LangChain - PromptTemplate

---

本文整理改编自：
https://www.langchain.com.cn/modules/prompts/prompt_templates
https://python.langchain.com.cn/docs/modules/model_io/prompts/

代码基于 langchain 0.1.14 测试

---

关于 Prompt

编写模型的新方式是通过提示。
一个 提示(prompt) 指的是 输入模型 的内容。
这个输入通常由多个组件构成。
LangChain 提供了多个类和函数，使构建和处理提示变得简单。

• 提示模板(Prompt templates): 为模型输入添加 参数
• 示例选择器(Example selectors): 动态选择 在提示中 包含的示例

---

关于 PromptTemplate

它包含一个文本字符串（称为：模板，template)，该字符串可以从最终用户那里 接收一组参数 并生成提示。

提示模板是生成语言模型提示的 预定义配方。

---

提示模板可能包含：

• 对语言模型的指令，
• 一组少量示例，以帮助语言模型生成更好的响应，
• 适用于特定任务的特定上下文和问题

---

LangChain提供了创建和使用提示模板的工具。

LangChain致力于创建 与模型无关 的模板，以便在不同的语言模型之间 轻松重用现有模板。

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基本创建

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您可以使用 PromptTemplate 类创建简单的 硬编码提示。
提示模板可以接受任意数量的输入变量，并可以 格式化 生成提示。

示例1

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(template = template,input_variables=['product'],
)

---

prompt 为：

    PromptTemplate(input_variables=['product'], output_parser=None, partial_variables={}, template='I want you to act as a naming consultant for new companies.\nWhat is a good name for a company that makes {product}?\n', template_format='f-string', validate_template=True)

---

prompt.format(product="colorful socks") 

---

    'I want you to act as a naming consultant for new companies.\nWhat is a good name for a company that makes colorful socks?\n'

---

---

手动指定 input_variables

---

无变量输入

no_input_prompt = PromptTemplate(input_variables=[], template="Tell me a joke.")
no_input_prompt.format()
#     'Tell me a joke.'

---

1个变量

one_input_prompt = PromptTemplate(input_variables=["adjective"], template="Tell me a {adjective} joke.")one_input_prompt.format(adjective="funny")
#    'Tell me a funny joke.'

---

多变量

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.'

---

使用 from_template 自动推断 input_variables

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.

---

聊天模板

聊天模型 以聊天消息列表作为输入 - 这个列表通常称为 prompt。
这些聊天消息与原始字符串不同（您会将其传递给 LLM 模型），因为每个消息都与一个 role 相关联。

---

要创建与角色相关联的消息模板，您可以使用 MessagePromptTemplate。

LangChain 提供了不同类型的 MessagePromptTemplate，其中最常用的是

• AIMessagePromptTemplate, 创建 AI 消息
• SystemMessagePromptTemplate , 系统消息
• HumanMessagePromptTemplate，人类消息
• ChatMessagePromptTemplate，允许用户指定角色名称

---

from langchain.prompts import (ChatPromptTemplate,PromptTemplate,SystemMessagePromptTemplate,AIMessagePromptTemplate,HumanMessagePromptTemplate,
)
from langchain.schema import (AIMessage,HumanMessage,SystemMessage
)

---

使用 from_template 方法构建

template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)

---

system_message_prompt

---

SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input_language', 'output_language'], output_parser=None, partial_variables={}, template='You are a helpful assistant that translates {input_language} to {output_language}.', template_format='f-string', validate_template=True), additional_kwargs={})

---

human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)human_message_prompt

---

HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['text'], output_parser=None, partial_variables={}, template='{text}', template_format='f-string', validate_template=True), additional_kwargs={})

---

使用 PromptTemplate 构建 MessagePromptTemplate

更直接地构建MessagePromptTemplate：可以在外部创建一个 PromptTemplate，然后将其传递进去

---

prompt=PromptTemplate(template="You are a helpful assistant that translates {input_language} to {output_language}.",input_variables=["input_language", "output_language"],
)system_message_prompt_2 = SystemMessagePromptTemplate(prompt=prompt)
system_message_prompt_2

---

    SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=['input_language', 'output_language'], output_parser=None, partial_variables={}, template='You are a helpful assistant that translates {input_language} to {output_language}.', template_format='f-string', validate_template=True), additional_kwargs={})

---

assert system_message_prompt == system_message_prompt_2

---

使用一或多个 MessagePromptTemplates 构建一个 ChatPromptTemplate

可以使用ChatPromptTemplate的format_prompt方法 - 这将返回一个PromptValue，
您可以将其转换为字符串或Message对象，具体取决于您是否想将格式化值用作llm或chat模型的输入。

chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])

