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Fix product data eval export format

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wuyang6
2026-05-11 15:27:11 +08:00
parent be731caed7
commit f22de8a40b
15 changed files with 414 additions and 71 deletions
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skills/product-data/README.md
+2 -2
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@@ -45,8 +45,8 @@ cat input.json | python skills/product-data/scripts/<tool>.py
| `scripts/validate_dataset_records.py` | 校验 canonical records |
| `scripts/export_dataset_records.py` | 导出紧凑 JSONL/JSON，并默认生成同目录 `records.csv` 表格 |
| `scripts/export_dataset_table.py` | 已有 canonical records 时，只补生成同事流转表格 |
| `scripts/export_training_jsonl.py` | 把 canonical records 转成训练 JSONL |
| `scripts/export_planning_eval_csv.py` | 把 canonical records 转成含 `newPrompt` 的评测 CSV |
| `scripts/export_training_jsonl.py` | 把 canonical records 转成训练 JSONL，`output` 自动组合 `complex` 和标签 |
| `scripts/export_planning_eval_csv.py` | 把 canonical records 转成含 chat-template `newPrompt` 的评测 CSV，`complex` 列来自元数据并输出为 `TRUE/FALSE` |
## 平台内优先级
skills/product-data/SKILL.md
+25 -9
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@@ -97,6 +97,7 @@ source_refs:
- `dataset_label`：数据集或专题名称。
- `target` / `target_definitions`：最终监督标签。单标签任务用 `target`，多标签边界任务必须用 `target_definitions` 列出每个标签和判定规则。
- `complex` 判定规则：复杂度是独立维度，必须和标签边界一起确认；如果用户没有说明复杂/不复杂，默认按“原子化单步操作=false，需要规划、分析、组合或多步骤推理=true”给出建议，并在 review 中让用户确认。
- 生成数量：总条数，以及单轮/多轮数量或比例。
- 覆盖范围：需要覆盖哪些 query 类型、意图边界或错误类型。
- 负例/排除项：哪些表达不要生成，或哪些边界容易误判。
@@ -114,12 +115,15 @@ source_refs:
如果用户在原始需求里已经写出 `Agent(tag="xxx")`、function 调用或其他完整标签表达，`target` 必须原样保留这个完整表达，不要简化成纯标签名。例如用户说 `Agent(tag="餐饮服务")`，则 `target_definitions[*].target` 和后续 draft 的 `target:` 都必须写 `Agent(tag="餐饮服务")`，不要写成 `餐饮服务`。
如果用户给的是训练格式里的两行输出，例如 `complex=false\nAgent(tag="地图导航")`，需要拆开处理：`complex=false` 进入复杂度维度，`Agent(tag="地图导航")` 才是 `target`。不要把 `complex=...` 作为 target 的一部分写入 generation plan。
review 展示必须简短清晰，不要重复解释工具和流程。每次 review 最多展示 6 行，格式优先如下：
```text
我先把生成目标整理好了，先确认边界，暂时不生成数据。
- 数据集：xxx
- 标签：A -> Agent(tag="A")；B -> Agent(tag="B")
- 复杂度：默认 false；复杂任务按规则单独标 true
- 边界：一句话说明核心判定规则
- 覆盖：一句话说明主要 case 类型
- 内部：goal_id `...`，revision `...`
@@ -165,7 +169,7 @@ review 展示必须简短清晰，不要重复解释工具和流程。每次 rev
9. 用户提出修改意见时，调用 `data_agent_update_generation_plan`，再展示计划。
10. 用户明确确认当前计划版本后，调用 `data_agent_confirm_generation_plan`。
11. 生成 dataset draft text v1。
12. 调用 `data_agent_normalize_dataset_draft`，必须传入 `confirmed_plan_id`。
12. 调用 `data_agent_normalize_dataset_draft`，必须传入 `confirmed_plan_id`；draft 中每条 case 都必须有 `complex: true/false`。
13. 调用 `data_agent_validate_dataset_records`。
14. 如果用户要求落盘 canonical records，调用 `data_agent_export_dataset_records`，`output_path` 固定传 `output/records.jsonl`，默认导出紧凑 JSONL，并同时生成同目录 `records.csv` 表格，不要用 `write_file` 手写 JSON 或 CSV。
15. 如果用户已经明确要训练数据，调用 `data_agent_export_training_jsonl`，优先传 `records_path`，输出固定为 `output/training.jsonl`。
@@ -176,6 +180,7 @@ review 展示必须简短清晰，不要重复解释工具和流程。每次 rev
- 单次 `data_agent_normalize_dataset_draft` 最多处理 8 条 case；计划数量更多时，分批生成、分批 normalize，再汇总校验和导出。
- dataset draft 中的 Agent target 推荐写成单引号形式，例如 `target: Agent(tag='地图导航')`。工具会规范化为 `Agent(tag="地图导航")`，这样可以降低 tool call JSON 里双引号转义失败的概率。
- `complex:` 独立写一行，不要写进 `target:`；工具会在导出训练数据和流转表格时自动组合成 `complex=false\nAgent(...)`。
- 如果 draft 已经保存在文件中，优先传 `draft_path`，不要再把大段 `draft_text` 作为工具参数传入。
- 校验和导出 records 时，如果 records 已经保存在 JSON/JSONL 文件中，优先传 `records_path`。
@@ -190,6 +195,7 @@ review 展示必须简短清晰，不要重复解释工具和流程。每次 rev
### case: case名称
用户: 本轮 query
complex: false
target: Agent(tag="xxx")
notes: 可选，说明覆盖的问题或边界
@@ -197,6 +203,7 @@ notes: 可选，说明覆盖的问题或边界
用户: 前一轮 query
小爱: 前一轮 tts
用户: 本轮 query
complex: false
target: Agent(tag="xxx")
notes: 可选，说明覆盖的问题或边界
```
@@ -207,11 +214,12 @@ notes: 可选，说明覆盖的问题或边界
- `用户:` 表示用户 query。
- `小爱:` 表示小爱回复 tts。
- 最后一个 `用户:` 是本轮 query。
- `complex:` 是复杂度维度，必须独立填写 `true` 或 `false`；无法确定时先问用户。
- `target:` 是本轮 query 对应的监督标签，必须存在。
- 为避免工具参数 JSON 转义失败，dataset draft 里 Agent 标签优先写 `target: Agent(tag='xxx')`；转换工具会统一规范为 `Agent(tag="xxx")`。
- 单轮数据只需要写一行 `用户:`，然后写 `target:`。
- 单轮数据只需要写一行 `用户:`，然后写 `complex:` 和 `target:`。
- 多轮数据需要按照时间顺序写多组 `用户:` / `小爱:`。
- 多轮数据的最后一轮只写 `用户:` 和 `target:`，不要写最后一轮 `小爱:`，因为本轮 query 不包含 tts。
- 多轮数据的最后一轮只写 `用户:`、`complex:` 和 `target:`，不要写最后一轮 `小爱:`，因为本轮 query 不包含 tts。
- 如果 `target` 无法确定，不要编造，必须向用户确认。
- 不要手写 `record_id`、`request_id`、`timestamp`、`context`。
- 线上挖掘数据如有真实 `request_id` 和 `timestamp`，可以附加在 case 中；没有则不写。
@@ -226,7 +234,8 @@ canonical records 落盘必须使用 `data_agent_export_dataset_records`，默
- 训练数据：调用 `data_agent_export_training_jsonl`，输出 `training.jsonl`，每行包含 `system`、`instruction`、`output`。
- 评测数据：调用 `data_agent_export_planning_eval_csv`，输出 `eval_planning.csv`，字段为 `request_id,newPrompt,query,类别真实标签,code标签,complex`。
- 两个派生格式默认复用相同 prompt 构造逻辑：`[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`。
- 训练 `output` 和流转表格 `function` 列都会自动组合为两行：第一行 `complex=true/false`，第二行原监督标签；评测 `complex` 列直接来自 canonical record 的 `dimensions.complex`，按评测表习惯输出 `TRUE/FALSE`。
- 训练 `instruction` 和评测 `newPrompt` 复用相同 prompt 主体：`[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`；评测 `newPrompt` 还会额外包上 `<|im_start|>system`、`<|im_start|>user`、`<|im_start|>assistant` chat template。
- 对话历史默认最多取 5 轮，且相邻轮间隔不超过 5 分钟；`context` 默认注入 `location` 和 `rag` 两个字段。
当前 canonical record v1 工作格式：
@@ -256,6 +265,9 @@ canonical records 落盘必须使用 `data_agent_export_dataset_records`，默
"target": "Agent(tag=\"xxx\")",
"target_type": "agent"
},
"dimensions": {
"complex": false
},
"meta": {
"case_name": "case名称",
"notes": ""
@@ -357,7 +369,7 @@ skills/product-data/scripts/normalize_dataset_draft.py
```json
{
"draft_text": "# dataset_label: 地图餐饮边界\n\n### case: 找附近美食\n用户: 附近有什么好吃的\ntarget: Agent(tag='餐饮服务')",
