LangChain Decorators ✨

Disclaimer: `LangChain decorators` is not created by the LangChain team and is not supported by it.

LangChain decorators is a layer on the top of LangChain that provides syntactic sugar 🍭 for writing custom langchain prompts and chains

For Feedback, Issues, Contributions - please raise an issue here: ju-bezdek/langchain-decorators

主要原则和优势：

编写代码的 pythonic 种方法
编写多行提示，不会因缩进而破坏你的代码流程
利用IDE内置的提示、类型检查和带文档的弹出窗口功能，快速查看函数以了解其提示、参数等。
利用 🦜🔗 LangChain 生态系统的全部力量
添加对可选参数的支持
通过将参数绑定到一个类，可以轻松在提示之间共享参数。

这是一个使用LangChain 装饰器 ✨编写的简单代码示例

@llm_prompt
def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers")->str:
    """
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)
    """
    return

# run it naturally
write_me_short_post(topic="starwars")
# or
write_me_short_post(topic="starwars", platform="redit")

快速入门

安装

pip install langchain_decorators

示例

一个好的开始方法是查看这里的示例：

定义其他参数

在这里，我们只是使用 llm_prompt 装饰器将一个函数标记为提示，从而将其有效地转换为 LLMChain。而不是运行它

标准的LLMchain需要比inputs_variables和prompt更多的初始化参数……这里有一个在装饰器中隐藏的实现细节。它是这样工作的：

使用全局设置：

# define global settings for all prompty (if not set - chatGPT is the current default)
from langchain_decorators import GlobalSettings

GlobalSettings.define_settings(
    default_llm=ChatOpenAI(temperature=0.0), this is default... can change it here globally
    default_streaming_llm=ChatOpenAI(temperature=0.0,streaming=True), this is default... can change it here for all ... will be used for streaming
)

使用预定义的提示类型

#You can change the default prompt types
from langchain_decorators import PromptTypes, PromptTypeSettings

PromptTypes.AGENT_REASONING.llm = ChatOpenAI()

# Or you can just define your own ones:
class MyCustomPromptTypes(PromptTypes):
    GPT4=PromptTypeSettings(llm=ChatOpenAI(model="gpt-4"))

@llm_prompt(prompt_type=MyCustomPromptTypes.GPT4) 
def write_a_complicated_code(app_idea:str)->str:
    ...

在装饰器中直接定义设置

from langchain_openai import OpenAI

@llm_prompt(
    llm=OpenAI(temperature=0.7),
    stop_tokens=["\nObservation"],
    ...
    )
def creative_writer(book_title:str)->str:
    ...

API 参考：OpenAI

传递记忆和/或回调：

要通过这些，只需在函数中声明它们（或使用kwargs传递任何内容）。

@llm_prompt()
async def write_me_short_post(topic:str, platform:str="twitter", memory:SimpleMemory = None):
    """
    {history_key}
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)
    """
    pass

await write_me_short_post(topic="old movies")

简化流式处理

如果我们想利用流式传输：

我们需要将提示定义为异步函数。
在装饰器中开启流式传输，或者我们可以定义带有流式传输的 PromptType。
使用 StreamingContext 捕获流

这样我们只需标记哪些提示应该流式传输，而无需纠结使用哪个大型语言模型，也不用在链的特定部分传递创建和分发流式处理程序……只需在提示/提示类型上开启或关闭流式传输即可。

流式传输仅在我们在流式上下文中调用时才会发生……在那里我们可以定义一个简单的函数来处理流。

# this code example is complete and should run as it is

from langchain_decorators import StreamingContext, llm_prompt

# this will mark the prompt for streaming (useful if we want stream just some prompts in our app... but don't want to pass distribute the callback handlers)
# note that only async functions can be streamed (will get an error if it's not)
@llm_prompt(capture_stream=True) 
async def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"):
    """
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)
    """
    pass



# just an arbitrary  function to demonstrate the streaming... will be some websockets code in the real world
tokens=[]
def capture_stream_func(new_token:str):
    tokens.append(new_token)

# if we want to capture the stream, we need to wrap the execution into StreamingContext... 
# this will allow us to capture the stream even if the prompt call is hidden inside higher level method
# only the prompts marked with capture_stream will be captured here
with StreamingContext(stream_to_stdout=True, callback=capture_stream_func):
    result = await run_prompt()
    print("Stream finished ... we can distinguish tokens thanks to alternating colors")


print("\nWe've captured",len(tokens),"tokens🎉\n")
print("Here is the result:")
print(result)

提示声明

默认情况下，提示是整个函数文档，除非你标记了你的提示。

记录您的提示

我们可以指定文档的哪一部分是提示定义，方法是通过指定一个带有 <prompt> 语言标签的代码块

@llm_prompt
def write_me_short_post(topic:str, platform:str="twitter", audience:str = "developers"):
    """
    Here is a good way to write a prompt as part of a function docstring, with additional documentation for devs.

