getvalue()
+
+
+def build_training_instruction(
+    record: dict[str, Any],
+    *,
+    session_num: int = 5,
+    session_time_minutes: int = 5,
+    context_fields: list[str] | None = None,
+) -> str:
+    turn = record.get('turn') if isinstance(record.get('turn'), dict) else {}
+    query = str(turn.get('query') or '')
+    context = record.get('context') if isinstance(record.get('context'), dict) else {}
+    prev_session = record.get('prev_session') if isinstance(record.get('prev_session'), list) else []
+    current_ts = _optional_int_for_export(turn.get('timestamp'))
+    if current_ts is None:
+        source = record.get('source') if isinstance(record.get('source'), dict) else {}
+        current_ts = _optional_int_for_export(source.get('timestamp'))
+    session, last_tts = _training_history(
+        prev_session,
+        current_ts=current_ts,
+        session_num=session_num,
+        session_time_minutes=session_time_minutes,
+    )
+    fields = _normalize_context_fields(context_fields)
+
+    instruction = '请参考用户的[当前query]、[对话历史]、[知识注入]、[系统状态]识别出[当前query]的[function]结果，[function]是python的code形式。\n'
+    instruction += '[知识注入]\n'
+    instruction += f'{_context_prompt_block(context, fields)}\n'
+    instruction += '[系统状态]\n'
+    instruction += '{}\n'
+    instruction += '[对话历史]\n'
+    if session:
+        history_parts = [f'用户: {session_query}' for session_query in session]
+        if last_tts is not None:
+            history_parts.append(f'小爱: {last_tts}')
+        instruction += '\n'.join(history_parts)
+        instruction += '\n'
+    instruction += '[当前query]\n'
+    instruction += f'用户: {query}\n'
+    instruction += '[function]\n'
+    return instruction
+
+
+def _training_jsonl_line(
+    record: dict[str, Any],
+    *,
+    session_num: int,
+    session_time_minutes: int,
+    context_fields: list[str],
+    system_prompt: str,
+) -> str:
+    label = record.get('label') if isinstance(record.get('label'), dict) else {}
+    payload = {
+        'system': system_prompt,
+        'instruction': build_training_instruction(
+            record,
+            session_num=session_num,
+            session_time_minutes=session_time_minutes,
+            context_fields=context_fields,
+        ),
+        'output': str(label.get('target') or ''),
+    }
+    return json.dumps(payload, ensure_ascii=False, separators=(',', ':'))
+
+
+def _training_history(
+    prev_session: list[Any],
+    *,
+    current_ts: int | None,
+    session_num: int,
+    session_time_minutes: int,
+) -> tuple[list[str], str | None]:
+    if current_ts is None:
+        return [], None
+    session_time_ms = session_time_minutes * 60 * 1000
+    valid_items: list[tuple[int, str, str]] = []
+    for item in prev_session:
+        if not isinstance(item, dict):
+            continue
+        ts = _optional_int_for_export(item.get('timestamp'))
+        query = str(item.get('query') or '')
+        tts = str(item.get('tts') or '')
+        if ts is not None and ts > 0 and query:
+            valid_items.append((ts, query, tts))
+    valid_items.sort(key=lambda item: item[0])
+
+    session: list[str] = []
+    last_tts: str | None = None
+    last_ts = current_ts
+    count = 0
+    for ts, query, tts in reversed(valid_items):
+        if last_ts - ts <= session_time_ms and last_ts >= ts:
+            session.append(query)
+            if count == 0:
+                last_tts = tts
+            last_ts = ts
+            count += 1
+            if count >= session_num:
+                break
+        else:
+            break
+    session.reverse()
+    return session, last_tts
+
+
+def _context_prompt_block(context: dict[str, Any], fields: list[str]) -> str:
+    lines = ['{']
+    for index, field in enumerate(fields):
+        comma = ',' if index < len(fields) - 1 else ''
+        value = str(context.get(field, ''))
+        lines.append(f'"{field}": {json.dumps(value, ensure_ascii=False)}{comma}')
+    lines.append('}')
+    return '\n'.join(lines)
+
+
+def _normalize_context_fields(context_fields: list[str] | None) -> list[str]:
+    fields = context_fields or list(DEFAULT_CONTEXT_FIELDS)
+    normalized = [field.strip() for field in fields if isinstance(field, str) and field.strip()]
+    return normalized or list(DEFAULT_CONTEXT_FIELDS)
+
+
+def _optional_int_for_export(value: Any) -> int | None:
+    if isinstance(value, bool) or value is None or value == '':
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _category_label_from_target(target: str) -> str:
+    agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target.strip())
+    if agent_match:
+        return agent_match.group(1)
+    function_names = re.findall(r'\b([A-Za-z_][A-Za-z0-9_]*)\s*\(', target)
+    if function_names:
+        return function_names[-1]
+    return target
+
+
 def _dataset_table_columns() -> list[str]:
     return [
         'request_id',