使用 RetrieveChat 进行检索增强代码生成和问答
AutoGen 提供了由 LLM 提供支持的可对话代理，可以通过自动聊天来共同执行任务，无论是工具还是人类。该框架通过多代理对话允许工具使用和人类参与。有关此功能的文档，请参见此处。
RetrieveChat 是一个用于检索增强代码生成和问答的对话系统。在这个笔记本中，我们演示了如何利用 RetrieveChat 根据不在 LLM 训练数据集中的自定义文档生成代码和回答问题。RetrieveChat 使用 RetrieveAssistantAgent 和 RetrieveUserProxyAgent，类似于其他笔记本中使用 AssistantAgent 和 UserProxyAgent 的用法（例如，使用代码生成、执行和调试自动解决任务）。实质上，RetrieveAssistantAgent 和 RetrieveUserProxyAgent 实现了与 RetrieveChat 提示相对应的不同自动回复机制。
目录
我们将演示使用 RetrieveChat 进行代码生成和问答的六个示例：
- 示例 1：根据文档字符串生成代码（无人类反馈）
- 示例 2：根据文档字符串回答问题（无人类反馈）
- 示例 3：根据文档字符串生成代码（有人类反馈）
- 示例 4：根据文档字 符串回答问题（有人类反馈）
- 示例 5：使用 RetrieveChat 的独特功能“更新上下文”解决综合性问答问题
- 示例 6：使用自定义提示和少样本学习解决综合性问答问题
需求
设置 API 端点
config_list_from_json
函数从环境变量或json文件中加载配置列表。
import json
import os
import chromadb
import autogen
from autogen.agentchat.contrib.retrieve_assistant_agent import RetrieveAssistantAgent
from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
# 可以存储在向量数据库实例中的接受的文件格式
from autogen.retrieve_utils import TEXT_FORMATS
config_list = [
{"model": "gpt-3.5-turbo-0125", "api_key": "<YOUR_API_KEY>", "api_type": "openai"},
]
assert len(config_list) > 0
print("要使用的模型：", [config_list[i]["model"] for i in range(len(config_list))])
要使用的模型： ['gpt-3.5-turbo-0125']
tip
了解有关为代理配置LLM的更多信息，请点击这里。
为RetrieveChat构建代理
我们首先初始化RetrieveAssistantAgent和RetrieveUserProxyAgent。对于RetrieveAssistantAgent，系统消息需要设置为“您是一个有帮助的助手。”。用户消息中提供了详细的说明。稍后，我们将使用RetrieveUserProxyAgent.message_generator将说明和检索增强生成任务组合起来，作为初始提示发送给LLM助手。
print("可接受的`docs_path`文件格式：")
print(TEXT_FORMATS)
可接受的`docs_path`文件格式：
['odt', 'xml', 'pdf', 'docx', 'html', 'md', 'htm', 'csv', 'rst', 'org', 'ppt', 'doc', 'log', 'json', 'epub', 'jsonl', 'pptx', 'yml', 'xlsx', 'tsv', 'txt', 'yaml', 'msg', 'rtf']
# 1. 创建一个名为“assistant”的 RetrieveAssistantAgent 实例
assistant = RetrieveAssistantAgent(
name="assistant",
system_message="您是一个乐于助人的助手。",
llm_config={
"timeout": 600,
"cache_seed": 42,
"config_list": config_list,
},
)
# 2. 创建一个名为“ragproxyagent”的 RetrieveUserProxyAgent 实例
# 默认情况下，human_input_mode 是 "ALWAYS"，这意味着代理将在每一步都要求人工输入。我们在这里将其设置为 "NEVER"。
# `docs_path` 是文档目录的路径。它也可以是单个文件的路径，或者是单个文件的 URL。默认情况下，
# 它设置为 None，仅在集合已经创建的情况下才起作用。
# `task` 表示我们正在处理的任务类型。在这个例子中，是一个 `code` 任务。
# `chunk_token_size` 是用于检索聊天的块令牌大小。默认情况下，它设置为 `max_tokens * 0.6`，这里我们将其设置为 2000。
# `custom_text_types` 是要处理的文件类型列表。默认值为 `autogen.retrieve_utils.TEXT_FORMATS`。
# 这仅适用于 `docs_path` 目录下的文件。无论其类型如何，显式包含的文件和 URL 都将被分块处理。
# 在这个例子中，我们将其设置为 ["non-existent-type"]，以仅处理 markdown 文件。由于 `websit/docs` 中没有包含任何 "non-existent-type" 文件，
# 因此那里的文件都不会被处理。但是，显式包含的 URL 仍然会被处理。
ragproxyagent = RetrieveUserProxyAgent(
name="ragproxyagent",
human_input_mode="NEVER",
max_consecutive_auto_reply=3,
retrieve_config={
"task": "code",
"docs_path": [
"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Examples/Integrate%20-%20Spark.md",
"https://raw.githubusercontent.com/microsoft/FLAML/main/website/docs/Research.md",
os.path.join(os.path.abspath(""), "..", "website", "docs"),
],
"custom_text_types": ["non-existent-type"],
"chunk_token_size": 2000,
"model": config_list[0]["model"],
# "client": chromadb.PersistentClient(path="/tmp/chromadb"), # 已弃用，请使用 "vector_db"
"vector_db": "chroma", # 要使用已弃用的 `client` 参数，请将其设置为 None，并取消上面一行的注释
"overwrite": False, # 如果要覆盖现有的集合，请将其设置为 True
},
code_execution_config=False, # 如果不想执行代码，请将其设置为 False
)
示例1
使用 RetrieveChat 来帮助生成示例代码，并自动运行代码并修复错误（如果有的话）。
问题：如果我想在一个分类任务中使用 FLAML，并且想要在30秒内训练模型，使用 spark 进行并行训练。如果达到时间限制，强制取消作业。
# 重置助手。在开始新对话之前，始终重置助手。
assistant.reset()
# 给定一个问题，我们使用 ragproxyagent 生 成一个要发送给助手作为初始消息的提示。
# 助手接收消息并生成回复。回复将发送回 ragproxyagent 进行处理。
# 对话将继续，直到满足终止条件，在 RetrieveChat 中，当没有检测到代码块时，终止条件是没有人在循环中。
# 在有人在循环中的情况下，对话将继续，直到用户说“退出”为止。
code_problem = "如何使用 FLAML 执行分类任务并使用 spark 进行并行训练。训练30秒并在达到时间限制时强制取消作业。"
chat_result = ragproxyagent.initiate_chat(
assistant, message=ragproxyagent.message_generator, problem=code_problem, search_string="spark"
) # search_string 用作嵌入搜索的额外过滤器，在这种情况下，我们只想搜索包含“spark”的文档。
2024-04-07 17:30:56,955 - autogen.agentchat.contrib.retrieve_user_proxy_agent - INFO - 使用现有集合 `autogen-docs`。
尝试创建集合。
2024-04-07 17:30:59,609 - autogen.agentchat.contrib.retrieve_user_proxy_agent - INFO - 找到2个块。
请求的结果数20大于索引中的元素数2，更新 n_results = 2
VectorDB 返回文档ID：[['bdfbc921']]
将文档 bdfbc921 的内容添加到上下文中。
ragproxyagent（给助手）：
你是一个增强型的检索编码助手。你根据自己的知识和用户提供的上下文来回答用户的问题。
如果你无法根据当前上下文回答问题，你应该回复“UPDATE CONTEXT”。
对于代码生成，你必须遵守以下规则：
规则1. 你不得安装任何包，因为所需的所有包都已经安装好了。
规则2. 你必须按照下面的格式编写你的代码：
```language
# 你的代码
用户的问题是：如何使用FLAML执行分类任务并使用spark进行并行训练。训练30秒，并在达到时间限制时强制取消作业。
背景是：# 集成 - Spark
FLAML已经集成了Spark进行分布式训练。与Spark的集成有两个主要方面：
- 使用Spark ML估计器进行自动机器学习。
- 使用Spark运行并行spark作业进行训练。
## Spark ML估计器
FLAML集成了基于Spark ML模型的估计器。这些模型使用Spark并行训练，因此我们称之为Spark估计器。要使用这些模型，您首先需要按照所需的格式组织数据。
### 数据
对于Spark估计器，AutoML只能使用Spark数据。FLAML在`flaml.automl.spark.utils`模块中提供了一个方便的函数`to_pandas_on_spark`，用于将数据转换为pandas-on-spark（`pyspark.pandas`）数据帧/系列，这是Spark估计器所需的。
此实用函数接受`pandas.Dataframe`或`pyspark.sql.Dataframe`形式的数据，并将其转换为pandas-on-spark数据帧。它还接受`pandas.Series`或`pyspark.sql.Dataframe`并将其转换为[pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html)系列。如果传入`pyspark.pandas.Dataframe`，它将不会进行任何更改。
该函数还接受可选参数`index_col`和`default_index_type`。
- `index_col`是要用作索引的列名，默认为None。
- `default_index_type`是默认索引类型，默认为"distributed-sequence"。有关默认索引类型的更多信息，请参阅Spark官方[文档](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)
下面是一个Spark数据的示例代码片段：
```python
```python
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# 创建一个字典
data = {
"Square_Feet": [800, 1200, 1800, 1500, 850],
"Age_Years": [20, 15, 10, 7, 25],
"Price": [100000, 200000, 300000, 240000, 120000],
}
# 创建一个 pandas DataFrame
dataframe = pd.DataFrame(data)
label = "Price"
# 转换为 pandas-on-spark DataFrame
psdf = to_pandas_on_spark(dataframe)
```
要使用 Spark ML 模型，您需要适当地格式化数据。具体来说，使用 [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) 将所有特征列合并为一个向量列。
以下是如何使用它的示例：
```python
from pyspark.ml.feature import VectorAssembler
columns = psdf.columns
feature_cols = [col for col in columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
```
在进行实验时，像处理非 Spark 数据一样使用您的 pandas-on-spark 数据，并使用 `X_train, y_train` 或 `dataframe, label` 进行传递。
### 估计器
#### 模型列表
- `lgbm_spark`：用于微调 Spark 版本 LightGBM 模型的类，使用 [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API。
#### 用法
首先，按照前面部分的描述，将数据准备成所需的格式。
通过将您打算尝试的模型包含在 `estimators_list` 参数中传递给 `flaml.automl`，FLAML 将开始尝试这些模型的配置。如果您的输入是 Spark 数据，则 FLAML 默认还会使用带有 `_spark` 后缀的估计器，即使您没有指定它们。
以下是使用 SparkML 模型的示例代码片段：
```python
import flaml
# 按照前面提到的方法，将数据准备成 pandas-on-spark 格式
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"], # 这个设置是可选的
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
[链接到笔记本](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [在colab中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)
## 并行 Spark 作业
您可以在[自动机器学习](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)和[超参数调整](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning)中，通过将 `use_spark` 设置为 `true`，将 Spark 激活为并行后端。FLAML 将使用 [`joblib-spark`](https://github.com/joblib/joblib-spark) 将您的作业分发到分布式 Spark 后端。
请注意，当应用于 Spark 数据的自动机器学习和调整时，不应将 `use_spark` 设置为 `true`。这是因为在自动机器学习和调整中，只会使用 SparkML 模型来处理 Spark 数据。由于 SparkML 模型可以并行运行，因此不需要再使用 `use_spark` 进行分发。
下面列出了所有与 Spark 相关的参数。这些参数在超参数调整和自动机器学习中都可用：
- `use_spark`：布尔值，默认为 False | 是否使用 Spark 运行并行 Spark 作业的训练。这可以用于加速大型模型和大型数据集的训练，但会增加时间开销，从而在某些情况下减慢训练速度。当 `use_spark` 为 True 时，不支持 GPU 训练。对于 Spark 集群，默认情况下，我们将为每个执行器启动一个试验。然而，有时我们希望启动的试验数量超过执行器的数量（例如，本地模式）。在这种情况下，我们可以设置环境变量 `FLAML_MAX_CONCURRENT` 来覆盖检测到的 `num_executors`。最终的并发试验数量将是 `n_concurrent_trials` 和 `num_executors` 中较小的那个。
- `n_concurrent_trials`：整数，默认为 1 | 并发试验的数量。当 n_concurrent_trials > 1 时，FLAML 执行并行调整。
- `force_cancel`：布尔值，默认为 False | 如果搜索时间超过时间预算，则是否强制取消 Spark 作业。Spark 作业包括并行调整作业和基于 Spark 的模型训练作业。
使用并行 Spark 作业的示例代码片段：
```python
导入 flaml
automl_experiment = flaml.AutoML()
automl_settings = {
"time_budget": 30,
"metric": "r2",
"task": "regression",
"n_concurrent_trials": 2,
"use_spark": True,
"force_cancel": True, # 激活 force_cancel 选项可以在超过分配的 time_budget 后立即停止 Spark 作业。
}
automl.fit(
dataframe=dataframe,
label=label,
**automl_settings,
)
```
[笔记本链接](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [在 colab 中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)
--------------------------------------------------------------------------------
assistant to=python code
请注意，上述代码假设数据量小到足以在30秒内训练完成。如果你有一个更大的数据集，你可能需要增加`time_budget`并相应调整并行作业的数量。
--------------------------------------------------------------------------------
ragproxyagent (to assistant):
ragproxyagent (给助手)：
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
助手 (给ragproxyagent)：
更新上下文
--------------------------------------------------------------------------------
更新上下文并重置对话。
``` text
请求的结果数量60大于索引2中的元素数量，更新n_results = 2
请求的结果数量100大于索引2中的元素数量，更新n_results = 2
请求的结果数量140大于索引2中的元素数量，更新n_results = 2
```
``` text
VectorDB返回的doc_ids：[['bdfbc921']]
VectorDB返 回的doc_ids：[['bdfbc921']]
VectorDB返回的doc_ids：[['bdfbc921']]
```
``` text
请求的结果数量180大于索引2中的元素数量，更新n_results = 2
```
``` text
VectorDB返回的doc_ids：[['bdfbc921']]
没有更多的上下文，将终止。
ragproxyagent（对assistant）：
终止
--------------------------------------------------------------------------------
```
``` text
ChatResult(chat_id=None, chat_history=[{'content': 'TERMINATE', 'role': 'assistant'}], summary='', cost=({'total_cost': 0.007691, 'gpt-35-turbo': {'cost': 0.007691, 'prompt_tokens': 4242, 'completion_tokens': 664, 'total_tokens': 4906}}, {'total_cost': 0}), human_input=[])
```
### 示例2
[返回顶部](#table-of-contents)
使用RetrieveChat来回答与代码生成无关的问题。
问题：FLAML的作者是谁？
```python
# 重置assistant。在开始新对话之前，始终重置assistant。
assistant.reset()
qa_problem = "FLAML的作者是谁？"
chat_result = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=qa_problem)
```
``` text
请求的结果数量20大于索引2中的元素数量，更新n_results = 2
```
```` text
VectorDB返回的doc_ids：[['7968cf3c'，'bdfbc921']]
将文档7968cf3c的内容添加到上下文中。
将文档bdfbc921的内容添加到上下文中。
ragproxyagent（对assistant）：
你是一个检索增强的编码助手。你根据自己的知识和用户提供的上下文来回答用户的问题。
如果你无论是否有当前上下文都无法回答问题，你应该回复“UPDATE CONTEXT”。
对于代码生成，你必须遵守以下规则：
规则1. 你不得安装任何包，因为所有需要的包都已经安装好了。
规则2. 你必须按照下面的格式编写你的代码：
```language
# 你的代码
```
用户的问题是：FLAML的作者是谁？
上下文是：# 研究
有关技术细节，请查看我们的研究出版物。
- [FLAML: A Fast and Lightweight AutoML Library](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/)。Chi Wang，Qingyun Wu，Markus Weimer，Erkang Zhu。MLSys 2021。
```bibtex
@inproceedings{wang2021flaml,
title={FLAML: A Fast and Lightweight AutoML Library},
author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
year={2021},
booktitle={MLSys},
}
```
- [成本相关超参数的节约优化](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
```bibtex
- [与混合搜索策略的经济超参数优化](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/)。作者：Chi Wang，Qingyun Wu，Silu Huang，Amin Saied。ICLR 2021。
```bibtex
@inproceedings{wang2021blendsearch,
title={与混合搜索策略的经济超参数优化},
author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},
year={2021},
booktitle={ICLR},
}
```
- [关于微调预训练语言模型的超参数优化的实证研究](https://aclanthology.org/2021.acl-long.178.pdf)。作者：Susan Xueqing Liu，Chi Wang。ACL 2021。
```bibtex
@inproceedings{liuwang2021hpolm,
title={关于微调预训练语言模型的超参数优化的实证研究},
author={Susan Xueqing Liu and Chi Wang},
year={2021},
booktitle={ACL},
}
```
- [用于在线自动机器学习的ChaCha](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/)。作者：Qingyun Wu，Chi Wang，John Langford，Paul Mineiro和Marco Rossi。ICML 2021。
```bibtex
@inproceedings{wu2021chacha,
title={用于在线自动机器学习的ChaCha},
author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
year={2021},
booktitle={ICML},
}
```
- [公平的自动机器学习](https://arxiv.org/abs/2111.06495)。作者：Qingyun Wu，Chi Wang。ArXiv预印本arXiv:2111.06495（2021年）。
```bibtex
@inproceedings{wuwang2021fairautoml,
title={公平的自动机器学习},
author={Qingyun Wu and Chi Wang},
year={2021},
booktitle={ArXiv预印本arXiv:2111.06495},
}
```
- [针对资源受限的自动机器学习挖掘稳健的默认配置](https://arxiv.org/abs/2202.09927)。作者：Moe Kayali，Chi Wang。ArXiv预印本arXiv:2202.09927（2022年）。
```bibtex
@inproceedings{kayaliwang2022default,
title={针对资源受限的自动机器学习挖掘稳健的默认配置},
author={Moe Kayali and Chi Wang},
year={2022},
booktitle={ArXiv预印本arXiv:2202.09927},
}
```
- [基于词典偏好的多目标超参数优化](https://openreview.net/forum?id=0Ij9_q567Ma)。作者：Shaokun Zhang，Feiran Jia，Chi Wang，Qingyun Wu。ICLR 2023（显著性前5%）。
```bibtex
@inproceedings{zhang2023targeted,
title={基于词典偏好的多目标超参数优化},
author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu},
booktitle={国际学习表示会议},
year={2023},
url={https://openreview.net/forum?id=0Ij9_q567Ma},
}
```
- [大规模语言模型生成推理的成本效益超参数优化](https://arxiv.org/abs/2303.04673)。Chi Wang，Susan Xueqing Liu，Ahmed H. Awadallah。ArXiv预印本arXiv:2303.04673（2023）。
```bibtex
# 集成 - Spark
FLAML已经集成了Spark用于分布式训练。Spark集成主要有两个方面：
- 使用Spark ML估计器进行自动机器学习。
- 使用Spark运行并行的Spark作业进行训练。
## Spark ML估计器
FLAML集成了基于Spark ML模型的估计器。这些模型使用Spark并行训练，因此我们称之为Spark估计器。要使用这些模型，您首先需要按照所需的格式组织数据。
### 数据
对于Spark估计器，AutoML只能使用Spark数据。FLAML在`flaml.automl.spark.utils`模块中提供了一个方便的函数`to_pandas_on_spark`，用于将数据转换为pandas-on-spark（`pyspark.pandas`）的DataFrame/series，这是Spark估计器所需的。
此实用函数接受`pandas.Dataframe`或`pyspark.sql.Dataframe`形式的数据，并将其转换为pandas-on-spark DataFrame。它还接受`pandas.Series`或`pyspark.sql.Dataframe`并将其转换为[pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) series。如果传入`pyspark.pandas.Dataframe`，它将不会进行任何更改。
该函数还接受可选参数`index_col`和`default_index_type`。
- `index_col`是要用作索引的列名，默认为None。
- `default_index_type`是默认的索引类型，默认为"distributed-sequence"。有关默认索引类型的更多信息，请参阅Spark官方[文档](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)。
以下是使用Spark数据的示例代码片段：
```python
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# 创建一个字典
data = {
"Square_Feet": [800, 1200, 1800, 1500, 850],
"Age_Years": [20, 15, 10, 7, 25],
"Price": [100000, 200000, 300000, 240000, 120000],
}
# 创建一个pandas DataFrame
dataframe = pd.DataFrame(data)
label = "Price"
# 转换为pandas-on-spark DataFrame
psdf = to_pandas_on_spark(dataframe)
```
要使用Spark ML模型，您需要适当地格式化数据。具体来说，使用[`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html)将所有特征列合并为一个向量列。
以下是如何使用它的示例：
```python
```python
from pyspark.ml.feature import VectorAssembler
# 获取列名
columns = psdf.columns
# 获取特征列，排除标签列
feature_cols = [col for col in columns if col != label]
# 创建特征向量转换器
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
# 对数据进行特征转换
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
```
在进行实验时，可以像处理非 Spark 数据一样使用你的 pandas-on-spark 数据，并使用 `X_train, y_train` 或 `dataframe, label` 进行传递。
### 估计器
#### 模型列表
- `lgbm_spark`：用于微调 Spark 版本 LightGBM 模型的类，使用 [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API。
#### 用法
首先，按照前面部分所述的要求格式准备你的数据。
通过将你打算尝试的模型包含在 `estimators_list` 参数中传递给 `flaml.automl`，FLAML 将开始尝试这些模型的配置。如果你的输入是 Spark 数据，FLAML 默认还会使用带有 `_spark` 后缀的估计器，即使你没有指定它们。
下面是一个使用 SparkML 模型的示例代码片段：
```python
import flaml
# 按照前面提到的方式准备你的 pandas-on-spark 格式数据
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"], # 这个设置是可选的
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
[链接到笔记本](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [在Colab中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)
## 并行 Spark 作业
您可以在[自动机器学习](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)和[超参数调整](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning)中，通过将`use_spark`设置为`true`，将Spark激活为并行后端。FLAML将使用[`joblib-spark`](https://github.com/joblib/joblib-spark)将您的作业分发到分布式Spark后端。
请注意，在应用于Spark数据的自动机器学习和调整时，不应将`use_spark`设置为`true`。这是因为自动机器学习和调整中只 会使用SparkML模型来处理Spark数据。由于SparkML模型可以并行运行，因此不需要再次使用`use_spark`进行分发。
下面列出了所有与Spark相关的参数。这些参数在超参数调整和自动机器学习中都可用：
- `use_spark`：布尔值，默认为False | 是否使用Spark在并行Spark作业中运行训练。这可以用于加速大型模型和大型数据集的训练，但会增加时间开销，从而在某些情况下减慢训练速度。当`use_spark`为True时，不支持GPU训练。对于Spark集群，默认情况下，我们将为每个执行器启动一个试验。然而，有时我们希望启动的试验数量超过执行器的数量（例如，本地模式）。在这种情况下，我们可以设置环境变量`FLAML_MAX_CONCURRENT`来覆盖检测到的`num_executors`。最终的并发试验数量将是`n_concurrent_trials`和`num_executors`中的最小值。
- `n_concurrent_trials`：整数，默认为1 | 并发试验的数量。当`n_concurrent_trials`大于1时，FLAML将执行并行调整。
- `force_cancel`：布尔值，默认为False | 如果搜索时间超过时间预算，则是否强制取消Spark作业。Spark作业包括并行调整作业和基于Spark的模型训练作业。
使用并行Spark作业的示例代码片段：
```python
```python
import flaml
automl_experiment = flaml.AutoML()
automl_settings = {
"time_budget": 30,
"metric": "r2",
"task": "regression",
"n_concurrent_trials": 2,
"use_spark": True,
"force_cancel": True, # 激活 force_cancel 选项可以在超过分配的 time_budget 后立即停止 Spark 作业。
}
automl.fit(
dataframe=dataframe,
label=label,
**automl_settings,
)
```
[笔记本链接](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [在 Colab 中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)
--------------------------------------------------------------------------------
助手（给 ragproxyagent）：
FLAML 的作者是王驰（Chi Wang），还有几位与 FLAML 相关的出版物的合著者。
--------------------------------------------------------------------------------
```
``` text
