data_juicer.ops.aggregator package

Submodules

data_juicer.ops.aggregator.entity_attribute_aggregator module

class data_juicer.ops.aggregator.entity_attribute_aggregator.EntityAttributeAggregator(api_model: str = 'gpt-4o', entity: str | None = None, attribute: str | None = None, input_key: str | None = None, output_key: str | None = None, word_limit: Annotated[int, Gt(gt=0)] = 100, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, example_prompt: str | None = None, input_template: str | None = None, output_pattern_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Return conclusion of the given entity’s attribute from some docs.

DEFAULT_SYSTEM_TEMPLATE = '给定与`{entity}`相关的一些文档，总结`{entity}`的`{attribute}`。\n要求：\n- 尽量使用原文专有名词\n- 联系上下文，自动忽略上下文不一致的细节错误\n- 只对文档中与`{entity}`的`{attribute}`有关的内容进行总结\n- 字数限制在**{word_limit}字以内**\n- 要求输出格式如下：\n# {entity}\n## {attribute}\n...\n{example}'

DEFAULT_EXAMPLE_PROMPT = '- 例如，根据相关文档总结`孙悟空`的`出身背景`，**100字**以内的样例如下：\n`孙悟空`的`出身背景`总结：\n# 孙悟空\n## 出身背景\n号称齐天大圣，花果山水帘洞的美猴王、西行取经队伍中的大师兄。师父是唐僧玄奘，曾拜菩提祖师学艺。亲生父母未知，自石头中孕育而生。自认斗战胜佛，最怕观世音菩萨和紧箍咒。\n'

DEFAULT_INPUT_TEMPLATE = '`{entity}`的相关文档：\n{sub_docs}\n\n`{entity}`的`{attribute}`总结：\n'

DEFAULT_OUTPUT_PATTERN_TEMPLATE = '\\#\\s*{entity}\\s*\\#\\#\\s*{attribute}\\s*(.*?)\\Z'

__init__(api_model: str = 'gpt-4o', entity: str | None = None, attribute: str | None = None, input_key: str | None = None, output_key: str | None = None, word_limit: Annotated[int, Gt(gt=0)] = 100, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, example_prompt: str | None = None, input_template: str | None = None, output_pattern_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param entity: The given entity. :param attribute: The given attribute. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• word_limit – Prompt the output length.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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_template – The system prompt template.

• example_prompt – The example part in the system prompt.

• input_template – The input template.

• output_pattern_template – The output template.

• 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.

parse_output(response)

attribute_summary(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample

data_juicer.ops.aggregator.most_relavant_entities_aggregator module

class data_juicer.ops.aggregator.most_relavant_entities_aggregator.MostRelavantEntitiesAggregator(api_model: str = 'gpt-4o', entity: str | None = None, query_entity_type: str | None = None, input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, input_template: str | None = None, output_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Extract entities closely related to a given entity from some texts, and sort them in descending order of importance.

DEFAULT_SYSTEM_TEMPLATE = '给定与`{entity}`相关的一些文档，总结一些与`{entity}`最为相关的`{entity_type}`。\n要求：\n- 不用包含与{entity}为同一{entity_type}的{entity_type}。\n- 请按照人物的重要性进行排序，**越重要人物在列表越前面**。\n- 你的返回格式如下：\n## 分析\n你对各个{entity_type}与{entity}关联度的分析\n## 列表\n人物1, 人物2, 人物3, ...'

DEFAULT_INPUT_TEMPLATE = '`{entity}`的相关文档：\n{sub_docs}\n\n与`{entity}`最相关的一些`{entity_type}`：\n'

DEFAULT_OUTPUT_PATTERN = '\\#\\#\\s*列表\\s*(.*?)\\Z'

__init__(api_model: str = 'gpt-4o', entity: str | None = None, query_entity_type: str | None = None, input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, input_template: str | None = None, output_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param entity: The given entity. :param query_entity_type: The type of queried relavant entities. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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_template – The system prompt template.

• input_template – The input template.

• output_pattern – The output pattern.

• 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.

parse_output(response)

query_most_relavant_entities(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample

data_juicer.ops.aggregator.nested_aggregator module

class data_juicer.ops.aggregator.nested_aggregator.NestedAggregator(api_model: str = 'gpt-4o', input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, sub_doc_template: str | None = None, input_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Considering the limitation of input length, nested aggregate contents for each given number of samples.