"draft_text": "# dataset_label: 地图餐饮边界\n\n### case: 找附近美食\n用户: 附近有什么好吃的\ncomplex: false\ntarget: Agent(tag='餐饮服务')",
"batch_id": "aabbccdd",
"source_type": "generated"
}
@@ -369,7 +381,7 @@ skills/product-data/scripts/normalize_dataset_draft.py
{"draft_path": "output/dataset_draft.txt", "batch_id": "aabbccdd", "source_type": "generated"}
```
用途：把 dataset draft text v1 转成 canonical records。
用途：把 dataset draft text v1 转成 canonical records。每条 case 推荐包含 `complex: true/false`；旧草稿缺失时会按 `false` 兼容。
### `product_data_validate_dataset_records`
@@ -419,6 +431,8 @@ skills/product-data/scripts/export_dataset_records.py
request_id,timestamp,query,prev_session,context,label,是否迁移Function,function
```
其中 `function` 列会自动组合为 `complex=true/false` 和原监督标签两行。
### `product_data_export_dataset_table`
脚本：
@@ -456,6 +470,7 @@ skills/product-data/scripts/export_training_jsonl.py
```
用途：把 canonical records 转成训练 JSONL，默认文件名 `training.jsonl`。
输出的 `output` 字段会自动组合 `complex=true/false` 和 `label.target` 两行。
### `product_data_export_planning_eval_csv`
@@ -475,6 +490,7 @@ skills/product-data/scripts/export_planning_eval_csv.py
```
用途：把 canonical records 转成含 `newPrompt` 的评测 CSV，默认文件名 `eval_planning.csv`。
`newPrompt` 会使用评测侧标准 chat template；`complex` 列来自 canonical records 的 `dimensions.complex`，输出为 `TRUE/FALSE`，不要用固定默认值覆盖。
## 当前可用工具
@@ -494,11 +510,11 @@ skills/product-data/scripts/export_planning_eval_csv.py
- `data_agent_show_generation_plan`：用户要求查看当前计划，或继续上下文时需要恢复计划详情时使用。
- `data_agent_update_generation_plan`：用户对计划提出修改意见后使用，更新计划并重新展示。
- `data_agent_confirm_generation_plan`：用户明确确认当前计划版本后使用，获取 `confirmed_plan_id`。
- `data_agent_normalize_dataset_draft`：用户确认计划后，把 dataset draft text v1 转成 canonical records；必须传入 `confirmed_plan_id`；支持 `draft_text` 或 `draft_path`，大草稿优先 `draft_path`。
- `data_agent_normalize_dataset_draft`：用户确认计划后，把 dataset draft text v1 转成 canonical records；必须传入 `confirmed_plan_id`；支持 `draft_text` 或 `draft_path`，大草稿优先 `draft_path`；每条 case 要写 `complex: true/false`。
- `data_agent_validate_dataset_records`：对 canonical records 做结构、标签、时间戳和多轮上下文校验；支持 `records` 或 `records_path`。
- `data_agent_export_dataset_records`：校验 canonical records 并落盘；支持 `records` 或 `records_path`；默认写紧凑 JSONL，一行一条，固定传 `output/records.jsonl`，并同时生成 `output/records.csv` 表格，不要再用 `write_file` 手写 records 或表格文件。
- `data_agent_export_training_jsonl`：把 canonical records 转成训练 JSONL；支持 `records` 或 `records_path`；默认写 `output/training.jsonl`。
- `data_agent_export_planning_eval_csv`：把 canonical records 转成评测 CSV；支持 `records` 或 `records_path`；默认写 `output/eval_planning.csv`。
- `data_agent_export_training_jsonl`：把 canonical records 转成训练 JSONL；支持 `records` 或 `records_path`；默认写 `output/training.jsonl`；`output` 自动包含 `complex` 行。
- `data_agent_export_planning_eval_csv`：把 canonical records 转成评测 CSV；支持 `records` 或 `records_path`；默认写 `output/eval_planning.csv`；`complex` 列从元数据生成。
## 约束
skills/product-data/knowledge/canonical_record_v1.md
+8 -4
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@@ -29,6 +29,9 @@ canonical record 是产品数据生成链路的中间元数据格式。它不是
"target": "Agent(tag=\"xxx\")",
"target_type": "agent"
},
"dimensions": {
"complex": false
},
"meta": {
"case_name": "case名称",
"notes": ""
@@ -44,6 +47,7 @@ canonical record 是产品数据生成链路的中间元数据格式。它不是
- 相邻轮时间间隔超过 5 分钟时给 warning。
- `label.target` 必须有值。
- `target_type` 只能是 `agent`、`function` 或 `unknown`。
- `dimensions.complex` 必须是布尔值，表示当前 query 是否为复杂任务；它是独立维度，不写入 `label.target`。
## 默认导出
@@ -52,7 +56,7 @@ canonical record 是产品数据生成链路的中间元数据格式。它不是
- `records.jsonl`：canonical records，一行一条紧凑 JSON。
- `records.csv`：同事流转表格，字段为 `request_id,timestamp,query,prev_session,context,label,是否迁移Function,function`。
`records.csv` 中的 `prev_session` 和 `context` 是紧凑 JSON 字符串；`prev_session` 中的 `timestamp` 按历史表格习惯输出为字符串。
`records.csv` 中的 `prev_session` 和 `context` 是紧凑 JSON 字符串；`prev_session` 中的 `timestamp` 按历史表格习惯输出为字符串。`function` 列会把复杂度和监督标签组合成两行，例如 `complex=false\nAgent(tag="地图导航")`。
如果已有 `records.jsonl` 或 `records.json`，只需要补表格，可以执行 portable `export_dataset_table.py` 或等价工具；默认仍输出到同目录的 `records.csv`。
@@ -60,7 +64,7 @@ canonical record 是产品数据生成链路的中间元数据格式。它不是
训练和评测格式都从 canonical records 转换，不由模型手写：
- `training.jsonl`：每行 `{"system": "...", "instruction": "...", "output": "..."}`，由 `export_training_jsonl.py` 或 `data_agent_export_training_jsonl` 生成。
- `eval_planning.csv`：字段为 `request_id,newPrompt,query,类别真实标签,code标签,complex`，由 `export_planning_eval_csv.py` 或 `data_agent_export_planning_eval_csv` 生成。
- `training.jsonl`：每行 `{"system": "...", "instruction": "...", "output": "..."}`，由 `export_training_jsonl.py` 或 `data_agent_export_training_jsonl` 生成；`output` 会输出 `complex=true/false` 加监督标签两行。
- `eval_planning.csv`：字段为 `request_id,newPrompt,query,类别真实标签,code标签,complex`，由 `export_planning_eval_csv.py` 或 `data_agent_export_planning_eval_csv` 生成；`newPrompt` 会包上 `<|im_start|>system/user/assistant` chat template；`complex` 列来自 `dimensions.complex`，按评测表习惯输出 `TRUE/FALSE`，不是固定默认值。
`instruction` 和 `newPrompt` 使用同一套 prompt 模板，默认包含 `[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`。历史轮次默认最多取 5 轮，且相邻时间间隔不超过 5 分钟。
`instruction` 和 `newPrompt` 使用同一套 prompt 主体，默认包含 `[知识注入]`、`[系统状态]`、`[对话历史]`、`[当前query]`、`[function]`。历史轮次默认最多取 5 轮，且相邻时间间隔不超过 5 分钟。
skills/product-data/knowledge/dataset_draft_v1.md
+5 -2
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@@ -9,6 +9,7 @@
### case: case名称
用户: 本轮 query
complex: false
target: Agent(tag='xxx')
notes: 可选，说明覆盖的问题或边界
@@ -16,6 +17,7 @@ notes: 可选，说明覆盖的问题或边界
用户: 前一轮 query
小爱: 前一轮 tts
用户: 本轮 query
complex: false
target: Agent(tag='xxx')
notes: 可选，说明覆盖的问题或边界
```
@@ -26,10 +28,11 @@ notes: 可选，说明覆盖的问题或边界
- `用户:` 表示用户 query。
- `小爱:` 表示小爱回复 tts。
- 最后一个 `用户:` 是本轮 query。
- `complex:` 是复杂度维度，必须独立填写 `true` 或 `false`；无法确定时先问用户，不要把它塞进 `target`。