ChatResult(chat_id=None, chat_history=[{'content': 'You\'re a retrieve augmented coding assistant. You answer user\'s questions based on your own knowledge and the\ncontext provided by the user.\nIf you can\'t answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.\nFor code generation, you must obey the following rules:\nRule 1. You MUST NOT install any packages because all the packages needed are already installed.\nRule 2. You must follow the formats below to write your code:\n```language\n# your code\n```\n\nUser\'s question is: Who is the author of FLAML?\n\nContext is: # Research\n\nFor technical details, please check our research publications.\n\n- [FLAML: A Fast and Lightweight AutoML Library](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/). Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.\n\n```bibtex\n@inproceedings{wang2021flaml,\n title={FLAML: A Fast and Lightweight AutoML Library},\n author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},\n year={2021},\n booktitle={MLSys},\n}\n```\n\n- [Frugal Optimization for Cost-related Hyperparameters](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.\n\n```bibtex\n@inproceedings{wu2021cfo,\n title={Frugal Optimization for Cost-related Hyperparameters},\n author={Qingyun Wu and Chi Wang and Silu Huang},\n year={2021},\n booktitle={AAAI},\n}\n```\n\n- [Economical Hyperparameter Optimization With Blended Search Strategy](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.\n\n```bibtex\n@inproceedings{wang2021blendsearch,\n title={Economical Hyperparameter Optimization With Blended Search Strategy},\n author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},\n year={2021},\n booktitle={ICLR},\n}\n```\n\n- [An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models](https://aclanthology.org/2021.acl-long.178.pdf). Susan Xueqing Liu, Chi Wang. ACL 2021.\n\n```bibtex\n@inproceedings{liuwang2021hpolm,\n title={An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models},\n author={Susan Xueqing Liu and Chi Wang},\n year={2021},\n booktitle={ACL},\n}\n```\n\n- [ChaCha for Online AutoML](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.\n\n```bibtex\n@inproceedings{wu2021chacha,\n title={ChaCha for Online AutoML},\n author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},\n year={2021},\n booktitle={ICML},\n}\n```\n\n- [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).\n\n```bibtex\n@inproceedings{wuwang2021fairautoml,\n title={Fair AutoML},\n author={Qingyun Wu and Chi Wang},\n year={2021},\n booktitle={ArXiv preprint arXiv:2111.06495},\n}\n```\n\n- [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).\n\n```bibtex\n@inproceedings{kayaliwang2022default,\n title={Mining Robust Default Configurations for Resource-constrained AutoML},\n author={Moe Kayali and Chi Wang},\n year={2022},\n booktitle={ArXiv preprint arXiv:2202.09927},\n}\n```\n\n- [Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives](https://openreview.net/forum?id=0Ij9_q567Ma). Shaokun Zhang, Feiran Jia, Chi Wang, Qingyun Wu. ICLR 2023 (notable-top-5%).\n\n```bibtex\n@inproceedings{zhang2023targeted,\n title={Targeted Hyperparameter Optimization with Lexicographic Preferences Over Multiple Objectives},\n author={Shaokun Zhang and Feiran Jia and Chi Wang and Qingyun Wu},\n booktitle={International Conference on Learning Representations},\n year={2023},\n url={https://openreview.net/forum?id=0Ij9_q567Ma},\n}\n```\n\n- [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673). Chi Wang, Susan Xueqing Liu, Ahmed H. Awadallah. ArXiv preprint arXiv:2303.04673 (2023).\n\n```bibtex\n@inproceedings{wang2023EcoOptiGen,\n title={Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference},\n author={Chi Wang and Susan Xueqing Liu and Ahmed H. Awadallah},\n year={2023},\n booktitle={ArXiv preprint arXiv:2303.04673},\n}\n```\n\n- [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).\n\n```bibtex\n@inproceedings{wu2023empirical,\n title={An Empirical Study on Challenging Math Problem Solving with GPT-4},\n author={Yiran Wu and Feiran Jia and Shaokun Zhang and Hangyu Li and Erkang Zhu and Yue Wang and Yin Tat Lee and Richard Peng and Qingyun Wu and Chi Wang},\n year={2023},\n booktitle={ArXiv preprint arXiv:2306.01337},\n}\n```\n# Integrate - Spark\n\nFLAML has integrated Spark for distributed training. There are two main aspects of integration with Spark:\n\n- Use Spark ML estimators for AutoML.\n- Use Spark to run training in parallel spark jobs.\n\n## Spark ML Estimators\n\nFLAML integrates estimators based on Spark ML models. These models are trained in parallel using Spark, so we called them Spark estimators. To use these models, you first need to organize your data in the required format.\n\n### Data\n\nFor Spark estimators, AutoML only consumes Spark data. FLAML provides a convenient function `to_pandas_on_spark` in the `flaml.automl.spark.utils` module to convert your data into a pandas-on-spark (`pyspark.pandas`) dataframe/series, which Spark estimators require.\n\nThis utility function takes data in the form of a `pandas.Dataframe` or `pyspark.sql.Dataframe` and converts it into a pandas-on-spark dataframe. It also takes `pandas.Series` or `pyspark.sql.Dataframe` and converts it into a [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) series. If you pass in a `pyspark.pandas.Dataframe`, it will not make any changes.\n\nThis function also accepts optional arguments `index_col` and `default_index_type`.\n\n- `index_col` is the column name to use as the index, default is None.\n- `default_index_type` is the default index type, default is "distributed-sequence". More info about default index type could be found on Spark official [documentation](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)\n\nHere is an example code snippet for Spark Data:\n\n```python\nimport pandas as pd\nfrom flaml.automl.spark.utils import to_pandas_on_spark\n\n# Creating a dictionary\ndata = {\n "Square_Feet": [800, 1200, 1800, 1500, 850],\n "Age_Years": [20, 15, 10, 7, 25],\n "Price": [100000, 200000, 300000, 240000, 120000],\n}\n\n# Creating a pandas DataFrame\ndataframe = pd.DataFrame(data)\nlabel = "Price"\n\n# Convert to pandas-on-spark dataframe\npsdf = to_pandas_on_spark(dataframe)\n```\n\nTo use Spark ML models you need to format your data appropriately. Specifically, use [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) to merge all feature columns into a single vector column.\n\nHere is an example of how to use it:\n\n```python\nfrom pyspark.ml.feature import VectorAssembler\n\ncolumns = psdf.columns\nfeature_cols = [col for col in columns if col != label]\nfeaturizer = VectorAssembler(inputCols=feature_cols, outputCol="features")\npsdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]\n```\n\nLater in conducting the experiment, use your pandas-on-spark data like non-spark data and pass them using `X_train, y_train` or `dataframe, label`.\n\n### Estimators\n\n#### Model List\n\n- `lgbm_spark`: The class for fine-tuning Spark version LightGBM models, using [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API.\n\n#### Usage\n\nFirst, prepare your data in the required format as described in the previous section.\n\nBy including the models you intend to try in the `estimators_list` argument to `flaml.automl`, FLAML will start trying configurations for these models. If your input is Spark data, FLAML will also use estimators with the `_spark` postfix by default, even if you haven\'t specified them.\n\nHere is an example code snippet using SparkML models in AutoML:\n\n```python\nimport flaml\n\n# prepare your data in pandas-on-spark format as we previously mentioned\n\nautoml = flaml.AutoML()\nsettings = {\n "time_budget": 30,\n "metric": "r2",\n "estimator_list": ["lgbm_spark"], # this setting is optional\n "task": "regression",\n}\n\nautoml.fit(\n dataframe=psdf,\n label=label,\n **settings,\n)\n```\n\n[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)\n\n## Parallel Spark Jobs\n\nYou can activate Spark as the parallel backend during parallel tuning in both [AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning) and [Hyperparameter Tuning](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning), by setting the `use_spark` to `true`. FLAML will dispatch your job to the distributed Spark backend using [`joblib-spark`](https://github.com/joblib/joblib-spark).\n\nPlease note that you should not set `use_spark` to `true` when applying AutoML and Tuning for Spark Data. This is because only SparkML models will be used for Spark Data in AutoML and Tuning. As SparkML models run in parallel, there is no need to distribute them with `use_spark` again.\n\nAll the Spark-related arguments are stated below. These arguments are available in both Hyperparameter Tuning and AutoML:\n\n- `use_spark`: boolean, default=False | Whether to use spark to run the training in parallel spark jobs. This can be used to accelerate training on large models and large datasets, but will incur more overhead in time and thus slow down training in some cases. GPU training is not supported yet when use_spark is True. For Spark clusters, by default, we will launch one trial per executor. However, sometimes we want to launch more trials than the number of executors (e.g., local mode). In this case, we can set the environment variable `FLAML_MAX_CONCURRENT` to override the detected `num_executors`. The final number of concurrent trials will be the minimum of `n_concurrent_trials` and `num_executors`.\n- `n_concurrent_trials`: int, default=1 | The number of concurrent trials. When n_concurrent_trials > 1, FLAML performes parallel tuning.\n- `force_cancel`: boolean, default=False | Whether to forcely cancel Spark jobs if the search time exceeded the time budget. Spark jobs include parallel tuning jobs and Spark-based model training jobs.\n\nAn example code snippet for using parallel Spark jobs:\n\n```python\nimport flaml\n\nautoml_experiment = flaml.AutoML()\nautoml_settings = {\n "time_budget": 30,\n "metric": "r2",\n "task": "regression",\n "n_concurrent_trials": 2,\n "use_spark": True,\n "force_cancel": True, # Activating the force_cancel option can immediately halt Spark jobs once they exceed the allocated time_budget.\n}\n\nautoml.fit(\n dataframe=dataframe,\n label=label,\n **automl_settings,\n)\n```\n\n[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)\n\n', 'role': 'assistant'}, {'content': 'The author of FLAML is Chi Wang, along with several co-authors for various publications related to FLAML.', 'role': 'user'}], summary='The author of FLAML is Chi Wang, along with several co-authors for various publications related to FLAML.', cost=({'total_cost': 0.004711, 'gpt-35-turbo': {'cost': 0.004711, 'prompt_tokens': 3110, 'completion_tokens': 23, 'total_tokens': 3133}}, {'total_cost': 0}), human_input=[])
```
### 示例 3
[返回目录](#目录)
使用 RetrieveChat 来帮助生成示例代码并请求人工反馈。
问题：如何使用 FLAML 构建股票价格的时间序列预测模型？
```python
# 重置助手。在开始新的对话之前，始终重置助手。
assistant.reset()
# 将 `human_input_mode` 设置为 `ALWAYS`，这样代理将在每一步都要求人工输入。
ragproxyagent.human_input_mode = "ALWAYS"
code_problem = "如何使用 FLAML 构建股票价格的时间序列预测模型？"
chat_result = ragproxyagent.initiate_chat(assistant, message=ragproxyagent.message_generator, problem=code_problem)
```
``` text
WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Number of requested results 20 is greater than number of elements in index 2, updating n_results = 2
```
```` text
doc_ids: [['doc_0', 'doc_1']]
将 doc_id doc_0 添加到上下文中。
将 doc_id doc_1 添加到上下文中。
ragproxyagent（对助手说）：
你是一个带有检索增强的编码助手。你根据自己的知识和用户提供的上下文来回答用户的问题。
如果你无法根据当前上下文回答问题，无论是否有上下文，你都应该回复 `UPDATE CONTEXT`。
对于代码生成，你必须遵守以下规则：
规则 1. 你不得安装任何包，因为所需的所有包已经安装好了。
规则 2. 你必须按照以下格式编写你的代码：
```language
# your code
用户的问题是：如何使用FLAML构建股票价格的时间序列预测模型？
背景是：# 集成 - Spark
FLAML已经集成了Spark进行分布式训练。与Spark的集成有两个主要方面：
- 使用Spark ML估计器进行自动机器学习。
- 使用Spark并行运行训练作业。
## Spark ML估计器
FLAML集成了基于Spark ML模型的估计器。这些模型使用Spark并行训练，因此我们称之为Spark估计器。要使用这些模型，首先需要按照所需的格式组织数据。
### 数据
对于Spark估计器，AutoML只能使用Spark数据。FLAML在`flaml.automl.spark.utils`模块中提供了一个方便的函数`to_pandas_on_spark`，用于将数据转换为pandas-on-spark（`pyspark.pandas`）数据帧/系列，这是Spark估计器所需的。
这个实用函数接受`pandas.Dataframe`或`pyspark.sql.Dataframe`形式的数据，并将其转换为pandas-on-spark数据帧。它还接受`pandas.Series`或`pyspark.sql.Dataframe`并将其转换为[pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html)系列。如果传入`pyspark.pandas.Dataframe`，它将不会进行任何更改。
该函数还接受可选参数`index_col`和`default_index_type`。
- `index_col`是要用作索引的列名，默认为None。
- `default_index_type`是默认的索引类型，默认为"distributed-sequence"。有关默认索引类型的更多信息，请参阅Spark官方[文档](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)
下面是一个Spark数据的示例代码片段：
```python
```python
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# 创建一个字典
data = {"Square_Feet": [800, 1200, 1800, 1500, 850],
"Age_Years": [20, 15, 10, 7, 25],
"Price": [100000, 200000, 300000, 240000, 120000]}
# 创建一个 pandas DataFrame
dataframe = pd.DataFrame(data)
label = "Price"
# 转换为 pandas-on-spark DataFrame
psdf = to_pandas_on_spark(dataframe)
```
要使用 Spark ML 模型，您需要适当地格式化数据。具体来说，使用 [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) 将所有特征列合并为一个向量列。
以下是如何使用的示例：
```python
from pyspark.ml.feature import VectorAssembler
columns = psdf.columns
feature_cols = [col for col in columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
```
在进行实验时，像处理非 Spark 数据一样使用您的 pandas-on-spark 数据，并使用 `X_train, y_train` 或 `dataframe, label` 进行传递。
### 估计器
#### 模型列表
- `lgbm_spark`：用于微调 Spark 版本 LightGBM 模型的类，使用 [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API。
#### 用法
首先，按照前一节中的描述，将您的数据准备成所需的格式。
通过将您打算尝试的模型包含在 `estimators_list` 参数中传递给 `flaml.automl`，FLAML 将开始尝试这些模型的配置。如果您的输入是 Spark 数据，则 FLAML 默认还会使用带有 `_spark` 后缀的估计器，即使您没有指定它们。
以下是使用 SparkML 模型进行 AutoML 的示例代码片段：
```python
import flaml
# 按照我们之前提到的方式，将数据准备成 pandas-on-spark 格式
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"], # 这个设置是可选的
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
[链接到笔记本](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [在colab中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)
## 并行 Spark 作业
您可以在[AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)和[超参数调整](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning)中将 Spark 激活为并行后端，方法是将 `use_spark` 设置为 `true`。FLAML 将使用 [`joblib-spark`](https://github.com/joblib/joblib-spark) 将您的作业分发到分布式 Spark 后端。
请注意，在应用于 Spark 数据的 AutoML 和调整过程中，不应将 `use_spark` 设置为 `true`。这是因为在 AutoML 和调整过程中，只会使用 SparkML 模型来处理 Spark 数据。由于 SparkML 模型可以并行运行，因此不需要再使用 `use_spark` 进行分发。
下面列出了所有与 Spark 相关的参数。这些参数在超参数调整和 AutoML 中都可用：
- `use_spark`：布尔值，默认为 False | 是否使用 Spark 在并行 Spark 作业中运行训练。这可以用于加速大型模型和大型数据集的训练，但会增加时间开销，从而在某些情况下减慢训练速度。当 `use_spark` 为 True 时，不支持 GPU 训练。对于 Spark 集群，默认情况下，我们将为每个执行器启动一个试验。然而，有时我们希望启动的试验数量超过执行器的数量（例如，本地模式）。在这种情况下，我们可以设置环境变量 `FLAML_MAX_CONCURRENT` 来覆盖检测到的 `num_executors`。最终的并发试验数量将是 `n_concurrent_trials` 和 `num_executors` 中的最小值。
- `n_concurrent_trials`：整数，默认为 1 | 并发试验的数量。当 `n_concurrent_trials` 大于 1 时，FLAML 执行并行调整。
- `force_cancel`：布尔值，默认为 False | 如果搜索时间超过时间预算，则是否强制取消 Spark 作业。Spark 作业包括并行调整作业和基于 Spark 的模型训练作业。
使用并行 Spark 作业的示例代码片段：
```python
```python
import flaml
automl_experiment = flaml.AutoML()
automl_settings = {
"time_budget": 30,
"metric": "r2",
"task": "regression",
"n_concurrent_trials": 2,
"use_spark": True,
"force_cancel": True, # 激活 force_cancel 选项可以在超出分配的 time_budget 后立即停止 Spark 作业。
}
automl.fit(
dataframe=dataframe,
label=label,
**automl_settings,
)
```
[Notebook 链接](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [在 colab 中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)
# 研究
有关技术细节，请查阅我们的研究论文。
* [FLAML: 一个快速且轻量级的 AutoML 库](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/)。Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.
```bibtex
@inproceedings{wang2021flaml,
title={FLAML: 一个快速且轻量级的 AutoML 库},
author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
year={2021},
booktitle={MLSys},
}
```
* [面向成本相关超参数的节俭优化](https://arxiv.org/abs/2005.01571)。Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
```bibtex
@inproceedings{wu2021cfo,
title={面向成本相关超参数的节俭优化},
author={Qingyun Wu and Chi Wang and Silu Huang},
year={2021},
booktitle={AAAI},
}
```
* [混合搜索策略的经济超参数优化](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/)。Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.
```bibtex
@inproceedings{wang2021blendsearch,
title={混合搜索策略的经济超参数优化},
author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},
year={2021},
booktitle={ICLR},
}
```
* [针对微调预训练语言模型的超参数优化的实证研究](https://aclanthology.org/2021.acl-long.178.pdf)。Susan Xueqing Liu, Chi Wang. ACL 2021.
```bibtex
@inproceedings{liuwang2021hpolm,
title={针对微调预训练语言模型的超参数优化的实证研究},
author={Susan Xueqing Liu and Chi Wang},
year={2021},
booktitle={ACL},
}
```
* [在线 AutoML 的 ChaCha](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/)。Qingyun Wu, Chi Wang, John Langford, Paul Mineiro 和 Marco Rossi. ICML 2021.
```bibtex
@inproceedings{wu2021chacha,
title={在线 AutoML 的 ChaCha},
author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
year={2021},
booktitle={ICML},
}
```
* [公平的 AutoML](https://arxiv.org/abs/2111.06495)。Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
```bibtex
@inproceedings{wuwang2021fairautoml,
title={公平的 AutoML},
```python
import flaml
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# Convert data to pandas-on-spark dataframe/series
data = to_pandas_on_spark(spark_data)
# Merge feature columns into a single vector column
assembler = VectorAssembler(
inputCols=data.columns[:-1],
outputCol="features"
)
data = assembler.transform(data)
# Initialize and run AutoML
automl = flaml.AutoML()
automl.fit(data, label_col=data.columns[-1])
```
This code snippet demonstrates how to use FLAML's `lgbm_spark` estimator for time series forecasting. It converts the Spark dataframe to a pandas-on-spark dataframe/series using the `to_pandas_on_spark` function. Then, it merges the feature columns into a single vector column using `VectorAssembler`. Finally, it initializes and runs the `flaml.AutoML` object to search for the best model configuration.
```python
import flaml
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
from pyspark.ml.feature import VectorAssembler
# 将股票价格数据加载到 pandas 数据帧中
data = pd.read_csv('stock_price.csv')
# 指定标签列名
label = 'price'
# 将 pandas 数据帧转换为 pandas-on-spark 数据帧
psdf = to_pandas_on_spark(data)
# 将特征列合并为单个向量列
feature_cols = [col for col in psdf.columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
# 使用 lgbm_spark 评估器开始一个 AutoML 实验
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"],
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
```
--------------------------------------------------------------------------------
ragproxyagent (to assistant):
我希望将 time_budget 设置为 10 分钟
--------------------------------------------------------------------------------
我希望将 time_budget 设置为 10 分钟
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
您可以将 `settings` 字典中的 `time_budget` 参数设置为 10 分钟（600 秒），如下所示：
```python
import flaml
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
from pyspark.ml.feature import VectorAssembler
# 将股票价格数据加载到 pandas 数据帧中
data = pd.read_csv('stock_price.csv')
# 指定标签列名
label = 'price'
# 将 pandas 数据帧转换为 pandas-on-spark 数据帧
psdf = to_pandas_on_spark(data)
# 将特征列合并为单个向量列
feature_cols = [col for col in psdf.columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
# 使用 lgbm_spark 评估器开始一个 AutoML 实验，时间预算为 10 分钟
automl = flaml.AutoML()
settings = {
"time_budget": 600, # 时间预算以秒为单位
"metric": "r2",
"estimator_list": ["lgbm_spark"],
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
```
在这个例子中，`time_budget` 参数被设置为 600，表示 FLAML AutoML 实验运行的秒数。您可以调整这个值来控制实验所花费的总时间。
--------------------------------------------------------------------------------
>>>>>>>> NO HUMAN INPUT RECEIVED.
>>>>>>>> USING AUTO REPLY...
ragproxyagent (to assistant):
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
还有什么我可以帮助您的吗？
--------------------------------------------------------------------------------
>>>>>>>> NO HUMAN INPUT RECEIVED.