---

# get a chat completion from the formatted messages
chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages()  

---

    [SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}),HumanMessage(content='I love programming.', additional_kwargs={}, example=False)]

---

使用ChatMessagePromptTemplate，指定角色名称

from langchain.prompts import ChatMessagePromptTemplateprompt = "May the {subject} be with you"chat_message_prompt = ChatMessagePromptTemplate.from_template(role="Jedi", template=prompt)chat_message_prompt.format(subject="force")

---

ChatMessage(content='May the force be with you', additional_kwargs={}, role='Jedi')

---

MessagesPlaceholder 占位符

当您不确定应该使用哪个消息提示模板的角色，或者希望在格式化期间插入消息列表时，这可能非常有用。

from langchain.prompts import MessagesPlaceholderhuman_prompt = "Summarize our conversation so far in {word_count} words."
human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)chat_prompt = ChatPromptTemplate.from_messages([ MessagesPlaceholder( variable_name="conversation" ), human_message_template] ) 

---

human_message = HumanMessage(content="What is the best way to learn programming?")
ai_message = AIMessage(content="""\
1. Choose a programming language: Decide on a programming language that you want to learn. 2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.3. Practice, practice, practice: The best way to learn programming is through hands-on experience\
""")chat_prompt.format_prompt( conversation = [human_message, ai_message],  			 word_count="10").to_messages()

---

[HumanMessage(content='What is the best way to learn programming?', additional_kwargs={}),AIMessage(content='1. Choose a programming language: Decide on a programming language that you want to learn.   2. Start with the basics: Familiarize yourself with the basic programming concepts such as variables, data types and control structures.  3. Practice, practice, practice: The best way to learn programming is through hands-on experience', additional_kwargs={}),HumanMessage(content='Summarize our conversation so far in 10 words.', additional_kwargs={})]

---

模板格式 jinja2、f-string

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：

目前，PromptTemplate 仅支持 jinja2 和 f-string 模板格式。

---

运行这些之前，需要保证 jinja2 已安装
Make sure jinja2 is installed before running this

jinja2_template = "Tell me a {{ adjective }} joke about {{ content }}"jinja2_template
# ->  'Tell me a {{ adjective }} joke about {{ content }}'

---

prompt_template = PromptTemplate.from_template(template=jinja2_template, template_format="jinja2")  
prompt_template

---

    PromptTemplate(input_variables=['adjective', 'content'], output_parser=None, partial_variables={}, template='Tell me a {{ adjective }} joke about {{ content }}', template_format='jinja2', validate_template=True)

---

prompt_template.format(adjective="funny", content="chickens")
# -> Tell me a funny joke about chickens.

---

template = "I am learning langchain because {reason}."
# ->     'I am learning langchain because {reason}.'

---

部分格式化提示模板

---

使用字符串进行部分格式化

提示模板是具有.format方法的类，该方法接受键-值映射并返回字符串（提示)，以传递给语言模型。

先获取某些变量

如：有一个需要两个变量foo和baz的提示模板。
如果在链条的早期就获取了foo值，但稍后才能获取了baz值，
那么等到在一个地方同时拥有两个变量 才将它们传递给提示模板可能会很麻烦。
相反，您可以使用foo值部分化提示模板，然后将部分化的提示模板传递下去，只需使用它即可。
代码如下：

prompt = PromptTemplate(template="{foo}{bar}", input_variables=["foo", "bar"])partial_prompt = prompt.partial(foo="foo");
partial_prompt

---

PromptTemplate(input_variables=['bar'], output_parser=None, partial_variables={'foo': 'foo'}, template='{foo}{bar}', template_format='f-string', validate_template=True)

partial_prompt.partial(foo="foo2") # 可移执行，修改无效；只能使用 partial，不能使用 format 

---

PromptTemplate(input_variables=['bar'], output_parser=None, partial_variables={'foo': 'foo2'}, template='{foo}{bar}', template_format='f-string', validate_template=True)

partial_prompt.format(bar="baz")

---

'foobaz'

partial_prompt.partial(foo="foo2") # 可移执行，修改无效；只能使用 partial，不能使用 format 

---

PromptTemplate(input_variables=['bar'], output_parser=None, partial_variables={'foo': 'foo2'}, template='{foo}{bar}', template_format='f-string', validate_template=True)

partial_prompt.format(bar="bar2") # 可以执行

---

'foobar2'