DEFAULT_SYSTEM_PROMPT = '给定一些文档碎片，将这些文档整合成一个文档总结。\n要求：\n- 总结的长度与文档碎片的平均长度基本一致\n- 不要包含主观看法\n- 注意要尽可能保留文本的专有名词\n- 只输出文档总结不要输出其他内容\n- 参考如下样例：\n文档碎片：\n唐僧师徒四人行至白虎岭，遇上了变化多端的白骨精。\n\n文档碎片：\n白骨精首次变身少女送斋，被孙悟空识破打死，唐僧责怪悟空。\n\n文档碎片：\n妖怪再变老妇寻女，又被悟空击毙，师傅更加不满，念紧箍咒惩罚。\n\n文档碎片：\n不甘心的白骨精第三次化作老公公来诱骗，依旧逃不过金睛火眼。\n\n文档碎片：\n最终，在观音菩萨的帮助下，真相大白，唐僧明白了自己的误解。\n\n\n文档总结：\n唐僧师徒在白虎岭三遇白骨精变化诱惑，悟空屡次识破击毙妖怪却遭误解，最终观音相助真相大白。'

DEFAULT_INPUT_TEMPLATE = '{sub_docs}\n\n文档总结：\n'

DEFAULT_SUB_DOC_TEMPLATE = '文档碎片：\n{text}\n'

__init__(api_model: str = 'gpt-4o', input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, sub_doc_template: str | None = None, input_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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 – The system prompt.

• sub_doc_template – The template for input text in each sample.

• input_template – The input template.

• 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.

parse_output(response)

recursive_summary(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample

Module contents

class data_juicer.ops.aggregator.NestedAggregator(api_model: str = 'gpt-4o', input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, sub_doc_template: str | None = None, input_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Considering the limitation of input length, nested aggregate contents for each given number of samples.

DEFAULT_SYSTEM_PROMPT = '给定一些文档碎片，将这些文档整合成一个文档总结。\n要求：\n- 总结的长度与文档碎片的平均长度基本一致\n- 不要包含主观看法\n- 注意要尽可能保留文本的专有名词\n- 只输出文档总结不要输出其他内容\n- 参考如下样例：\n文档碎片：\n唐僧师徒四人行至白虎岭，遇上了变化多端的白骨精。\n\n文档碎片：\n白骨精首次变身少女送斋，被孙悟空识破打死，唐僧责怪悟空。\n\n文档碎片：\n妖怪再变老妇寻女，又被悟空击毙，师傅更加不满，念紧箍咒惩罚。\n\n文档碎片：\n不甘心的白骨精第三次化作老公公来诱骗，依旧逃不过金睛火眼。\n\n文档碎片：\n最终，在观音菩萨的帮助下，真相大白，唐僧明白了自己的误解。\n\n\n文档总结：\n唐僧师徒在白虎岭三遇白骨精变化诱惑，悟空屡次识破击毙妖怪却遭误解，最终观音相助真相大白。'

DEFAULT_INPUT_TEMPLATE = '{sub_docs}\n\n文档总结：\n'

DEFAULT_SUB_DOC_TEMPLATE = '文档碎片：\n{text}\n'

__init__(api_model: str = 'gpt-4o', input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt: str | None = None, sub_doc_template: str | None = None, input_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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 – The system prompt.

• sub_doc_template – The template for input text in each sample.

• input_template – The input template.

• 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.

parse_output(response)

recursive_summary(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample

class data_juicer.ops.aggregator.EntityAttributeAggregator(api_model: str = 'gpt-4o', entity: str | None = None, attribute: str | None = None, input_key: str | None = None, output_key: str | None = None, word_limit: Annotated[int, Gt(gt=0)] = 100, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, example_prompt: str | None = None, input_template: str | None = None, output_pattern_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Return conclusion of the given entity’s attribute from some docs.

