使用基于Embedding的搜索来实现问答

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注：本文来自OpenAI的Cookbook，原文是英文，以下译文完全是由AI翻译的：

GPT擅长回答问题，但仅限于它从训练数据中记住的内容。

如果你希望GPT回答有关不熟悉的内容，该怎么办？例如：

2021年9月之后，最近发生的事件；
你的非公开文档；
来自过去对话的信息等。

本文演示了一种基于搜索-提问的两步法，使GPT能够使用参考文本库回答问题。

搜索：在文本库中搜索相关文本片段；
提问：将检索到的文本片段连同消息一并发送至GPT并提问问题。

为什么搜索比微调更好

GPT可以通过两种方式学习知识：

通过模型权重（即在训练集上微调模型）
通过模型输入（即将知识插入在输入消息中）

尽管微调感觉像是更自然的选择（毕竟，数据训练是GPT学习所有其他知识的方式），但我们通常不推荐将其作为教授模型知识的方式。微调更适合教授专门的任务或风格，但对于事实来说不太可靠。

打个比方，模型权重就像长期记忆。当你对模型进行微调时，就像为一周后的考试做准备一样。当考试到来时，模型可能会忘记细节，或者记错它从未读过的事实。

相比之下，消息输入就像短期记忆。当你在消息中插入知识时，就像用现成的笔记参加考试一样。有了笔记，模型更有可能得出正确答案。

文本搜索相对于微调的一个缺点是每个模型都受到一次可以读取的最大文本量的限制：

模型	最大文本长度
gpt-3.5-turbo	4,096个Token（约5页）
gpt-4	8,192个Token（约10页）
gpt-4-32k	32,768个Token（约40页）

继续这个类比，你可以把这个模型想象成一个学生，尽管书架上可能有很多课本可供参考，但一次只能看几页笔记。

因此，为了构建一个能够利用大量文本来回答问题的系统，我们建议使用“搜索-提问”的方法。

搜索

可以通过多种方式搜索文本。例如：

基于词汇的搜索
基于图的搜索
基于Embedding的搜索

此示例文档使用基于Embedding（嵌入）的搜索。Embedding很容易实现，并且特别适用于回答问题，因为问题在词汇上通常不会与其答案重叠。

可以将使用Embedding搜索视为你自己的系统的起点。更好的搜索系统可能会结合多种搜索方法，以及诸如流行度、新鲜度、用户历史记录、先前搜索结果的冗余度、点击率数据等特征。问答检索性能也可以通过HyDE等技术来提高，在Embedding之前，问题首先被转换为假设的答案。同样，GPT还可以通过自动将问题转换为关键字或搜索词组来改善搜索结果。

完整程序

具体来说，本文演示了以下过程：

准备搜索数据（每个文档仅需准备一次）
1. 收集：我们将下载数百篇有关2022年奥运会的维基百科文章；
2. 分块：文档被分成简短的、独立的片段用于Embedding；
3. Embedding：每个部分都使用OpenAI API来实现Embedding；
4. 保存：Embedding被存储起来（对于大型数据集，使用矢量数据库）。
搜索（针对每次查询）
1. 给定用户问题，使用OpenAI API将查询转换成Embedding；
2. 使用Embedding，根据查询的相关性对文本部分进行排名。
提问（针对每次查询）
1. 将问题和最相关的部分插入至发送到GPT的消息中；
2. 返回GPT给出的答案。

成本

由于GPT比Embedding搜索更昂贵，因此具有大量查询的系统成本将主要由步骤3决定。

对于每个查询使用约1,000个Token的gpt-3.5-turbo，每个查询的成本约为0.002美元，或1美元约500个查询（截至2023年4月）；
对于gpt-4，再次假设每个查询约1,000个Token，每个查询的成本约为0.03美元，或1美元约30个查询（截至2023年4月）。

当然，确切的成本将取决于系统的具体实现和使用模式。

前言

我们将从以下开始：

导入必要的库；
选择Embedding搜索和问答的模型。

# imports
import ast  # 用于将Embedding的数组转换为String
import openai  # 访问OpenAI API
import pandas as pd  # 存储文本和Embedding数据
import tiktoken  # 计算Token
from scipy import spatial  # 计算Vector的相似度以便用于搜索