# 只使用部分变量初始化Prompt
prompt = PromptTemplate(template="{foo}{bar}", input_variables=["bar"], partial_variables={"foo": "foo"})prompt.format(bar="baz") 

---

foobaz

---

函数部分化Partial With Functions

from datetime import datetimedef _get_datetime():now = datetime.now()return now.strftime("%m/%d/%Y, %H:%M:%S")

---

prompt = PromptTemplate(template="Tell me a {adjective} joke about the day {date}", input_variables=["adjective", "date"]
);partial_prompt = prompt.partial(date=_get_datetime) # 函数作为参数partial_prompt.format(adjective="funny") 

---

Tell me a funny joke about the day 04/07/2024, 14:08:22

# 使用部分变量初始化 Promptprompt = PromptTemplate(template="Tell me a {adjective} joke about the day {date}", input_variables=["adjective"],partial_variables={"date": _get_datetime}  # 
);prompt.format(adjective="funny")

---

'Tell me a funny joke about the day 04/07/2024, 14:09:11' 

---

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。

prompt_template = PromptTemplate(template=template, input_variables=["reason", "foo"]) # ValueError due to extra variables

会报错： ValidationError: 1 validation error for PromptTemplate
因为设置需要校验

---

prompt_template = PromptTemplate(template=template, input_variables=["reason", "foo"],  validate_template=False) # 设置不校验，不会报错

---

    PromptTemplate(input_variables=['reason', 'foo'], output_parser=None, partial_variables={}, template='I am learning langchain because {reason}.', template_format='f-string', validate_template=False)

---

自定义提示模板

基本上有两种不同的提示模板可用-字符串提示模板和聊天提示模板。
字符串提示模板提供一个简单的字符串格式提示，而聊天提示模板生成一个更结构化的提示，可用于与聊天API一起使用。

在本指南中，我们将使用字符串提示模板创建自定义提示。

---

要创建一个自定义的字符串提示模板，需要满足两个要求：

1. 它具有input_variables属性，公开了提示模板预期的输入变量。
2. 它公开了一个format方法，该方法接受与预期的input_variables相对应的关键字参数，并返回格式化后的提示。

---

下例创建一个自定义的提示模板，它以函数名作为输入，并格式化提示以提供函数的源代码。
为了实现这一点，让我们首先创建一个根据函数名 返回 函数源代码的函数。

import inspectdef get_source_code(function_name):# Get the source code of the functionreturn inspect.getsource(function_name)

---

接下来，我们将创建一个自定义提示模板，该模板将函数名称作为输入，并格式化提示模板 以提供函数的源代码。

from langchain.prompts import StringPromptTemplate
from pydantic import BaseModel, validatorclass FunctionExplainerPromptTemplate(StringPromptTemplate, BaseModel):"""A custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function."""@validator("input_variables")def validate_input_variables(cls, v):"""Validate that the input variables are correct."""if len(v) != 1 or "function_name" not in v:raise ValueError("function_name must be the only input_variable.")return vdef format(self, **kwargs) -> str:# Get the source code of the functionsource_code = get_source_code(kwargs["function_name"])# Generate the prompt to be sent to the language modelprompt = f"""Given the function name and source code, generate an English language explanation of the function.Function Name: {kwargs["function_name"].__name__}Source Code:{source_code}Explanation:"""return promptdef _prompt_type(self):return "function-explainer"

---

使用它来为我们的任务生成提示

fn_explainer = FunctionExplainerPromptTemplate(input_variables=["function_name"])# 为函数"get_source_code"生成一个提示
prompt = fn_explainer.format(function_name=get_source_code)
print(prompt)

---

FewShotPromptTemplate

from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.prompt import PromptTemplateexamples = [{"question": "Who lived longer, Muhammad Ali or Alan Turing?","answer": 
"""
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Alan Turing when he died?
Intermediate answer: Alan Turing was 41 years old when he died.
So the final answer is: Muhammad Ali
"""},{"question": "When was the founder of craigslist born?","answer": 
"""
Are follow up questions needed here: Yes.
Follow up: Who was the founder of craigslist?
Intermediate answer: Craigslist was founded by Craig Newmark.
Follow up: When was Craig Newmark born?
Intermediate answer: Craig Newmark was born on December 6, 1952.
So the final answer is: December 6, 1952
"""},{"question": "Who was the maternal grandfather of George Washington?","answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who was the mother of George Washington?
Intermediate answer: The mother of George Washington was Mary Ball Washington.
Follow up: Who was the father of Mary Ball Washington?
Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
So the final answer is: Joseph Ball
"""},{"question": "Are both the directors of Jaws and Casino Royale from the same country?","answer":
"""
Are follow up questions needed here: Yes.
Follow up: Who is the director of Jaws?
Intermediate Answer: The director of Jaws is Steven Spielberg.
Follow up: Where is Steven Spielberg from?
Intermediate Answer: The United States.
Follow up: Who is the director of Casino Royale?
Intermediate Answer: The director of Casino Royale is Martin Campbell.
Follow up: Where is Martin Campbell from?
Intermediate Answer: New Zealand.
So the final answer is: No
"""}
]

