data_juicer.ops.mapper.dialog_topic_detection_mapper module¶
- class data_juicer.ops.mapper.dialog_topic_detection_mapper.DialogTopicDetectionMapper(api_model: str = 'gpt-4o', topic_candidates: List[str] | None = None, max_round: Annotated[int, Ge(ge=0)] = 10, *, labels_key: str = 'dialog_topic_labels', analysis_key: str = 'dialog_topic_labels_analysis', api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, query_template: str | None = None, response_template: str | None = None, candidate_template: str | None = None, analysis_template: str | None = None, labels_template: str | None = None, analysis_pattern: str | None = None, labels_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)[源代码]¶
基类：
MapperMapper to generate user's topic labels in dialog. Input from history_key, query_key and response_key. Output lists of labels and analysis for queries in the dialog.
- DEFAULT_SYSTEM_PROMPT = '请判断用户和LLM多轮对话中用户所讨论的话题。\n要求：\n- 针对用户的每个query，需要先进行分析，然后列出用户正在讨论的话题，下面是一个样例，请模仿样例格式输出。\n用户：你好，今天我们来聊聊秦始皇吧。\n话题分析：用户提到秦始皇，这是中国历史上第一位皇帝。\n话题类别：历史\nLLM：当然可以，秦始皇是中国历史上第一个统一全国的皇帝，他在公元前221年建立了秦朝，并采取了一系列重要的改革措施，如统一文字、度量衡和货币等。\n用户：秦始皇修建的长城和现在的长城有什么区别？\n话题分析：用户提到秦始皇修建的长城，并将其与现代长城进行比较，涉及建筑历史和地理位置。\n话题类别：历史LLM：秦始皇时期修建的长城主要是为了抵御北方游牧民族的入侵，它的规模和修建技术相对较为简陋。现代人所看到的长城大部分是明朝时期修建和扩建的，明长城不仅规模更大、结构更坚固，而且保存得比较完好。\n用户：有意思，那么长城的具体位置在哪些省份呢？\n话题分析：用户询问长城的具体位置，涉及到地理知识。\n话题类别：地理\nLLM：长城横跨中国北方多个省份，主要包括河北、山西、内蒙古、宁夏、陕西、甘肃和北京等。每一段长城都建在关键的战略位置，以便最大限度地发挥其防御作用。\n'¶
- DEFAULT_QUERY_TEMPLATE = '用户：{query}\n'¶
- DEFAULT_RESPONSE_TEMPLATE = 'LLM：{response}\n'¶
- DEFAULT_CANDIDATES_TEMPLATE = '备选话题类别：[{candidate_str}]'¶
- DEFAULT_ANALYSIS_TEMPLATE = '话题分析：{analysis}\n'¶
- DEFAULT_LABELS_TEMPLATE = '话题类别：{labels}\n'¶
- DEFAULT_ANALYSIS_PATTERN = '话题分析：(.*?)\n'¶
- DEFAULT_LABELS_PATTERN = '话题类别：(.*?)($|\n)'¶
- __init__(api_model: str = 'gpt-4o', topic_candidates: List[str] | None = None, max_round: Annotated[int, Ge(ge=0)] = 10, *, labels_key: str = 'dialog_topic_labels', analysis_key: str = 'dialog_topic_labels_analysis', api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, query_template: str | None = None, response_template: str | None = None, candidate_template: str | None = None, analysis_template: str | None = None, labels_template: str | None = None, analysis_pattern: str | None = None, labels_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)[源代码]¶
Initialization method.
- 参数:
api_model -- API model name.
topic_candidates -- The output topic candidates. Use open-domain topic labels if it is None.
max_round -- The max num of round in the dialog to build the prompt.
labels_key -- The key name in the meta field to store the output labels. It is 'dialog_topic_labels' in default.
analysis_key -- The key name in the meta field to store the corresponding analysis. It is 'dialog_topic_labels_analysis' in default.
api_endpoint -- URL endpoint for the API.
response_path -- Path to extract content from the API response. Defaults to 'choices.0.message.content'.
system_prompt -- System prompt for the task.
query_template -- Template for query part to build the input prompt.
response_template -- Template for response part to build the input prompt.
candidate_template -- Template for topic candidates to build the input prompt.
analysis_template -- Template for analysis part to build the input prompt.
labels_template -- Template for labels part to build the input prompt.
analysis_pattern -- Pattern to parse the return topic analysis.
labels_pattern -- Pattern to parse the return topic labels.
try_num -- The number of retry attempts when there is an API call error or output parsing error.
model_params -- Parameters for initializing the API model.
sampling_params -- Extra parameters passed to the API call. e.g {'temperature': 0.9, 'top_p': 0.95}
kwargs -- Extra keyword arguments.