# models
EMBEDDING_MODEL = "text-embedding-ada-002"
GPT_MODEL = "gpt-3.5-turbo"

疑难解答：安装库

如果你需要安装上述任何库，请在终端中运行pip install {library_name}。

例如，要安装openai库，请运行：

pip install openai

（你也可以在notebook中使用!pip install openai或%pip install openai来执行此操作。）

安装后，重新启动notebook，以便可以加载库。

疑难解答：设置API密钥

OpenAI库将尝试从OPENAI_API_KEY环境变量中读取你的API密钥。如果你还没有设置此环境变量，则可以按照此说明进行设置。

一个示例：GPT无法回答有关时事的问题

由于gpt-3.5-turbo和gpt-4的训练数据大多于2021年9月结束，因此模型无法回答有关近期事件的问题，例如2022年冬季奥运会。

例如，让我们尝试提问“哪些运动员赢得了2022年冰壶金牌？”：

# an example question about the 2022 Olympics
query = 'Which athletes won the gold medal in curling at the 2022 Winter Olympics?'

response = openai.ChatCompletion.create(
    messages=[
        {'role': 'system', 'content': 'You answer questions about the 2022 Winter Olympics.'},
        {'role': 'user', 'content': query},
    ],
    model=GPT_MODEL,
    temperature=0,
)

print(response['choices'][0]['message']['content'])

I'm sorry, but as an AI language model, I don't have information about the future events. The 2022 Winter Olympics will be held in Beijing, China, from February 4 to 20, 2022. The curling events will take place during the games, and the winners of the gold medal in curling will be determined at that time.

在这种情况下，模型不知道2022年发生的事情，无法回答该问题。

你可以通过将某个主题的内容插入到输入消息中来提供有关该主题的GPT知识

为了帮助模型了解2022年冬季奥运会的冰壶知识，我们可以将相关维基百科文章的上半部分复制并粘贴到我们的消息中：

# text copied and pasted from: https://en.wikipedia.org/wiki/Curling_at_the_2022_Winter_Olympics
# I didn't bother to format or clean the text, but GPT will still understand it
# the entire article is too long for gpt-3.5-turbo, so I only included the top few sections

wikipedia_article_on_curling = """Curling at the 2022 Winter Olympics

Article
Talk
Read
Edit
View history
From Wikipedia, the free encyclopedia
Curling
at the XXIV Olympic Winter Games
Curling pictogram.svg
Curling pictogram
Venue	Beijing National Aquatics Centre
Dates	2–20 February 2022
No. of events	3 (1 men, 1 women, 1 mixed)
Competitors	114 from 14 nations
← 20182026 →
Men's curling
at the XXIV Olympic Winter Games
Medalists
1st place, gold medalist(s)		 Sweden
2nd place, silver medalist(s)		 Great Britain
3rd place, bronze medalist(s)		 Canada
Women's curling
at the XXIV Olympic Winter Games
Medalists
1st place, gold medalist(s)		 Great Britain
2nd place, silver medalist(s)		 Japan
3rd place, bronze medalist(s)		 Sweden
Mixed doubles's curling
at the XXIV Olympic Winter Games
Medalists
1st place, gold medalist(s)		 Italy
2nd place, silver medalist(s)		 Norway
3rd place, bronze medalist(s)		 Sweden
Curling at the
2022 Winter Olympics
(更多内容略......)
"""

query = f"""Use the below article on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found, write "I don't know."

Article:
\"\"\"
{wikipedia_article_on_curling}
\"\"\"

Question: Which athletes won the gold medal in curling at the 2022 Winter Olympics?"""

response = openai.ChatCompletion.create(
    messages=[
        {'role': 'system', 'content': 'You answer questions about the 2022 Winter Olympics.'},
        {'role': 'user', 'content': query},
    ],
    model=GPT_MODEL,
    temperature=0,
)

print(response['choices'][0]['message']['content'])

There were three events in curling at the 2022 Winter Olympics, so there were three sets of athletes who won gold medals. The gold medalists in men's curling were Sweden's Niklas Edin, Oskar Eriksson, Rasmus Wranå, Christoffer Sundgren, and Daniel Magnusson. The gold medalists in women's curling were Great Britain's Eve Muirhead, Vicky Wright, Jennifer Dodds, Hailey Duff, and Mili Smith. The gold medalists in mixed doubles curling were Italy's Stefania Constantini and Amos Mosaner.