---

配置一个将少量示例 格式化为字符串的格式化程序。
该格式化程序应该是一个 PromptTemplate 对象。

example_prompt = PromptTemplate(input_variables=["question", "answer"], template="Question: {question}\n{answer}")print(example_prompt.format(**examples[0]))

    Question: Who lived longer, Muhammad Ali or Alan Turing?Are follow up questions needed here: Yes.Follow up: How old was Muhammad Ali when he died?Intermediate answer: Muhammad Ali was 74 years old when he died.Follow up: How old was Alan Turing when he died?Intermediate answer: Alan Turing was 41 years old when he died.So the final answer is: Muhammad Ali

---

将示例和格式化程序提供给 FewShotPromptTemplate

prompt = FewShotPromptTemplate(examples=examples, example_prompt=example_prompt, suffix="Question: {input}", input_variables=["input"]
)print(prompt.format(input="Who was the father of Mary Ball Washington?"))

---

PipelinePrompt 组合提示

PipelinePrompt 由两个主要部分组成:

• 最终提示: 返回的最终提示
• 管道提示: 由一个字符串名称和一个提示模板组成的元组列表。
  每个提示模板将被格式化，然后作为相同名称的变量传递给未来的提示模板。

from langchain.prompts.pipeline import PipelinePromptTemplate
from langchain.prompts.prompt import PromptTemplate

full_template = """{introduction}{example}{start}"""
full_prompt = PromptTemplate.from_template(full_template)

introduction_template = """You are impersonating {person}."""
introduction_prompt = PromptTemplate.from_template(introduction_template)

example_template = """Here's an example of an interaction: Q: {example_q}
A: {example_a}"""
example_prompt = PromptTemplate.from_template(example_template)

start_template = """Now, do this for real!Q: {input}
A:"""
start_prompt = PromptTemplate.from_template(start_template)

input_prompts = [("introduction", introduction_prompt),("example", example_prompt),("start", start_prompt)
]
pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt, pipeline_prompts=input_prompts)

pipeline_prompt.input_variables
# -> ['example_a', 'person', 'example_q', 'input']

print(pipeline_prompt.format(person="Elon Musk",example_q="What's your favorite car?",example_a="Telsa",input="What's your favorite social media site?"
))

---

    You are impersonating Elon Musk.Here's an example of an interaction: Q: What's your favorite car?A: TelsaNow, do this for real!Q: What's your favorite social media site?A:

---

示例选择器

我们不会直接将示例提供给 FewShotPromptTemplate 对象，而是将它们提供给一个 ExampleSelector 对象。

在本例中，我们将使用 SemanticSimilarityExampleSelector 类。该类根据输入与少量示例的相似性选择少量示例。
它使用嵌入模型计算输入与少量示例之间的相似性，以及向量存储执行最近邻搜索。

from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddingsexample_selector = SemanticSimilarityExampleSelector.from_examples(# This is the list of examples available to select from.examples,# This is the embedding class used to produce embeddings which are used to measure semantic similarity.OpenAIEmbeddings(),# This is the VectorStore class that is used to store the embeddings and do a similarity search over.Chroma,# This is the number of examples to produce.k=1
)# Select the most similar example to the input.
question = "Who was the father of Mary Ball Washington?"
selected_examples = example_selector.select_examples({"question": question})
print(f"Examples most similar to the input: {question}")
for example in selected_examples:print("\n")for k, v in example.items():print(f"{k}: {v}")

---

示例选择器提供给 FewShotPromptTemplate
最后，创建一个 FewShotPromptTemplate 对象。该对象接受示例选择器和少量示例的格式化程序。

prompt = FewShotPromptTemplate(example_selector=example_selector, example_prompt=example_prompt, suffix="Question: {input}", input_variables=["input"]
)print(prompt.format(input="Who was the father of Mary Ball Washington?"))

