LangChain 表达式语言速查表

这是所有最重要的LCEL原语的快速参考。有关更高级的用法，请参阅LCEL操作指南和完整的API参考。

调用一个可运行对象

Runnable.invoke() / Runnable.ainvoke()

from langchain_core.runnables import RunnableLambda

runnable = RunnableLambda(lambda x: str(x))
runnable.invoke(5)

# Async variant:
# await runnable.ainvoke(5)

API Reference:RunnableLambda

'5'

批量处理可运行对象

Runnable.batch() / Runnable.abatch()

from langchain_core.runnables import RunnableLambda

runnable = RunnableLambda(lambda x: str(x))
runnable.batch([7, 8, 9])

# Async variant:
# await runnable.abatch([7, 8, 9])

API Reference:RunnableLambda

['7', '8', '9']

流式运行一个可执行程序

Runnable.stream() / Runnable.astream()

from langchain_core.runnables import RunnableLambda


def func(x):
    for y in x:
        yield str(y)


runnable = RunnableLambda(func)

for chunk in runnable.stream(range(5)):
    print(chunk)

# Async variant:
# async for chunk in await runnable.astream(range(5)):
#     print(chunk)

API Reference:RunnableLambda

组合可运行对象

管道操作符 `|`

from langchain_core.runnables import RunnableLambda

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)

chain = runnable1 | runnable2

chain.invoke(2)

API Reference:RunnableLambda

[{'foo': 2}, {'foo': 2}]

并行调用可运行对象

RunnableParallel

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)

chain = RunnableParallel(first=runnable1, second=runnable2)

chain.invoke(2)

API Reference:RunnableLambda | RunnableParallel

{'first': {'foo': 2}, 'second': [2, 2]}

将任何函数转换为可运行的

RunnableLambda

from langchain_core.runnables import RunnableLambda

def func(x):
    return x + 5

runnable = RunnableLambda(func)
runnable.invoke(2)

API Reference:RunnableLambda

合并输入和输出字典

RunnablePassthrough.assign

from langchain_core.runnables import RunnableLambda, RunnablePassthrough

runnable1 = RunnableLambda(lambda x: x["foo"] + 7)

chain = RunnablePassthrough.assign(bar=runnable1)

chain.invoke({"foo": 10})

API Reference:RunnableLambda | RunnablePassthrough

{'foo': 10, 'bar': 17}

在输出字典中包含输入字典

RunnablePassthrough

from langchain_core.runnables import (
    RunnableLambda,
    RunnableParallel,
    RunnablePassthrough,
)

runnable1 = RunnableLambda(lambda x: x["foo"] + 7)

chain = RunnableParallel(bar=runnable1, baz=RunnablePassthrough())

chain.invoke({"foo": 10})

API Reference:RunnableLambda | RunnableParallel | RunnablePassthrough

{'bar': 17, 'baz': {'foo': 10}}

添加默认调用参数

Runnable.bind

from typing import Optional

from langchain_core.runnables import RunnableLambda


def func(main_arg: dict, other_arg: Optional[str] = None) -> dict:
    if other_arg:
        return {**main_arg, **{"foo": other_arg}}
    return main_arg


runnable1 = RunnableLambda(func)
bound_runnable1 = runnable1.bind(other_arg="bye")

bound_runnable1.invoke({"bar": "hello"})

API Reference:RunnableLambda

{'bar': 'hello', 'foo': 'bye'}

添加回退

Runnable.with_fallbacks

from langchain_core.runnables import RunnableLambda

runnable1 = RunnableLambda(lambda x: x + "foo")
runnable2 = RunnableLambda(lambda x: str(x) + "foo")

chain = runnable1.with_fallbacks([runnable2])

chain.invoke(5)

API Reference:RunnableLambda

'5foo'

添加重试

Runnable.with_retry

from langchain_core.runnables import RunnableLambda

counter = -1


def func(x):
    global counter
    counter += 1
    print(f"attempt with {counter=}")
    return x / counter


chain = RunnableLambda(func).with_retry(stop_after_attempt=2)

chain.invoke(2)

API Reference:RunnableLambda

attempt with counter=0
attempt with counter=1

2.0

配置可运行执行

RunnableConfig

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)
runnable3 = RunnableLambda(lambda x: str(x))

chain = RunnableParallel(first=runnable1, second=runnable2, third=runnable3)

chain.invoke(7, config={"max_concurrency": 2})

API Reference:RunnableLambda | RunnableParallel

{'first': {'foo': 7}, 'second': [7, 7], 'third': '7'}

添加默认配置到可运行对象

Runnable.with_config

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)
runnable3 = RunnableLambda(lambda x: str(x))

chain = RunnableParallel(first=runnable1, second=runnable2, third=runnable3)
configured_chain = chain.with_config(max_concurrency=2)

chain.invoke(7)