由于输入消息中包含维基百科文章，因此GPT能够正确回答。

在这个特殊案例中，GPT足够聪明，意识到最初的问题未明确说明，因为冰壶金牌项目有三项，而不仅仅是一项。

当然，这个例子部分依赖于人类智慧。我们知道问题是关于冰壶的，所以我们插入了一篇关于冰壶的维基百科文章。

本文的其余部分展示了如何通过基于嵌入的搜索来自动实现知识的插入。

1. 准备搜索数据

为了节省您的时间和费用，我们准备了一个预嵌入数据集，其中包含数百篇有关2022年冬季奥运会的维基百科文章。

要了解我们如何构建此数据集或自行修改它，请参阅嵌入维基百科文章以进行搜索。

# download pre-chunked text and pre-computed embeddings
# this file is ~200 MB, so may take a minute depending on your connection speed
embeddings_path = "https://cdn.openai.com/API/examples/data/winter_olympics_2022.csv"

df = pd.read_csv(embeddings_path)

# convert embeddings from CSV str type back to list type
df['embedding'] = df['embedding'].apply(ast.literal_eval)

# the dataframe has two columns: "text" and "embedding"
df

text	embedding
0	Lviv bid for the 2022 Winter Olympics\n\n{{Oly...	[-0.005021067801862955, 0.00026050032465718687...
1	Lviv bid for the 2022 Winter Olympics\n\n==His...	[0.0033927420154213905, -0.007447326090186834,...
2	Lviv bid for the 2022 Winter Olympics\n\n==Ven...	[-0.00915789045393467, -0.008366798982024193, ...
3	Lviv bid for the 2022 Winter Olympics\n\n==Ven...	[0.0030951891094446182, -0.006064314860850573,...
4	Lviv bid for the 2022 Winter Olympics\n\n==Ven...	[-0.002936174161732197, -0.006185177247971296,...
...	...	...
6054	Anaïs Chevalier-Bouchet\n\n==Personal life==\n...	[-0.027750400826334953, 0.001746018067933619, ...
6055	Uliana Nigmatullina\n\n{{short description|Rus...	[-0.021714167669415474, 0.016001321375370026, ...
6056	Uliana Nigmatullina\n\n==Biathlon results==\n\...	[-0.029143543913960457, 0.014654331840574741, ...
6057	Uliana Nigmatullina\n\n==Biathlon results==\n\...	[-0.024266039952635765, 0.011665306985378265, ...
6058	Uliana Nigmatullina\n\n==Biathlon results==\n\...	[-0.021818075329065323, 0.005420385394245386, ...
6059 rows × 2 columns

2. 搜索

现在我们将定义一个搜索函数：

接受用户查询和带有文本和Embedding列的数据框；
使用OpenAI API嵌入用户查询；
根据查询和文本的Embedding距离对文本进行排序；
返回两个列表：
- 前N个文本，按相关性排名；
- 它们对应的相关性分数。

# search function
def strings_ranked_by_relatedness(
    query: str,
    df: pd.DataFrame,
    relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),
    top_n: int = 100
) -> tuple[list[str], list[float]]:
    """Returns a list of strings and relatednesses, sorted from most related to least."""
    query_embedding_response = openai.Embedding.create(
        model=EMBEDDING_MODEL,
        input=query,
    )
    query_embedding = query_embedding_response["data"][0]["embedding"]
    strings_and_relatednesses = [
        (row["text"], relatedness_fn(query_embedding, row["embedding"]))
        for i, row in df.iterrows()
    ]
    strings_and_relatednesses.sort(key=lambda x: x[1], reverse=True)
    strings, relatednesses = zip(*strings_and_relatednesses)
    return strings[:top_n], relatednesses[:top_n]