示例 4
使用 RetrieveChat 来回答问题并请求人工反馈。
问题：FLAML 中是否有一个名为 tune_automl 的函数？
# 重置助手。在开始新对话之前，始终重置助手。
assistant.reset()
# 将 `human_input_mode` 设置为 `ALWAYS`，这样代理将在每一步都要求人工输入。
ragproxyagent.human_input_mode = "ALWAYS"
qa_problem = "FLAML 中是否有一个名为 `tune_automl` 的函数？"
chat_result = ragproxyagent.initiate_chat(
assistant, message=ragproxyagent.message_generator, problem=qa_problem
) # 输入 "exit" 退出对话
WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:请求的结果数 20 大于索引中的元素数 2，更新 n_results = 2
doc_ids: [['doc_0', 'doc_1']]
将 doc_id doc_0 添加到上下文中。
将 doc_id doc_1 添加到上下文中。
ragproxyagent（对助手）：
你是一个带有检索增强的编码助手。你根据自己的知识和用户提供的上下文来回答用户的问题。
如果你无法根据当前上下文回答问题，你应该回复 `UPDATE CONTEXT`。
对于代码生成，你必须遵守以下规则：
规则 1. 你绝对不能安装任何包，因为所需的所有包都已经安装好了。
规则 2. 你必须按照以下格式编写你的代码：
```language
# your code
用户的问题是：FLAML 中是否有一个名为 `tune_automl` 的函数？
上下文是：# 集成 - Spark
FLAML 已经集成了 Spark 用于分布式训练。与 Spark 的集成有两个主要方面：
- 使用 Spark ML 估计器进行自动机器学习。
- 使用 Spark 运行并行的 Spark 作业进行训练。
## Spark ML 估计器
FLAML 集成了基于 Spark ML 模型的估计器。这些模型使用 Spark 并行训练，因此我们称之为 Spark 估计器。要使用这些模型，您首先需要将数据组织成所需的格式。
### 数据
对于 Spark 估计器，AutoML 只能使用 Spark 数据。FLAML 在 `flaml.automl.spark.utils` 模块中提供了一个方便的函数 `to_pandas_on_spark`，用于将数据转换为 pandas-on-spark (`pyspark.pandas`) 数据帧/系列，这是 Spark 估计器所需的。
这个实用函数接受 `pandas.Dataframe` 或 `pyspark.sql.Dataframe` 形式的数据，并将其转换为 pandas-on-spark 数据帧。它还接受 `pandas.Series` 或 `pyspark.sql.Dataframe` 并将其转换为 [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) 系列。如果传入 `pyspark.pandas.Dataframe`，它将不会进行任何更改。
该函数还接受可选参数 `index_col` 和 `default_index_type`。
- `index_col` 是要用作索引的列名，默认为 None。
- `default_index_type` 是默认的索引类型，默认为 "distributed-sequence"。有关默认索引类型的更多信息，请参阅 Spark 官方 [文档](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)。
下面是一个使用 Spark 数据的示例代码片段：
```python
```python
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# 创建一个字典
data = {"Square_Feet": [800, 1200, 1800, 1500, 850],
"Age_Years": [20, 15, 10, 7, 25],
"Price": [100000, 200000, 300000, 240000, 120000]}
# 创建一个 pandas DataFrame
dataframe = pd.DataFrame(data)
label = "Price"
# 转换为 pandas-on-spark DataFrame
psdf = to_pandas_on_spark(dataframe)
```
要使用 Spark ML 模型，您需要适当地格式化数据。具体来说，使用 [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) 将所有特征列合并为一个向量列。
以下是如何使用的示例代码：
```python
from pyspark.ml.feature import VectorAssembler
columns = psdf.columns
feature_cols = [col for col in columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
```
在进行实验时，像处理非 Spark 数据一样使用您的 pandas-on-spark 数据，并使用 `X_train, y_train` 或 `dataframe, label` 进行传递。
### 估计器
#### 模型列表
- `lgbm_spark`：用于微调 Spark 版本 LightGBM 模型的类，使用 [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API。
#### 用法
首先，按照前面部分的描述，将数据准备成所需的格式。
通过将您打算尝试的模型包含 在 `estimators_list` 参数中传递给 `flaml.automl`，FLAML 将开始尝试这些模型的配置。如果您的输入是 Spark 数据，则 FLAML 默认还会使用带有 `_spark` 后缀的估计器，即使您没有指定它们。
以下是在 AutoML 中使用 SparkML 模型的示例代码片段：
```python
import flaml
# 按照前面提到的方式准备您的 pandas-on-spark 格式数据
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"], # 此设置是可选的
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
[链接到笔记本](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [在colab中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)
## 并行 Spark 作业
您可以在[自动机器学习](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)和[超参数调整](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning)中将 Spark 激活为并行后端，方法是将 `use_spark` 设置为 `true`。FLAML 将使用 [`joblib-spark`](https://github.com/joblib/joblib-spark) 将您的作业分发到分布式 Spark 后端。
请注意，在应用于 Spark 数据的自动机器学习和调整过程中，不应将 `use_spark` 设置为 `true`。这是因为自动机器学习和调整过程中只会使用 SparkML 模型来处理 Spark 数据。由于 SparkML 模型是并行运行的，因此不需要再使用 `use_spark` 进行分发。
下面列出了所有与 Spark 相关的参数。这些参数在超参数调整和自动机器学习中都可用：
- `use_spark`：布尔值，默认为 False | 是否使用 Spark 在并行 Spark 作业中运行训练。这可以用于加速大型模型和大型数据集的训练，但会增加时间开销，从而在某些情况下减慢训练速度。当 use_spark 为 True 时，不支持 GPU 训练。对于 Spark 集群，默认情况下，我们将为每个执行器启动一个试验。但是，有时我们希望启动的试验数量多于执行器的数量（例如，本地模式）。在这种情况下，我们可以设置环境变量 `FLAML_MAX_CONCURRENT` 来覆盖检测到的 `num_executors`。最终的并发试验数量将是 `n_concurrent_trials` 和 `num_executors` 中的最小值。
- `n_concurrent_trials`：整数，默认为 1 | 并发试验的数量。当 n_concurrent_trials > 1 时，FLAML 执行并行调整。
- `force_cancel`：布尔值，默认为 False | 如果搜索时间超出时间预算，则是否强制取消 Spark 作业。Spark 作业包括并行调整作业和基于 Spark 的模型训练作业。
使用并行 Spark 作业的示例代码片段：
```python
```python
import flaml
automl_experiment = flaml.AutoML()
automl_settings = {
"time_budget": 30,
"metric": "r2",
"task": "regression",
"n_concurrent_trials": 2,
"use_spark": True,
"force_cancel": True, # 激活 force_cancel 选项可以在超过分配的 time_budget 后立即停止 Spark 作业。
}
automl.fit(
dataframe=dataframe,
label=label,
**automl_settings,
)
```
[笔记本链接](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [在 Colab 中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)
# 研究
有关技术细节，请查阅我们的研究论文。
* [FLAML: 一个快速轻量级的 AutoML 库](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/)。Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.
```bibtex
@inproceedings{wang2021flaml,
title={FLAML: 一个快速轻量级的 AutoML 库},
author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
year={2021},
booktitle={MLSys},
}
```
* [面向成本相关超参数的节俭优化](https://arxiv.org/abs/2005.01571)。Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
```bibtex
@inproceedings{wu2021cfo,
title={面向成本相关超参数的节俭优化},
author={Qingyun Wu and Chi Wang and Silu Huang},
year={2021},
booktitle={AAAI},
}
```
* [混合搜索策略的经济超参数优化](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/)。Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.
```bibtex
@inproceedings{wang2021blendsearch,
title={混合搜索策略的经济超参数优化},
author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},
year={2021},
booktitle={ICLR},
}
```
* [针对微调预训练语言模型的超参数优化的实证研究](https://aclanthology.org/2021.acl-long.178.pdf)。Susan Xueqing Liu, Chi Wang. ACL 2021.
```bibtex
@inproceedings{liuwang2021hpolm,
title={针对微调预训练语言模型的超参数优化的实证研究},
author={Susan Xueqing Liu and Chi Wang},
year={2021},
booktitle={ACL},
}
```
* [在线 AutoML 的 ChaCha](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/)。Qingyun Wu, Chi Wang, John Langford, Paul Mineiro 和 Marco Rossi. ICML 2021.
```bibtex
@inproceedings{wu2021chacha,
title={在线 AutoML 的 ChaCha},
author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
year={2021},
booktitle={ICML},
}
```
* [公平 AutoML](https://arxiv.org/abs/2111.06495)。Qingyun Wu, Chi Wang. ArXiv 预印本 arXiv:2111.06495 (2021).
```bibtex
@inproceedings{wuwang2021fairautoml,
title={公平 AutoML},
```python
# your code
```
Rule 3. You can write auxiliary functions outside the main function, but please do not write any executable code outside the main function.
Rule 4. You should not change the name or signature of the main function.
Rule 5. If your code implementation is not complete, you should add the following comment:
```python
# TODO: complete the code implementation
```
Do you understand the rules?
用户的问题是：FLAML 中是否有一个名为 `tune_automl` 的函数？
上下文是：# 集成 - Spark
FLAML 已经集成了 Spark 用于分布式训练。与 Spark 的集成有两个主要方面：
- 使用 Spark ML 估计器进行自动机器学习。
- 使用 Spark 运行并行的 Spark 作业进行训练。
## Spark ML 估计器
FLAML 集成了基于 Spark ML 模型的估计器。这些模型使用 Spark 并行训练，因此我们称之为 Spark 估计器。要使用这些模型，您首先需要将数据组织成所需的格式。
### 数据
对于 Spark 估计器，AutoML 只能使用 Spark 数据。FLAML 在 `flaml.automl.spark.utils` 模块中提供了一个方便的函数 `to_pandas_on_spark`，用于将数据转换为 pandas-on-spark (`pyspark.pandas`) 数据帧/系列，这是 Spark 估计器所需的。
这个实用函数接受 `pandas.Dataframe` 或 `pyspark.sql.Dataframe` 形式的数据，并将其转换为 pandas-on-spark 数据帧。它还接受 `pandas.Series` 或 `pyspark.sql.Dataframe` 并将其转换为 [pandas-on-spark](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/index.html) 系列。如果传入 `pyspark.pandas.Dataframe`，它将不会进行任何更改。
该函数还接受可选参数 `index_col` 和 `default_index_type`。
- `index_col` 是要用作索引的列名，默认为 None。
- `default_index_type` 是默认的索引类型，默认为 "distributed-sequence"。有关默认 索引类型的更多信息，请参阅 Spark 官方 [文档](https://spark.apache.org/docs/latest/api/python/user_guide/pandas_on_spark/options.html#default-index-type)。
下面是一个使用 Spark 数据的示例代码片段：
```python
```python
import pandas as pd
from flaml.automl.spark.utils import to_pandas_on_spark
# 创建一个字典
data = {"Square_Feet": [800, 1200, 1800, 1500, 850],
"Age_Years": [20, 15, 10, 7, 25],
"Price": [100000, 200000, 300000, 240000, 120000]}
# 创建一个 pandas DataFrame
dataframe = pd.DataFrame(data)
label = "Price"
# 转换为 pandas-on-spark DataFrame
psdf = to_pandas_on_spark(dataframe)
```
要使用 Spark ML 模型，您需要适当地格式化数据。具体来说，使用 [`VectorAssembler`](https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.feature.VectorAssembler.html) 将所有特征列合并为一个向量列。
以下是如何使用的示例：
```python
from pyspark.ml.feature import VectorAssembler
columns = psdf.columns
feature_cols = [col for col in columns if col != label]
featurizer = VectorAssembler(inputCols=feature_cols, outputCol="features")
psdf = featurizer.transform(psdf.to_spark(index_col="index"))["index", "features"]
```
在进行实验时，可以像处理非 Spark 数据一样使用 pandas-on-spark 数据，并使用 `X_train, y_train` 或 `dataframe, label` 进行传递。
### 估计器
#### 模型列表
- `lgbm_spark`：用于微调 Spark 版本 LightGBM 模型的类，使用 [SynapseML](https://microsoft.github.io/SynapseML/docs/features/lightgbm/about/) API。
#### 用法
首先，按照前面部分所述的要求格式化您的数据。
通过将您打算尝试的模型包含在 `estimators_list` 参数中传递给 `flaml.automl`，FLAML 将开始尝试这些模型的配置。如果您的输入是 Spark 数据，FLAML 默认还会使用带有 `_spark` 后缀的估计器，即使您没有指定它们。
以下是在 AutoML 中使用 SparkML 模型的示例代码片段：
```python
import flaml
# 按照前面提到的方式准备您的 pandas-on-spark 格式数据
automl = flaml.AutoML()
settings = {
"time_budget": 30,
"metric": "r2",
"estimator_list": ["lgbm_spark"], # 此设置是可选的
"task": "regression",
}
automl.fit(
dataframe=psdf,
label=label,
**settings,
)
[链接到笔记本](https://github.com/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb) | [在colab中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_bankrupt_synapseml.ipynb)
## 并行 Spark 作业
您可以在[AutoML](/docs/Use-Cases/Task-Oriented-AutoML#parallel-tuning)和[超参数调整](/docs/Use-Cases/Tune-User-Defined-Function#parallel-tuning)中将Spark激活为并行后端，方法是将`use_spark`设置为`true`。FLAML将使用[`joblib-spark`](https://github.com/joblib/joblib-spark)将您的作业分发到分布式Spark后端。
请注意，在应用于Spark数据的AutoML和调整时，不应将`use_spark`设置为`true`。这是因为在AutoML和调整中，仅使用SparkML模型来处理Spark数据。由于SparkML模型可以并行运行，因此不需要再使用`use_spark`进行分发。
下面列出了与Spark相关的所有参数。这些参数在超参数调 整和AutoML中都可用：
- `use_spark`：布尔值，默认为False | 是否使用Spark在并行Spark作业中运行训练。这可以用于加速大型模型和大型数据集的训练，但会增加时间开销，从而在某些情况下减慢训练速度。当`use_spark`为True时，不支持GPU训练。对于Spark集群，默认情况下，我们将每个执行器启动一个试验。然而，有时我们希望启动的试验数量超过执行器的数量（例如，本地模式）。在这种情况下，我们可以设置环境变量`FLAML_MAX_CONCURRENT`来覆盖检测到的`num_executors`。最终的并发试验数量将是`n_concurrent_trials`和`num_executors`的最小值。
- `n_concurrent_trials`：整数，默认为1 | 并发试验的数量。当`n_concurrent_trials` > 1时，FLAML执行并行调整。
- `force_cancel`：布尔值，默认为False | 如果搜索时间超过时间预算，则是否强制取消Spark作业。Spark作业包括并行调整作业和基于Spark的模型训练作业。
使用并行Spark作业的示例代码片段：
```python
```python
import flaml
automl_experiment = flaml.AutoML()
automl_settings = {
"time_budget": 30,
"metric": "r2",
"task": "regression",
"n_concurrent_trials": 2,
"use_spark": True,
"force_cancel": True, # 激活 force_cancel 选项可以在超过分配的 time_budget 后立即停止 Spark 作业。
}
automl.fit(
dataframe=dataframe,
label=label,
**automl_settings,
)
```
[笔记本链接](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb) | [在 Colab 中打开](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_spark.ipynb)
# 研究
有关技术细节，请查看我们的研究出版物。
* [FLAML: 一种快速且轻量级的 AutoML 库](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/). Chi Wang, Qingyun Wu, Markus Weimer, Erkang Zhu. MLSys 2021.
```bibtex
@inproceedings{wang2021flaml,
title={FLAML: 一种快速且轻量级的 AutoML 库},
author={Chi Wang and Qingyun Wu and Markus Weimer and Erkang Zhu},
year={2021},
booktitle={MLSys},
}
```
* [面向成本相关超参数的节俭优化](https://arxiv.org/abs/2005.01571). Qingyun Wu, Chi Wang, Silu Huang. AAAI 2021.
```bibtex
@inproceedings{wu2021cfo,
title={面向成本相关超参数的节俭优化},
author={Qingyun Wu and Chi Wang and Silu Huang},
year={2021},
booktitle={AAAI},
}
```
* [混合搜索策略的经济超参数优化](https://www.microsoft.com/en-us/research/publication/economical-hyperparameter-optimization-with-blended-search-strategy/). Chi Wang, Qingyun Wu, Silu Huang, Amin Saied. ICLR 2021.
```bibtex
@inproceedings{wang2021blendsearch,
title={混合搜索策略的经济超参数优化},
author={Chi Wang and Qingyun Wu and Silu Huang and Amin Saied},
year={2021},
booktitle={ICLR},
}
```
* [针对微调预训练语言模型的超参数优化的实证研究](https://aclanthology.org/2021.acl-long.178.pdf). Susan Xueqing Liu, Chi Wang. ACL 2021.
```bibtex
@inproceedings{liuwang2021hpolm,
title={针对微调预训练语言模型的超参数优化的实证研究},
author={Susan Xueqing Liu and Chi Wang},
year={2021},
booktitle={ACL},
}
```
* [在线 AutoML 的 ChaCha](https://www.microsoft.com/en-us/research/publication/chacha-for-online-automl/). Qingyun Wu, Chi Wang, John Langford, Paul Mineiro and Marco Rossi. ICML 2021.
```bibtex
@inproceedings{wu2021chacha,
title={在线 AutoML 的 ChaCha},
author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
year={2021},
booktitle={ICML},
}
```
* [公平的 AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
```bibtex
@inproceedings{wuwang2021fairautoml,
title={公平的 AutoML},
### 示例 5
[返回顶部](#目录)
使用 RetrieveChat 来回答 [NaturalQuestion](https://ai.google.com/research/NaturalQuestions) 数据集的问题。
首先，我们将创建一个新的文档集合，其中包含所有的文档。
上述代码段展示了如何使用RetrieveChat来回答一些问题。在这个例子中，我们将使用`gpt-3.5-turbo`模型，并展示RetrieveChat的自动更新上下文的功能，以防检索到的文档不包含足够的信 息。
首先，我们将更改模型为`gpt-3.5-turbo`：
```python
config_list[0]["model"] = "gpt-3.5-turbo" # 将模型更改为gpt-3.5-turbo
```
然后，我们将使用`https://huggingface.co/datasets/thinkall/NaturalQuestionsQA/resolve/main/corpus.txt`作为语料库文件，并创建一个新的NaturalQuestions数据集的集合。在这个例子中，我们的任务是`qa`（问答）任务。
```python
corpus_file = "https://huggingface.co/datasets/thinkall/NaturalQuestionsQA/resolve/main/corpus.txt"
# 为NaturalQuestions数据集创建一个新的集合
# `task`指示我们正在处理的任务类型。在这个例子中，是一个`qa`任务。
ragproxyagent = RetrieveUserProxyAgent(
name="ragproxyagent",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
retrieve_config={
"task": "qa",
"docs_path": corpus_file,
"chunk_token_size": 2000,
"model": config_list[0]["model"],
"client": chromadb.PersistentClient(path="/tmp/chromadb"),
"collection_name": "natural-questions",
"chunk_mode": "one_line",
"embedding_model": "all-MiniLM-L6-v2",
},
)
```
接下来，我们定义了一些问题和答案，并使用RetrieveChat来回答这些问题。这些问题和答案是从`https://huggingface.co/datasets/thinkall/NaturalQuestionsQA/resolve/main/queries.jsonl`中获取的。
```python
queries = """{"_id": "ce2342e1feb4e119cb273c05356b33309d38fa132a1cbeac2368a337e38419b8", "text": "what is non controlling interest on balance sheet", "metadata": {"answer": ["the portion of a subsidiary corporation 's stock that is not owned by the parent corporation"]}}
{"_id": "3a10ff0e520530c0aa33b2c7e8d989d78a8cd5d699201fc4b13d3845010994ee", "text": "how many episodes are in chicago fire season 4", "metadata": {"answer": ["23"]}}
{"_id": "fcdb6b11969d5d3b900806f52e3d435e615c333405a1ff8247183e8db6246040", "text": "what are bulls used for on a farm", "metadata": {"answer": ["breeding", "as work oxen", "slaughtered for meat"]}}
{"_id": "26c3b53ec44533bbdeeccffa32e094cfea0cc2a78c9f6a6c7a008ada1ad0792e", "text": "has been honoured with the wisden leading cricketer in the world award for 2016", "metadata": {"answer": ["Virat Kohli"]}}
{"_id": "0868d0964c719a52cbcfb116971b0152123dad908ac4e0a01bc138f16a907ab3", "text": "who carried the usa flag in opening ceremony", "metadata": {"answer": ["Erin Hamlin"]}}
"""
queries = [json.loads(line) for line in queries.split("\n") if line]
questions = [q["text"] for q in queries]
answers = [q["metadata"]["answer"] for q in queries]
print(questions)
print(answers)
```
以上是问题和答案的示例。
```python
for i in range(len(questions)):
print(f"\n\n>>>>>>>>>>>> 下面是第 {i+1} 个案例的输出 <<<<<<<<<<<<\n\n")
# 重置助手。在开始新对话之前，始终重置助手。
assistant.reset()
qa_problem = questions[i]
chat_result = ragproxyagent.initiate_chat(
assistant, message=ragproxyagent.message_generator, problem=qa_problem, n_results=30
)
```
``` text
>>>>>>>>>>>> 下面是案例1的输出 <<<<<<<<<<<<
尝试创建集合。
```
``` text
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```
# 机器学习的基本概念
机器学习是一种人工智能的分支，它研究如何使计算机系统能够从数据中学习和改进。与传统的编程方法不同，机器学习的目标是通过让计算机自己从数据中发现模式和规律，从而实现自主学习和预测能力。
在机器学习中，我们通常使用训练数据来训练模型，然后使用测试数据来评估模型的性能。训练数据是已知 结果的数据集，而测试数据是未知结果的数据集。通过训练模型并使用测试数据进行验证，我们可以评估模型的准确性和泛化能力。
机器学习可以分为监督学习、无监督学习和强化学习三种类型。在监督学习中，我们提供带有标签的训练数据，让模型学习如何预测标签。在无监督学习中，我们只提供无标签的训练数据，让模型自己发现数据中的模式和结构。在强化学习中，模型通过与环境进行交互来学习最优的行为策略。
机器学习在各个领域都有广泛的应用，包括图像识别、语音识别、自然语言处理、推荐系统等。它已经成为现代科技和商业领域中不可或缺的一部分。
## 机器学习的基本步骤
机器学习的基本步骤包括数据准备、特征工程、模型选择和训练、模型评估和调优等。
1. 数据准备：在机器学习中，数据是非常重要的。我们需要 收集和准备合适的数据集，包括清洗、去噪、处理缺失值等操作。
2. 特征工程：特征工程是将原始数据转换为适合机器学习算法处理的特征的过程。这包括特征选择、特征提取和特征转换等操作。
3. 模型选择和训练：在选择模型时，我们需要考虑问题的性质和数据的特点。常见的机器学习模型包括线性回归、决策树、支持向量机等。训练模型是指使用训练数据来调整模型的参数，使其能够更好地拟合数据。
4. 模型评估和调优：在训练模型后，我们需要使用测试数据来评估模型的性能。常见的评估指标包括准确率、精确率、召回率等。如果模型性能不理想，我们可以通过调整模型的超参数或改进数据处理方法来提高模型的性能。
## 机器学习的挑战
尽管机器学习在许多领域取得了显著的成果，但它仍然面临一些挑战。
1. 数据质量：机器学习的性能很大程度上依赖于数据的质量。如果数据存在噪声、缺失值或不平衡等问题，模型的性能可能会受到影响。
2. 维度灾难：当特征的数量非常大时，模型的训练和预测可能会变得非常困难。这被称为维度灾难，需要采用特征选择、降维等方法来解决。
3. 模型选择：选择合适的模型对于机器学习的成功非常重要。不同的问题和数据可能需要不同类型的模型，因此选择合适的模型是一个挑战。
4. 解释性：一些机器学习模型，如深度神经网络，具有很强的预测能力，但缺乏解释性。这使得人们很难理解模型的决策过程和原因。
尽管存在这些挑战，机器学习仍然是一项非常有前景和潜力的技术。随着数据量的增加和算法的改进，我们可以期待机器学习在未来的发展和应用中发挥更大的作用。
参考文献：
[1] Mitchell, T. M. (1997). Machine learning. McGraw Hill.