API Reference:RunnableLambda | RunnableParallel

{'first': {'foo': 7}, 'second': [7, 7], 'third': '7'}

使可运行属性可配置

Runnable.with_configurable_fields

from typing import Any, Optional

from langchain_core.runnables import (
    ConfigurableField,
    RunnableConfig,
    RunnableSerializable,
)


class FooRunnable(RunnableSerializable[dict, dict]):
    output_key: str

    def invoke(
        self, input: Any, config: Optional[RunnableConfig] = None, **kwargs: Any
    ) -> list:
        return self._call_with_config(self.subtract_seven, input, config, **kwargs)

    def subtract_seven(self, input: dict) -> dict:
        return {self.output_key: input["foo"] - 7}


runnable1 = FooRunnable(output_key="bar")
configurable_runnable1 = runnable1.configurable_fields(
    output_key=ConfigurableField(id="output_key")
)

configurable_runnable1.invoke(
    {"foo": 10}, config={"configurable": {"output_key": "not bar"}}
)

API Reference:ConfigurableField | RunnableConfig | RunnableSerializable

{'not bar': 3}

configurable_runnable1.invoke({"foo": 10})

{'bar': 3}

使链组件可配置

Runnable.with_configurable_alternatives

from typing import Any, Optional

from langchain_core.runnables import RunnableConfig, RunnableLambda, RunnableParallel


class ListRunnable(RunnableSerializable[Any, list]):
    def invoke(
        self, input: Any, config: Optional[RunnableConfig] = None, **kwargs: Any
    ) -> list:
        return self._call_with_config(self.listify, input, config, **kwargs)

    def listify(self, input: Any) -> list:
        return [input]


class StrRunnable(RunnableSerializable[Any, str]):
    def invoke(
        self, input: Any, config: Optional[RunnableConfig] = None, **kwargs: Any
    ) -> list:
        return self._call_with_config(self.strify, input, config, **kwargs)

    def strify(self, input: Any) -> str:
        return str(input)


runnable1 = RunnableLambda(lambda x: {"foo": x})

configurable_runnable = ListRunnable().configurable_alternatives(
    ConfigurableField(id="second_step"), default_key="list", string=StrRunnable()
)
chain = runnable1 | configurable_runnable

chain.invoke(7, config={"configurable": {"second_step": "string"}})

API Reference:RunnableConfig | RunnableLambda | RunnableParallel

"{'foo': 7}"

chain.invoke(7)

[{'foo': 7}]

根据输入动态构建链

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)

chain = RunnableLambda(lambda x: runnable1 if x > 6 else runnable2)

chain.invoke(7)

API Reference:RunnableLambda | RunnableParallel

{'foo': 7}

chain.invoke(5)

[5, 5]

生成事件流

Runnable.astream_events

# | echo: false

import nest_asyncio

nest_asyncio.apply()

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x}, name="first")

async def func(x):
    for _ in range(5):
        yield x

runnable2 = RunnableLambda(func, name="second")

chain = runnable1 | runnable2

async for event in chain.astream_events("bar", version="v2"):
    print(f"event={event['event']} | name={event['name']} | data={event['data']}")

API Reference:RunnableLambda | RunnableParallel

event=on_chain_start | name=RunnableSequence | data={'input': 'bar'}
event=on_chain_start | name=first | data={}
event=on_chain_stream | name=first | data={'chunk': {'foo': 'bar'}}
event=on_chain_start | name=second | data={}
event=on_chain_end | name=first | data={'output': {'foo': 'bar'}, 'input': 'bar'}
event=on_chain_stream | name=second | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=RunnableSequence | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=second | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=RunnableSequence | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=second | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=RunnableSequence | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=second | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=RunnableSequence | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=second | data={'chunk': {'foo': 'bar'}}
event=on_chain_stream | name=RunnableSequence | data={'chunk': {'foo': 'bar'}}
event=on_chain_end | name=second | data={'output': {'foo': 'bar'}, 'input': {'foo': 'bar'}}
event=on_chain_end | name=RunnableSequence | data={'output': {'foo': 'bar'}}

在完成时生成批量输出

Runnable.batch_as_completed / Runnable.abatch_as_completed

import time

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: time.sleep(x) or print(f"slept {x}"))

for idx, result in runnable1.batch_as_completed([5, 1]):
    print(idx, result)

# Async variant:
# async for idx, result in runnable1.abatch_as_completed([5, 1]):
#     print(idx, result)

API Reference:RunnableLambda | RunnableParallel

slept 1
1 None
slept 5
0 None

返回输出字典的子集

Runnable.pick

from langchain_core.runnables import RunnableLambda, RunnablePassthrough

runnable1 = RunnableLambda(lambda x: x["baz"] + 5)
chain = RunnablePassthrough.assign(foo=runnable1).pick(["foo", "bar"])

chain.invoke({"bar": "hi", "baz": 2})

API Reference:RunnableLambda | RunnablePassthrough

{'foo': 7, 'bar': 'hi'}

声明式地创建一个可运行对象的批处理版本

Runnable.map

from langchain_core.runnables import RunnableLambda

runnable1 = RunnableLambda(lambda x: list(range(x)))
runnable2 = RunnableLambda(lambda x: x + 5)

chain = runnable1 | runnable2.map()

chain.invoke(3)

