LangChain入门-Runnable 协议完全解析

LangChain入门-Runnable 协议完全解析

标签: LangChain, Runnable, LCEL, 协议设计, Python, 异步编程

深入讲解 LangChain 核心 Runnable 协议，包括 LCEL 管道操作符、并行执行、路由分支、流式输出和批量处理

Runnable 协议

用途

LangChain 的核心抽象协议，为所有组件提供统一的执行接口。这是 LangChain 表达式语言（LCEL）的基础。

位置

libs/core/langchain_core/runnables/

知识图谱上下文

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组合

使用

Runnable

语言模型

工具

检索器

链

嵌入模型

Agent

核心类/模块

Runnable 类

• Runnable: 基础抽象类，定义核心接口
• RunnableLambda: 将函数转换为 Runnable
• RunnableParallel: 并行执行多个 Runnable
• RunnableSequence: 顺序执行（LCEL 的 | 操作符）
• RunnablePassthrough: 传递输入不变
• RunnableConfig: 运行时配置

核心接口

依赖关系

内部依赖

• langchain_core.messages: 消息类型
• langchain_core.callbacks: 回调系统
• langchain_core.output_parsers: 输出解析

外部依赖

• typing: 类型提示
• asyncio: 异步支持
• operator: 管道操作符

数据流

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invoke

处理

return

stream

yield

yield

输入

Runnable

转换

输出

输入

Runnable

数据块

数据块

LCEL 管道操作符

使用 | 组合组件

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

# 管道组合
chain = (
ChatPromptTemplate.from_template("Tell me a joke about {topic}")
| ChatOpenAI(model="gpt-4")
| StrOutputParser()
)

实际执行流程

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格式化

生成消息

提取文本

提示模板

LLM

解析器

最终输出

并行执行

RunnableParallel

from langchain_core.runnables import RunnableParallel

# 并行执行多个链
chain = RunnableParallel(
joke=chain1,
poem=chain2,
fact=chain3
)

并行流程

#mermaid-svg-OLrycbryzRJDwp0V{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-OLrycbryzRJDwp0V .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-OLrycbryzRJDwp0V .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-OLrycbryzRJDwp0V .error-icon{fill:#552222;}#mermaid-svg-OLrycbryzRJDwp0V .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-OLrycbryzRJDwp0V .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-OLrycbryzRJDwp0V .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-OLrycbryzRJDwp0V .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-OLrycbryzRJDwp0V .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-OLrycbryzRJDwp0V .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-OLrycbryzRJDwp0V .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-OLrycbryzRJDwp0V .marker{fill:#333333;stroke:#333333;}#mermaid-svg-OLrycbryzRJDwp0V .marker.cross{stroke:#333333;}#mermaid-svg-OLrycbryzRJDwp0V svg{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-OLrycbryzRJDwp0V p{margin:0;}#mermaid-svg-OLrycbryzRJDwp0V .label{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;color:#333;}#mermaid-svg-OLrycbryzRJDwp0V .cluster-label text{fill:#333;}#mermaid-svg-OLrycbryzRJDwp0V .cluster-label span{color:#333;}#mermaid-svg-OLrycbryzRJDwp0V .cluster-label span p{background-color:transparent;}#mermaid-svg-OLrycbryzRJDwp0V .label text,#mermaid-svg-OLrycbryzRJDwp0V span{fill:#333;color:#333;}#mermaid-svg-OLrycbryzRJDwp0V .node rect,#mermaid-svg-OLrycbryzRJDwp0V .node circle,#mermaid-svg-OLrycbryzRJDwp0V .node ellipse,#mermaid-svg-OLrycbryzRJDwp0V .node polygon,#mermaid-svg-OLrycbryzRJDwp0V .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-OLrycbryzRJDwp0V .rough-node .label text,#mermaid-svg-OLrycbryzRJDwp0V .node .label text,#mermaid-svg-OLrycbryzRJDwp0V .image-shape .label,#mermaid-svg-OLrycbryzRJDwp0V .icon-shape .label{text-anchor:middle;}#mermaid-svg-OLrycbryzRJDwp0V .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-OLrycbryzRJDwp0V .rough-node .label,#mermaid-svg-OLrycbryzRJDwp0V .node .label,#mermaid-svg-OLrycbryzRJDwp0V .image-shape .label,#mermaid-svg-OLrycbryzRJDwp0V .icon-shape .label{text-align:center;}#mermaid-svg-OLrycbryzRJDwp0V .node.clickable{cursor:pointer;}#mermaid-svg-OLrycbryzRJDwp0V .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-OLrycbryzRJDwp0V .arrowheadPath{fill:#333333;}#mermaid-svg-OLrycbryzRJDwp0V .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-OLrycbryzRJDwp0V .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-OLrycbryzRJDwp0V .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-OLrycbryzRJDwp0V .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-OLrycbryzRJDwp0V .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-OLrycbryzRJDwp0V .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-OLrycbryzRJDwp0V .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-OLrycbryzRJDwp0V .cluster text{fill:#333;}#mermaid-svg-OLrycbryzRJDwp0V .cluster span{color:#333;}#mermaid-svg-OLrycbryzRJDwp0V div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-OLrycbryzRJDwp0V .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-OLrycbryzRJDwp0V rect.text{fill:none;stroke-width:0;}#mermaid-svg-OLrycbryzRJDwp0V .icon-shape,#mermaid-svg-OLrycbryzRJDwp0V .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-OLrycbryzRJDwp0V .icon-shape p,#mermaid-svg-OLrycbryzRJDwp0V .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-OLrycbryzRJDwp0V .icon-shape rect,#mermaid-svg-OLrycbryzRJDwp0V .image-shape rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-OLrycbryzRJDwp0V .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-OLrycbryzRJDwp0V .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-OLrycbryzRJDwp0V :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}