``` text
doc_ids: [['doc_0', 'doc_3334', 'doc_720', 'doc_2732', 'doc_2510', 'doc_5084', 'doc_5068', 'doc_3727', 'doc_1938', 'doc_4689', 'doc_5249', 'doc_1751', 'doc_480', 'doc_3989', 'doc_2115', 'doc_1233', 'doc_2264', 'doc_633', 'doc_2376', 'doc_2293', 'doc_5274', 'doc_5213', 'doc_3991', 'doc_2880', 'doc_2737', 'doc_1257', 'doc_1748', 'doc_2038', 'doc_4073', 'doc_2876']]
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ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: what is non controlling interest on balance sheet
Context is: <P> In accounting , minority interest ( or non-controlling interest ) is the portion of a subsidiary corporation 's stock that is not owned by the parent corporation . The magnitude of the minority interest in the subsidiary company is generally less than 50 % of outstanding shares , or the corporation would generally cease to be a subsidiary of the parent . </P>
<P> The balance sheet is the financial statement showing a firm 's assets , liabilities and equity ( capital ) at a set point in time , usually the end of the fiscal year reported on the accompanying income statement . The total assets always equal the total combined liabilities and equity in dollar amount . This statement best demonstrates the basic accounting equation - Assets = Liabilities + Equity . The statement can be used to help show the status of a company . </P>
<P> The comptroller ( who is also auditor general and head of the National Audit Office ) controls both the Consolidated Fund and the National Loans Fund . The full official title of the role is Comptroller General of the Receipt and Issue of Her Majesty 's Exchequer . </P>
<P> Financing activities include the inflow of cash from investors such as banks and shareholders , as well as the outflow of cash to shareholders as dividends as the company generates income . Other activities which impact the long - term liabilities and equity of the company are also listed in the financing activities section of the cash flow statement . </P>
<P> It is frequently claimed that annual accounts have not been certified by the external auditor since 1994 . In its annual report on the implementation of the 2009 EU Budget , the Court of Auditors found that the two biggest areas of the EU budget , agriculture and regional spending , have not been signed off on and remain `` materially affected by error '' . </P>
<P> The Ministry of Finance , Government of India announces the rate of interest for PPF account every quarter . The current interest rate effective from 1 January 2018 is 7.6 % Per Annum ' ( compounded annually ) . Interest will be paid on 31 March every year . Interest is calculated on the lowest balance between the close of the fifth day and the last day of every month . </P>
<Table> <Tr> <Th> Quarter </Th> <Th> Interest Rate </Th> </Tr> <Tr> <Td> April 2018 - June 2018 </Td> <Td> 7.6 % </Td> </Tr> </Table>
<P> For a percentage of the settlement amount , Public adjusters work exclusively for the policyholder . This means there should be no inherent conflict of interest when it comes to advocating on the policyholder 's behalf to the insurance company . </P>
<P> Accounts receivable is a legally enforceable claim for payment held by a business for goods supplied and / or services rendered that customers / clients have ordered but not paid for . These are generally in the form of invoices raised by a business and delivered to the customer for payment within an agreed time frame . Accounts receivable is shown in a balance sheet as an asset . It is one of a series of accounting transactions dealing with the billing of a customer for goods and services that the customer has ordered . These may be distinguished from notes receivable , which are debts created through formal legal instruments called promissory notes . </P>
<P> A common synonym for net profit when discussing financial statements ( which include a balance sheet and an income statement ) is the bottom line . This term results from the traditional appearance of an income statement which shows all allocated revenues and expenses over a specified time period with the resulting summation on the bottom line of the report . </P>
<Table> Electronic Fund Transfer Act <Tr> <Td colspan="2"> </Td> </Tr> <Tr> <Th> Other short titles </Th> <Td> <Ul> <Li> Financial Institutions Regulatory and Interest Rate Control Act of 1978 </Li> <Li> Change in Bank Control Act </Li> <Li> Change in Savings and Loan Control Act </Li> <Li> Depository Institution Management Interlocks Act </Li> <Li> Export - Import Bank Act Amendments </Li> <Li> Federal Financial Institutions Examination Council Act </Li> <Li> National Credit Union Central Liquidity Facility Act </Li> <Li> Right to Financial Privacy Act </Li> </Ul> </Td> </Tr> <Tr> <Th> Long title </Th> <Td> An Act to extend the authority for the flexible regulation of interest rates on deposits and accounts in depository institutions . </Td> </Tr> <Tr> <Th> Nicknames </Th> <Td> American Arts Gold Medallion Act </Td> </Tr> <Tr> <Th> Enacted by </Th> <Td> the 95th United States Congress </Td> </Tr> <Tr> <Th> Effective </Th> <Td> November 10 , 1978 </Td> </Tr> <Tr> <Th colspan="2"> Citations </Th> </Tr> <Tr> <Th> Public law </Th> <Td> 95 - 630 </Td> </Tr> <Tr> <Th> Statutes at Large </Th> <Td> 92 Stat. 3641 aka 92 Stat. 3728 </Td> </Tr> <Tr> <Th colspan="2"> Codification </Th> </Tr> <Tr> <Th> Titles amended </Th> <Td> <Ul> <Li> 12 U.S.C. : Banks and Banking </Li> <Li> 15 U.S.C. : Commerce and Trade </Li> </Ul> </Td> </Tr> <Tr> <Th> U.S.C. sections amended </Th> <Td> <Ul> <Li> 12 U.S.C. ch. 3 § 226 et seq . </Li> <Li> 15 U.S.C. ch. 41 § 1601 et seq . </Li> <Li> 15 U.S.C. ch. 41 § 1693 et seq . </Li> </Ul> </Td> </Tr> <Tr> <Th colspan="2"> Legislative history </Th> </Tr> <Tr> <Td colspan="2"> <Ul> <Li> Introduced in the House as H.R. 14279 by Fernand St. Germain ( D - RI ) on October 10 , 1978 </Li> <Li> Committee consideration by House Banking , Finance , and Urban Affairs , Senate Banking , Housing , and Urban Affairs </Li> <Li> Passed the House on October 11 , 1978 ( passed ) </Li> <Li> Passed the Senate on October 12 , 1978 ( passed ) with amendment </Li> <Li> House agreed to Senate amendment on October 14 , 1978 ( 341 - 32 , in lieu of H. Res. 1439 ) with further amendment </Li> <Li> Senate agreed to House amendment on October 14 , 1978 ( agreed ) </Li> <Li> Signed into law by President Jimmy Carter on November 10 , 1978 </Li> </Ul> </Td> </Tr> <Tr> <Th colspan="2"> Major amendments </Th> </Tr> <Tr> <Td colspan="2"> Credit CARD Act of 2009 </Td> </Tr> </Table>
<P> Financial management refers to the efficient and effective management of money ( funds ) in such a manner as to accomplish the objectives of the organization . It is the specialized function directly associated with the top management . The significance of this function is not seen in the ' Line ' but also in the capacity of the ' Staff ' in overall of a company . It has been defined differently by different experts in the field . </P>
<P> Form 990 ( officially , the `` Return of Organization Exempt From Income Tax '' ) is a United States Internal Revenue Service form that provides the public with financial information about a nonprofit organization . It is often the only source of such information . It is also used by government agencies to prevent organizations from abusing their tax - exempt status . Certain nonprofits have more comprehensive reporting requirements , such as hospitals and other health care organizations ( Schedule H ) . </P>
<P> The Board of Governors of the Federal Reserve System , commonly known as the Federal Reserve Board , is the main governing body of the Federal Reserve System . It is charged with overseeing the Federal Reserve Banks and with helping implement monetary policy of the United States . Governors are appointed by the President of the United States and confirmed by the Senate for staggered 14 - year terms . </P>
<P> The International Monetary Fund ( IMF ) is an international organization headquartered in Washington , D.C. , of `` 189 countries working to foster global monetary cooperation , secure financial stability , facilitate international trade , promote high employment and sustainable economic growth , and reduce poverty around the world . '' Formed in 1945 at the Bretton Woods Conference primarily by the ideas of Harry Dexter White and John Maynard Keynes , it came into formal existence in 1945 with 29 member countries and the goal of reconstructing the international payment system . It now plays a central role in the management of balance of payments difficulties and international financial crises . Countries contribute funds to a pool through a quota system from which countries experiencing balance of payments problems can borrow money . As of 2016 , the fund had SDR 477 billion ( about $668 billion ) . </P>
<Li> Callability -- Some bonds give the issuer the right to repay the bond before the maturity date on the call dates ; see call option . These bonds are referred to as callable bonds . Most callable bonds allow the issuer to repay the bond at par . With some bonds , the issuer has to pay a premium , the so - called call premium . This is mainly the case for high - yield bonds . These have very strict covenants , restricting the issuer in its operations . To be free from these covenants , the issuer can repay the bonds early , but only at a high cost . </Li>
<P> On November 7 , 2016 , debt held by the public was $14.3 trillion or about 76 % of the previous 12 months of GDP . Intragovernmental holdings stood at $5.4 trillion , giving a combined total gross national debt of $19.8 trillion or about 106 % of the previous 12 months of GDP ; $6.2 trillion or approximately 45 % of the debt held by the public was owned by foreign investors , the largest of which were Japan and China at about $1.09 trillion for Japan and $1.06 trillion for China as of December 2016 . </P>
<P> A currency transaction report ( CTR ) is a report that U.S. financial institutions are required to file with FinCEN for each deposit , withdrawal , exchange of currency , or other payment or transfer , by , through , or to the financial institution which involves a transaction in currency of more than $10,000 . Used in this context , currency means the coin and / or paper money of any country that is designated as legal tender by the country of issuance . Currency also includes U.S. silver certificates , U.S. notes , Federal Reserve notes , and official foreign bank notes . </P>
<P> Checks and balances is the principle that each of the Branches has the power to limit or check the other two and this creates a balance between the three separate powers of the state , this principle induces that the ambitions of one branch prevent that one of the other branches become supreme , and thus be eternally confronting each other and in that process leaving the people free from government abuses . Checks and Balances are designed to maintain the system of separation of powers keeping each branch in its place . This is based on the idea that it is not enough to separate the powers and guarantee their independence but to give the various branches the constitutional means to defend their own legitimate powers from the encroachments of the other branches . They guarantee that the powers of the State have the same weight ( co-equal ) , that is , to be balanced , so that they can limit each other , avoiding the abuse of state power . the origin of checks and balances , like separation of powers itself , is specifically credited to Montesquieu in the Enlightenment ( in The Spirit of the Laws , 1748 ) , under this influence was implemented in 1787 in the Constitution of the United States . </P>
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
Non controlling interest on balance sheet refers to the portion of a subsidiary corporation's stock that is not owned by the parent corporation. It represents ownership of less than 50% of the outstanding shares. It is shown as a separate line item in the equity section of the balance sheet.
--------------------------------------------------------------------------------
>>>>>>>>>>>> Below are outputs of Case 2 <<<<<<<<<<<<
doc_ids: [['doc_1', 'doc_1097', 'doc_4221', 'doc_4972', 'doc_1352', 'doc_96', 'doc_988', 'doc_2370', 'doc_2414', 'doc_5038', 'doc_302', 'doc_1608', 'doc_980', 'doc_2112', 'doc_562', 'doc_4204', 'doc_3298', 'doc_2995', 'doc_3978', 'doc_1258', 'doc_2971', 'doc_2171', 'doc_1065', 'doc_17', 'doc_2683', 'doc_87', 'doc_1767', 'doc_158', 'doc_482', 'doc_3850']]
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Adding doc_id doc_1352 to context.
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Adding doc_id doc_1065 to context.
Adding doc_id doc_17 to context.
Adding doc_id doc_2683 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: how many episodes are in chicago fire season 4
Context is: <P> The fourth season of Chicago Fire , an American drama television series with executive producer Dick Wolf , and producers Derek Haas , Michael Brandt , and Matt Olmstead , was ordered on February 5 , 2015 , by NBC , and premiered on October 13 , 2015 and concluded on May 17 , 2016 . The season contained 23 episodes . </P>
<P> The fourth season began airing on October 10 , 2017 , and is set to run for 23 episodes on The CW until May 22 , 2018 . </P>
<P> The fourth season began airing on October 10 , 2017 , on The CW . </P>
<P> The fifth season of Chicago P.D. , an American police drama television series with executive producer Dick Wolf , and producers Derek Haas , Michael Brandt , and Rick Eid , premiered on September 27 , 2017 . This season featured its 100th episode . </P>
<P> This was the city of Chicago 's first professional sports championship since the Chicago Fire won MLS Cup ' 98 ( which came four months after the Chicago Bulls ' sixth NBA championship that year ) . The next major Chicago sports championship came in 2010 , when the NHL 's Chicago Blackhawks ended a 49 - year Stanley Cup title drought . With the Chicago Bears ' win in Super Bowl XX and the Chicago Cubs ' own World Series championship in 2016 , all Chicago sports teams have won at least one major championship since 1985 . Meanwhile , the Astros themselves made it back to the World Series in 2017 , but this time as an AL team , where they defeated the Los Angeles Dodgers in seven games , resulting in Houston 's first professional sports championship since the 2006 -- 07 Houston Dynamo won their back - to - back MLS Championships . </P>
<P> The season was ordered in May 2017 , and production began the following month . Ben McKenzie stars as Gordon , alongside Donal Logue , David Mazouz , Morena Baccarin , Sean Pertwee , Robin Lord Taylor , Erin Richards , Camren Bicondova , Cory Michael Smith , Jessica Lucas , Chris Chalk , Drew Powell , Crystal Reed and Alexander Siddig . The fourth season premiered on September 21 , 2017 , on Fox , while the second half premiered on March 1 , 2018 . </P>
<P> As of May 24 , 2017 , 58 episodes of The 100 have aired , concluding the fourth season . In March 2017 , The CW renewed the series for a fifth season , set to premiere on April 24 , 2018 . </P>
<P> The fifth book , River of Fire , is scheduled to be released on April 10 , 2018 . </P>
<P> On September 10 , 2013 , AMC officially cancelled the series after 38 episodes and three seasons . However , on November 15 , 2013 , Netflix ordered a fourth and final season of six episodes , that was released on Netflix on August 1 , 2014 . </P>
<P> The second season of Fargo , an American anthology black comedy -- crime drama television series created by Noah Hawley , premiered on October 12 , 2015 , on the basic cable network FX . Its principal cast consists of Kirsten Dunst , Patrick Wilson , Jesse Plemons , Jean Smart , and Ted Danson . The season had ten episodes , and its initial airing concluded on December 14 , 2015 . As an anthology , each Fargo season possesses its own self - contained narrative , following a disparate set of characters in various settings . </P>
<P> The Great Fire of London was a major conflagration that swept through the central parts of the English city of London from Sunday , 2 September to Wednesday , 5 September 1666 . The fire gutted the medieval City of London inside the old Roman city wall . It threatened but did not reach the aristocratic district of Westminster , Charles II 's Palace of Whitehall , and most of the suburban slums . It consumed 13,200 houses , 87 parish churches , St Paul 's Cathedral , and most of the buildings of the City authorities . It is estimated to have destroyed the homes of 70,000 of the City 's 80,000 inhabitants . </P>
<P> The first season consisted of eight one - hour - long episodes which were released worldwide on Netflix on July 15 , 2016 , in Ultra HD 4K . The second season , consisting of nine episodes , was released on October 27 , 2017 in HDR . A teaser for the second season , which also announced the release date , aired during Super Bowl LI . </P>
<P> `` Two Days Before the Day After Tomorrow '' is the eighth episode in the ninth season of the American animated television series South Park . The 133rd overall episode overall , it originally aired on Comedy Central in the United States on October 19 , 2005 . In the episode , Stan and Cartman accidentally destroy a dam , causing the town of Beaverton to be destroyed . </P>
<P> The fourth season consists of a double order of twenty episodes , split into two parts of ten episodes ; the second half premiered on November 30 , 2016 . The season follows the battles between Ragnar and Rollo in Francia , Bjorn 's raid into the Mediterranean , and the Viking invasion of England . It concluded in its entirety on February 1 , 2017 . </P>
<P> This is an episode list for Sabrina the Teenage Witch , an American sitcom that debuted on ABC in 1996 . From Season 5 , the program was aired on The WB . The series ran for seven seasons totaling 163 episodes . It originally premiered on September 27 , 1996 on ABC and ended on April 24 , 2003 on The WB . </P>
<P> Hart of Dixie was renewed by The CW for 10 episode season on May 8 , 2014 . The show 's fourth and final season premiered on November 15 , 2014 . The series was later cancelled on May 7 , 2015 . </P>
<P> The Burning Maze is the third book in the series . It is scheduled to be released on May 1 , 2018 . </P>
<Table> <Tr> <Th colspan="2"> My Name Is Earl ( season 4 ) </Th> </Tr> <Tr> <Td colspan="2"> DVD cover </Td> </Tr> <Tr> <Th> Country of origin </Th> <Td> United States </Td> </Tr> <Tr> <Th> No. of episodes </Th> <Td> 27 </Td> </Tr> <Tr> <Th colspan="2"> Release </Th> </Tr> <Tr> <Th> Original network </Th> <Td> NBC </Td> </Tr> <Tr> <Th> Original release </Th> <Td> September 25 , 2008 -- May 14 , 2009 </Td> </Tr> <Tr> <Th colspan="2"> Season chronology </Th> </Tr> <Tr> <Td colspan="2"> ← Previous Season 3 </Td> </Tr> <Tr> <Td colspan="2"> List of My Name Is Earl episodes </Td> </Tr> </Table>
<P> The eighteenth season of Law & Order : Special Victims Unit debuted on Wednesday , September 21 , 2016 , on NBC and finished on Wednesday , May 24 , 2017 , with a two - hour season finale . </P>
<P> The eighth and final season of the fantasy drama television series Game of Thrones was announced by HBO in July 2016 . Unlike the first six seasons that each had ten episodes and the seventh that had seven episodes , the eighth season will have only six episodes . Like the previous season , it will largely consist of original content not found currently in George R.R. Martin 's A Song of Ice and Fire series , and will instead adapt material Martin has revealed to showrunners about the upcoming novels in the series , The Winds of Winter and A Dream of Spring . </P>
<P> A total of 49 episodes of The Glades were produced and aired over four seasons . </P>
<P> Sneaky Pete is an American crime drama series created by David Shore and Bryan Cranston . The series follows Marius Josipović ( Giovanni Ribisi ) , a released convict who adopts the identity of his cell mate , Pete Murphy , in order to avoid his past life . The series also stars Marin Ireland , Shane McRae , Libe Barer , Michael Drayer , Peter Gerety , and Margo Martindale . The pilot debuted on August 7 , 2015 , and was followed by a full series order that September . Shore left the project in early 2016 and was replaced by Graham Yost , who served as executive producer and showrunner for the remaining nine episodes . The first season premiered in its entirety on January 13 , 2017 , exclusively on Amazon Video . On January 19 , 2017 , Amazon announced that Sneaky Pete had been renewed for a second season , which was released on March 9 , 2018 . </P>
<P> The eighth season of Blue Bloods , a police procedural drama series created by Robin Green and Mitchell Burgess , premiered on CBS on September 29 , 2017 . The season is set to contain 22 episodes . </P>
<P> The first five seasons of Prison Break have been released on DVD and Blu - ray in Regions 1 , 2 , and 4 . Each DVD boxed set includes all of the broadcast episodes from that season , the associated special episode , commentary from cast and crew , and profiles of various parts of Prison Break , such as Fox River State Penitentiary or the tattoo . Prison Break is also available online , including iTunes , Amazon Video , and Netflix . After the premiere of the second season of Prison Break , Fox began online streaming of the prior week 's episode , though it originally restricted viewing to the United States . </P>
<P> In June 2017 , Remini was upped to a series regular starting with Season 2 ; shortly after , it was announced that Erinn Hayes would not be returning for the show 's second season . Sources cited in a Variety article confirmed that Remini would be returning as Detective Vanessa Cellucci , the character she portrayed in the first - season finale , and that Hayes ' dismissal was for creative reasons and `` not a reflection '' of the actress ' performance . In August 2017 , it was reported Hayes ' character will be killed off before season two begins and the season will take place 7 -- 10 months after season one ended , in order to make room for Remini . </P>
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
There are 23 episodes in Chicago Fire season 4.
--------------------------------------------------------------------------------
>>>>>>>>>>>> Below are outputs of Case 3 <<<<<<<<<<<<
doc_ids: [['doc_47', 'doc_45', 'doc_2570', 'doc_2851', 'doc_4033', 'doc_5320', 'doc_3849', 'doc_4172', 'doc_3202', 'doc_2282', 'doc_1896', 'doc_949', 'doc_103', 'doc_1552', 'doc_2791', 'doc_392', 'doc_1175', 'doc_5315', 'doc_832', 'doc_3185', 'doc_2532', 'doc_3409', 'doc_824', 'doc_4075', 'doc_1201', 'doc_4116', 'doc_1448', 'doc_2545', 'doc_2251', 'doc_2485']]
Adding doc_id doc_47 to context.
Adding doc_id doc_45 to context.
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Adding doc_id doc_4033 to context.
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Adding doc_id doc_5315 to context.
Adding doc_id doc_832 to context.
Adding doc_id doc_3185 to context.