- `target:` 是本轮 query 对应的监督标签，必须存在。
- Agent 标签推荐写单引号形式 `Agent(tag='xxx')`，转换工具会规范成 `Agent(tag="xxx")`。
- 单轮数据只需要写一行 `用户:`，然后写 `target:`。
- 单轮数据只需要写一行 `用户:`，然后写 `complex:` 和 `target:`。
- 多轮数据需要按照时间顺序写多组 `用户:` / `小爱:`。
- 多轮数据的最后一轮只写 `用户:` 和 `target:`，不要写最后一轮 `小爱:`。
- 多轮数据的最后一轮只写 `用户:`、`complex:` 和 `target:`，不要写最后一轮 `小爱:`。
- 如果 `target` 无法确定，不要编造，必须向用户确认。
- 不要手写 `record_id`、`request_id`、`timestamp`、`context`。
skills/product-data/schemas/export_planning_eval_csv.input.schema.json
+9 -1
View File
@@ -18,7 +18,15 @@
"items": {"type": "string"},
"default": ["location", "rag"]
},
"complex_default": {"type": "boolean", "default": false},
"system_prompt": {
"type": "string",
"default": "你是小爱同学，中文智能语音助手。"
},
"complex_default": {
"type": "boolean",
"default": false,
"description": "Fallback only for legacy records missing dimensions.complex."
},
"require_validation_ok": {"type": "boolean", "default": true},
"overwrite": {"type": "boolean", "default": true}
}
skills/product-data/schemas/normalize_dataset_draft.input.schema.json
+1 -1
View File
@@ -3,7 +3,7 @@
"properties": {
"draft_text": {
"type": "string",
"description": "Dataset draft text. Provide exactly one of draft_text or draft_path."
"description": "Dataset draft text. Each case should include complex: true/false. Provide exactly one of draft_text or draft_path."
},
"draft_path": {
"type": "string",
skills/product-data/scripts/export_planning_eval_csv.py
+2
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@@ -1,6 +1,7 @@
from __future__ import annotations
from product_data_portable import (
DEFAULT_SYSTEM_PROMPT,
default_planning_eval_output_path,
emit_error,
emit_success,
@@ -20,6 +21,7 @@ def main() -> int:
session_num=int(payload.get("session_num", 5)),
session_time_minutes=int(payload.get("session_time_minutes", 5)),
context_fields=payload.get("context_fields"),
system_prompt=str(payload.get("system_prompt") or DEFAULT_SYSTEM_PROMPT),
complex_default=bool(payload.get("complex_default", False)),
require_validation_ok=bool(payload.get("require_validation_ok", True)),
overwrite=bool(payload.get("overwrite", True)),
skills/product-data/scripts/product_data_portable.py
+120 -9
View File
@@ -159,7 +159,7 @@ def parse_draft_text(draft_text: str) -> dict[str, Any]:
continue
field_match = re.match(
r"^(用户|小爱|target|notes|dataset_label|request_id|timestamp)\s*[:：]\s*(.*)$",
r"^(用户|小爱|target|complex|notes|dataset_label|request_id|timestamp)\s*[:：]\s*(.*)$",
line,
re.I,
)
@@ -207,9 +207,14 @@ def case_to_record(
if not current_query:
raise ProductDataError(f"case {index} current 用户 line must be non-empty")
target = canonical_target(str(case.get("target") or "").strip())
raw_target, prefixed_complex = split_complex_prefixed_target(str(case.get("target") or "").strip())
target = canonical_target(raw_target)
if not target:
raise ProductDataError(f"case {index} target is required")
complex_value = parse_complex(
case.get("complex"),
default=False if prefixed_complex is None else prefixed_complex,
)
prev_session = build_prev_session(turns[:final_user_index], case_index=index)
if len(prev_session) > 10:
@@ -248,6 +253,9 @@ def case_to_record(
"target": target,
"target_type": target_type(target),
},
"dimensions": {
"complex": complex_value,
},
"meta": {
"case_name": str(case.get("case_name") or "").strip(),
"notes": str(case.get("notes") or "").strip(),
@@ -364,6 +372,14 @@ def validate_dataset_records(records: list[dict[str, Any]]) -> dict[str, Any]:
if label.get("target_type") not in {"agent", "function", "unknown"}:
errors.append(issue(f"{prefix}.label.target_type", "label.target_type is invalid"))
dimensions = record.get("dimensions")
if not isinstance(dimensions, dict):
warnings.append(issue(f"{prefix}.dimensions", "dimensions.complex is missing; false will be used as fallback"))
elif "complex" not in dimensions:
warnings.append(issue(f"{prefix}.dimensions.complex", "complex is missing; false will be used as fallback"))
elif not isinstance(dimensions.get("complex"), bool):
errors.append(issue(f"{prefix}.dimensions.complex", "complex must be a boolean"))
return {
"ok": not errors,
"error_count": len(errors),
@@ -500,6 +516,7 @@ def export_planning_eval_csv(
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
complex_default: bool = False,
require_validation_ok: bool = True,
overwrite: bool = True,
@@ -520,6 +537,7 @@ def export_planning_eval_csv(
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=context_fields,
system_prompt=system_prompt,
complex_default=complex_default,
)
path.write_text(content, encoding="utf-8-sig")
@@ -547,6 +565,7 @@ def render_planning_eval_csv(
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
complex_default: bool = False,
) -> str:
fields = normalize_context_fields(context_fields)
@@ -560,21 +579,21 @@ def render_planning_eval_csv(
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 "")
target = record_target(record)
writer.writerow(
{
"request_id": str(source.get("request_id") or ""),
"newPrompt": build_training_instruction(
"newPrompt": build_planning_prompt(
record,
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=fields,
system_prompt=system_prompt,
),
"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",
"complex": eval_complex_literal(record_complex(record, default=complex_default)),
}
)
return output.getvalue()
@@ -621,6 +640,29 @@ def build_training_instruction(
return instruction
def build_planning_prompt(
record: dict[str, Any],
*,
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
) -> str:
"""把训练 instruction 包成评测侧使用的 chat template。"""
instruction = build_training_instruction(
record,
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=context_fields,
)
return (