    It needs to be a code block, marked as a `<prompt>` language
    ```<prompt>
    Write me a short header for my post about {topic} for {platform} platform. 
    It should be for {audience} audience.
    (Max 15 words)

现在只有上面的代码块将用作提示，其余的文档字符串将用作开发人员的描述。（它还有一个很好的好处，即 IDE（如 VS Code）会正确显示提示（不会尝试将其解析为 Markdown，因此无法正确显示换行）。） """ return

## Chat messages prompt

For chat models is very useful to define prompt as a set of message templates... here is how to do it:

``` python
@llm_prompt
def simulate_conversation(human_input:str, agent_role:str="a pirate"):
    """
    ## System message
     - note the `:system` suffix inside the <prompt:_role_> tag
     

    ```<prompt:system>
    You are a {agent_role} hacker. You mus act like one.
    You reply always in code, using python or javascript code block...
    for example:
    
    ... do not reply with anything else.. just with code - respecting your role.

人类消息

（我们使用的是由大型语言模型（LLM）强制执行的真实角色——GPT支持system、assistant和user角色）

Helo, who are you

回复：

\``` python <<- escaping inner code block with \ that should be part of the prompt
def hello():
    print("Argh... hello you pesky pirate")
\```

我们还可以使用占位符添加一些历史记录。

{history}

{human_input}

现在只有上面的代码块将用作提示，其余的文档字符串将用作开发人员的描述。（它还有一个很好的好处，即 IDE（如 VS Code）会正确显示提示（不会尝试将其解析为 Markdown，因此无法正确显示换行）。） """ pass

the roles here are model native roles (assistant, user, system for chatGPT)



# Optional sections
- you can define a whole sections of your prompt that should be optional
- if any input in the section is missing, the whole section won't be rendered

the syntax for this is as follows:

``` python
@llm_prompt
def prompt_with_optional_partials():
    """
    this text will be rendered always, but

    {? anything inside this block will be rendered only if all the {value}s parameters are not empty (None | "")   ?}

    you can also place it in between the words
    this too will be rendered{? , but
        this  block will be rendered only if {this_value} and {this_value}
        is not empty?} !
    """

输出解析器

llm_prompt 装饰器会原生地尝试根据输出类型检测最佳的输出解析器。（如果未设置，则返回原始字符串）
列表、字典和 Pydantic 输出也原生（自动）支持。

# this code example is complete and should run as it is

from langchain_decorators import llm_prompt

@llm_prompt
def write_name_suggestions(company_business:str, count:int)->list:
    """ Write me {count} good name suggestions for company that {company_business}
    """
    pass

write_name_suggestions(company_business="sells cookies", count=5)

更复杂的结构

对于字典或 pydantic，你需要指定格式化指令…… 这可能会很繁琐，这就是为什么你可以让输出解析器根据模型（pydantic）为你生成指令。

from langchain_decorators import llm_prompt
from pydantic import BaseModel, Field


class TheOutputStructureWeExpect(BaseModel):
    name:str = Field (description="The name of the company")
    headline:str = Field( description="The description of the company (for landing page)")
    employees:list[str] = Field(description="5-8 fake employee names with their positions")

@llm_prompt()
def fake_company_generator(company_business:str)->TheOutputStructureWeExpect:
    """ Generate a fake company that {company_business}
    {FORMAT_INSTRUCTIONS}
    """
    return

company = fake_company_generator(company_business="sells cookies")

# print the result nicely formatted
print("Company name: ",company.name)
print("company headline: ",company.headline)
print("company employees: ",company.employees)

将提示绑定到一个对象

from pydantic import BaseModel
from langchain_decorators import llm_prompt

class AssistantPersonality(BaseModel):
    assistant_name:str
    assistant_role:str
    field:str

    @property
    def a_property(self):
        return "whatever"

    def hello_world(self, function_kwarg:str=None):
        """
        We can reference any {field} or {a_property} inside our prompt... and combine it with {function_kwarg} in the method
        """

    
    @llm_prompt
    def introduce_your_self(self)->str:
        """
        ``` <prompt:system>
        You are an assistant named {assistant_name}. 
        Your role is to act as {assistant_role}

Introduce your self (in less than 20 words)

"""

personality = AssistantPersonality(assistant_name="约翰", assistant_role="一名海盗")

print(personality.introduce_your_self(personality))

# More examples:

- these and few more examples are also available in the [colab notebook here](https://colab.research.google.com/drive/1no-8WfeP6JaLD9yUtkPgym6x0G9ZYZOG#scrollTo=N4cf__D0E2Yk)
- including the [ReAct Agent re-implementation](https://colab.research.google.com/drive/1no-8WfeP6JaLD9yUtkPgym6x0G9ZYZOG#scrollTo=3bID5fryE2Yp) using purely langchain decorators