Adding doc_id doc_2532 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: what are bulls used for on a farm
Context is: <P> Many cattle ranches and stations run bulls with cows , and most dairy or beef farms traditionally had at least one , if not several , bulls for purposes of herd maintenance . However , the problems associated with handling a bull ( particularly where cows must be removed from its presence to be worked ) has prompted many dairy farmers to restrict themselves to artificial insemination ( AI ) of the cows . Semen is removed from the bulls and stored in canisters of liquid nitrogen , where it is kept until it can be sold , at which time it can be very profitable , in fact , many ranchers keep bulls specifically for this purpose . AI is also used to increase the quality of a herd , or to introduce an outcross of bloodlines . Some ranchers prefer to use AI to allow them to breed to several different bulls in a season or to breed their best stock to a higher quality bull than they could afford to purchase outright . AI may also be used in conjunction with embryo transfer to allow cattle producers to add new breeding to their herds . </P>
<P> Other than the few bulls needed for breeding , the vast majority of male cattle are slaughtered for meat before the age of three years , except where they are needed ( castrated ) as work oxen for haulage . Most of these beef animals are castrated as calves to reduce aggressive behavior and prevent unwanted mating , although some are reared as uncastrated bull beef . A bull is typically ready for slaughter one or two months sooner than a castrated male or a female , and produces proportionately more , leaner muscle . </P>
<P> Pastoral farming is the major land use but there are increases in land area devoted to horticulture . </P>
<P> Animal fibers are natural fibers that consist largely of particular proteins . Instances are silk , hair / fur ( including wool ) and feathers . The animal fibers used most commonly both in the manufacturing world as well as by the hand spinners are wool from domestic sheep and silk . Also very popular are alpaca fiber and mohair from Angora goats . Unusual fibers such as Angora wool from rabbits and Chiengora from dogs also exist , but are rarely used for mass production . </P>
<P> In 2012 , there were 3.2 million farmers , ranchers and other agricultural managers and an estimated 757,900 agricultural workers were legally employed in the US . Animal breeders accounted for 11,500 of those workers with the rest categorized as miscellaneous agricultural workers . The median pay was $9.12 per hour or $18,970 per year . In 2009 , about 519,000 people under age 20 worked on farms owned by their family . In addition to the youth who lived on family farms , an additional 230,000 youth were employed in agriculture . In 2004 , women made up approximately 24 % of farmers ; that year , there were 580,000 women employed in agriculture , forestry , and fishing . </P>
<P> The recipe can vary widely . The defining ingredients are minced meat ( commonly beef when named cottage pie or lamb when named shepherd 's pie ) , typically cooked in a gravy with onions and sometimes other vegetables , such as peas , celery or carrots , and topped with mashed potato . The pie is sometimes also topped with grated cheese . </P>
<P> The history of the domesticated sheep goes back to between 11000 and 9000 BC , and the domestication of the wild mouflon in ancient Mesopotamia . Sheep are among the first animals to have been domesticated by humans , and there is evidence of sheep farming in Iranian statuary dating to that time period . These sheep were primarily raised for meat , milk , and skins . Woolly sheep began to be developed around 6000 BC in Iran , and cultures such as the Persians relied on sheep 's wool for trading . They were then imported to Africa and Europe via trading . </P>
<P> Although large - scale use of wheels did not occur in the Americas prior to European contact , numerous small wheeled artifacts , identified as children 's toys , have been found in Mexican archeological sites , some dating to about 1500 BC . It is thought that the primary obstacle to large - scale development of the wheel in the Americas was the absence of domesticated large animals which could be used to pull wheeled carriages . The closest relative of cattle present in Americas in pre-Columbian times , the American Bison , is difficult to domesticate and was never domesticated by Native Americans ; several horse species existed until about 12,000 years ago , but ultimately became extinct . The only large animal that was domesticated in the Western hemisphere , the llama , did not spread far beyond the Andes by the time of the arrival of Columbus . </P>
<P> The Call of the Wild is a short adventure novel by Jack London published in 1903 and set in Yukon , Canada during the 1890s Klondike Gold Rush , when strong sled dogs were in high demand . The central character of the novel is a dog named Buck . The story opens at a ranch in Santa Clara Valley , California , when Buck is stolen from his home and sold into service as a sled dog in Alaska . He becomes progressively feral in the harsh environment , where he is forced to fight to survive and dominate other dogs . By the end , he sheds the veneer of civilization , and relies on primordial instinct and learned experience to emerge as a leader in the wild . </P>
<P> The Three Little Pigs was included in The Nursery Rhymes of England ( London and New York , c. 1886 ) , by James Halliwell - Phillipps . The story in its arguably best - known form appeared in English Fairy Tales by Joseph Jacobs , first published in 1890 and crediting Halliwell as his source . The story begins with the title characters being sent out into the world by their mother , to `` seek out their fortune '' . The first little pig builds a house of straw , but a wolf blows it down and devours him . The second little pig builds a house of sticks , which the wolf also blows down , and the second little pig is also devoured . Each exchange between wolf and pig features ringing proverbial phrases , namely : </P>
<P> `` How now brown cow '' ( / ˈhaʊ ˈnaʊ ˈbraʊn ˈkaʊ / ) is a phrase used in elocution teaching to demonstrate rounded vowel sounds . Each `` ow '' sound in the phrase represents the diphthong / aʊ / . Although orthographies for each of the four words in this utterance is represented by the English spelling `` ow '' , the articulation required to create this same diphthong represented by the International Phonetic Association 's phonetic alphabet as / aʊ / is also represented by the spelling `` ou '' . Some examples of these homophonic / aʊ / 's are the English words `` house '' , `` blouse '' , `` noun '' , and `` cloud '' . The use of the phrase `` how now brown cow '' in teaching elocution can be dated back to at least 1926 . Although not in use today , the phrase `` how now '' is a greeting , short for `` how say you now '' , and can be found in archaic literature , such as the plays of William Shakespeare . </P>
<P> Brisket is a cut of meat from the breast or lower chest of beef or veal . The beef brisket is one of the nine beef primal cuts , though the precise definition of the cut differs internationally . The brisket muscles include the superficial and deep pectorals . As cattle do not have collar bones , these muscles support about 60 % of the body weight of standing / moving cattle . This requires a significant amount of connective tissue , so the resulting meat must be cooked correctly to tenderize the connective tissue . </P>
<P> The music to `` Man Gave Names to All the Animals '' is reggae - inspired . The lyrics were inspired by the biblical Book of Genesis , verses 2 : 19 -- 20 in which Adam named the animals and birds . The lyrics have an appeal to children , rhyming the name of the animal with one of its characteristics . So after describing an animal 's `` muddy trail '' and `` curly tail , '' Dylan sings that `` he was n't too small and he was n't too big '' and so that animal was named a pig . Similarly , the cow got its name because Adam `` saw milk comin ' out but he did n't know how '' and the bear got its name because it has a `` great big furry back and furry hair . '' </P>
<P> As early as 1671 railed roads were in use in Durham to ease the conveyance of coal ; the first of these was the Tanfield Wagonway . Many of these tramroads or wagon ways were built in the 17th and 18th centuries . They used simply straight and parallel rails of timber on which carts with simple flanged iron wheels were drawn by horses , enabling several wagons to be moved simultaneously . </P>
<P> Unicorns are not found in Greek mythology , but rather in the accounts of natural history , for Greek writers of natural history were convinced of the reality of unicorns , which they believed lived in India , a distant and fabulous realm for them . The earliest description is from Ctesias , who in his book Indika ( `` On India '' ) described them as wild asses , fleet of foot , having a horn a cubit and a half ( 700 mm , 28 inches ) in length , and colored white , red and black . Aristotle must be following Ctesias when he mentions two one - horned animals , the oryx ( a kind of antelope ) and the so - called `` Indian ass '' . Strabo says that in the Caucasus there were one - horned horses with stag - like heads . Pliny the Elder mentions the oryx and an Indian ox ( perhaps a rhinoceros ) as one - horned beasts , as well as `` a very fierce animal called the monoceros which has the head of the stag , the feet of the elephant , and the tail of the boar , while the rest of the body is like that of the horse ; it makes a deep lowing noise , and has a single black horn , which projects from the middle of its forehead , two cubits ( 900 mm , 35 inches ) in length . '' In On the Nature of Animals ( Περὶ Ζῴων Ἰδιότητος , De natura animalium ) , Aelian , quoting Ctesias , adds that India produces also a one - horned horse ( iii. 41 ; iv. 52 ) , and says ( xvi. 20 ) that the monoceros ( Greek : μονόκερως ) was sometimes called cartazonos ( Greek : καρτάζωνος ) , which may be a form of the Arabic karkadann , meaning `` rhinoceros '' . </P>
<P> The First Battle of Bull Run ( the name used by Union forces ) , also known as the First Battle of Manassas ( the name used by Confederate forces ) , was fought on July 21 , 1861 in Prince William County , Virginia , just north of the city of Manassas and about 25 miles west - southwest of Washington , D.C. It was the first major battle of the American Civil War . The Union 's forces were slow in positioning themselves , allowing Confederate reinforcements time to arrive by rail . Each side had about 18,000 poorly trained and poorly led troops in their first battle . It was a Confederate victory , followed by a disorganized retreat of the Union forces . </P>
<P> Hops production is concentrated in moist temperate climates , with much of the world 's production occurring near the 48th parallel north . Hop plants prefer the same soils as potatoes and the leading potato - growing states in the United States are also major hops - producing areas ; however , not all potato - growing areas can produce good hops naturally : soils in the Maritime Provinces of Canada , for example , lack the boron that hops prefer . Historically , hops were not grown in Ireland , but were imported from England . In 1752 more than 500 tons of English hops were imported through Dublin alone . </P>
<P> Shepherd 's pie or cottage pie is a meat pie with a crust of mashed potato . </P>
<P> Castles served a range of purposes , the most important of which were military , administrative , and domestic . As well as defensive structures , castles were also offensive tools which could be used as a base of operations in enemy territory . Castles were established by Norman invaders of England for both defensive purposes and to pacify the country 's inhabitants . As William the Conqueror advanced through England , he fortified key positions to secure the land he had taken . Between 1066 and 1087 , he established 36 castles such as Warwick Castle , which he used to guard against rebellion in the English Midlands . </P>
<P> The Rocky and Bullwinkle Show remained in syndicated reruns and was still available for local television stations through The Program Exchange as late as 2016 ; WBBZ - TV , for instance , aired the show in a strip to counterprogram 10 PM newscasts in the Buffalo , New York market during the summer 2013 season . The underlying rights are now owned by Universal Pictures , which holds the library of predecessor companies DreamWorks Animation and Classic Media , and who in turn with copyright holder Ward Productions forms the joint venture Bullwinkle Studios , which manages the Rocky and Bullwinkle properties ; Universal 's purchase of Classic Media coincided with The Program Exchange 's shutdown . </P>
<P> When Yellowstone National Park was created in 1872 , gray wolf ( Canis lupus ) populations were already in decline in Montana , Wyoming and Idaho . The creation of the national park did not provide protection for wolves or other predators , and government predator control programs in the first decades of the 1900s essentially helped eliminate the gray wolf from Yellowstone . The last wolves were killed in Yellowstone in 1926 . After that time , sporadic reports of wolves still occurred , but scientists confirmed that sustainable wolf populations had been extirpated and were absent from Yellowstone during the mid-1900s . </P>
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assistant (to ragproxyagent):
Bulls are used for breeding and often kept for their semen to sell for AI purposes. Some male cattle are also kept as work oxen for haulage. The vast majority, however, are slaughtered for meat before the age of three years.
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>>>>>>>>>>>> Below are outputs of Case 4 <<<<<<<<<<<<
doc_ids: [['doc_3031', 'doc_819', 'doc_4521', 'doc_3980', 'doc_3423', 'doc_5275', 'doc_745', 'doc_753', 'doc_3562', 'doc_4139', 'doc_3678', 'doc_4931', 'doc_2347', 'doc_1115', 'doc_2806', 'doc_5204', 'doc_2707', 'doc_3653', 'doc_1122', 'doc_2398', 'doc_309', 'doc_3891', 'doc_2087', 'doc_330', 'doc_4844', 'doc_2155', 'doc_2674', 'doc_5357', 'doc_1581', 'doc_9']]
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ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: has been honoured with the wisden leading cricketer in the world award for 2016
Context is: <P> The first recipient was Uttam Kumar from Bengali cinema , who was honoured at the 15th National Film Awards in 1968 for his performances in Anthony Firingee and Chiriyakhana . As of 2017 , Amitabh Bachchan is the most honoured actor , with four awards . Two actors -- Kamal Haasan and Mammootty -- have been honoured three times , while six actors -- Sanjeev Kumar , Mithun Chakraborty , Om Puri , Naseeruddin Shah , Mohanlal , and Ajay Devgn -- have won the award two times . Two actors have achieved the honour for performing in two languages -- Mithun Chakraborty ( Hindi and Bengali ) and Mammootty ( Malayalam and English ) . The most recent recipient is Riddhi Sen , who was honoured at the 65th National Film Awards for his performance in the Bengali film Nagarkirtan . </P>
<P> There was controversy over the National Film Award for Best Actor , which the committee awarded to Akshay Kumar for his performance in Rustom , snubbing Aamir Khan 's performance for Dangal . Committee member Priyadarshan , who has worked with Kumar on several films , gave the following explanation for awarding Kumar instead of Khan : </P>
<P> The 2017 ICC Champions Trophy was the eighth ICC Champions Trophy , a cricket tournament for the eight top - ranked One Day International ( ODI ) teams in the world . It was held in England and Wales from 1 June to 18 June 2017 . Pakistan won the competition for the first time with a 180 - run victory over India in the final at The Oval . The margin of victory was the largest by any team in the final of an ICC ODI tournament in terms of runs . </P>
<Table> List of One Day International cricket double centuries <Tr> <Th> No . </Th> <Th> Runs </Th> <Th> Batsman </Th> <Th> S / R </Th> <Th> For </Th> <Th> Against </Th> <Th> ODI </Th> <Th> Venue </Th> <Th> Date </Th> </Tr> <Tr> <Td> </Td> <Td> 200 * </Td> <Td> Tendulkar , Sachin Sachin Tendulkar </Td> <Td> 136.05 </Td> <Td> India </Td> <Td> South Africa </Td> <Td> 2962 </Td> <Td> Captain Roop Singh Stadium , Gwalior , India </Td> <Td> 24 February 2010 </Td> </Tr> <Tr> <Td> </Td> <Td> 219 </Td> <Td> Sehwag , Virender Virender Sehwag </Td> <Td> 146.98 </Td> <Td> India </Td> <Td> West Indies </Td> <Td> 3223 </Td> <Td> Holkar Stadium , Indore , India </Td> <Td> 8 December 2011 </Td> </Tr> <Tr> <Td> </Td> <Td> 209 </Td> <Td> Sharma , Rohit Rohit Sharma </Td> <Td> 132.28 </Td> <Td> India </Td> <Td> Australia </Td> <Td> 3428 </Td> <Td> M. Chinnaswamy Stadium , Bangalore , India </Td> <Td> 2 November 2013 </Td> </Tr> <Tr> <Td> </Td> <Td> 264 </Td> <Td> Sharma , Rohit Rohit Sharma </Td> <Td> 152.60 </Td> <Td> India </Td> <Td> Sri Lanka </Td> <Td> 3544 </Td> <Td> Eden Gardens , India </Td> <Td> 13 November 2014 </Td> </Tr> <Tr> <Td> 5 </Td> <Td> 215 </Td> <Td> Gayle , Chris Chris Gayle </Td> <Td> 146.30 </Td> <Td> West Indies </Td> <Td> Zimbabwe </Td> <Td> 3612 </Td> <Td> Manuka Oval , Canberra , Australia </Td> <Td> 24 February 2015 </Td> </Tr> <Tr> <Td> 6 </Td> <Td> 237 * </Td> <Td> Guptill , Martin Martin Guptill </Td> <Td> 145.40 </Td> <Td> New Zealand </Td> <Td> West Indies </Td> <Td> 3643 </Td> <Td> Wellington Regional Stadium , Wellington , New Zealand </Td> <Td> 22 March 2015 </Td> </Tr> <Tr> <Td> 7 </Td> <Td> 208 * </Td> <Td> Sharma , Rohit Rohit Sharma </Td> <Td> 135.95 </Td> <Td> India </Td> <Td> Sri Lanka </Td> <Td> 3941 </Td> <Td> Punjab Cricket Association IS Bindra Stadium , Mohali , India </Td> <Td> 13 December 2017 </Td> </Tr> </Table>
<P> G. Sankara Kurup , ( 3 June 1901 , Nayathode , Kingdom of Cochin ( now in Ernakulam district , Kerala , India ) -- 2 February 1978 , Vappalassery , Angamaly , Ernakulam district , Kerala ) , better known as Mahakavi G ( The Great Poet G ) , was the first winner of the Jnanpith Award , India 's highest literary award . He won the prize in 1965 for his collection of poems in Malayalam Odakkuzhal ( The Bamboo Flute , 1950 ) . With part of the prize money he established the literary award Odakkuzhal in 1968 . He was also the recipient of the Soviet Land Nehru Award , in 1967 , and the Padma Bhushan in 1968 . His poetry collection Viswadarshanam won the Kerala Sahitya Akademi Award in 1961 and Kendra Sahitya Akademi Award in 1963 . </P>
<P> The 2019 Cricket World Cup ( officially ICC Cricket World Cup 2019 ) is the 12th edition of the Cricket World Cup , scheduled to be hosted by England and Wales , from 30 May to 14 July 2019 . </P>
<Table> 2018 Under - 19 Cricket World Cup <Tr> <Td colspan="2"> </Td> </Tr> <Tr> <Th> Dates </Th> <Td> 13 January -- 3 February 2018 </Td> </Tr> <Tr> <Th> Administrator ( s ) </Th> <Td> International Cricket Council </Td> </Tr> <Tr> <Th> Cricket format </Th> <Td> 50 overs </Td> </Tr> <Tr> <Th> Tournament format ( s ) </Th> <Td> Round - robin and knockout </Td> </Tr> <Tr> <Th> Host ( s ) </Th> <Td> New Zealand </Td> </Tr> <Tr> <Th> Champions </Th> <Td> India ( 4th title ) </Td> </Tr> <Tr> <Th> Runners - up </Th> <Td> Australia </Td> </Tr> <Tr> <Th> Participants </Th> <Td> 16 </Td> </Tr> <Tr> <Th> Matches played </Th> <Td> 48 </Td> </Tr> <Tr> <Th> Player of the series </Th> <Td> Shubman Gill </Td> </Tr> <Tr> <Th> Most runs </Th> <Td> Alick Athanaze ( 418 ) </Td> </Tr> <Tr> <Th> Most wickets </Th> <Td> Anukul Roy ( 14 ) Qais Ahmad ( 14 ) Faisal Jamkhandi ( 14 ) </Td> </Tr> <Tr> <Th> Official website </Th> <Td> Official website </Td> </Tr> <Tr> <Td colspan="2"> ← 2016 2020 → </Td> </Tr> </Table>
<P> The 2018 ICC Under - 19 Cricket World Cup was an international limited - overs cricket tournament that was held in New Zealand from 13 January to 3 February 2018 . It was the twelfth edition of the Under - 19 Cricket World Cup , and the third to be held in New Zealand ( after the 2002 and 2010 events ) . New Zealand was the first country to host the event three times . The opening ceremony took place on 7 January 2018 . The West Indies were the defending champions . However , they failed to defend their title , after losing their first two group fixtures . </P>
<P> Scoring over 10,000 runs across a playing career in any format of cricket is considered a significant achievement . In the year 2001 , Sachin Tendulkar became the first player to score 10,000 runs in ODIs , while playing a match during the bi-lateral series against Australia at home . In the chase for achieving top scores , West Indies ' Desmond Haynes retired as the most prolific run - scorer in One Day Internationals ( ODIs ) , with a total of 8,648 runs in 1994 . The record stood for four years until it was broken by India 's Mohammed Azharuddin . Azharuddin remained the top - scorer in the format until his compatriot Sachin Tendulkar passed him in October 2000 . As of August 2016 , eleven players -- from six teams that are Full members of the International Cricket Council -- have scored more than 10,000 runs in ODIs . Four of these are from Sri Lanka and three from India . The rest are one player each from Pakistan , Australia , West Indies , and South Africa . Bangladesh , England , New Zealand , and Zimbabwe are yet to have a player reach the 10,000 - run mark in this format . </P>
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assistant (to ragproxyagent):
I'm sorry, I couldn't find any information about who has been honoured with the Wisden Leading Cricketer in the World award for 2016. UPDATE CONTEXT.