f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
f"<|im_start|>user\n{instruction}<|im_end|>\n"
"<|im_start|>assistant\n"
)
def training_jsonl_line(
record: dict[str, Any],
*,
@@ -629,7 +671,6 @@ def training_jsonl_line(
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(
@@ -638,7 +679,7 @@ def training_jsonl_line(
session_time_minutes=session_time_minutes,
context_fields=context_fields,
),
"output": str(label.get("target") or ""),
"output": combined_function_label(record),
}
return json.dumps(payload, ensure_ascii=False, separators=(",", ":"))
@@ -711,6 +752,7 @@ def optional_int_for_export(value: Any) -> int | None:
def category_label_from_target(target: str) -> str:
target = strip_complex_prefix(target)
agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target.strip())
if agent_match:
return agent_match.group(1)
@@ -749,7 +791,7 @@ def dataset_record_table_row(record: dict[str, Any]) -> dict[str, str]:
"context": json.dumps(context, ensure_ascii=False, separators=(",", ":")),
"label": str(label.get("dataset_label") or ""),
"是否迁移Function": "",
"function": str(label.get("target") or ""),
"function": combined_function_label(record),
}
@@ -808,7 +850,75 @@ def default_planning_eval_output_path(payload: dict[str, Any]) -> str:
return str(Path("output") / "eval_planning.csv")
def normalize_target_expression(target: str) -> str:
"""把可能带 complex 前缀的标签表达式收敛为纯 target。"""
return canonical_target(strip_complex_prefix(target))
def strip_complex_prefix(target: str) -> str:
stripped_target, _complex_value = split_complex_prefixed_target(target)
return stripped_target
def split_complex_prefixed_target(target: str) -> tuple[str, bool | None]:
lines = target.strip().splitlines()
if not lines:
return "", None
first_line = lines[0].strip()
match = re.fullmatch(r"complex\s*=\s*(.+)", first_line, flags=re.I)
if not match:
return target.strip(), None
complex_value = parse_complex(match.group(1), default=False)
return "\n".join(lines[1:]).strip(), complex_value
def parse_complex(value: Any, *, default: bool) -> bool:
if value is None or value == "":
return default
if isinstance(value, bool):
return value
text = str(value).strip().lower()
if text in {"true", "1", "yes", "y", "是", "复杂", "complex"}:
return True
if text in {"false", "0", "no", "n", "否", "不复杂", "简单", "simple"}:
return False
raise ProductDataError("complex must be a boolean value such as true/false")
def complex_literal(value: bool) -> str:
return "true" if value else "false"
def eval_complex_literal(value: bool) -> str:
return "TRUE" if value else "FALSE"
def record_target(record: dict[str, Any]) -> str:
label = record.get("label") if isinstance(record.get("label"), dict) else {}
return normalize_target_expression(str(label.get("target") or ""))
def record_complex(record: dict[str, Any], *, default: bool = False) -> bool:
dimensions = record.get("dimensions")
if isinstance(dimensions, dict) and "complex" in dimensions:
return parse_complex(dimensions.get("complex"), default=default)
label = record.get("label") if isinstance(record.get("label"), dict) else {}
if "complex" in label:
return parse_complex(label.get("complex"), default=default)
_target, prefixed_complex = split_complex_prefixed_target(str(label.get("target") or ""))
if prefixed_complex is not None:
return prefixed_complex
return default
def combined_function_label(record: dict[str, Any], *, default_complex: bool = False) -> str:
target = record_target(record)
return f"complex={complex_literal(record_complex(record, default=default_complex))}\n{target}"
def target_type(target: str) -> str:
target = strip_complex_prefix(target)
if re.match(r"^Agent\s*\(\s*tag\s*=", target):
return "agent"
if target:
@@ -817,6 +927,7 @@ def target_type(target: str) -> str:
def canonical_target(target: str) -> str:
target = strip_complex_prefix(target)
agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target)
if agent_match:
return f'Agent(tag="{agent_match.group(1)}")'
skills/product-data/tools.yaml
+6 -6
View File
@@ -7,7 +7,7 @@ runtime:
dependencies: []
tools:
- name: product_data_normalize_dataset_draft
description: Parse dataset draft text v1 into canonical records with generated ids, timestamps, source metadata, context and labels.
description: Parse dataset draft text v1 into canonical records with generated ids, timestamps, source metadata, context, labels and dimensions.complex.
script: scripts/normalize_dataset_draft.py
input_schema: schemas/normalize_dataset_draft.input.schema.json
output_schema: schemas/normalize_dataset_draft.output.schema.json
@@ -15,7 +15,7 @@ tools:
command: python skills/product-data/scripts/normalize_dataset_draft.py --input <input.json>
stdin: true
- name: product_data_validate_dataset_records
description: Validate canonical product-data records for required fields, labels, timestamp order and prompt stitching constraints.
description: Validate canonical product-data records for required fields, labels, dimensions.complex, timestamp order and prompt stitching constraints.
script: scripts/validate_dataset_records.py
input_schema: schemas/validate_dataset_records.input.schema.json
output_schema: schemas/validate_dataset_records.output.schema.json
@@ -23,7 +23,7 @@ tools:
command: python skills/product-data/scripts/validate_dataset_records.py --input <input.json>
stdin: true
- name: product_data_export_dataset_records
description: Validate canonical records and write compact JSONL or JSON files plus a sibling records.csv sharing table without asking the model to hand-write files.
description: Validate canonical records and write compact JSONL or JSON files plus a sibling records.csv sharing table whose function column combines complex and label target.