# examples
strings, relatednesses = strings_ranked_by_relatedness("curling gold medal", df, top_n=5)
for string, relatedness in zip(strings, relatednesses):
    print(f"{relatedness=:.3f}")
    display(string)

relatedness=0.879
'Curling at the 2022 Winter Olympics\n\n==Medal summary==\n\n===Medal table===\n\n{{Medals table\n | caption        = \n | host           = \n | flag_template  = flagIOC\n | event          = 2022 Winter\n | team           = \n | gold_CAN = 0 | silver_CAN = 0 | bronze_CAN = 1\n | gold_ITA = 1 | silver_ITA = 0 | bronze_ITA = 0\n | gold_NOR = 0 | silver_NOR = 1 | bronze_NOR = 0\n | gold_SWE = 1 | silver_SWE = 0 | bronze_SWE = 2\n | gold_GBR = 1 | silver_GBR = 1 | bronze_GBR = 0\n | gold_JPN = 0 | silver_JPN = 1 | bronze_JPN - 0\n}}'
relatedness=0.872
"Curling at the 2022 Winter Olympics\n\n==Results summary==\n\n===Women's tournament===\n\n====Playoffs====\n\n=====Gold medal game=====\n\n''Sunday, 20 February, 9:05''\n{{#lst:Curling at the 2022 Winter Olympics – Women's tournament|GM}}\n{{Player percentages\n| team1 = {{flagIOC|JPN|2022 Winter}}\n| [[Yurika Yoshida]] | 97%\n| [[Yumi Suzuki]] | 82%\n| [[Chinami Yoshida]] | 64%\n| [[Satsuki Fujisawa]] | 69%\n| teampct1 = 78%\n| team2 = {{flagIOC|GBR|2022 Winter}}\n| [[Hailey Duff]] | 90%\n| [[Jennifer Dodds]] | 89%\n| [[Vicky Wright]] | 89%\n| [[Eve Muirhead]] | 88%\n| teampct2 = 89%\n}}"
relatedness=0.869
'Curling at the 2022 Winter Olympics\n\n==Results summary==\n\n===Mixed doubles tournament===\n\n====Playoffs====\n\n=====Gold medal game=====\n\n\'\'Tuesday, 8 February, 20:05\'\'\n{{#lst:Curling at the 2022 Winter Olympics – Mixed doubles tournament|GM}}\n{| class="wikitable"\n!colspan=4 width=400|Player percentages\n|-\n!colspan=2 width=200 style="white-space:nowrap;"| {{flagIOC|ITA|2022 Winter}}\n!colspan=2 width=200 style="white-space:nowrap;"| {{flagIOC|NOR|2022 Winter}}\n|-\n| [[Stefania Constantini]] || 83%\n| [[Kristin Skaslien]] || 70%\n|-\n| [[Amos Mosaner]] || 90%\n| [[Magnus Nedregotten]] || 69%\n|-\n| \'\'\'Total\'\'\' || 87%\n| \'\'\'Total\'\'\' || 69%\n|}'
relatedness=0.868
"Curling at the 2022 Winter Olympics\n\n==Medal summary==\n\n===Medalists===\n\n{| {{MedalistTable|type=Event|columns=1}}\n|-\n|Men<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Men's tournament}}\n|{{flagIOC|SWE|2022 Winter}}<br>[[Niklas Edin]]<br>[[Oskar Eriksson]]<br>[[Rasmus Wranå]]<br>[[Christoffer Sundgren]]<br>[[Daniel Magnusson (curler)|Daniel Magnusson]]\n|{{flagIOC|GBR|2022 Winter}}<br>[[Bruce Mouat]]<br>[[Grant Hardie]]<br>[[Bobby Lammie]]<br>[[Hammy McMillan Jr.]]<br>[[Ross Whyte]]\n|{{flagIOC|CAN|2022 Winter}}<br>[[Brad Gushue]]<br>[[Mark Nichols (curler)|Mark Nichols]]<br>[[Brett Gallant]]<br>[[Geoff Walker (curler)|Geoff Walker]]<br>[[Marc Kennedy]]\n|-\n|Women<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Women's tournament}}\n|{{flagIOC|GBR|2022 Winter}}<br>[[Eve Muirhead]]<br>[[Vicky Wright]]<br>[[Jennifer Dodds]]<br>[[Hailey Duff]]<br>[[Mili Smith]]\n|{{flagIOC|JPN|2022 Winter}}<br>[[Satsuki Fujisawa]]<br>[[Chinami Yoshida]]<br>[[Yumi Suzuki]]<br>[[Yurika Yoshida]]<br>[[Kotomi Ishizaki]]\n|{{flagIOC|SWE|2022 Winter}}<br>[[Anna Hasselborg]]<br>[[Sara McManus]]<br>[[Agnes Knochenhauer]]<br>[[Sofia Mabergs]]<br>[[Johanna Heldin]]\n|-\n|Mixed doubles<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Mixed doubles tournament}}\n|{{flagIOC|ITA|2022 Winter}}<br>[[Stefania Constantini]]<br>[[Amos Mosaner]]\n|{{flagIOC|NOR|2022 Winter}}<br>[[Kristin Skaslien]]<br>[[Magnus Nedregotten]]\n|{{flagIOC|SWE|2022 Winter}}<br>[[Almida de Val]]<br>[[Oskar Eriksson]]\n|}"
relatedness=0.867
"Curling at the 2022 Winter Olympics\n\n==Results summary==\n\n===Men's tournament===\n\n====Playoffs====\n\n=====Gold medal game=====\n\n''Saturday, 19 February, 14:50''\n{{#lst:Curling at the 2022 Winter Olympics – Men's tournament|GM}}\n{{Player percentages\n| team1 = {{flagIOC|GBR|2022 Winter}}\n| [[Hammy McMillan Jr.]] | 95%\n| [[Bobby Lammie]] | 80%\n| [[Grant Hardie]] | 94%\n| [[Bruce Mouat]] | 89%\n| teampct1 = 90%\n| team2 = {{flagIOC|SWE|2022 Winter}}\n| [[Christoffer Sundgren]] | 99%\n| [[Rasmus Wranå]] | 95%\n| [[Oskar Eriksson]] | 93%\n| [[Niklas Edin]] | 87%\n| teampct2 = 94%\n}}"