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ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: has been honoured with the wisden leading cricketer in the world award for 2016
Context is: <Table> List of the Indian Oscar nominee ( s ) / recipient ( s ) , also showing the year , film , category , and result <Tr> <Th> Year </Th> <Th> Nominee ( s ) / recipient ( s ) </Th> <Th> Film </Th> <Th> Category / Honorary Award </Th> <Th> Result / received </Th> <Th> Ref . </Th> </Tr> <Tr> <Td> 1958 ( 30th ) </Td> <Td> Mehboob Khan </Td> <Td> Mother India </Td> <Td> Best Foreign Language Film </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 1961 ( 33rd ) </Td> <Td> Ismail Merchant </Td> <Td> The Creation of Woman </Td> <Td> Best Short Subject ( Live Action ) </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 1979 ( 51st ) </Td> <Td> Vidhu Vinod Chopra and K.K. Kapil </Td> <Td> An Encounter with Faces </Td> <Td> Best Documentary ( Short Subject ) </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> ( 55th ) </Td> <Td> Bhanu Athaiya </Td> <Td> Gandhi </Td> <Td> Best Costume Design </Td> <Td> Won </Td> <Td> </Td> </Tr> <Tr> <Td> Ravi Shankar </Td> <Td> Best Original Score </Td> <Td> Nominated </Td> </Tr> <Tr> <Td> ( 59th ) </Td> <Td> Ismail Merchant </Td> <Td> A Room with a View </Td> <Td> Best Picture </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> ( 61st ) </Td> <Td> Mira Nair </Td> <Td> Salaam Bombay ! </Td> <Td> Best Foreign Language Film </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 1992 ( 64th ) </Td> <Td> Satyajit Ray </Td> <Td> Pather Pachali </Td> <Td> Honorary Award </Td> <Td> Received </Td> <Td> </Td> </Tr> <Tr> <Td> ( 65th ) </Td> <Td> Ismail Merchant </Td> <Td> Howards End </Td> <Td> Best Picture </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> ( 66th ) </Td> <Td> Ismail Merchant </Td> <Td> The Remains of the Day </Td> <Td> Best Picture </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 2002 ( 74th ) </Td> <Td> Ashutosh Gowarikar </Td> <Td> Lagaan </Td> <Td> Best Foreign Language Film </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 2005 ( 77th ) </Td> <Td> Ashvin Kumar </Td> <Td> Little Terrorist </Td> <Td> Best Short Subject ( Live Action ) </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 2007 ( 79th ) </Td> <Td> Deepa Mehta </Td> <Td> Water </Td> <Td> Best Foreign Language Film </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 2009 ( 81st ) </Td> <Td> Resul Pookutty </Td> <Td> Slumdog Millionaire </Td> <Td> Best Sound Mixing </Td> <Td> Won </Td> <Td> </Td> </Tr> <Tr> <Td> A.R. Rahman </Td> <Td> Best Original Score </Td> <Td> Won </Td> </Tr> <Tr> <Td> A.R. Rahman and Gulzar </Td> <Td> Best Original Song </Td> <Td> Won </Td> </Tr> <Tr> <Td> 2011 ( 83rd ) </Td> <Td> A.R. Rahman </Td> <Td> 127 Hours </Td> <Td> Best Original Score </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> A.R. Rahman </Td> <Td> Best Original Song </Td> <Td> Nominated </Td> </Tr> <Tr> <Td> 2013 ( 85th ) </Td> <Td> Bombay Jayashri </Td> <Td> Life of Pi </Td> <Td> Best Original Song </Td> <Td> Nominated </Td> <Td> </Td> </Tr> <Tr> <Td> 2016 </Td> <Td> Rahul Thakkar </Td> <Td> n / a </Td> <Td> Sci - Tech Award </Td> <Td> Received </Td> <Td> </Td> </Tr> <Tr> <Td> 2016 </Td> <Td> Cottalango Leon </Td> <Td> n / a </Td> <Td> Sci - Tech Award </Td> <Td> Received </Td> <Td> </Td> </Tr> <Tr> <Td> 2018 </Td> <Td> Vikas Sathaye </Td> <Td> n / a </Td> <Td> Sci - Tech Award </Td> <Td> Received </Td> <Td> </Td> </Tr> </Table>
<P> The 2017 Nobel Peace Prize was awarded to the International Campaign to Abolish Nuclear Weapons ( ICAN ) `` for its work to draw attention to the catastrophic humanitarian consequences of any use of nuclear weapons and for its ground - breaking efforts to achieve a treaty - based prohibition on such weapons , '' according to the Norwegian Nobel Committee announcement on October 6 , 2017 . The award announcement acknowledged the fact that `` the world 's nine nuclear - armed powers and their allies '' neither signed nor supported the treaty - based prohibition known as the Treaty on the Prohibition of Nuclear Weapons or nuclear ban treaty , yet in an interview Committee Chair Berit Reiss - Andersen told reporters that the award was intended to give `` encouragement to all players in the field '' to disarm . The award was hailed by civil society as well as governmental and intergovernmental representatives who support the nuclear ban treaty , but drew criticism from those opposed . At the Nobel Peace Prize award ceremony held in Oslo City Hall on December 10 , 2017 , Setsuko Thurlow , an 85 - year - old survivor of the 1945 atomic bombing of Hiroshima , and ICAN Executive Director Beatrice Fihn jointly received a medal and diploma of the award on behalf of ICAN and delivered the Nobel lecture . </P>
<P> Career records for batting average are usually subject to a minimum qualification of 20 innings played or completed , in order to exclude batsmen who have not played enough games for their skill to be reliably assessed . Under this qualification , the highest Test batting average belongs to Australia 's Sir Donald Bradman , with 99.94 . Given that a career batting average over 50 is exceptional , and that only five other players have averages over 60 , this is an outstanding statistic . The fact that Bradman 's average is so far above that of any other cricketer has led several statisticians to argue that , statistically at least , he was the greatest athlete in any sport . </P>
<Table> <Tr> <Th colspan="4"> Indian cricket team in South Africa in 2017 -- 18 </Th> </Tr> <Tr> <Th> </Th> <Td> </Td> <Td> </Td> </Tr> <Tr> <Th> </Th> <Td> South Africa </Td> <Td> India </Td> </Tr> <Tr> <Th> Dates </Th> <Td colspan="3"> 5 January 2018 -- 24 February 2018 </Td> </Tr> <Tr> <Th> Captains </Th> <Td> Faf du Plessis ( Tests and ODIs ) JP Duminy ( T20Is ) </Td> <Td> Virat Kohli </Td> </Tr> <Tr> <Th colspan="4"> Test series </Th> </Tr> <Tr> <Th> Result </Th> <Td colspan="3"> South Africa won the 3 - match series 2 -- 1 </Td> </Tr> <Tr> <Th> Most runs </Th> <Td> AB de Villiers ( 211 ) </Td> <Td> Virat Kohli ( 286 ) </Td> </Tr> <Tr> <Th> Most wickets </Th> <Td> Vernon Philander ( 15 ) Kagiso Rabada ( 15 ) </Td> <Td> Mohammed Shami ( 15 ) </Td> </Tr> <Tr> <Th> Player of the series </Th> <Td colspan="3"> Vernon Philander ( SA ) </Td> </Tr> <Tr> <Th colspan="4"> One Day International series </Th> </Tr> <Tr> <Th> Results </Th> <Td colspan="3"> India won the 6 - match series 5 -- 1 </Td> </Tr> <Tr> <Th> Most runs </Th> <Td> Hashim Amla ( 154 ) </Td> <Td> Virat Kohli ( 558 ) </Td> </Tr> <Tr> <Th> Most wickets </Th> <Td> Lungi Ngidi ( 8 ) </Td> <Td> Kuldeep Yadav ( 17 ) </Td> </Tr> <Tr> <Th> Player of the series </Th> <Td colspan="3"> Virat Kohli ( Ind ) </Td> </Tr> <Tr> <Th colspan="4"> Twenty20 International series </Th> </Tr> <Tr> <Th> Results </Th> <Td colspan="3"> India won the 3 - match series 2 -- 1 </Td> </Tr> <Tr> <Th> Most runs </Th> <Td> JP Duminy ( 122 ) </Td> <Td> Shikhar Dhawan ( 143 ) </Td> </Tr> <Tr> <Th> Most wickets </Th> <Td> Junior Dala ( 7 ) </Td> <Td> Bhuvneshwar Kumar ( 7 ) </Td> </Tr> <Tr> <Th> Player of the series </Th> <Td colspan="3"> Bhuvneshwar Kumar ( Ind ) </Td> </Tr> </Table>
<P> Brian Lara took the least number of innings ( 195 ) to reach the 10,000 run mark , later equalled by Sachin Tendulkar and Kumar Sangakkara , while Australia 's Steve Waugh took 244 innings to achieve the feat . Alastair Cook is the fastest in terms of time span , taking 10 years and 87 days . The time taken by Shivnarine Chanderpaul ( 18 years and 37 days ) is the slowest among all . As of May 2017 , Tendulkar leads the list with 15,921 runs followed by Ricky Ponting of Australia with 13,378 . </P>
<Table> <Tr> <Th> 50 + </Th> <Th> Player </Th> <Th> Matches </Th> <Th> Innings </Th> </Tr> <Tr> <Th> 119 </Th> <Td> Sachin Tendulkar </Td> <Td> 200 </Td> <Td> 329 </Td> </Tr> <Tr> <Th> 103 </Th> <Td> Jacques Kallis </Td> <Td> 166 </Td> <Td> 280 </Td> </Tr> <Tr> <Th> 103 </Th> <Td> Ricky Ponting </Td> <Td> 168 </Td> <Td> 287 </Td> </Tr> <Tr> <Th> 99 </Th> <Td> Rahul Dravid </Td> <Td> 164 </Td> <Td> 286 </Td> </Tr> <Tr> <Th> 96 </Th> <Td> Shivnarine Chanderpaul </Td> <Td> 164 </Td> <Td> 280 </Td> </Tr> <Tr> <Td colspan="4"> <P> Last updated : 15 June 2016 </P> </Td> </Tr> </Table>
<P> Chandan Shetty emerged as the winner of this season on 28. January. 2018 with Karthik being the runner up . Other finalists Niveditha , Diwakar , Shruti were eliminated </P>
<P> Arthur Chung ( January 10 , 1918 -- June 23 , 2008 ) was the first President of Guyana from 1970 to 1980 . During his time as President of Guyana , the office was that of a ceremonial head of state , with real power in the hands of Prime Minister Forbes Burnham . He was honoured with Guyana 's highest national honour , the Order of Excellence ( O.E. ) . </P>
<Tr> <Td colspan="2"> Incumbent Achal Kumar Jyoti since 6 July 2017 </Td> </Tr>
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assistant (to ragproxyagent):
UPDATE CONTEXT. The current context does not provide information related to the question.
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ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: has been honoured with the wisden leading cricketer in the world award for 2016
Context is: <Table> <Tr> <Th> No </Th> <Th> Name ( birth -- death ) </Th> <Th> Portrait </Th> <Th> Elected ( % votes ) </Th> <Th> Took office </Th> <Th> Left office </Th> <Th> Term ( in years ) </Th> <Th> Notes </Th> <Th> President ( s ) </Th> <Th colspan="2"> Candidate of </Th> </Tr> <Tr> <Th> </Th> <Td> Sarvepalli Radhakrishnan ( 1888 -- 1975 ) </Td> <Td> </Td> <Td> 1952 ( Unopposed ) <P> 1957 ( Unopposed ) </P> </Td> <Td> 13 May 1952 </Td> <Td> 12 May 1962 </Td> <Td> 10 </Td> <Td> Radhakrishnan was a prominent scholar . Besides being awarded the Bharat Ratna he also held the position of vice-chancellor in the Banaras Hindu University and the Andhra college . He served as the Vice-President for two terms . </Td> <Td> Rajendra Prasad </Td> <Td> </Td> <Td> Independent </Td> </Tr> <Tr> <Th> </Th> <Td> Zakir Husain ( 1897 -- 1969 ) </Td> <Td> -- </Td> <Td> 1962 ( 97.59 ) </Td> <Td> 13 May 1962 </Td> <Td> 12 May 1967 </Td> <Td> 5 </Td> <Td> </Td> <Td> Sarvepalli Radhakrishnan </Td> <Td> </Td> <Td> Independent </Td> </Tr> <Tr> <Th> </Th> <Td> Varahagiri Venkata Giri ( 1894 -- 1980 ) </Td> <Td> -- </Td> <Td> 1967 ( 71.45 ) </Td> <Td> 13 May 1967 </Td> <Td> 3 May 1969 </Td> <Td> </Td> <Td> </Td> <Td> Zakir Husain </Td> <Td> </Td> <Td> Independent </Td> </Tr> <Tr> <Th> </Th> <Td> Gopal Swarup Pathak ( 1896 -- 1982 ) </Td> <Td> -- </Td> <Td> 1969 -- </Td> <Td> 31 August 1969 </Td> <Td> 30 August 1974 </Td> <Td> 5 </Td> <Td> </Td> <Td> Varahagiri Venkata Giri ( 1969 -- 1974 ) <P> Fakhruddin Ali Ahmed ( 1974 ) </P> </Td> <Td> </Td> <Td> Independent </Td> </Tr> <Tr> <Th> 5 </Th> <Td> Basappa Danappa Jatti ( 1912 -- 2002 ) </Td> <Td> -- </Td> <Td> ( 78.70 ) </Td> <Td> 31 August 1974 </Td> <Td> 30 August 1979 </Td> <Td> 5 </Td> <Td> </Td> <Td> Fakhruddin Ali Ahmed ( 1974 -- 1977 ) Neelam Sanjiva Reddy ( 1977 -- 1979 ) </Td> <Td> </Td> <Td> Indian National Congress </Td> </Tr> <Tr> <Th> 6 </Th> <Td> Mohammad Hidayatullah ( 1905 -- 1992 ) </Td> <Td> -- </Td> <Td> 1979 ( Unopposed ) </Td> <Td> 31 August 1979 </Td> <Td> 30 August 1984 </Td> <Td> 5 </Td> <Td> </Td> <Td> Neelam Sanjiva Reddy ( 1979 -- 1982 ) Giani Zail Singh ( 1982 -- 1984 ) </Td> <Td> </Td> <Td> Independent </Td> </Tr> <Tr> <Th> 7 </Th> <Td> Ramaswamy Venkataraman ( 1910 -- 2009 ) </Td> <Td> </Td> <Td> 1984 ( 71.05 ) </Td> <Td> 31 August 1984 </Td> <Td> 24 July 1987 </Td> <Td> </Td> <Td> </Td> <Td> Giani Zail Singh </Td> <Td> </Td> <Td> Indian National Congress </Td> </Tr> <Tr> <Th> 8 </Th> <Td> Shankar Dayal Sharma ( 1918 -- 1999 ) </Td> <Td> </Td> <Td> ( Unopposed ) </Td> <Td> 3 September 1987 </Td> <Td> 24 July 1992 </Td> <Td> 5 </Td> <Td> </Td> <Td> Ramaswamy Venkataraman </Td> <Td> </Td> <Td> Indian National Congress </Td> </Tr> <Tr> <Th> 9 </Th> <Td> Kocheril Raman Narayanan ( 1920 -- 2005 ) </Td> <Td> </Td> <Td> 1992 ( 99.86 ) </Td> <Td> 21 August 1992 </Td> <Td> 24 July 1997 </Td> <Td> 5 </Td> <Td> </Td> <Td> Shankar Dayal Sharma </Td> <Td> </Td> <Td> Indian National Congress </Td> </Tr> <Tr> <Th> 10 </Th> <Td> Krishan Kant ( 1927 -- 2002 ) </Td> <Td> -- </Td> <Td> 1997 ( 61.76 ) </Td> <Td> 21 August 1997 </Td> <Td> 27 July 2002 </Td> <Td> </Td> <Td> </Td> <Td> Kocheril Raman Narayanan ( 1997 -- 2002 ) A.P.J. Abdul Kalam ( 2002 ) </Td> <Td> </Td> <Td> Janata Dal </Td> </Tr> <Tr> <Th> 11 </Th> <Td> Bhairon Singh Shekhawat ( 1923 -- 2010 ) </Td> <Td> </Td> <Td> 2002 ( 59.82 ) </Td> <Td> 19 August 2002 </Td> <Td> 21 July 2007 </Td> <Td> 5 </Td> <Td> </Td> <Td> A.P.J. Abdul Kalam </Td> <Td> </Td> <Td> Bharatiya Janata Party </Td> </Tr> <Tr> <Th> 12 </Th> <Td> Mohammad Hamid Ansari ( 1937 -- ) </Td> <Td> </Td> <Td> 2007 ( 60.51 ) 2012 ( 67.31 ) </Td> <Td> 11 August 2007 </Td> <Td> 11 August 2017 </Td> <Td> 10 </Td> <Td> </Td> <Td> Pratibha Patil ( 2007 -- 2012 ) Pranab Mukherjee ( 2012 -- 2017 ) Ram Nath Kovind ( 2017 ) </Td> <Td> </Td> <Td> Indian National Congress </Td> </Tr> <Tr> <Th> 13 </Th> <Td> Muppavarapu Venkaiah Naidu ( 1949 -- ) </Td> <Td> </Td> <Td> 2017 ( 67.89 ) </Td> <Td> 11 August 2017 </Td> <Td> Incumbent </Td> <Td> -- </Td> <Td> </Td> <Td> Ram Nath Kovind </Td> <Td> </Td> <Td> Bharatiya Janata Party </Td> </Tr> </Table>
<Table> <Tr> <Th colspan="2"> Governor of Maharashtra </Th> </Tr> <Tr> <Td colspan="2"> Incumbent Chennamaneni Vidyasagar Rao since 30 August 2014 </Td> </Tr> <Tr> <Th> Style </Th> <Td> His Excellency </Td> </Tr> <Tr> <Th> Residence </Th> <Td> Main : Raj Bhavan ( Mumbai ) Additional : Raj Bhavan ( Nagpur ) ; Raj Bhavan ( Pune ) & Raj Bhavan ( Mahabaleshwar ) </Td> </Tr> <Tr> <Th> Appointer </Th> <Td> President of India </Td> </Tr> <Tr> <Th> Term length </Th> <Td> Five Years </Td> </Tr> <Tr> <Th> Inaugural holder </Th> <Td> John Colville , PC , GCIE </Td> </Tr> <Tr> <Th> Formation </Th> <Td> 15 August 1947 ; 70 years ago ( 1947 - 08 - 15 ) </Td> </Tr> </Table>
<P> Every player who has won this award and has been eligible for the Naismith Memorial Basketball Hall of Fame has been inducted . Kareem Abdul - Jabbar won the award a record six times . Both Bill Russell and Michael Jordan won the award five times , while Wilt Chamberlain and LeBron James won the award four times . Russell and James are the only players to have won the award four times in five seasons . Moses Malone , Larry Bird and Magic Johnson each won the award three times , while Bob Pettit , Karl Malone , Tim Duncan , Steve Nash and Stephen Curry have each won it twice . Only two rookies have won the award : Wilt Chamberlain in the 1959 -- 60 season and Wes Unseld in the 1968 -- 69 season . Hakeem Olajuwon of Nigeria , Tim Duncan of the U.S. Virgin Islands , Steve Nash of Canada and Dirk Nowitzki of Germany are the only MVP winners considered `` international players '' by the NBA . </P>
<P> The Jawaharlal Nehru Centre for Advanced Scientific Research ( JNCASR ) is a multidisciplinary research institute located at Jakkur , Bangalore , India . It was established by the Department of Science and Technology of the Government of India , to mark the birth centenary of Pandit Jawaharlal Nehru . </P>
<P> Ajay Tyagi was appointed chairman on 10 January 2017 replacing UK Sinha . And took charge of chairman office on 1 March 2017 . The Board comprises </P>
<Table> <Tr> <Th> Year </Th> <Th> Player </Th> <Th> Country </Th> </Tr> <Tr> <Td> 2003 </Td> <Th> Ponting , Ricky Ricky Ponting </Th> <Td> Australia </Td> </Tr> <Tr> <Td> </Td> <Th> Warne , Shane Shane Warne </Th> <Td> Australia </Td> </Tr> <Tr> <Td> 2005 </Td> <Th> Flintoff , Andrew Andrew Flintoff </Th> <Td> England </Td> </Tr> <Tr> <Td> 2006 </Td> <Th> Muralitharan , Muttiah Muttiah Muralitharan </Th> <Td> Sri Lanka </Td> </Tr> <Tr> <Td> 2007 </Td> <Th> Kallis , Jacques Jacques Kallis </Th> <Td> South Africa </Td> </Tr> <Tr> <Td> 2008 </Td> <Th> Sehwag , Virender Virender Sehwag </Th> <Td> India </Td> </Tr> <Tr> <Td> 2009 </Td> <Th> Sehwag , Virender Virender Sehwag </Th> <Td> India </Td> </Tr> <Tr> <Td> </Td> <Th> Tendulkar , Sachin Sachin Tendulkar </Th> <Td> India </Td> </Tr> <Tr> <Td> 2011 </Td> <Th> Sangakkara , Kumar Kumar Sangakkara </Th> <Td> Sri Lanka </Td> </Tr> <Tr> <Td> 2012 </Td> <Th> Clarke , Michael Michael Clarke </Th> <Td> Australia </Td> </Tr> <Tr> <Td> 2013 </Td> <Th> Steyn , Dale Dale Steyn </Th> <Td> South Africa </Td> </Tr> <Tr> <Td> 2014 </Td> <Th> Sangakkara , Kumar Kumar Sangakkara </Th> <Td> Sri Lanka </Td> </Tr> <Tr> <Td> 2015 </Td> <Th> Williamson , Kane Kane Williamson </Th> <Td> New Zealand </Td> </Tr> <Tr> <Td> 2016 </Td> <Th> Kohli , Virat Virat Kohli </Th> <Td> India </Td> </Tr> <Tr> <Td> 2017 </Td> <Th> Kohli , Virat Virat Kohli </Th> <Td> India </Td> </Tr> </Table>
<P> Mankombu Sambasivan Swaminathan ( born 7 August 1925 ) is an Indian geneticist and international administrator , renowned for his leading role in India 's Green Revolution a program under which high - yield varieties of wheat and rice seedlings were planted in the fields of poor farmers . Swaminathan is known as `` Indian Father of Green Revolution '' for his leadership and success in introducing and further developing high - yielding varieties of wheat in India . He is the founder and chairman of the MS Swaminathan Research Foundation . His stated vision is to rid the world of hunger and poverty . Swaminathan is an advocate of moving India to sustainable development , especially using environmentally sustainable agriculture , sustainable food security and the preservation of biodiversity , which he calls an `` evergreen revolution . '' </P>
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assistant (to ragproxyagent):
I'm sorry, the provided context doesn't contain information about any cricketer being honored with the Wisden Leading Cricketer in the World award for 2016. UPDATE CONTEXT if you have any other query.