script: scripts/export_dataset_records.py
input_schema: schemas/export_dataset_records.input.schema.json
output_schema: schemas/export_dataset_records.output.schema.json
@@ -31,7 +31,7 @@ tools:
command: python skills/product-data/scripts/export_dataset_records.py --input <input.json>
stdin: true
- name: product_data_export_dataset_table
description: Convert existing canonical records into the shared records.csv table format without rewriting the metadata file.
description: Convert existing canonical records into the shared records.csv table format without rewriting the metadata file; function column combines complex and label target.
script: scripts/export_dataset_table.py
input_schema: schemas/export_dataset_table.input.schema.json
output_schema: schemas/export_dataset_table.output.schema.json
@@ -39,7 +39,7 @@ tools:
command: python skills/product-data/scripts/export_dataset_table.py --input <input.json>
stdin: true
- name: product_data_export_training_jsonl
description: Convert canonical records into training JSONL lines with system, instruction and output fields.
description: Convert canonical records into training JSONL lines with system, instruction and output fields; output combines complex and label target.
script: scripts/export_training_jsonl.py
input_schema: schemas/export_training_jsonl.input.schema.json
output_schema: schemas/export_training_jsonl.output.schema.json
@@ -47,7 +47,7 @@ tools:
command: python skills/product-data/scripts/export_training_jsonl.py --input <input.json>
stdin: true
- name: product_data_export_planning_eval_csv
description: Convert canonical records into evaluation CSV with request_id, newPrompt, query, 类别真实标签, code标签 and complex columns.
description: Convert canonical records into evaluation CSV with request_id, newPrompt, query, 类别真实标签, code标签 and complex columns; newPrompt uses chat-template tags and complex is read from dimensions.complex as TRUE/FALSE.
script: scripts/export_planning_eval_csv.py
input_schema: schemas/export_planning_eval_csv.input.schema.json
output_schema: schemas/export_planning_eval_csv.output.schema.json
src/agent_tool_specs/data_agent.py
+20 -7
View File
@@ -346,6 +346,10 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
'type': 'string',
'description': 'Target label used for included candidates unless review_decisions provides a target.',
},
'default_complex': {
'type': 'boolean',
'description': 'Default complex dimension for included candidates unless review_decisions provides complex.',
},
'review_decisions': {
'type': 'array',
'description': 'Optional include/exclude/uncertain decisions keyed by semantic_session_id or req_id.',
@@ -357,6 +361,7 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
'matched_turn_index': {'type': 'integer'},
'decision': {'type': 'string', 'enum': ['include', 'exclude', 'uncertain']},
'target': {'type': 'string'},
'complex': {'type': 'boolean'},
'notes': {'type': 'string'},
},
},
@@ -428,7 +433,8 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
name='data_agent_normalize_dataset_draft',
description=(
'Parse dataset draft text written with 用户/小爱/target blocks and build canonical '
'data-agent records with generated record IDs, timestamps, source metadata, context, and labels. '
'data-agent records with generated record IDs, timestamps, source metadata, context, labels, '
'and dimensions.complex. '
'Generated data requires a confirmed_plan_id from data_agent_confirm_generation_plan. '
'Use draft_path instead of draft_text when the draft is large or contains many quoted targets.'
),
@@ -437,7 +443,7 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
'properties': {
'draft_text': {
'type': 'string',
'description': 'Dataset draft text in dataset draft text v1 format. Provide exactly one of draft_text or draft_path.',
'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.',
},
'draft_path': {
'type': 'string',
@@ -476,7 +482,7 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
name='data_agent_validate_dataset_records',
description=(
'Validate canonical data-agent records for required fields, labels, source metadata, '
'prev_session structure, timestamp order, and 5-minute prompt stitching constraints.'
'prev_session structure, dimensions.complex, timestamp order, and 5-minute prompt stitching constraints.'
),
parameters={
'type': 'object',
@@ -501,7 +507,8 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
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.'
'in the same output directory for human sharing. The table function column combines '
'complex=true/false and label.target.'
),
parameters={
'type': 'object',
@@ -551,7 +558,8 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
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.'
'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',
@@ -600,7 +608,8 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
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.'
'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',
@@ -635,9 +644,13 @@ def build_data_agent_tools(handlers: Mapping[str, ToolHandler]) -> list[AgentToo
'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': 'Default complex column value. Defaults to false.',
'description': 'Fallback complex value only for legacy records missing dimensions.complex. Defaults to false.',
},
'require_validation_ok': {'type': 'boolean'},
'overwrite': {'type': 'boolean'},
src/agent_tools.py
+8
View File
@@ -1417,11 +1417,15 @@ def _data_agent_convert_router_candidates_to_records_tool(
include_uncertain = arguments.get('include_uncertain', False)
if not isinstance(include_uncertain, bool):
raise ToolExecutionError('include_uncertain must be a boolean')
default_complex = arguments.get('default_complex', False)
if not isinstance(default_complex, bool):
raise ToolExecutionError('default_complex must be a boolean')
try:
payload = convert_router_candidates_to_records(
arguments['candidates'],
dataset_label=_require_string(arguments, 'dataset_label'),
default_target=_optional_string(arguments, 'default_target'),
default_complex=default_complex,
review_decisions=review_decisions,
batch_id=_optional_string(arguments, 'batch_id') or 'router',
include_uncertain=include_uncertain,
@@ -1596,6 +1600,9 @@ def _export_planning_eval_csv_tool(arguments: dict[str, Any], context: ToolExecu
complex_default = arguments.get('complex_default', False)
if not isinstance(complex_default, bool):
raise ToolExecutionError('complex_default 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_planning_eval_csv(
@@ -1609,6 +1616,7 @@ def _export_planning_eval_csv_tool(arguments: dict[str, Any], context: ToolExecu
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,
complex_default=complex_default,
require_validation_ok=require_validation_ok,
overwrite=overwrite,
src/data_agent_records.py
+131 -19
View File
@@ -53,6 +53,7 @@ def prepare_generation_goal(
