入门指南#
在本教程中，我们将学习：
-
什么是提示模板，以及为什么需要它，
-
如何创建提示模板，
-
如何将少量示例传递给提示模板，
-
如何为提示模板选择示例。
什么是提示模板？#
提示模板是生成提示的可重复方法。它包含一个文本字符串（“模板”），该字符串可以从最终用户那里接收一组参数并生成提示。
提示模板可能包含：
-
对语言模型的指导，
-
一组少量示例，以帮助语言模型生成更好的响应，
-
对语言模型的提问。
以下代码片段包含提示模板的示例：
from langchain import PromptTemplate
template = """
I want you to act as a naming consultant for new companies.
What is a good name for a company that makes {product}?
"""
prompt = PromptTemplate(
input_variables=["product"],
template=template,
)
prompt.format(product="colorful socks")
# -> I want you to act as a naming consultant for new companies.
# -> What is a good name for a company that makes colorful socks?
创建提示模板#
您可以使用PromptTemplate类创建简单的硬编码提示。提示模板可以使用任意数量的输入变量，并可以进行格式化以生成提示。
from langchain import PromptTemplate
# An example prompt with no input variables
no_input_prompt = PromptTemplate(input_variables=[], template="Tell me a joke.")
no_input_prompt.format()
# -> "Tell me a joke."
# An example prompt with one input variable
one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")
one_input_prompt.format(adjective="funny")
# -> "Tell me a funny joke."
# An example prompt with multiple input variables
multiple_input_prompt = PromptTemplate(
input_variables=["adjective", "content"],
template="Tell me a {adjective} joke about {content}."
)
multiple_input_prompt.format(adjective="funny", content="chickens")
# -> "Tell me a funny joke about chickens."
如果您不想手动指定input_variables，您也可以使用from_template类方法创建PromptTemplate。 langchain将根据传递的template自动推断input_variables。
template = "Tell me a {adjective} joke about {content}."
prompt_template = PromptTemplate.from_template(template)
prompt_template.input_variables
# -> ['adjective', 'content']
prompt_template.format(adjective="funny", content="chickens")
# -> Tell me a funny joke about chickens.
您可以创建自定义提示模板，以任何您想要的方式格式化提示。有关更多信息，请参见自定义提示模板。
模板格式#
默认情况下，PromptTemplate 会将提供的模板作为 Python f-string 处理。您可以通过 template_format 参数指定其他模板格式：
# Make sure jinja2 is installed before running this
jinja2_template = "Tell me a {{ adjective }} joke about {{ content }}"
prompt_template = PromptTemplate.from_template(template=jinja2_template, template_format="jinja2")
prompt_template.format(adjective="funny", content="chickens")
# -> Tell me a funny joke about chickens.
目前，PromptTemplate 仅支持 jinja2 和 f-string 模板格式。如果您想使用其他模板格式，请随时在 Github (opens in a new tab) 页面上开启一个 issue。
验证模板#
默认情况下，PromptTemplate 会通过检查 template 字符串中定义的变量是否与 input_variables 中的变量匹配来验证模板。您可以通过将 validate_template 设置为 False 来禁用此行为。
template = "I am learning langchain because {reason}."
prompt_template = PromptTemplate(template=template,
input_variables=["reason", "foo"]) # ValueError due to extra variables
prompt_template = PromptTemplate(template=template,
input_variables=["reason", "foo"],
validate_template=False) # No error
序列化提示模板#
您可以将PromptTemplate保存到本地文件系统中。 langchain会自动通过文件扩展名推断文件格式。当前，langchain支持将模板保存为YAML和JSON文件。
prompt_template.save("awesome_prompt.json") # Save to JSON file
from langchain.prompts import load_prompt
loaded_prompt = load_prompt("awesome_prompt.json")
assert prompt_template == loaded_prompt
langchain还支持从LangChainHub加载提示模板，其中包含您可以在项目中使用的有用提示的集合。您可以在此处 (opens in a new tab)了解有关LangChainHub和可用提示的更多信息。
from langchain.prompts import load_prompt
prompt = load_prompt("lc://prompts/conversation/prompt.json")
prompt.format(history="", input="What is 1 + 1?")