分支1

分支2

分支3

结果

结果

结果

输入

RunnableParallel

链1

链2

链3

合并结果

最终输出

路由和条件分支

RunnableBranch

from langchain_core.runnables import RunnableBranch

# 条件路由
branch = RunnableBranch(
(lambda x: x["language"] == "spanish", spanish_chain),
(lambda x: x["language"] == "english", english_chain),
default_chain
)

路由流程

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条件1

条件2

默认

输入

条件判断

链1

链2

默认链

输出

流式输出

stream() 方法

# 流式文本生成
for chunk in chain.stream({"topic": "cats"}):
print(chunk, end="", flush=True)

astream() 异步流

async for chunk in chain.astream({"topic": "cats"}):
print(chunk, end="", flush=True)

批量处理

batch() 方法

# 批量执行
results = chain.batch([
{"topic": "cats"},
{"topic": "dogs"},
{"topic": "birds"}
])

并行批量

# 使用 max_concurrency 控制并发
results = chain.batch(inputs, config={"max_concurrency": 5})

配置和元数据

RunnableConfig

from langchain_core.runnables import RunnableConfig

config = RunnableConfig(
tags=["my-tag"],
metadata={"user_id": "123"},
callbacks=[my_handler],
run_name="my-run"
)

配置传递

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传递

传递

传递

使用

使用

使用

RunnableConfig

invoke/ainvoke

batch/abatch

stream/astream

标签

元数据

回调

自定义 Runnable

使用 RunnableLambda

from langchain_core.runnables import RunnableLambda

# 将函数转为 Runnable
def custom_function(input: dict) –> str:
return f"Processed: {input['text']}"

custom_runnable = RunnableLambda(custom_function)

继承 Runnable 类

from langchain_core.runnables import Runnable

class CustomRunnable(Runnable):
def invoke(self, input, config=None):
# 自定义处理逻辑
return self._process(input)

重要设计模式

1. 装饰器模式

# 使用 bind() 绑定参数
llm = ChatOpenAI(model="gpt-4")
llm_with_stop = llm.bind(stop=["\\n"])

2. 组合模式

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组合

组合

形成

Runnable A

Runnable B

Runnable C

复杂链

3. 适配器模式

# 将函数适配为 Runnable
@runnable
def my_function(x: str) –> str:
return x.upper()

# 现在可以使用管道
chain = my_function | another_runnable

错误处理

重试机制

from langchain_core.runnables import RunnableLambda
from tenacity import retry

@retry(stop=stop_after_attempt(3))
def flaky_function(x):
# 可能失败的函数
return process(x)

runnable_with_retry = RunnableLambda(flaky_function)

回退机制

from langchain_core.runnables import RunnableWithFallbacks

# 主流程失败时使用备选
chain_with_fallback = primary_chain.with_fallbacks([fallback_chain])

性能优化

1. 流式处理

• 大数据集处理
• 实时响应
• 减少内存使用

2. 批量处理

• 并行执行多个请求
• 减少 API 调用开销
• 提高吞吐量

3. 异步执行

• 非阻塞 I/O
• 提高并发能力
• 更好的资源利用