if missing:
raise DataRecordError('missing required goal fields: ' + ', '.join(missing))
normalized_targets = _normalize_target_definitions(target_definitions)
target = _normalize_target_expression(target)
if not target.strip() and not normalized_targets:
raise DataRecordError('generation goal must include target or target_definitions')
state = _load_goal_state(root)
@@ -63,7 +64,7 @@ def prepare_generation_goal(
'status': 'pending_confirmation',
'dataset_label': dataset_label.strip(),
'goal_summary': goal_summary.strip(),
'target': target.strip(),
'target': target,
'target_definitions': normalized_targets,
'plan_hint': plan_hint.strip(),
'coverage': coverage.strip(),
@@ -191,13 +192,14 @@ def prepare_generation_plan(
'output_path': output_path,
}
)
target = _normalize_target_expression(target)
normalized_targets = _normalize_target_definitions(target_definitions)
if confirmed_goal is not None and not target.strip() and not normalized_targets:
normalized_targets = _normalize_target_definitions(
confirmed_goal.get('target_definitions') if isinstance(confirmed_goal, dict) else None
)
if not normalized_targets:
target = str(confirmed_goal.get('target') or '').strip()
target = _normalize_target_expression(str(confirmed_goal.get('target') or ''))
if not target.strip() and not normalized_targets:
missing.append('target')
if missing:
@@ -220,7 +222,7 @@ def prepare_generation_plan(
'plan_id': plan_id,
'status': 'pending_confirmation',
'dataset_label': dataset_label.strip(),
'target': target.strip(),
'target': target,
'target_definitions': normalized_targets,
'total_count': total_count,
'turn_mix': turn_mix.strip(),
@@ -448,6 +450,14 @@ def validate_dataset_records(records: list[dict[str, Any]]) -> dict[str, Any]:
if label.get('target_type') not in {'agent', 'function', 'unknown'}:
errors.append(_issue(f'{prefix}.label.target_type', 'label.target_type is invalid'))
dimensions = record.get('dimensions')
if not isinstance(dimensions, dict):
warnings.append(_issue(f'{prefix}.dimensions', 'dimensions.complex is missing; false will be used as fallback'))
elif 'complex' not in dimensions:
warnings.append(_issue(f'{prefix}.dimensions.complex', 'complex is missing; false will be used as fallback'))
elif not isinstance(dimensions.get('complex'), bool):
errors.append(_issue(f'{prefix}.dimensions.complex', 'complex must be a boolean'))
return {
'ok': not errors,
'error_count': len(errors),
@@ -579,6 +589,7 @@ def export_planning_eval_csv(
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
complex_default: bool = False,
require_validation_ok: bool = True,
overwrite: bool = True,
@@ -604,6 +615,7 @@ def export_planning_eval_csv(
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=fields,
system_prompt=system_prompt,
complex_default=complex_default,
)
path.write_text(content, encoding='utf-8-sig')
@@ -633,6 +645,7 @@ def render_planning_eval_csv(
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
complex_default: bool = False,
) -> str:
"""把 canonical records 转成含 newPrompt 的评测 CSV。"""
@@ -648,21 +661,21 @@ def render_planning_eval_csv(
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 '')
target = _record_target(record)
writer.writerow(
{
'request_id': str(source.get('request_id') or ''),
'newPrompt': build_training_instruction(
'newPrompt': build_planning_prompt(
record,
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=fields,
system_prompt=system_prompt,
),
'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',
'complex': _eval_complex_literal(_record_complex(record, default=complex_default)),
}
)
return output.getvalue()
@@ -709,6 +722,29 @@ def build_training_instruction(
return instruction
def build_planning_prompt(
record: dict[str, Any],
*,
session_num: int = 5,
session_time_minutes: int = 5,
context_fields: list[str] | None = None,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
) -> str:
"""把训练 instruction 包成评测侧使用的 chat template。"""
instruction = build_training_instruction(
record,
session_num=session_num,
session_time_minutes=session_time_minutes,
context_fields=context_fields,
)
return (
f'<|im_start|>system\n{system_prompt}<|im_end|>\n'
f'<|im_start|>user\n{instruction}<|im_end|>\n'
'<|im_start|>assistant\n'
)
def _training_jsonl_line(
record: dict[str, Any],
*,
@@ -717,7 +753,6 @@ def _training_jsonl_line(
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(
@@ -726,7 +761,7 @@ def _training_jsonl_line(
session_time_minutes=session_time_minutes,
context_fields=context_fields,
),
'output': str(label.get('target') or ''),
'output': _combined_function_label(record),
}
return json.dumps(payload, ensure_ascii=False, separators=(',', ':'))
@@ -797,6 +832,7 @@ def _optional_int_for_export(value: Any) -> int | None:
def _category_label_from_target(target: str) -> str:
target = _strip_complex_prefix(target)
agent_match = re.match(r'''^Agent\s*\(\s*tag\s*=\s*["']([^"']+)["']\s*\)$''', target.strip())
if agent_match:
return agent_match.group(1)
@@ -835,7 +871,7 @@ def _dataset_record_table_row(record: dict[str, Any]) -> dict[str, str]:
'context': json.dumps(context, ensure_ascii=False, separators=(',', ':')),
'label': str(label.get('dataset_label') or ''),
'是否迁移Function': '',
'function': str(label.get('target') or ''),
'function': _combined_function_label(record),
}
@@ -875,7 +911,7 @@ def _parse_draft_text(draft_text: str) -> dict[str, Any]:
if current is None:
continue
field_match = re.match(r'^(用户|小爱|target|notes|dataset_label|request_id|timestamp)\s*[:：]\s*(.*)$', line, re.IGNORECASE)
field_match = re.match(r'^(用户|小爱|target|complex|notes|dataset_label|request_id|timestamp)\s*[:：]\s*(.*)$', line, re.IGNORECASE)
if not field_match:
continue
key = field_match.group(1).lower()
@@ -925,7 +961,7 @@ def _normalize_target_definitions(value: list[dict[str, Any]] | None) -> list[di
if not isinstance(item, dict):
raise DataRecordError(f'target_definitions[{index}] must be an object')
name = str(item.get('name') or '').strip()
target = str(item.get('target') or '').strip()
target = _normalize_target_expression(str(item.get('target') or ''))
rule = str(item.get('rule') or '').strip()
if not target:
raise DataRecordError(f'target_definitions[{index}].target is required')
@@ -958,10 +994,10 @@ def _allowed_targets_for_plan(plan: dict[str, Any]) -> set[str]:
if isinstance(target_definitions, list):
for item in target_definitions:
if isinstance(item, dict) and isinstance(item.get('target'), str) and item['target'].strip():
allowed.add(item['target'].strip())
allowed.add(_normalize_target_expression(item['target']))
target = plan.get('target')
if isinstance(target, str) and target.strip():
allowed.add(target.strip())
allowed.add(_normalize_target_expression(target))
return allowed
@@ -980,7 +1016,7 @@ def _validate_plan_against_goal(
goal_targets = _allowed_targets_for_goal(goal)
plan_targets = set()
if target.strip():
plan_targets.add(target.strip())
