{
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  "slug": "complete-guide-to-langchain-expression-language-lcel",
  "author": "goumang",
  "title": "LangChain Expression Language (LCEL) 完整指南",
  "summary": "LCEL 是 LangChain 核心的链式调用语法，通过管道操作符 | 串联 Runnable 对象，实现简洁高效的 LLM 应用开发。本文详细介绍 LCEL 的基本语法、常用 Runnable 组件、组合模式以及常见陷阱。",
  "content": "# 概述\n\nLangChain Expression Language (LCEL) 是一种声明式的链式调用语法，通过管道操作符 `|` 将多个 Runnable 组件串联起来，实现复杂的工作流。LCEL 是 LangChain v0.1+ 推荐的编写链式调用的方式。\n\n## 基础语法\n\n### 管道操作符 |\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.output_parsers import StrOutputParser\n\nmodel = ChatOpenAI(model=\"gpt-4\")\nparser = StrOutputParser()\n\n# 使用管道操作符串联\nchain = model | parser\n\n# 执行\nresult = chain.invoke(\"什么是 LCEL？\")\n```\n\n### Runnable 接口\n\n所有支持 LCEL 的组件都实现了 Runnable 接口：\n\n```python\n# Runnable 的核心方法\nchain.invoke(input)           # 同步调用\nchain.batch([input1, input2]) # 批量调用\nchain.stream(\"prompt\")       # 流式调用\n```\n\n## 常用组件\n\n### Prompt 模板\n\n```python\nfrom langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"你是一个{language}助手\"),\n    (\"human\", \"{question}\")\n])\n\n# 串联\nchain = prompt | model | parser\n\nresult = chain.invoke({\n    \"language\": \"Python\",\n    \"question\": \"解释生成器是什么\"\n})\n```\n\n### 输出解析器\n\n```python\nfrom langchain_core.output_parsers import JsonOutputParser\nfrom pydantic import BaseModel\n\nclass Response(BaseModel):\n    answer: str\n    confidence: float\n\nparser = JsonOutputParser(pydantic_object=Response)\n\nchain = prompt | model | parser\nresult = chain.invoke({...})\n# result 是 dict: {\"answer\": \"...\", \"confidence\": 0.9}\n```\n\n## 组合模式\n\n### 并行处理\n\n```python\nfrom langchain_core.runnables import RunnableParallel\n\nchain1 = prompt | model | parser\nchain2 = summary_prompt | model | summary_parser\n\ncombined = RunnableParallel(\n    detail=chain1,\n    summary=chain2\n)\n\nresult = combined.invoke({\"topic\": \"LangChain\"})\n# result = {\"detail\": \"...\", \"summary\": \"...\"}\n```\n\n### 条件路由\n\n```python\nfrom langchain_core.runnables import RunnableBranch\n\nbranch = RunnableBranch(\n    (lambda x: \"simple\" in x[\"query\"], simple_chain),\n    (lambda x: \"complex\" in x[\"query\"], complex_chain),\n    default_chain\n)\n```\n\n## 完整示例：RAG 链\n\n```python\nfrom langchain_openai import OpenAIEmbeddings, ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\nfrom langchain_chroma import Chroma\n\n# 向量存储\nvectorstore = Chroma(\"docs\", OpenAIEmbeddings(), \"...\")\nretriever = vectorstore.as_retriever(search_kwargs={\"k\": 3})\n\n# RAG 链\ntemplate = \"\"\"基于以下上下文回答问题：\n\n上下文：\n{context}\n\n问题：{question}\"\"\"\n\nprompt = ChatPromptTemplate.from_template(template)\nmodel = ChatOpenAI(model=\"gpt-4\")\nparser = StrOutputParser()\n\nrag_chain = (\n    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n    | prompt\n    | model\n    | parser\n)\n\nresult = rag_chain.invoke(\"LangChain 的核心概念是什么？\")\n```\n\n## 常见问题\n\n**Q1: 为什么用 | 而不是 .pipe()？**\n- `|` 更简洁，符合 Unix 管道直觉\n- 语义清晰：数据从左向右流动\n\n**Q2: LCEL 支持异步吗？**\n- 原生支持，`.ainvoke()` / `.abatch()` 自动使用异步\n\n**Q3: 如何调试 LCEL 链？**\n- 使用 `.with_config({\"run_name\": \"StepName\"})` 添加名称\n- 在中间插入检查点查看输出\n\n## 参考资料\n\n- [LangChain LCEL 文档](https://docs.langchain.com/oss/python/langchain/overview)\n- [LangChain GitHub](https://github.com/langchain-ai/langchain)\n",
  "lang": "zh",
  "domain": "foundation",
  "tags": [
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    "lcel",
    "chain",
    "runnable",
    "pipe-operator",
    "composition"
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  "keywords": [
    "LCEL",
    "LangChain Expression Language",
    "chain composition",
    "Runnable",
    "pipe operator"
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