API Reference:RunnableLambda

[5, 6, 7]

获取可运行对象的图形表示

Runnable.get_graph

from langchain_core.runnables import RunnableLambda, RunnableParallel

runnable1 = RunnableLambda(lambda x: {"foo": x})
runnable2 = RunnableLambda(lambda x: [x] * 2)
runnable3 = RunnableLambda(lambda x: str(x))

chain = runnable1 | RunnableParallel(second=runnable2, third=runnable3)

chain.get_graph().print_ascii()

API Reference:RunnableLambda | RunnableParallel

                             +-------------+                              
                             | LambdaInput |                              
                             +-------------+                              
                                    *                                     
                                    *                                     
                                    *                                     
                    +------------------------------+                      
                    | Lambda(lambda x: {'foo': x}) |                      
                    +------------------------------+                      
                                    *                                     
                                    *                                     
                                    *                                     
                     +-----------------------------+                      
                     | Parallel<second,third>Input |                      
                     +-----------------------------+                      
                        ****                  ***                         
                    ****                         ****                     
                  **                                 **                   
+---------------------------+               +--------------------------+  
| Lambda(lambda x: [x] * 2) |               | Lambda(lambda x: str(x)) |  
+---------------------------+               +--------------------------+  
                        ****                  ***                         
                            ****          ****                            
                                **      **                                
                    +------------------------------+                      
                    | Parallel<second,third>Output |                      
                    +------------------------------+

获取链中的所有提示

Runnable.get_prompts

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda

prompt1 = ChatPromptTemplate.from_messages(
    [("system", "good ai"), ("human", "{input}")]
)
prompt2 = ChatPromptTemplate.from_messages(
    [
        ("system", "really good ai"),
        ("human", "{input}"),
        ("ai", "{ai_output}"),
        ("human", "{input2}"),
    ]
)
fake_llm = RunnableLambda(lambda prompt: "i am good ai")
chain = prompt1.assign(ai_output=fake_llm) | prompt2 | fake_llm

for i, prompt in enumerate(chain.get_prompts()):
    print(f"**prompt {i=}**\n")
    print(prompt.pretty_repr())
    print("\n" * 3)

API Reference:ChatPromptTemplate | RunnableLambda

**prompt i=0**

================================ System Message ================================

good ai

================================ Human Message =================================

{input}

**prompt i=1**

================================ System Message ================================

really good ai

================================ Human Message =================================

{input}

================================== AI Message ==================================

{ai_output}

================================ Human Message =================================

{input2}

添加生命周期监听器

Runnable.with_listeners

import time

from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run

def on_start(run_obj: Run):
    print("start_time:", run_obj.start_time)

def on_end(run_obj: Run):
    print("end_time:", run_obj.end_time)

runnable1 = RunnableLambda(lambda x: time.sleep(x))
chain = runnable1.with_listeners(on_start=on_start, on_end=on_end)
chain.invoke(2)

API Reference:RunnableLambda | Run

start_time: 2024-05-17 23:04:00.951065+00:00
end_time: 2024-05-17 23:04:02.958765+00:00

调用一个可运行对象​

Runnable.invoke() / Runnable.ainvoke()​

批量处理可运行对象​

Runnable.batch() / Runnable.abatch()​

流式运行一个可执行程序​

Runnable.stream() / Runnable.astream()​

组合可运行对象​

管道操作符 |​

并行调用可运行对象​

将任何函数转换为可运行的​

合并输入和输出字典​

在输出字典中包含输入字典​

添加默认调用参数​

添加回退​

添加重试​

配置可运行执行​

添加默认配置到可运行对象​

使可运行属性可配置​

使链组件可配置​

根据输入动态构建链​

生成事件流​

在完成时生成批量输出​

Runnable.batch_as_completed / Runnable.abatch_as_completed​

返回输出字典的子集​

声明式地创建一个可运行对象的批处理版本​

获取可运行对象的图形表示​

获取链中的所有提示​

添加生命周期监听器​

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调用一个可运行对象

Runnable.invoke() / Runnable.ainvoke()

批量处理可运行对象

Runnable.batch() / Runnable.abatch()

流式运行一个可执行程序

Runnable.stream() / Runnable.astream()

组合可运行对象

管道操作符 `|`

并行调用可运行对象

将任何函数转换为可运行的

合并输入和输出字典

在输出字典中包含输入字典

添加默认调用参数

添加回退

添加重试

配置可运行执行

添加默认配置到可运行对象

使可运行属性可配置

使链组件可配置

根据输入动态构建链

生成事件流

在完成时生成批量输出

Runnable.batch_as_completed / Runnable.abatch_as_completed

返回输出字典的子集

声明式地创建一个可运行对象的批处理版本

获取可运行对象的图形表示

获取链中的所有提示

添加生命周期监听器