3. 提问

通过上面的搜索功能，我们现在可以自动检索相关知识并将其插入到GPT的消息中。

下面，我们定义一个函数ask：

接受用户查询；
搜索与查询相关的文本；
将该文本填充到GPT消息中；
将消息发送到GPT；
返回GPT的答案。

def num_tokens(text: str, model: str = GPT_MODEL) -> int:
    """Return the number of tokens in a string."""
    encoding = tiktoken.encoding_for_model(model)
    return len(encoding.encode(text))


def query_message(
    query: str,
    df: pd.DataFrame,
    model: str,
    token_budget: int
) -> str:
    """Return a message for GPT, with relevant source texts pulled from a dataframe."""
    strings, relatednesses = strings_ranked_by_relatedness(query, df)
    introduction = 'Use the below articles on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found in the articles, write "I could not find an answer."'
    question = f"\n\nQuestion: {query}"
    message = introduction
    for string in strings:
        next_article = f'\n\nWikipedia article section:\n"""\n{string}\n"""'
        if (
            num_tokens(message + next_article + question, model=model)
            > token_budget
        ):
            break
        else:
            message += next_article
    return message + question


def ask(
    query: str,
    df: pd.DataFrame = df,
    model: str = GPT_MODEL,
    token_budget: int = 4096 - 500,
    print_message: bool = False,
) -> str:
    """Answers a query using GPT and a dataframe of relevant texts and embeddings."""
    message = query_message(query, df, model=model, token_budget=token_budget)
    if print_message:
        print(message)
    messages = [
        {"role": "system", "content": "You answer questions about the 2022 Winter Olympics."},
        {"role": "user", "content": message},
    ]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0
    )
    response_message = response["choices"][0]["message"]["content"]
    return response_message

示例问题

最后，让我们向系统提问有关金牌冰壶的原始问题：

ask('Which athletes won the gold medal in curling at the 2022 Winter Olympics?')

"There were two gold medal-winning teams in curling at the 2022 Winter Olympics: the Swedish men's team consisting of Niklas Edin, Oskar Eriksson, Rasmus Wranå, Christoffer Sundgren, and Daniel Magnusson, and the British women's team consisting of Eve Muirhead, Vicky Wright, Jennifer Dodds, Hailey Duff, and Mili Smith."

尽管gpt-3.5-turbo对2022年冬季奥运会一无所知，但我们的搜索系统能够检索参考文本供模型阅读，从而使其能够正确列出男子和女子锦标赛的金牌获得者。

然而，它仍然不太完美——该模型未能列出混双项目的金牌获得者。

解决错误答案问题

要查看错误是否是由于缺乏相关源文本（即搜索步骤失败）或缺乏推理可靠性（即提问步骤失败）造成的，你可以通过设置print_message=True。

在这个特定情况下，查看下面的文本，看起来为模型提供的第1篇文章确实包含所有三个项目的奖牌获得者，但后来的结果强调了男子和女子比赛，这可能分散了模型给出的注意力更完整的答案。

# set print_message=True to see the source text GPT was working off of
ask('Which athletes won the gold medal in curling at the 2022 Winter Olympics?', print_message=True)

Use the below articles on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found in the articles, write "I could not find an answer."