plan_targets.add(_normalize_target_expression(target))
for item in target_definitions:
item_target = item.get('target', '').strip()
if item_target:
@@ -998,9 +1034,8 @@ def _validate_records_against_plan(records: list[dict[str, Any]], plan: dict[str
return
unexpected: list[str] = []
for index, record in enumerate(records):
label = record.get('label')
target = label.get('target') if isinstance(label, dict) else None
if isinstance(target, str) and target.strip() in allowed_targets:
target = _record_target(record)
if target and target in allowed_targets:
continue
unexpected.append(f'records[{index}].label.target={target!r}')
if unexpected:
@@ -1148,9 +1183,14 @@ def _case_to_record(
if not current_query:
raise DataRecordError(f'case {index} current 用户 line must be non-empty')
target = _canonical_target(str(case.get('target') or '').strip())
raw_target, prefixed_complex = _split_complex_prefixed_target(str(case.get('target') or '').strip())
target = _canonical_target(raw_target)
if not target:
raise DataRecordError(f'case {index} target is required')
complex_value = _parse_complex(
case.get('complex'),
default=False if prefixed_complex is None else prefixed_complex,
)
prev_session = _build_prev_session(turns[:final_user_index], case_index=index)
if len(prev_session) > 10:
@@ -1183,6 +1223,9 @@ def _case_to_record(
'target': target,
'target_type': _target_type(target),
},
'dimensions': {
'complex': complex_value,
},
'meta': {
'case_name': str(case.get('case_name') or '').strip(),
'notes': str(case.get('notes') or '').strip(),
@@ -1209,7 +1252,75 @@ def _build_prev_session(turns: list[dict[str, Any]], *, case_index: int) -> list
return prev_session
def _normalize_target_expression(target: str) -> str:
"""把可能带 complex 前缀的标签表达式收敛为纯 target。"""
return _canonical_target(_strip_complex_prefix(target))
def _strip_complex_prefix(target: str) -> str:
stripped_target, _complex_value = _split_complex_prefixed_target(target)
return stripped_target
def _split_complex_prefixed_target(target: str) -> tuple[str, bool | None]:
lines = target.strip().splitlines()
if not lines:
return '', None
first_line = lines[0].strip()
match = re.fullmatch(r'complex\s*=\s*(.+)', first_line, flags=re.IGNORECASE)
if not match:
return target.strip(), None
complex_value = _parse_complex(match.group(1), default=False)
return '\n'.join(lines[1:]).strip(), complex_value
def _parse_complex(value: Any, *, default: bool) -> bool:
if value is None or value == '':
return default
if isinstance(value, bool):
return value
text = str(value).strip().lower()
if text in {'true', '1', 'yes', 'y', '是', '复杂', 'complex'}:
return True
if text in {'false', '0', 'no', 'n', '否', '不复杂', '简单', 'simple'}:
return False
raise DataRecordError('complex must be a boolean value such as true/false')
def _complex_literal(value: bool) -> str:
return 'true' if value else 'false'
def _eval_complex_literal(value: bool) -> str:
return 'TRUE' if value else 'FALSE'
def _record_target(record: dict[str, Any]) -> str:
label = record.get('label') if isinstance(record.get('label'), dict) else {}
return _normalize_target_expression(str(label.get('target') or ''))
def _record_complex(record: dict[str, Any], *, default: bool = False) -> bool:
dimensions = record.get('dimensions')
if isinstance(dimensions, dict) and 'complex' in dimensions:
return _parse_complex(dimensions.get('complex'), default=default)
label = record.get('label') if isinstance(record.get('label'), dict) else {}
if 'complex' in label:
return _parse_complex(label.get('complex'), default=default)
_target, prefixed_complex = _split_complex_prefixed_target(str(label.get('target') or ''))
if prefixed_complex is not None:
return prefixed_complex
return default
def _combined_function_label(record: dict[str, Any], *, default_complex: bool = False) -> str:
target = _record_target(record)
return f'complex={_complex_literal(_record_complex(record, default=default_complex))}\n{target}'
def _target_type(target: str) -> str:
target = _strip_complex_prefix(target)
if re.match(r'^Agent\s*\(\s*tag\s*=', target):
return 'agent'
if target:
@@ -1220,6 +1331,7 @@ def _target_type(target: str) -> str:
def _canonical_target(target: str) -> str:
"""规范常见 Agent 标签写法，允许草稿里用单引号降低 JSON 转义风险。"""
target = _strip_complex_prefix(target)
match = re.fullmatch(r'Agent\s*\(\s*tag\s*=\s*([\'"])(.+?)\1\s*\)', target)
if not match:
return target
src/data_agent_router_sessions.py
+41 -1
View File
@@ -197,6 +197,7 @@ def convert_router_candidates_to_records(
*,
dataset_label: str,
default_target: str = '',
default_complex: bool = False,
review_decisions: list[dict[str, Any]] | None = None,
batch_id: str = 'router',
include_uncertain: bool = False,
@@ -228,11 +229,17 @@ def convert_router_candidates_to_records(
raise DataAgentRouterSessionError(
f'target is required for candidate {candidate.get("semantic_session_id")}#{candidate.get("matched_turn_index")}'
)
raw_target, prefixed_complex = _split_complex_prefixed_target(target)
complex_value = _parse_complex(
decision.get('complex'),
default=default_complex if prefixed_complex is None else prefixed_complex,
)
records.append(
_router_candidate_to_record(
candidate,
dataset_label=str(decision.get('dataset_label') or dataset_label).strip(),
target=target,
target=raw_target,
complex_value=complex_value,
notes=str(decision.get('notes') or '').strip(),
batch_id=batch_id,
index=len(records) + 1,
@@ -556,6 +563,7 @@ def _router_candidate_to_record(
*,
dataset_label: str,
target: str,
complex_value: bool,
notes: str,
batch_id: str,
index: int,
@@ -604,6 +612,9 @@ def _router_candidate_to_record(
'target': target,
'target_type': _target_type(target),
},
'dimensions': {
'complex': complex_value,
},
'meta': {
'case_name': '',
'notes': notes,
@@ -642,6 +653,7 @@ def _skip_item(candidate: dict[str, Any], decision: str, notes: Any) -> dict[str
def _target_type(target: str) -> str:
target = _strip_complex_prefix(target)
if target.startswith('Agent('):
return 'agent'
if target:
@@ -649,6 +661,34 @@ def _target_type(target: str) -> str:
return 'unknown'
def _strip_complex_prefix(target: str) -> str:
stripped_target, _complex_value = _split_complex_prefixed_target(target)
return stripped_target
def _split_complex_prefixed_target(target: str) -> tuple[str, bool | None]:
lines = target.strip().splitlines()
if not lines:
return '', None
match = re.fullmatch(r'complex\s*=\s*(.+)', lines[0].strip(), flags=re.IGNORECASE)
if not match:
return target.strip(), None
return '\n'.join(lines[1:]).strip(), _parse_complex(match.group(1), default=False)
def _parse_complex(value: Any, *, default: bool) -> bool:
if value is None or value == '':
return default
if isinstance(value, bool):
return value
text = str(value).strip().lower()
if text in {'true', '1', 'yes', 'y', '是', '复杂', 'complex'}:
return True
if text in {'false', '0', 'no', 'n', '否', '不复杂', '简单', 'simple'}:
return False