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>>>>>>>>>>>> Below are outputs of Case 5 <<<<<<<<<<<<
doc_ids: [['doc_20', 'doc_2943', 'doc_2059', 'doc_3293', 'doc_4056', 'doc_1914', 'doc_2749', 'doc_1796', 'doc_3468', 'doc_1793', 'doc_876', 'doc_2577', 'doc_27', 'doc_366', 'doc_321', 'doc_3103', 'doc_715', 'doc_3534', 'doc_142', 'doc_5337', 'doc_2426', 'doc_5346', 'doc_3021', 'doc_1596', 'doc_316', 'doc_1103', 'doc_1602', 'doc_1677', 'doc_1670', 'doc_2853']]
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ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: who carried the usa flag in opening ceremony
Context is: <P> On January 17 , 1899 , under orders from President William McKinley , Commander Edward D. Taussig of USS Bennington landed on Wake and formally took possession of the island for the United States . After a 21 - gun salute , the flag was raised and a brass plate was affixed to the flagstaff with the following inscription : </P>
<Li> 1960 Flag with 50 stars ( Hawaii ) </Li>
<P> The flag of the United States of America , often referred to as the American flag , is the national flag of the United States . It consists of thirteen equal horizontal stripes of red ( top and bottom ) alternating with white , with a blue rectangle in the canton ( referred to specifically as the `` union '' ) bearing fifty small , white , five - pointed stars arranged in nine offset horizontal rows , where rows of six stars ( top and bottom ) alternate with rows of five stars . The 50 stars on the flag represent the 50 states of the United States of America , and the 13 stripes represent the thirteen British colonies that declared independence from the Kingdom of Great Britain , and became the first states in the U.S. Nicknames for the flag include The Stars and Stripes , Old Glory , and The Star - Spangled Banner . </P>
<P> The Pledge of Allegiance of the United States is an expression of allegiance to the Flag of the United States and the republic of the United States of America . It was originally composed by Captain George Thatcher Balch , a Union Army Officer during the Civil War and later a teacher of patriotism in New York City schools . The form of the pledge used today was largely devised by Francis Bellamy in 1892 , and formally adopted by Congress as the pledge in 1942 . The official name of The Pledge of Allegiance was adopted in 1945 . The most recent alteration of its wording came on Flag Day in 1954 , when the words `` under God '' were added . </P>
<P> In modern times , the U.S. military plays ( or sounds ) `` Reveille '' in the morning , generally near sunrise , though its exact time varies from base to base . On U.S. Army posts and Air Force bases , `` Reveille '' is played by itself or followed by the bugle call `` To the Colors '' at which time the national flag is raised and all U.S. military personnel outdoors are required to come to attention and present a salute in uniform , either to the flag or in the direction of the music if the flag is not visible . While in formation , soldiers are brought to the position of parade rest while `` Reveille '' plays then called to attention and present arms as the national flag is raised . On board U.S. Navy , Marine Corps , and Coast Guard facilities , the flag is generally raised at 0800 ( 8 am ) while `` The Star Spangled Banner '' or the bugle call `` To the Colors '' is played . On some U.S. military bases , `` Reveille '' is accompanied by a cannon shot . </P>
<P> When the National Anthem was first recognized by law in 1932 , there was no prescription as to behavior during its playing . On June 22 , 1942 , the law was revised indicating that those in uniform should salute during its playing , while others should simply stand at attention , men removing their hats . ( The same code also required that women should place their hands over their hearts when the flag is displayed during the playing of the Anthem , but not if the flag was not present . ) On December 23 , 1942 the law was again revised instructing men and women to stand at attention and face in the direction of the music when it was played . That revision also directed men and women to place their hands over their hearts only if the flag was displayed . Those in uniform were required to salute . On July 7 , 1976 , the law was simplified . Men and women were instructed to stand with their hands over their hearts , men removing their hats , irrespective of whether or not the flag was displayed and those in uniform saluting . On August 12 , 1998 , the law was rewritten keeping the same instructions , but differentiating between `` those in uniform '' and `` members of the Armed Forces and veterans '' who were both instructed to salute during the playing whether or not the flag was displayed . Because of the changes in law over the years and confusion between instructions for the Pledge of Allegence versus the National Anthem , throughout most of the 20th century many people simply stood at attention or with their hands folded in front of them during the playing of the Anthem , and when reciting the Pledge they would hold their hand ( or hat ) over their heart . After 9 / 11 , the custom of placing the hand over the heart during the playing of the Anthem became nearly universal . </P>
<P> A flag designed by John McConnell in 1969 for the first Earth Day is a dark blue field charged with The Blue Marble , a famous NASA photo of the Earth as seen from outer space . The first edition of McConnell 's flag used screen - printing and used different colors : ocean and land were blue and the clouds were white . McConnell presented his flag to the United Nations as a symbol for consideration . </P>
<P> The torch - bearing arm was displayed at the Centennial Exposition in Philadelphia in 1876 , and in Madison Square Park in Manhattan from 1876 to 1882 . Fundraising proved difficult , especially for the Americans , and by 1885 work on the pedestal was threatened by lack of funds . Publisher Joseph Pulitzer , of the New York World , started a drive for donations to finish the project and attracted more than 120,000 contributors , most of whom gave less than a dollar . The statue was built in France , shipped overseas in crates , and assembled on the completed pedestal on what was then called Bedloe 's Island . The statue 's completion was marked by New York 's first ticker - tape parade and a dedication ceremony presided over by President Grover Cleveland . </P>
<P> The horizontal stripes on the flag represent the nine original departments of Uruguay , based on the U.S flag , where the stripes represent the original 13 colonies . The first flag designed in 1828 had 9 light blue stripes ; this number was reduced to 4 in 1830 due to visibility problems from distance . The Sun of May represents the May Revolution of 1810 ; according to the historian Diego Abad de Santillán , the Sun of May is a figurative sun that represents Inti , the sun god of the Inca religion . It also appears in the Flag of Argentina and the Coat of Arms of Bolivia . </P>
<P> The anthem has been recorded and performed in many different languages , usually as a result of the hosting of either form of the Games in various countries . The IOC does n't require that the anthem be performed in either English or Greek . But in the 2008 Olympic opening and closing ceremonies in Beijing , China , Greek was sung instead of the host country 's official language , Mandarin . Also in the 2016 Olympic opening ceremonies in Rio de Janeiro , Brazil , English was also sung instead of host country 's official language , Portuguese . </P>
<P> The United States Oath of Allegiance , officially referred to as the `` Oath of Allegiance , '' 8 C.F.R. Part 337 ( 2008 ) , is an allegiance oath that must be taken by all immigrants who wish to become United States citizens . </P>
<P> During the first half of the 19th century , seven stars were added to the flag to represent the seven signatories to the Venezuelan declaration of independence , being the provinces of Caracas , Cumaná , Barcelona , Barinas , Margarita , Mérida , and Trujillo . </P>
<P> With the annexation of Hawaii in 1898 and the seizure of Guam and the Philippines during the Spanish -- American War that same year , the United States began to consider unclaimed and uninhabited Wake Island , located approximately halfway between Honolulu and Manila , as a good location for a telegraph cable station and coaling station for refueling warships of the rapidly expanding United States Navy and passing merchant and passenger steamships . On July 4 , 1898 , United States Army Brigadier General Francis V. Greene of the 2nd Brigade , Philippine Expeditionary Force , of the Eighth Army Corps , stopped at Wake Island and raised the American flag while en route to the Philippines on the steamship liner SS China . </P>
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assistant (to ragproxyagent):
I don't have the answer with the provided context. UPDATE CONTEXT.
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Updating context and resetting conversation.
Adding doc_id doc_366 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the
context provided by the user.
If you can't answer the question with or without the current context, you should reply exactly `UPDATE CONTEXT`.
You must give as short an answer as possible.
User's question is: who carried the usa flag in opening ceremony
Context is: <Table> <Tr> <Th> # </Th> <Th> Event year </Th> <Th> Season </Th> <Th> Ceremony </Th> <Th> Flag bearer </Th> <Th> Sex </Th> <Th> State / Country </Th> <Th> Sport </Th> </Tr> <Tr> <Td> 62 </Td> <Td> 2018 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Diggins , Jessica Jessica Diggins </Td> <Td> </Td> <Td> Minnesota </Td> <Td> Cross-country skiing </Td> </Tr> <Tr> <Td> 61 </Td> <Td> 2018 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Hamlin , Erin Erin Hamlin </Td> <Td> </Td> <Td> New York </Td> <Td> Luge </Td> </Tr> <Tr> <Td> 60 </Td> <Td> 2016 </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Biles , Simone Simone Biles </Td> <Td> </Td> <Td> Texas </Td> <Td> Gymnastics </Td> </Tr> <Tr> <Td> 59 </Td> <Td> 2016 </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Phelps , Michael Michael Phelps </Td> <Td> </Td> <Td> Maryland </Td> <Td> Swimming </Td> </Tr> <Tr> <Td> 58 </Td> <Td> 2014 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Chu , Julie Julie Chu </Td> <Td> </Td> <Td> Connecticut </Td> <Td> Hockey </Td> </Tr> <Tr> <Td> 57 </Td> <Td> 2014 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Lodwick , Todd Todd Lodwick </Td> <Td> </Td> <Td> Colorado </Td> <Td> Nordic combined </Td> </Tr> <Tr> <Td> 56 </Td> <Td> 2012 </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Nellum , Bryshon Bryshon Nellum </Td> <Td> </Td> <Td> California </Td> <Td> Athletics </Td> </Tr> <Tr> <Td> 55 </Td> <Td> 2012 </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Zagunis , Mariel Mariel Zagunis </Td> <Td> </Td> <Td> Oregon </Td> <Td> Fencing </Td> </Tr> <Tr> <Td> 54 </Td> <Td> </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Demong , Bill Bill Demong </Td> <Td> </Td> <Td> New York </Td> <Td> Nordic combined </Td> </Tr> <Tr> <Td> 53 </Td> <Td> </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Grimmette , Mark Mark Grimmette </Td> <Td> </Td> <Td> Michigan </Td> <Td> Luge </Td> </Tr> <Tr> <Td> 52 </Td> <Td> 2008 </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Lorig , Khatuna Khatuna Lorig </Td> <Td> </Td> <Td> Georgia ( country ) </Td> <Td> Archery </Td> </Tr> <Tr> <Td> 51 </Td> <Td> 2008 </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Lomong , Lopez Lopez Lomong </Td> <Td> </Td> <Td> Sudan ( now South Sudan ) </Td> <Td> Athletics </Td> </Tr> <Tr> <Td> 50 </Td> <Td> 2006 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Cheek , Joey Joey Cheek </Td> <Td> </Td> <Td> North Carolina </Td> <Td> Speed skating </Td> </Tr> <Tr> <Td> 49 </Td> <Td> 2006 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Witty , Chris Chris Witty </Td> <Td> </Td> <Td> Wisconsin </Td> <Td> Speed skating </Td> </Tr> <Tr> <Td> 48 </Td> <Td> </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Hamm , Mia Mia Hamm </Td> <Td> </Td> <Td> Texas </Td> <Td> Women 's soccer </Td> </Tr> <Tr> <Td> 47 </Td> <Td> </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Staley , Dawn Dawn Staley </Td> <Td> </Td> <Td> Pennsylvania </Td> <Td> Basketball </Td> </Tr> <Tr> <Td> 46 </Td> <Td> 2002 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Shimer , Brian Brian Shimer </Td> <Td> </Td> <Td> Florida </Td> <Td> Bobsleigh </Td> </Tr> <Tr> <Td> 45 </Td> <Td> 2002 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Peterson , Amy Amy Peterson </Td> <Td> </Td> <Td> Minnesota </Td> <Td> Short track speed skating </Td> </Tr> <Tr> <Td> 44 </Td> <Td> 2000 </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Gardner , Rulon Rulon Gardner </Td> <Td> </Td> <Td> Wyoming </Td> <Td> Wrestling </Td> </Tr> <Tr> <Td> 43 </Td> <Td> 2000 </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Meidl , Cliff Cliff Meidl </Td> <Td> </Td> <Td> California </Td> <Td> Canoeing </Td> </Tr> <Tr> <Td> 42 </Td> <Td> 1998 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Granato , Cammi Cammi Granato </Td> <Td> </Td> <Td> Illinois </Td> <Td> Hockey </Td> </Tr> <Tr> <Td> 41 </Td> <Td> 1998 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Flaim , Eric Eric Flaim </Td> <Td> </Td> <Td> Massachusetts </Td> <Td> Speed skating </Td> </Tr> <Tr> <Td> 40 </Td> <Td> </Td> <Td> Summer </Td> <Td> Closing </Td> <Td> Matz , Michael Michael Matz </Td> <Td> </Td> <Td> Pennsylvania </Td> <Td> Equestrian </Td> </Tr> <Tr> <Td> 39 </Td> <Td> </Td> <Td> Summer </Td> <Td> Opening </Td> <Td> Baumgartner , Bruce Bruce Baumgartner </Td> <Td> </Td> <Td> New Jersey </Td> <Td> Wrestling </Td> </Tr> <Tr> <Td> 38 </Td> <Td> 1994 </Td> <Td> Winter </Td> <Td> Closing </Td> <Td> Jansen , Dan Dan Jansen </Td> <Td> </Td> <Td> Wisconsin </Td> <Td> Speed skating </Td> </Tr> <Tr> <Td> 37 </Td> <Td> 1994 </Td> <Td> Winter </Td> <Td> Opening </Td> <Td> Myler , Cammy Cammy Myler </Td> <Td> </Td> <Td> New York </Td>
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
Erin Hamlin carried the USA flag in the opening ceremony.
--------------------------------------------------------------------------------
```
在这个例子中，问题是直接从数据集中选择的。在前两个案例中，RetrieveChat能够在第一次尝试中正确回答问题，因为检索到的上下文中包含了所需的信息。然而，在最后三个案例中，与问题嵌入最相似的上下文并不包含回答问题所需的信息。因此，LLM模型回复了“UPDATE CONTEXT”。通过RetrieveChat在更新上下文方面的独特和创新能力，代理自动更新了上下文并再次将其发送给LLM模型。经过几轮这样的过程，代理能够生成正确的问题答案。
### 示例 6
[返回顶部](#table-of-contents)
使用RetrieveChat来回答[2WikiMultihopQA](https://github.com/Alab-NII/2wikimultihop)数据集中的多跳问题，并使用自定义提示和少样本学习。
首先，我们将创建一个包含所有上下文语料库的新文档集合。然后，我们将选择一些问题，并利用RetrieveChat来回答这些问题。对于这个特定的例子，我们将使用`gpt-3.5-turbo`模型，并演示RetrieveChat在检索到的文档不包含足够信 息时自动更新上下文的功能。此外，我们还将演示如何使用自定义提示和少样本学习来解决RetrieveChat中未预定义的任务。
```python
PROMPT_MULTIHOP = """你是一位增强型检索聊天机器人。你根据自己的知识和用户提供的上下文来回答用户的问题。你必须逐步思考。
首先，请学习以下上下文和问题对以及它们对应的答案的示例。
上下文：
Kurram Garhi: Kurram Garhi是位于巴基斯坦开伯尔-普什图省班努市附近的一个小村庄。它的人口约为35000人。
Trojkrsti: Trojkrsti是马其顿共和国普里莱普市的一个村庄。
Q: Kurram Garhi和Trojkrsti是否位于同一个国家？
A: Kurram Garhi位于巴基斯坦。Trojkrsti位于马其顿共和国。因此，它们不在同一个国家。所以答案是：不在。
上下文：
Early Side of Later: Early Side of Later是英国歌手兼词曲创作人Matt Goss的第三张录音室专辑。它于2004年6月21日由Concept Music发行，并在英国专辑榜上排名第78位。
What's Inside: What's Inside是英国歌手兼词曲创作人Joan Armatrading的第14张录音室专辑。
Q: 专辑What's Inside和专辑Cassandra's Dream哪个先发行？
A: What's Inside发行于1995年。Cassandra's Dream专辑发行于2008年。因此，在这两个专辑中，先发行的是What's Inside。所以答案是：What's Inside。
上下文：
Maria Alexandrovna（Marie of Hesse）: Maria Alexandrovna，原名玛丽·冯·黑森和莱茵（1824年8月8日-1880年6月3日），是俄罗斯皇帝亚历山大二世的第一任妻子，也是俄罗斯的皇后。
Grand Duke Alexei Alexandrovich of Russia: Grand Duke Alexei Alexandrovich of Russia（俄语：Алексей Александрович；1850年1月14日（旧历1月2日）-1908年11月14日），是俄罗斯亚历山大二世和他的第一任妻子Maria Alexandrovna（Marie of Hesse）的第五个孩子和第四个儿子。
Q: Grand Duke Alexei Alexandrovich Of Russia的母亲死于什么原因？
A: Grand Duke Alexei Alexandrovich of Russia的母亲是Maria Alexandrovna。Maria Alexandrovna死于肺结核。所以答案是：肺结核。
上下文：
Laughter in Hell: Laughter in Hell是一部1933年的美国Pre-Code剧情片，由Edward L. Cahn执导，Pat O'Brien主演。这部电影的标题是许多Pre-Code电影的耸人标题的典型。
Edward L. Cahn: Edward L. Cahn（1899年2月12日-1963年8月25日）是一位美国电影导演。
Q: 电影Laughter In Hell的导演是什么时候去世的？
A: 电影Laughter In Hell的导演是Edward L. Cahn。Edward L. Cahn于1963年8月25日去世。所以答案是：1963年8月25日。
接下来，请逐步思考，完成答案。
上下文：
{input_context}
Q: {input_question}
A:
"""
```
```python
# 创建名为 "ragproxyagent" 的 RetrieveUserProxyAgent 实例
corpus_file = "https://huggingface.co/datasets/thinkall/2WikiMultihopQA/resolve/main/corpus.txt"
# 为 NaturalQuestions 数据集创建一个新的集合
ragproxyagent = RetrieveUserProxyAgent(
name="ragproxyagent",
human_input_mode="NEVER",
max_consecutive_auto_reply=3,
retrieve_config={
"task": "qa",
"docs_path": corpus_file,
"chunk_token_size": 2000,
"model": config_list[0]["model"],
"client": chromadb.PersistentClient(path="/tmp/chromadb"),
"collection_name": "2wikimultihopqa",
"chunk_mode": "one_line",
"embedding_model": "all-MiniLM-L6-v2",
"customized_prompt": PROMPT_MULTIHOP,
"customized_answer_prefix": "the answer is",
},
)
```
```python
# queries_file = "https://huggingface.co/datasets/thinkall/2WikiMultihopQA/resolve/main/queries.jsonl"
queries = """{"_id": "61a46987092f11ebbdaeac1f6bf848b6", "text": "哪部电影先上映，盲井还是傅满洲的面具？", "metadata": {"answer": ["傅满洲的面具"]}}
{"_id": "a7b9672009c311ebbdb0ac1f6bf848b6", "text": "北马里昂高中（俄勒冈州）和首尔高中是否位于同一个国家？", "metadata": {"answer": ["不是"]}}
"""
queries = [json.loads(line) for line in queries.split("\n") if line]
questions = [q["text"] for q in queries]
answers = [q["metadata"]["answer"] for q in queries]
print(questions)
print(answers)
```
``` text
['哪部电影先上映，盲井还是傅满洲的面具？', '北马里昂高中（俄勒冈州）和首尔高中是否位于同一个国家？']
[['傅满洲的面具'], ['不是']]
```
```python
for i in range(len(questions)):
print(f"\n\n>>>>>>>>>>>> 以下是第 {i+1} 个案例的输出 <<<<<<<<<<<<\n\n")
# 重置助手。在开始新对话之前，总是要重置助手。
assistant.reset()
qa_problem = questions[i]
chat_result = ragproxyagent.initiate_chat(
assistant, message=ragproxyagent.message_generator, problem=qa_problem, n_results=10
)
```
``` text
>>>>>>>>>>>> 以下是第 1 个案例的输出 <<<<<<<<<<<<
正在尝试创建集合。
```
``` text
max_tokens 太小，无法容纳一行文本。将此行拆分为：
Clyde Thompson: Clyde Thompson（1910年 - 1979年7月1日）是一位美国囚犯转行的牧师。他是...
max_tokens 太小，无法容纳一行文本。将此行拆分为：
Australian Historical Monographs: The Australian Historical Monographs 是一系列历史学 ...
```
``` text
doc_ids: [['doc_12', 'doc_11', 'doc_16', 'doc_19', 'doc_13116', 'doc_14', 'doc_13', 'doc_18', 'doc_977', 'doc_10']]
Adding doc_id doc_12 to context.
Adding doc_id doc_11 to context.
Adding doc_id doc_16 to context.
Adding doc_id doc_19 to context.
Adding doc_id doc_13116 to context.
Adding doc_id doc_14 to context.
Adding doc_id doc_13 to context.
Adding doc_id doc_18 to context.
Adding doc_id doc_977 to context.
Adding doc_id doc_10 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the context provided by the user. You must think step-by-step.
First, please learn the following examples of context and question pairs and their corresponding answers.
Context:
Kurram Garhi: Kurram Garhi is a small village located near the city of Bannu, which is the part of Khyber Pakhtunkhwa province of Pakistan. Its population is approximately 35000.
Trojkrsti: Trojkrsti is a village in Municipality of Prilep, Republic of Macedonia.
Q: Are both Kurram Garhi and Trojkrsti located in the same country?
A: Kurram Garhi is located in the country of Pakistan. Trojkrsti is located in the country of Republic of Macedonia. Thus, they are not in the same country. So the answer is: no.
Context:
Early Side of Later: Early Side of Later is the third studio album by English singer- songwriter Matt Goss. It was released on 21 June 2004 by Concept Music and reached No. 78 on the UK Albums Chart.
What's Inside: What's Inside is the fourteenth studio album by British singer- songwriter Joan Armatrading.
Q: Which album was released earlier, What'S Inside or Cassandra'S Dream (Album)?
A: What's Inside was released in the year 1995. Cassandra's Dream (album) was released in the year 2008. Thus, of the two, the album to release earlier is What's Inside. So the answer is: What's Inside.
Context:
Maria Alexandrovna (Marie of Hesse): Maria Alexandrovna , born Princess Marie of Hesse and by Rhine (8 August 1824 – 3 June 1880) was Empress of Russia as the first wife of Emperor Alexander II.
Grand Duke Alexei Alexandrovich of Russia: Grand Duke Alexei Alexandrovich of Russia,(Russian: Алексей Александрович; 14 January 1850 (2 January O.S.) in St. Petersburg – 14 November 1908 in Paris) was the fifth child and the fourth son of Alexander II of Russia and his first wife Maria Alexandrovna (Marie of Hesse).
Q: What is the cause of death of Grand Duke Alexei Alexandrovich Of Russia's mother?
A: The mother of Grand Duke Alexei Alexandrovich of Russia is Maria Alexandrovna. Maria Alexandrovna died from tuberculosis. So the answer is: tuberculosis.
Context:
Laughter in Hell: Laughter in Hell is a 1933 American Pre-Code drama film directed by Edward L. Cahn and starring Pat O'Brien. The film's title was typical of the sensationalistic titles of many Pre-Code films.
Edward L. Cahn: Edward L. Cahn (February 12, 1899 – August 25, 1963) was an American film director.
Q: When did the director of film Laughter In Hell die?
A: The film Laughter In Hell was directed by Edward L. Cahn. Edward L. Cahn died on August 25, 1963. So the answer is: August 25, 1963.
Second, please complete the answer by thinking step-by-step.
Context:
The Mask of Fu Manchu: The Mask of Fu Manchu is a 1932 pre-Code adventure film directed by Charles Brabin. It was written by Irene Kuhn, Edgar Allan Woolf and John Willard based on the 1932 novel of the same name by Sax Rohmer. Starring Boris Karloff as Fu Manchu, and featuring Myrna Loy as his depraved daughter, the movie revolves around Fu Manchu's quest for the golden sword and mask of Genghis Khan. Lewis Stone plays his nemesis. Dr. Petrie is absent from this film.
The Mysterious Dr. Fu Manchu: The Mysterious Dr. Fu Manchu is a 1929 American pre-Code drama film directed by Rowland V. Lee and starring Warner Oland as Dr. Fu Manchu. It was the first Fu Manchu film of the talkie era. Since this was during the transition period to sound, a silent version was also released in the United States.
The Face of Fu Manchu: The Face of Fu Manchu is a 1965 thriller film directed by Don Sharp and based on the characters created by Sax Rohmer. It stars Christopher Lee as the eponymous villain, a Chinese criminal mastermind, and Nigel Green as his pursuing rival Nayland Smith, a Scotland Yard detective. The film was a British- West German co-production, and was the first in a five- part series starring Lee and produced by Harry Alan Towers for Constantin Film, the second of which was" The Brides of Fu Manchu" released the next year, with the final entry being" The Castle of Fu Manchu" in 1969. It was shot in Technicolor and Techniscope, on- location in County Dublin, Ireland.
The Return of Dr. Fu Manchu: The Return of Dr. Fu Manchu is a 1930 American pre-Code film directed by Rowland V. Lee. It is the second of three films starring Warner Oland as the fiendish Fu Manchu, who returns from apparent death in the previous film," The Mysterious Dr. Fu Manchu"( 1929), to seek revenge on those he holds responsible for the death of his wife and child.
The Vengeance of Fu Manchu: The Vengeance of Fu Manchu is a 1967 British film directed by Jeremy Summers and starring Christopher Lee, Horst Frank, Douglas Wilmer and Tsai Chin. It was the third British/ West German Constantin Film co-production of the Dr. Fu Manchu series and the first to be filmed in Hong Kong. It was generally released in the U.K. through Warner- Pathé( as a support feature to the Lindsay Shonteff film" The Million Eyes of Sumuru") on 3 December 1967.