---

Chat模型的少样本示例

AIMessagePromptTemplate / HumanMessagePromptTemplate 交替

from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate, LLMChain
from langchain.prompts.chat import (ChatPromptTemplate,SystemMessagePromptTemplate,AIMessagePromptTemplate,HumanMessagePromptTemplate,
)
from langchain.schema import AIMessage, HumanMessage, SystemMessage

---

chat = ChatOpenAI(temperature=0)template = "You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = HumanMessagePromptTemplate.from_template("Hi")
example_ai = AIMessagePromptTemplate.from_template("Argh me mateys")
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, example_human, example_ai, human_message_prompt]
)
chain = LLMChain(llm=chat, prompt=chat_prompt)
# get a chat completion from the formatted messages
chain.run("I love programming.")
# -> "I be lovin' programmin', me hearty!"

---

SystemMessagePromptTemplate

template = "You are a helpful assistant that translates english to pirate."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
example_human = SystemMessagePromptTemplate.from_template("Hi", additional_kwargs={"name": "example_user"}
)
example_ai = SystemMessagePromptTemplate.from_template("Argh me mateys", additional_kwargs={"name": "example_assistant"}
)
human_template = "{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, example_human, example_ai, human_message_prompt]
)
chain = LLMChain(llm=chat, prompt=chat_prompt)
# get a chat completion from the formatted messages
chain.run("I love programming.")
# -> "I be lovin' programmin', me hearty."

---

---

---

---

---

输出的格式 Format output

format_prompt方法的输出可以作为字符串、消息列表和ChatPromptValue使用。

---

1、作为字符串

output = chat_prompt.format(input_language="English", output_language="French", text="I love programming.")output
# -> 'System: You are a helpful assistant that translates English to French.\nHuman: I love programming.'

---

下面代码和上面等效

output_2 = chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_string()assert output == output_2

---

2、作为ChatPromptValue

chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.")

---

    ChatPromptValue(messages=[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}), HumanMessage(content='I love programming.', additional_kwargs={}, example=False)])

---

3、作为消息对象列表：

chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages() 

---

[SystemMessage(content='You are a helpful assistant that translates English to French.', additional_kwargs={}), HumanMessage(content='I love programming.', additional_kwargs={})] 

---

---

输出解析器

https://www.langchain.com.cn/modules/prompts/output_parsers

输出解析器是帮助 结构化语言模型响应 的类。

有两种主要的方法，一个输出解析器必须实现：

• get_format_instructions() -> str：一个方法，返回一个包含有关 如何格式化语言模型输出 的字符串。
• parse(str) -> Any：一个方法，接受一个字符串（假定为语言模型的响应) 并将其解析为某个结构。

然后是一个可选的：

• parse_with_prompt(str) -> Any：一个方法，它接受一个字符串（假设是语言模型的响应）和一个提示（假设是生成这样的响应的提示），并将其解析为某种结构。提示在此大多数情况下是为了提供信息以便OutputParser重新尝试或以某种方式修复输出。

---

序列化

在高层次上，序列化遵循以下设计原则：

1. 支持JSON和YAML。我们希望支持人类在磁盘上可读的序列化方法，而YAML和JSON是其中最流行的方法之一。
   请注意，此规则适用于提示。对于其他资产，如示例，可能支持不同的序列化方法。
2. 我们支持将所有内容都存储在一个文件中，或者将不同的组件（模板、示例等）存储在不同的文件中并进行引用。
   对于某些情况，将所有内容存储在一个文件中是最合理的，但对于其他情况，最好拆分一些资产（长模板、大型示例、可复用组件）。LangChain同时支持两种方式。

还有一个单一入口点可以从磁盘加载提示，这样可以轻松加载任何类型的提示。

更多本地存储示例，可见：https://python.langchain.com.cn/docs/modules/model_io/prompts/prompt_templates/prompt_serialization

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本地存储加载 json

prompt_template.save("awesome_prompt.json")

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查看文件

!cat awesome_prompt.json

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内容如下

    {"input_variables": ["reason","foo"],"output_parser": null,"partial_variables": {},"template": "I am learning langchain because {reason}.","template_format": "f-string","validate_template": false,"_type": "prompt"}

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from langchain.prompts import load_prompt
loaded_prompt = load_prompt("awesome_prompt.json")

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loaded_prompt

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    PromptTemplate(input_variables=['reason', 'foo'], output_parser=None, partial_variables={}, template='I am learning langchain because {reason}.', template_format='f-string', validate_template=False)

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2024-04-08（一）