DEFAULT_SYSTEM_TEMPLATE = '给定与`{entity}`相关的一些文档，总结`{entity}`的`{attribute}`。\n要求：\n- 尽量使用原文专有名词\n- 联系上下文，自动忽略上下文不一致的细节错误\n- 只对文档中与`{entity}`的`{attribute}`有关的内容进行总结\n- 字数限制在**{word_limit}字以内**\n- 要求输出格式如下：\n# {entity}\n## {attribute}\n...\n{example}'

DEFAULT_EXAMPLE_PROMPT = '- 例如，根据相关文档总结`孙悟空`的`出身背景`，**100字**以内的样例如下：\n`孙悟空`的`出身背景`总结：\n# 孙悟空\n## 出身背景\n号称齐天大圣，花果山水帘洞的美猴王、西行取经队伍中的大师兄。师父是唐僧玄奘，曾拜菩提祖师学艺。亲生父母未知，自石头中孕育而生。自认斗战胜佛，最怕观世音菩萨和紧箍咒。\n'

DEFAULT_INPUT_TEMPLATE = '`{entity}`的相关文档：\n{sub_docs}\n\n`{entity}`的`{attribute}`总结：\n'

DEFAULT_OUTPUT_PATTERN_TEMPLATE = '\\#\\s*{entity}\\s*\\#\\#\\s*{attribute}\\s*(.*?)\\Z'

__init__(api_model: str = 'gpt-4o', entity: str | None = None, attribute: str | None = None, input_key: str | None = None, output_key: str | None = None, word_limit: Annotated[int, Gt(gt=0)] = 100, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, example_prompt: str | None = None, input_template: str | None = None, output_pattern_template: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param entity: The given entity. :param attribute: The given attribute. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• word_limit – Prompt the output length.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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_template – The system prompt template.

• example_prompt – The example part in the system prompt.

• input_template – The input template.

• output_pattern_template – The output template.

• 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.

parse_output(response)

attribute_summary(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample

class data_juicer.ops.aggregator.MostRelavantEntitiesAggregator(api_model: str = 'gpt-4o', entity: str | None = None, query_entity_type: str | None = None, input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, input_template: str | None = None, output_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Bases: Aggregator

Extract entities closely related to a given entity from some texts, and sort them in descending order of importance.

DEFAULT_SYSTEM_TEMPLATE = '给定与`{entity}`相关的一些文档，总结一些与`{entity}`最为相关的`{entity_type}`。\n要求：\n- 不用包含与{entity}为同一{entity_type}的{entity_type}。\n- 请按照人物的重要性进行排序，**越重要人物在列表越前面**。\n- 你的返回格式如下：\n## 分析\n你对各个{entity_type}与{entity}关联度的分析\n## 列表\n人物1, 人物2, 人物3, ...'

DEFAULT_INPUT_TEMPLATE = '`{entity}`的相关文档：\n{sub_docs}\n\n与`{entity}`最相关的一些`{entity_type}`：\n'

DEFAULT_OUTPUT_PATTERN = '\\#\\#\\s*列表\\s*(.*?)\\Z'

__init__(api_model: str = 'gpt-4o', entity: str | None = None, query_entity_type: str | None = None, input_key: str | None = None, output_key: str | None = None, max_token_num: Annotated[int, Gt(gt=0)] | None = None, *, api_endpoint: str | None = None, response_path: str | None = None, system_prompt_template: str | None = None, input_template: str | None = None, output_pattern: str | None = None, try_num: Annotated[int, Gt(gt=0)] = 3, model_params: Dict = {}, sampling_params: Dict = {}, **kwargs)

Initialization method. :param api_model: API model name. :param entity: The given entity. :param query_entity_type: The type of queried relavant entities. :param input_key: The input field key in the samples. Support for

nested keys such as “__dj__stats__.text_len”. It is text_key in default.

Parameters:

• output_key – The output field key in the samples. Support for nested keys such as “__dj__stats__.text_len”. It is same as the input_key in default.

• max_token_num – The max token num of the total tokens of the sub documents. Without limitation if it is None.

• 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_template – The system prompt template.

• input_template – The input template.

• output_pattern – The output pattern.

• 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.

parse_output(response)

query_most_relavant_entities(sub_docs, rank=None)

process_single(sample=None, rank=None)

For sample level, batched sample –> sample, the input must be the output of some Grouper OP.

Parameters:

sample – batched sample to aggregate

Returns:

aggregated sample