Wikipedia article section:
"""
List of 2022 Winter Olympics medal winners

==Curling==

{{main|Curling at the 2022 Winter Olympics}}
{|{{MedalistTable|type=Event|columns=1|width=225|labelwidth=200}}
|-valign="top"
|Men<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Men's tournament}}
|{{flagIOC|SWE|2022 Winter}}<br/>[[Niklas Edin]]<br/>[[Oskar Eriksson]]<br/>[[Rasmus Wranå]]<br/>[[Christoffer Sundgren]]<br/>[[Daniel Magnusson (curler)|Daniel Magnusson]]
|{{flagIOC|GBR|2022 Winter}}<br/>[[Bruce Mouat]]<br/>[[Grant Hardie]]<br/>[[Bobby Lammie]]<br/>[[Hammy McMillan Jr.]]<br/>[[Ross Whyte]]
|{{flagIOC|CAN|2022 Winter}}<br/>[[Brad Gushue]]<br/>[[Mark Nichols (curler)|Mark Nichols]]<br/>[[Brett Gallant]]<br/>[[Geoff Walker (curler)|Geoff Walker]]<br/>[[Marc Kennedy]]
|-valign="top"
|Women<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Women's tournament}}
|{{flagIOC|GBR|2022 Winter}}<br/>[[Eve Muirhead]]<br/>[[Vicky Wright]]<br/>[[Jennifer Dodds]]<br/>[[Hailey Duff]]<br/>[[Mili Smith]]
|{{flagIOC|JPN|2022 Winter}}<br/>[[Satsuki Fujisawa]]<br/>[[Chinami Yoshida]]<br/>[[Yumi Suzuki]]<br/>[[Yurika Yoshida]]<br/>[[Kotomi Ishizaki]]
|{{flagIOC|SWE|2022 Winter}}<br/>[[Anna Hasselborg]]<br/>[[Sara McManus]]<br/>[[Agnes Knochenhauer]]<br/>[[Sofia Mabergs]]<br/>[[Johanna Heldin]]
|-valign="top"
|Mixed doubles<br/>{{DetailsLink|Curling at the 2022 Winter Olympics – Mixed doubles tournament}}
|{{flagIOC|ITA|2022 Winter}}<br/>[[Stefania Constantini]]<br/>[[Amos Mosaner]]
|{{flagIOC|NOR|2022 Winter}}<br/>[[Kristin Skaslien]]<br/>[[Magnus Nedregotten]]
|{{flagIOC|SWE|2022 Winter}}<br/>[[Almida de Val]]<br/>[[Oskar Eriksson]]
|}
"""

Wikipedia article section:
(更多内容略......)

Question: Which athletes won the gold medal in curling at the 2022 Winter Olympics?

"There were two gold medal-winning teams in curling at the 2022 Winter Olympics: the Swedish men's team consisting of Niklas Edin, Oskar Eriksson, Rasmus Wranå, Christoffer Sundgren, and Daniel Magnusson, and the British women's team consisting of Eve Muirhead, Vicky Wright, Jennifer Dodds, Hailey Duff, and Mili Smith."

知道这个错误是由于提问步骤中的推理不完善，而不是搜索步骤中的检索不完善造成的，所以让我们重点改进提问步骤。

改善结果的最简单方法是使用功能更强大的模型，例如GPT-4。我们来试试吧。

ask('Which athletes won the gold medal in curling at the 2022 Winter Olympics?', model="gpt-4")

"The gold medal winners in curling at the 2022 Winter Olympics are as follows:\n\nMen's tournament: Team Sweden, consisting of Niklas Edin, Oskar Eriksson, Rasmus Wranå, Christoffer Sundgren, and Daniel Magnusson.\n\nWomen's tournament: Team Great Britain, consisting of Eve Muirhead, Vicky Wright, Jennifer Dodds, Hailey Duff, and Mili Smith.\n\nMixed doubles tournament: Team Italy, consisting of Stefania Constantini and Amos Mosaner."

GPT-4的回答十分完美，正确识别了所有12名冰壶金牌得主。