raise DataAgentRouterSessionError('complex must be a boolean value such as true/false')
def _safe_token(value: str) -> str:
token = re.sub(r'[^A-Za-z0-9_.-]+', '_', value.strip())
return token.strip('_.-') or 'item'
tests/test_data_agent_records.py
+32 -10
View File
@@ -1,6 +1,7 @@
from __future__ import annotations
import csv
import io
import json
import tempfile
import unittest
@@ -57,6 +58,7 @@ notes: 多轮承接附近生活服务查询
self.assertEqual(records[0]['turn']['query'], '帮我看看附近有什么好吃的')
self.assertEqual(records[0]['prev_session'], [])
self.assertEqual(records[0]['label']['target_type'], 'agent')
self.assertEqual(records[0]['dimensions'], {'complex': False})
self.assertEqual(records[1]['turn']['query'], '看看附近有什么好吃的')
self.assertEqual(
records[1]['prev_session'],
@@ -85,6 +87,23 @@ target: Agent(tag='地图导航')
self.assertEqual(record['label']['target'], 'Agent(tag="地图导航")')
self.assertEqual(record['label']['target_type'], 'agent')
def test_normalize_dataset_draft_keeps_complex_dimension_independent(self) -> None:
payload = normalize_dataset_draft(
'''
# dataset_label: 地图导航复杂样例
### case: 复杂导航
用户: 帮我规划一条先去加油站再去公司的路线
complex: true
target: Agent(tag='地图导航')
'''.strip(),
batch_id='demo',
base_timestamp=1_755_567_930_500,
)
record = payload['records'][0]
self.assertEqual(record['label']['target'], 'Agent(tag="地图导航")')
self.assertEqual(record['dimensions']['complex'], True)
def test_validate_dataset_records_reports_valid_payload(self) -> None:
records = normalize_dataset_draft(
'''
@@ -138,7 +157,7 @@ target: Agent(tag="life_service")
output_path = Path(tmp_dir) / payload['output_path']
lines = output_path.read_text(encoding='utf-8').splitlines()
table_path = Path(tmp_dir) / payload['table_output_path']
table_rows = list(csv.DictReader(table_path.read_text(encoding='utf-8-sig').splitlines()))
table_rows = list(csv.DictReader(io.StringIO(table_path.read_text(encoding='utf-8-sig'))))
self.assertEqual(payload['output_format'], 'jsonl')
self.assertEqual(payload['table_output_format'], 'csv')
@@ -157,7 +176,7 @@ target: Agent(tag="life_service")
self.assertEqual(table_rows[0]['context'], '{}')
self.assertEqual(table_rows[0]['label'], '地图和生活边界数据')
self.assertEqual(table_rows[0]['是否迁移Function'], '')
self.assertEqual(table_rows[0]['function'], 'Agent(tag="life_service")')
self.assertEqual(table_rows[0]['function'], 'complex=false\nAgent(tag="life_service")')
def test_render_dataset_records_table_csv_keeps_prev_session_json(self) -> None:
records = normalize_dataset_draft(
@@ -174,13 +193,13 @@ target: Agent(tag='地图导航')
timestamp_step_ms=60_000,
)['records']
rows = list(csv.DictReader(render_dataset_records_table_csv(records).splitlines()))
rows = list(csv.DictReader(io.StringIO(render_dataset_records_table_csv(records))))
prev_session = json.loads(rows[0]['prev_session'])
self.assertEqual(prev_session[0]['query'], '查一下附近停车场')
self.assertEqual(prev_session[0]['tts'], '找到了附近停车场')
self.assertEqual(prev_session[0]['timestamp'], '1755567870500')
self.assertEqual(rows[0]['function'], 'Agent(tag="地图导航")')
self.assertEqual(rows[0]['function'], 'complex=false\nAgent(tag="地图导航")')
def test_export_training_jsonl_uses_prompt_template_and_history(self) -> None:
records = normalize_dataset_draft(
@@ -208,7 +227,7 @@ target: Agent(tag='地图导航')
self.assertEqual(payload['output_format'], 'jsonl')
self.assertEqual(line['system'], '你是小爱同学，中文智能语音助手。')
self.assertEqual(line['output'], 'Agent(tag="地图导航")')
self.assertEqual(line['output'], 'complex=false\nAgent(tag="地图导航")')
self.assertIn('[知识注入]\n{\n"location": "北京",\n"rag": "地图服务可用"\n}', line['instruction'])
self.assertIn('用户: 查一下附近停车场\n小爱: 找到了附近停车场', line['instruction'])
self.assertIn('[当前query]\n用户: 帮我找个顺路的', line['instruction'])
@@ -227,7 +246,7 @@ target: Agent(tag='地图导航')
base_timestamp=1_755_567_930_500,
)['records']
rows = list(csv.DictReader(render_planning_eval_csv(records).splitlines()))
rows = list(csv.DictReader(io.StringIO(render_planning_eval_csv(records))))
self.assertEqual(
list(rows[0].keys()),
@@ -237,8 +256,11 @@ target: Agent(tag='地图导航')
self.assertEqual(rows[0]['query'], '帮我找个顺路的')
self.assertEqual(rows[0]['类别真实标签'], '地图导航')
self.assertEqual(rows[0]['code标签'], 'Agent(tag="地图导航")')
self.assertEqual(rows[0]['complex'], 'false')
self.assertEqual(rows[0]['complex'], 'FALSE')
self.assertTrue(rows[0]['newPrompt'].startswith('<|im_start|>system\n你是小爱同学，中文智能语音助手。<|im_end|>'))
self.assertIn('<|im_start|>user\n请参考用户的[当前query]', rows[0]['newPrompt'])
self.assertIn('[function]', rows[0]['newPrompt'])
self.assertTrue(rows[0]['newPrompt'].endswith('<|im_start|>assistant\n'))
def test_export_planning_eval_csv_writes_file(self) -> None:
records = normalize_dataset_draft(
@@ -246,6 +268,7 @@ target: Agent(tag='地图导航')
# dataset_label: 时间工具数据
### case: 几点
用户: 现在几点
complex: true
target: CalendarQA(type="TIME")
'''.strip(),
batch_id='demo',
@@ -256,15 +279,14 @@ target: CalendarQA(type="TIME")
records,
root=tmp_dir,
output_path='output/eval_planning.csv',
complex_default=True,
)
output_path = Path(tmp_dir) / payload['output_path']
rows = list(csv.DictReader(output_path.read_text(encoding='utf-8-sig').splitlines()))
rows = list(csv.DictReader(io.StringIO(output_path.read_text(encoding='utf-8-sig'))))
self.assertEqual(payload['output_format'], 'csv')
self.assertEqual(rows[0]['类别真实标签'], 'CalendarQA')
self.assertEqual(rows[0]['code标签'], 'CalendarQA(type="TIME")')
self.assertEqual(rows[0]['complex'], 'true')
self.assertEqual(rows[0]['complex'], 'TRUE')
def test_normalize_online_draft_uses_real_request_metadata(self) -> None:
records = normalize_dataset_draft(
tests/test_data_agent_router_sessions.py
+4
View File
@@ -57,6 +57,7 @@ class DataAgentRouterSessionTests(unittest.TestCase):
[candidate],
dataset_label='总结类边界评测集',
default_target='Summarize',
default_complex=True,
batch_id='summary',
)
@@ -68,6 +69,7 @@ class DataAgentRouterSessionTests(unittest.TestCase):
self.assertEqual(record['prev_session'], [{'query': '这篇文章讲了什么', 'tts': '', 'timestamp': 10}])
self.assertEqual(record['context']['domain'], 'QA')
self.assertEqual(record['label']['target'], 'Summarize')
self.assertEqual(record['dimensions'], {'complex': True})
def test_convert_router_candidates_to_records_applies_review_decisions(self) -> None:
candidates = [
@@ -84,6 +86,7 @@ class DataAgentRouterSessionTests(unittest.TestCase):
'matched_turn_index': 0,
'decision': 'include',
'target': 'Summarize',
'complex': True,
'notes': '前文有可总结内容',
},
{
@@ -98,6 +101,7 @@ class DataAgentRouterSessionTests(unittest.TestCase):
self.assertEqual(result['record_count'], 1)
self.assertEqual(result['skipped_count'], 1)
self.assertEqual(result['records'][0]['meta']['notes'], '前文有可总结内容')
self.assertEqual(result['records'][0]['dimensions']['complex'], True)
@unittest.skipUnless(HAS_PYARROW, 'pyarrow is required for parquet tests')
def test_profile_and_search_router_sessions_read_parquet(self) -> None: