【langchain】prompt
prompt源码分析PromptTemplate
提示词模板，由一个字符串模板组成，接受用户输入的参数用来生成语言模型的提示词。
from_template方法
@classmethod
def from_template(
cls,
template: str,
*,
template_format: PromptTemplateFormat = "f-string",
partial_variables: Optional] = None,
**kwargs: Any,
) -> PromptTemplate:
"""
20250708 -
template 模板内容，如果是是用f""那么变量就需要赋值，如果是不带f直接使用{}那么就不需要赋值
PromptTemplateFormat 模板的格式类型，默认是f-string
partial_variables 带变量参数的模板,部分参数
Load a prompt template from a template.
Args:
template: The template to load.
template_format: The format of the template. Use `jinja2` for jinja2,
`mustache` for mustache, and `f-string` for f-strings.
Defaults to `f-string`.
partial_variables: A dictionary of variables that can be used to partially
fill in the template. For example, if the template is
`"{variable1} {variable2}"`, and `partial_variables` is
`{"variable1": "foo"}`, then the final prompt will be
`"foo {variable2}"`. Defaults to None.
kwargs: Any other arguments to pass to the prompt template.
Returns:
The prompt template loaded from the template.
"""
# 20250708 - 从模板中提取变量，默认是{}类型的变量
input_variables = get_template_variables(template, template_format)
# 20250708 - 字典对象，可以部分不设置
_partial_variables = partial_variables or {}
# 20250708 - 去掉部分不需要设置的参数
if _partial_variables:
input_variables = [
var for var in input_variables if var not in _partial_variables
]
# 20250708 - 实例化对象
return cls(
input_variables=input_variables,
template=template,
template_format=template_format,# type: ignore
partial_variables=_partial_variables,
**kwargs,
)from langchain_core.prompts import PromptTemplate
t1 = "这是一个变量t1"
PromptTemplate.from_template(f"测试template_format：{t1}")
PromptTemplate.from_template("测试template_format：{t2}")
template1 = PromptTemplate.from_template("可以提前设置的变量{t1}，测试template_format：{t2}", partial_variables={"t1":t1})
template1.format(t2 = 'xxxxx')
ChatPromptTemplate
这个提示词模板主要用于格式化消息列表，适用于多轮对话
template = ChatPromptTemplate([
("system", "You are a helpful AI bot. Your name is {name}."),
("human", "Hello, how are you doing?"),
("ai", "I'm doing well, thanks!"),
("human", "{user_input}"),
])
from langchain_core.prompts import ChatPromptTemplate
prompt_template = ChatPromptTemplate([
("system", "You are a helpful assistant."),
("user", "给我讲一个关于 {topic} 的笑话"),
("assistant", "有一天，小明问他爸爸：“爸爸，我是不是傻孩子啊？”\n爸爸说：“傻孩子，你怎么会是傻孩子呢？”")
])
prompt_template.invoke({"topic": "小孩"})
prompt_template1 = ChatPromptTemplate([
("assistant", "有一天，小明问他爸爸：“爸爸，我是不是傻孩子啊？”\n爸爸说：“傻孩子，你怎么会是傻孩子呢？”"),
("ai", "有一天sssssssscccccxxx"),
("placeholder", "{param_test1}"),
("placeholder", ["{param_test1}", True]),
])
prompt_template1
MessagesPlaceholder & callback
在刚刚的chat提示词模板中可以格式化多个消息列表，每个消息都是字符串，但是如果我们希望直接传一个消息数组，并放入特定位置，那么就需要用到这个类
其中ChatPromptTemplate的runnableconfig的callbacks对象是从外部传入handler对象，这个handler对象会传递到内部的callbackmanager对象，然后在通过configure内部的构造方法设置inheritable_callbacks对象为外部传入的callbackhandler对象
最后触发调用on_chain_start -》 handle_event -》 event = getattr(handler, event_name)(*args, **kwargs) -》 触发回调
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.messages import HumanMessage, AIMessage
from langchain.callbacks.base import BaseCallbackHandler
from langchain_core.runnables import RunnableConfig
prompt_template = ChatPromptTemplate([
("system", "You are a helpful assistant"),
MessagesPlaceholder("msgs"),
("placeholder", ["{param_test1}", True])
])
# 自定义回调类
class MyCustomHandler(BaseCallbackHandler):
def on_chain_start(self, serialized, input, **kwargs):
print(f"开始执行: {input}")
def on_chain_end(self, output, **kwargs):
print(f"执行完成: {output}")
config = RunnableConfig(
callbacks=
)
pv1 = prompt_template.invoke({"msgs": , "param_test1":[
("user", "给我讲一个关于 {topci1} 的笑话"),
("ai", "有一天sssssssscccccxxx")
], "topci1":"cess"}, config=config)
print(prompt_template.pretty_repr())
pv1
也可以直接用placeholder代表MessagesPlaceholder
prompt_template = ChatPromptTemplate([
("system", "You are a helpful assistant"),
("placeholder", "{msgs}") # <-- This is the changed part
])
prompt_template.invoke({"msgs": })实质的底层调用format_prompt函数，进行数据格式化
少样本提示词模板:
# 调用invoke的时候
return self._call_with_config(
self._format_prompt_with_error_handling,
input,
config,
run_type="prompt",
serialized=self._serialized,
)
with set_config_context(child_config) as context:
output = cast(
"Output",
context.run(
call_func_with_variable_args,# type: ignore
func,
input,
config,
run_manager,
**kwargs,
),
)# 调用invoke的时候
return self._call_with_config(
self._format_prompt_with_error_handling,
input,
config,
run_type="prompt",
serialized=self._serialized,
)
with set_config_context(child_config) as context:
output = cast(
"Output",
context.run(
call_func_with_variable_args,# type: ignore
func,
input,
config,
run_manager,
**kwargs,
),
)
在调用大模型的时候，光有角色设定，用户输入得到的效果一般会比较差，需要通过给大模型一些参考样例，才能得到比较好的效果
from langchain_core.prompts import PromptTemplateexample_prompt = PromptTemplate.from_template("Question: {question}\n{answer}")examples = [ { "question": "Who lived longer, Muhammad Ali or Alan Turing?", "answer": """1Are 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""", },]from langchain_core.prompts import # 调用invoke的时候
return self._call_with_config(
self._format_prompt_with_error_handling,
input,
config,
run_type="prompt",
serialized=self._serialized,
)
with set_config_context(child_config) as context:
output = cast(
"Output",
context.run(
call_func_with_variable_args,# type: ignore
func,
input,
config,
run_manager,
**kwargs,
),
)prompt = # 调用invoke的时候
return self._call_with_config(
self._format_prompt_with_error_handling,
input,
config,
run_type="prompt",
serialized=self._serialized,
)
with set_config_context(child_config) as context:
output = cast(
"Output",
context.run(
call_func_with_variable_args,# type: ignore
func,
input,
config,
run_manager,
**kwargs,
),
)( examples=examples, example_prompt=example_prompt, suffix="Question: {input}", input_variables=["input"],)print( prompt.invoke({"input": "Who was the father of Mary Ball Washington?"}).to_string())
[*]提示词模板部分格式化：
FewShotPromptTemplatefrom langchain_core.prompts import PromptTemplate
example_prompt = PromptTemplate.from_template("Question: {question}\n{answer}")
examples = [
{
"question": "Who lived longer, Muhammad Ali or Alan Turing?",
"answer": """1
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
""",
},
]
from langchain_core.prompts import FewShotPromptTemplate
prompt = FewShotPromptTemplate(
examples=examples,
example_prompt=example_prompt,
suffix="Question: {input}",
input_variables=["input"],
)
print(
prompt.invoke({"input": "Who was the father of Mary Ball Washington?"}).to_string()
)
[*]提示词组合
多个提示词连接起来，提示词内容可以有字符串，也可以是变量占位符
Partial with strings
Partial with functions总结
[*]prompt默认一般就是f-string类型的字符串，通过变量替换的方式支持修改prompt
[*]message支持有4种类型的消息：human，ai，system，placeholder，分别代表用户输入，ai生成，系统提示，最后有个位置占位符用来匹配不确定的内容，内容是数组
参考：
python中的@classmethod的作用
https://blog.csdn.net/qq_23981335/article/details/103798741
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