The Brides of Fu Manchu: The Brides of Fu Manchu is a 1966 British/ West German Constantin Film co-production adventure crime film based on the fictional Chinese villain Dr. Fu Manchu, created by Sax Rohmer. It was the second film in a series, and was preceded by" The Face of Fu ManchuThe Vengeance of Fu Manchu" followed in 1967," The Blood of Fu Manchu" in 1968, and" The Castle of Fu Manchu" in 1969. It was produced by Harry Alan Towers for Hallam Productions. Like the first film, it was directed by Don Sharp, and starred Christopher Lee as Fu Manchu. Nigel Green was replaced by Douglas Wilmer as Scotland Yard detective Nayland Smith. The action takes place mainly in London, where much of the location filming took place.
The Castle of Fu Manchu: The Castle of Fu Manchu( also known as The Torture Chamber of Dr. Fu Manchu and also known by its German title Die Folterkammer des Dr. Fu Man Chu) is a 1969 film and the fifth and final Dr. Fu Manchu film with Christopher Lee portraying the title character.
The Blood of Fu Manchu: The Blood of Fu Manchu, also known as Fu Manchu and the Kiss of Death, Kiss of Death, Kiss and Kill( U.S. title) and Against All Odds( original U.S. video title), is a 1968 British adventure crime film directed by Jesús Franco, based on the fictional Asian villain Dr. Fu Manchu created by Sax Rohmer. It was the fourth film in a series, and was preceded by" The Vengeance of Fu Manchu The Castle of Fu Manchu" followed in 1969. It was produced by Harry Alan Towers for Udastex Films. It starred Christopher Lee as Dr. Fu Manchu, Richard Greene as Scotland Yard detective Nayland Smith, and Howard Marion- Crawford as Dr. Petrie. The movie was filmed in Spain and Brazil. Shirley Eaton appears in a scene that she claimed she was never paid for; apparently, the director Jesús Franco had inserted some stock footage of her from one of her films(" The Girl from Rio"( 1968)) into the film without telling her. She only found out years later that she had been in a Fu Manchu film.
Don Sharp: Donald Herman Sharp( 19 April 192114 December 2011) was an Australian- born British film director. His best known films were made for Hammer in the 1960s, and included" The Kiss of the Vampire"( 1962) and" Rasputin, the Mad Monk"( 1966). In 1965 he directed" The Face of Fu Manchu", based on the character created by Sax Rohmer, and starring Christopher Lee. Sharp also directed the sequel" The Brides of Fu Manchu"( 1966). In the 1980s he was also responsible for several hugely popular miniseries adapted from the novels of Barbara Taylor Bradford.
Blind Shaft: Blind Shaft is a 2003 film about a pair of brutal con artists operating in the illegal coal mines of present- day northern China. The film was written and directed by Li Yang( 李杨), and is based on Chinese writer Liu Qingbang's short novel" Shen MuSacred Wood").
Q: Which film came out first, Blind Shaft or The Mask Of Fu Manchu?
A:
--------------------------------------------------------------------------------
Adding doc_id doc_11 to context.
Adding doc_id doc_16 to context.
Adding doc_id doc_19 to context.
Adding doc_id doc_13116 to context.
Adding doc_id doc_14 to context.
Adding doc_id doc_13 to context.
Adding doc_id doc_18 to context.
Adding doc_id doc_977 to context.
Adding doc_id doc_10 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the context provided by the user. You must think step-by-step.
First, please learn the following examples of context and question pairs and their corresponding answers.
Context:
Kurram Garhi: Kurram Garhi is a small village located near the city of Bannu, which is the part of Khyber Pakhtunkhwa province of Pakistan. Its population is approximately 35000.
Trojkrsti: Trojkrsti is a village in Municipality of Prilep, Republic of Macedonia.
Q: Are both Kurram Garhi and Trojkrsti located in the same country?
A: Kurram Garhi is located in the country of Pakistan. Trojkrsti is located in the country of Republic of Macedonia. Thus, they are not in the same country. So the answer is: no.
Context:
Early Side of Later: Early Side of Later is the third studio album by English singer- songwriter Matt Goss. It was released on 21 June 2004 by Concept Music and reached No. 78 on the UK Albums Chart.
What's Inside: What's Inside is the fourteenth studio album by British singer- songwriter Joan Armatrading.
Q: Which album was released earlier, What'S Inside or Cassandra'S Dream (Album)?
A: What's Inside was released in the year 1995. Cassandra's Dream (album) was released in the year 2008. Thus, of the two, the album to release earlier is What's Inside. So the answer is: What's Inside.
Context:
Maria Alexandrovna (Marie of Hesse): Maria Alexandrovna , born Princess Marie of Hesse and by Rhine (8 August 1824 – 3 June 1880) was Empress of Russia as the first wife of Emperor Alexander II.
Grand Duke Alexei Alexandrovich of Russia: Grand Duke Alexei Alexandrovich of Russia,(Russian: Алексей Александрович; 14 January 1850 (2 January O.S.) in St. Petersburg – 14 November 1908 in Paris) was the fifth child and the fourth son of Alexander II of Russia and his first wife Maria Alexandrovna (Marie of Hesse).
Q: What is the cause of death of Grand Duke Alexei Alexandrovich Of Russia's mother?
A: The mother of Grand Duke Alexei Alexandrovich of Russia is Maria Alexandrovna. Maria Alexandrovna died from tuberculosis. So the answer is: tuberculosis.
Context:
Laughter in Hell: Laughter in Hell is a 1933 American Pre-Code drama film directed by Edward L. Cahn and starring Pat O'Brien. The film's title was typical of the sensationalistic titles of many Pre-Code films.
Edward L. Cahn: Edward L. Cahn (February 12, 1899 – August 25, 1963) was an American film director.
Q: When did the director of film Laughter In Hell die?
A: The film Laughter In Hell was directed by Edward L. Cahn. Edward L. Cahn died on August 25, 1963. So the answer is: August 25, 1963.
Second, please complete the answer by thinking step-by-step.
Context:
The Mask of Fu Manchu: The Mask of Fu Manchu is a 1932 pre-Code adventure film directed by Charles Brabin. It was written by Irene Kuhn, Edgar Allan Woolf and John Willard based on the 1932 novel of the same name by Sax Rohmer. Starring Boris Karloff as Fu Manchu, and featuring Myrna Loy as his depraved daughter, the movie revolves around Fu Manchu's quest for the golden sword and mask of Genghis Khan. Lewis Stone plays his nemesis. Dr. Petrie is absent from this film.
The Mysterious Dr. Fu Manchu: The Mysterious Dr. Fu Manchu is a 1929 American pre-Code drama film directed by Rowland V. Lee and starring Warner Oland as Dr. Fu Manchu. It was the first Fu Manchu film of the talkie era. Since this was during the transition period to sound, a silent version was also released in the United States.
The Face of Fu Manchu: The Face of Fu Manchu is a 1965 thriller film directed by Don Sharp and based on the characters created by Sax Rohmer. It stars Christopher Lee as the eponymous villain, a Chinese criminal mastermind, and Nigel Green as his pursuing rival Nayland Smith, a Scotland Yard detective. The film was a British- West German co-production, and was the first in a five- part series starring Lee and produced by Harry Alan Towers for Constantin Film, the second of which was" The Brides of Fu Manchu" released the next year, with the final entry being" The Castle of Fu Manchu" in 1969. It was shot in Technicolor and Techniscope, on- location in County Dublin, Ireland.
The Return of Dr. Fu Manchu: The Return of Dr. Fu Manchu is a 1930 American pre-Code film directed by Rowland V. Lee. It is the second of three films starring Warner Oland as the fiendish Fu Manchu, who returns from apparent death in the previous film," The Mysterious Dr. Fu Manchu"( 1929), to seek revenge on those he holds responsible for the death of his wife and child.
The Vengeance of Fu Manchu: The Vengeance of Fu Manchu is a 1967 British film directed by Jeremy Summers and starring Christopher Lee, Horst Frank, Douglas Wilmer and Tsai Chin. It was the third British/ West German Constantin Film co-production of the Dr. Fu Manchu series and the first to be filmed in Hong Kong. It was generally released in the U.K. through Warner- Pathé( as a support feature to the Lindsay Shonteff film" The Million Eyes of Sumuru") on 3 December 1967.
The Brides of Fu Manchu: The Brides of Fu Manchu is a 1966 British/ West German Constantin Film co-production adventure crime film based on the fictional Chinese villain Dr. Fu Manchu, created by Sax Rohmer. It was the second film in a series, and was preceded by" The Face of Fu ManchuThe Vengeance of Fu Manchu" followed in 1967," The Blood of Fu Manchu" in 1968, and" The Castle of Fu Manchu" in 1969. It was produced by Harry Alan Towers for Hallam Productions. Like the first film, it was directed by Don Sharp, and starred Christopher Lee as Fu Manchu. Nigel Green was replaced by Douglas Wilmer as Scotland Yard detective Nayland Smith. The action takes place mainly in London, where much of the location filming took place.
The Castle of Fu Manchu: The Castle of Fu Manchu( also known as The Torture Chamber of Dr. Fu Manchu and also known by its German title Die Folterkammer des Dr. Fu Man Chu) is a 1969 film and the fifth and final Dr. Fu Manchu film with Christopher Lee portraying the title character.
The Blood of Fu Manchu: The Blood of Fu Manchu, also known as Fu Manchu and the Kiss of Death, Kiss of Death, Kiss and Kill( U.S. title) and Against All Odds( original U.S. video title), is a 1968 British adventure crime film directed by Jesús Franco, based on the fictional Asian villain Dr. Fu Manchu created by Sax Rohmer. It was the fourth film in a series, and was preceded by" The Vengeance of Fu Manchu The Castle of Fu Manchu" followed in 1969. It was produced by Harry Alan Towers for Udastex Films. It starred Christopher Lee as Dr. Fu Manchu, Richard Greene as Scotland Yard detective Nayland Smith, and Howard Marion- Crawford as Dr. Petrie. The movie was filmed in Spain and Brazil. Shirley Eaton appears in a scene that she claimed she was never paid for; apparently, the director Jesús Franco had inserted some stock footage of her from one of her films(" The Girl from Rio"( 1968)) into the film without telling her. She only found out years later that she had been in a Fu Manchu film.
Don Sharp: Donald Herman Sharp( 19 April 192114 December 2011) was an Australian- born British film director. His best known films were made for Hammer in the 1960s, and included" The Kiss of the Vampire"( 1962) and" Rasputin, the Mad Monk"( 1966). In 1965 he directed" The Face of Fu Manchu", based on the character created by Sax Rohmer, and starring Christopher Lee. Sharp also directed the sequel" The Brides of Fu Manchu"( 1966). In the 1980s he was also responsible for several hugely popular miniseries adapted from the novels of Barbara Taylor Bradford.
Blind Shaft: Blind Shaft is a 2003 film about a pair of brutal con artists operating in the illegal coal mines of present- day northern China. The film was written and directed by Li Yang( 李杨), and is based on Chinese writer Liu Qingbang's short novel" Shen MuSacred Wood").
Q: Which film came out first, Blind Shaft or The Mask Of Fu Manchu?
A:
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
Blind Shaft is a 2003 film while The Mask of Fu Manchu is a 1932 pre-Code adventure film. Thus, The Mask of Fu Manchu came out earlier than Blind Shaft. So the answer is: The Mask of Fu Manchu.
--------------------------------------------------------------------------------
>>>>>>>>>>>> Below are outputs of Case 2 <<<<<<<<<<<<
doc_ids: [['doc_74', 'doc_76', 'doc_68', 'doc_42890', 'doc_75', 'doc_19596', 'doc_45135', 'doc_995', 'doc_7274', 'doc_23187']]
Adding doc_id doc_74 to context.
Adding doc_id doc_76 to context.
Adding doc_id doc_68 to context.
Adding doc_id doc_42890 to context.
Adding doc_id doc_75 to context.
Adding doc_id doc_19596 to context.
Adding doc_id doc_45135 to context.
Adding doc_id doc_995 to context.
Adding doc_id doc_7274 to context.
Adding doc_id doc_23187 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the context provided by the user. You must think step-by-step.
First, please learn the following examples of context and question pairs and their corresponding answers.
Context:
Kurram Garhi: Kurram Garhi is a small village located near the city of Bannu, which is the part of Khyber Pakhtunkhwa province of Pakistan. Its population is approximately 35000.
Trojkrsti: Trojkrsti is a village in Municipality of Prilep, Republic of Macedonia.
Q: Are both Kurram Garhi and Trojkrsti located in the same country?
A: Kurram Garhi is located in the country of Pakistan. Trojkrsti is located in the country of Republic of Macedonia. Thus, they are not in the same country. So the answer is: no.
Context:
Early Side of Later: Early Side of Later is the third studio album by English singer- songwriter Matt Goss. It was released on 21 June 2004 by Concept Music and reached No. 78 on the UK Albums Chart.
What's Inside: What's Inside is the fourteenth studio album by British singer- songwriter Joan Armatrading.
Q: Which album was released earlier, What'S Inside or Cassandra'S Dream (Album)?
A: What's Inside was released in the year 1995. Cassandra's Dream (album) was released in the year 2008. Thus, of the two, the album to release earlier is What's Inside. So the answer is: What's Inside.
Context:
Maria Alexandrovna (Marie of Hesse): Maria Alexandrovna , born Princess Marie of Hesse and by Rhine (8 August 1824 – 3 June 1880) was Empress of Russia as the first wife of Emperor Alexander II.
Grand Duke Alexei Alexandrovich of Russia: Grand Duke Alexei Alexandrovich of Russia,(Russian: Алексей Александрович; 14 January 1850 (2 January O.S.) in St. Petersburg – 14 November 1908 in Paris) was the fifth child and the fourth son of Alexander II of Russia and his first wife Maria Alexandrovna (Marie of Hesse).
Q: What is the cause of death of Grand Duke Alexei Alexandrovich Of Russia's mother?
A: The mother of Grand Duke Alexei Alexandrovich of Russia is Maria Alexandrovna. Maria Alexandrovna died from tuberculosis. So the answer is: tuberculosis.
Context:
Laughter in Hell: Laughter in Hell is a 1933 American Pre-Code drama film directed by Edward L. Cahn and starring Pat O'Brien. The film's title was typical of the sensationalistic titles of many Pre-Code films.
Edward L. Cahn: Edward L. Cahn (February 12, 1899 – August 25, 1963) was an American film director.
Q: When did the director of film Laughter In Hell die?
A: The film Laughter In Hell was directed by Edward L. Cahn. Edward L. Cahn died on August 25, 1963. So the answer is: August 25, 1963.
Second, please complete the answer by thinking step-by-step.
Context:
Seoul High School: Seoul High School( Hangul: 서울고등학교) is a public high school located in the heart of Seoul, South Korea.
North Marion High School (Oregon): North Marion High School is a public high school in Aurora, Oregon, United States. The school is part of the North Marion School District with all four schools being located on the same campus. The school draws students from the cities of Aurora, Hubbard, and Donald as well as the communities of Broadacres and Butteville.
Marion High School (Kansas): Marion High School is a public high school in Marion, Kansas, USA. It is one of three schools operated by Marion USD 408, and is the sole high school in the district.
Northwest High School: Northwest High School or North West High School may refer to:
Marion High School (Indiana): Marion High School is a high school in Marion, Indiana with more than 1,000 students.
Macon County High School: Macon County High School is located in Montezuma, Georgia, United States, which is a part of Macon County. Enrollment as of the 2017- 2018 school year is 491.
Canyon High School (Ogden, Utah): Canyon High School was a high school in Ogden, Utah.
Northside High School: Northside High School or North Side High School or Northside Christian School or similar can refer to:
Springs Boys' High School: Springs Boys' High School is a high school in Springs, Gauteng, South Africa.
International School of Koje: International School of Koje( ISK) is a privately funded international school located in Geoje, South Korea.
Q: Are North Marion High School (Oregon) and Seoul High School both located in the same country?
A:
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
No, North Marion High School (Oregon) is located in the United States, specifically in the state of Oregon, while Seoul High School is located in South Korea. So they are not in the same country.
--------------------------------------------------------------------------------
Updating context and resetting conversation.
doc_ids: [['doc_76', 'doc_68', 'doc_74', 'doc_75', 'doc_19596', 'doc_42890', 'doc_24819', 'doc_69', 'doc_995', 'doc_7274']]
Adding doc_id doc_24819 to context.
Adding doc_id doc_69 to context.
ragproxyagent (to assistant):
You're a retrieve augmented chatbot. You answer user's questions based on your own knowledge and the context provided by the user. You must think step-by-step.
First, please learn the following examples of context and question pairs and their corresponding answers.
Context:
Kurram Garhi: Kurram Garhi is a small village located near the city of Bannu, which is the part of Khyber Pakhtunkhwa province of Pakistan. Its population is approximately 35000.
Trojkrsti: Trojkrsti is a village in Municipality of Prilep, Republic of Macedonia.
Q: Are both Kurram Garhi and Trojkrsti located in the same country?
A: Kurram Garhi is located in the country of Pakistan. Trojkrsti is located in the country of Republic of Macedonia. Thus, they are not in the same country. So the answer is: no.
Context:
Early Side of Later: Early Side of Later is the third studio album by English singer- songwriter Matt Goss. It was released on 21 June 2004 by Concept Music and reached No. 78 on the UK Albums Chart.
What's Inside: What's Inside is the fourteenth studio album by British singer- songwriter Joan Armatrading.
Q: Which album was released earlier, What'S Inside or Cassandra'S Dream (Album)?
A: What's Inside was released in the year 1995. Cassandra's Dream (album) was released in the year 2008. Thus, of the two, the album to release earlier is What's Inside. So the answer is: What's Inside.
Context:
Maria Alexandrovna (Marie of Hesse): Maria Alexandrovna , born Princess Marie of Hesse and by Rhine (8 August 1824 – 3 June 1880) was Empress of Russia as the first wife of Emperor Alexander II.
Grand Duke Alexei Alexandrovich of Russia: Grand Duke Alexei Alexandrovich of Russia,(Russian: Алексей Александрович; 14 January 1850 (2 January O.S.) in St. Petersburg – 14 November 1908 in Paris) was the fifth child and the fourth son of Alexander II of Russia and his first wife Maria Alexandrovna (Marie of Hesse).
Q: What is the cause of death of Grand Duke Alexei Alexandrovich Of Russia's mother?
A: The mother of Grand Duke Alexei Alexandrovich of Russia is Maria Alexandrovna. Maria Alexandrovna died from tuberculosis. So the answer is: tuberculosis.
Context:
Laughter in Hell: Laughter in Hell is a 1933 American Pre-Code drama film directed by Edward L. Cahn and starring Pat O'Brien. The film's title was typical of the sensationalistic titles of many Pre-Code films.
Edward L. Cahn: Edward L. Cahn (February 12, 1899 – August 25, 1963) was an American film director.
Q: When did the director of film Laughter In Hell die?
A: The film Laughter In Hell was directed by Edward L. Cahn. Edward L. Cahn died on August 25, 1963. So the answer is: August 25, 1963.
Second, please complete the answer by thinking step-by-step.
Context:
Seoul High School: Seoul High School( Hangul: 서울고등학교) is a public high school located in the heart of Seoul, South Korea.
North Marion High School (Oregon): North Marion High School is a public high school in Aurora, Oregon, United States. The school is part of the North Marion School District with all four schools being located on the same campus. The school draws students from the cities of Aurora, Hubbard, and Donald as well as the communities of Broadacres and Butteville.
Marion High School (Kansas): Marion High School is a public high school in Marion, Kansas, USA. It is one of three schools operated by Marion USD 408, and is the sole high school in the district.
Northwest High School: Northwest High School or North West High School may refer to:
Marion High School (Indiana): Marion High School is a high school in Marion, Indiana with more than 1,000 students.
Macon County High School: Macon County High School is located in Montezuma, Georgia, United States, which is a part of Macon County. Enrollment as of the 2017- 2018 school year is 491.
Canyon High School (Ogden, Utah): Canyon High School was a high school in Ogden, Utah.
Northside High School: Northside High School or North Side High School or Northside Christian School or similar can refer to:
Springs Boys' High School: Springs Boys' High School is a high school in Springs, Gauteng, South Africa.
International School of Koje: International School of Koje( ISK) is a privately funded international school located in Geoje, South Korea.
Anderson High School (Anderson, Indiana): Anderson High School is a public high school located in Anderson, Indiana.
North Marion High School (West Virginia): North Marion High School is a public Double A ("AA") high school in the U.S. state of West Virginia, with a current enrollment of 851 students. North Marion High School is located approximately 4 miles from Farmington, West Virginia on US Route 250 north. While it is closer to the city of Mannington, West Virginia, and is often considered to be located in Rachel, West Virginia, the school mailing address is Farmington. Rachel is a small coal mining community located adjacent to the school, and is an unincorporated municipality. North Marion High School is represented as "Grantville High School" in the popular alternative history novel "1632" by writer Eric Flint. The novel is set in the fictional town of Grantville, which is based on the real town and surroundings of Mannington.
Q: Are North Marion High School (Oregon) and Seoul High School both located in the same country?
A:
--------------------------------------------------------------------------------
assistant (to ragproxyagent):
North Marion High School (Oregon) is located in the country of United States. Seoul High School is located in the country of South Korea. Thus, they are not in the same country. So the answer is: no.
--------------------------------------------------------------------------------
```
# 标题一
这是一篇关于某个主题的科普文章。在这篇文章中，我们将介绍一些关于该主题的基本概念和背景信息。
## 段落一
在过去的几十年中，人工智能（Artificial Intelligence，简称AI）已经取得了巨大的进展。AI技术已经应用于各个领域，包括医疗、交通、金融等。然而，尽管AI在许多任务上表现出色，但在某些情况下，它仍然存在一些局限性。
## 段落二
最近，一种新的AI技术被提出，它被称为生成对抗网络（Generative Adversarial Networks，简称GANs）。GANs是由两个神经网络组成的系统，一个生成器网络和一个判别器网络。生成器网络的目标是生成看起来与真实数据相似的新数据，而判别器网络的目标是区分生成的数据和真实数据。
## 段落三
GANs的一个重要应用是图像生成。通过训练生成器网络，我们可以生成逼真的图像，这些图像看起来与真实图像非常相似。这项技术在许多领域都有潜在的应用，例如电影特效、游戏开发和虚拟现实。
## 段落四
然而，GANs也存在一些挑战和限制。例如，生成的图像可能存在一些细微的错误或不真实之处。此外，GANs的训练过程通常需要大量的计算资源和时间。
## 段落五
尽管 存在一些挑战，但GANs仍然是一个非常有前景的研究领域。许多研究人员正在努力改进GANs的性能和稳定性，并探索其在更多领域的应用。
## 引用
[20] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