您可以在如何序列化提示中了解有关序列化提示模板的更多信息。
将少量示例传递给提示模板#
few shot examples 是一组示例，可用于帮助语言模型生成更好的响应。
要使用 few shot examples 生成提示，可以使用 FewShotPromptTemplate。这个类接受一个 PromptTemplate 和一个 few shot examples 列表。然后，它将用 few shot examples 格式化提示模板。
在这个例子中，我们将创建一个提示来生成单词的反义词。
from langchain import PromptTemplate, FewShotPromptTemplate
# First, create the list of few shot examples.
examples = [
{"word": "happy", "antonym": "sad"},
{"word": "tall", "antonym": "short"},
]
# Next, we specify the template to format the examples we have provided.
# We use the `PromptTemplate` class for this.
example_formatter_template = """
Word: {word}
Antonym: {antonym}\n
"""
example_prompt = PromptTemplate(
input_variables=["word", "antonym"],
template=example_formatter_template,
)
# Finally, we create the `FewShotPromptTemplate` object.
few_shot_prompt = FewShotPromptTemplate(
# These are the examples we want to insert into the prompt.
examples=examples,
# This is how we want to format the examples when we insert them into the prompt.
example_prompt=example_prompt,
# The prefix is some text that goes before the examples in the prompt.
# Usually, this consists of intructions.
prefix="Give the antonym of every input",
# The suffix is some text that goes after the examples in the prompt.
# Usually, this is where the user input will go
suffix="Word: {input}\nAntonym:",
# The input variables are the variables that the overall prompt expects.
input_variables=["input"],
# The example_separator is the string we will use to join the prefix, examples, and suffix together with.
example_separator=" ",
)
# We can now generate a prompt using the `format` method.
print(few_shot_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: tall
# -> Antonym: short
# ->
# -> Word: big
# -> Antonym:
选择提示模板的示例#
如果你有大量的示例，可以使用 ExampleSelector 来选择一组最具信息量的示例，以帮助语言模型生成更好的响应。这将帮助你生成更可能生成良好响应的提示。
在下面的示例中，我们将使用基于输入长度选择示例的 LengthBasedExampleSelector。当你担心构建的提示会超过上下文窗口的长度时，这很有用。对于较长的输入，它将选择较少的示例进行包含，而对于较短的输入，它将选择更多的示例。
我们将继续使用前一个部分的示例，但这次我们将使用 LengthBasedExampleSelector 来选择示例。
from langchain.prompts.example_selector import LengthBasedExampleSelector
# These are a lot of examples of a pretend task of creating antonyms.
examples = [
{"word": "happy", "antonym": "sad"},
{"word": "tall", "antonym": "short"},
{"word": "energetic", "antonym": "lethargic"},
{"word": "sunny", "antonym": "gloomy"},
{"word": "windy", "antonym": "calm"},
]
# We'll use the `LengthBasedExampleSelector` to select the examples.
example_selector = LengthBasedExampleSelector(
# These are the examples is has available to choose from.
examples=examples,
# This is the PromptTemplate being used to format the examples.
example_prompt=example_prompt,
# This is the maximum length that the formatted examples should be.
# Length is measured by the get_text_length function below.
max_length=25,
)
# We can now use the `example_selector` to create a `FewShotPromptTemplate`.
dynamic_prompt = FewShotPromptTemplate(
# We provide an ExampleSelector instead of examples.
example_selector=example_selector,
example_prompt=example_prompt,
prefix="Give the antonym of every input",
suffix="Word: {input}\nAntonym:",
input_variables=["input"],
example_separator=" ",
)
# We can now generate a prompt using the `format` method.
print(dynamic_prompt.format(input="big"))
# -> Give the antonym of every input
# ->
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: tall
# -> Antonym: short
# ->
# -> Word: energetic
# -> Antonym: lethargic
# ->
# -> Word: sunny
# -> Antonym: gloomy
# ->
# -> Word: windy
# -> Antonym: calm
# ->
# -> Word: big
# -> Antonym:
相比之下，如果我们提供一个非常长的输入，LengthBasedExampleSelector将选择较少的示例包含在提示中。
long_string = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"
print(dynamic_prompt.format(input=long_string))
# -> Give the antonym of every input
# -> Word: happy
# -> Antonym: sad
# ->
# -> Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else
# -> Antonym:
LangChain带有一些示例选择器，您可以使用它们。有关如何使用它们的更多详细信息，请参见示例选择器。
您可以创建自定义示例选择器，根据任何您想要的标准选择示例。有关如何执行此操作的更多详细信息，请参见创建自定义示例选择器。