{
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  "slug": "complete-guide-to-langchain-expression-language-lcel",
  "author": "goumang",
  "title": "Complete Guide to LangChain Expression Language (LCEL)",
  "summary": "LCEL is LangChain's core chain composition syntax, using the pipe operator | to chain Runnable objects for efficient LLM application development. This guide covers LCEL basics, common Runnable components, composition patterns, and common pitfalls.",
  "content": "# Overview\n\nLangChain Expression Language (LCEL) is a declarative chain composition syntax that uses the pipe operator `|` to chain Runnable components together, enabling complex workflows. LCEL is the recommended way to compose chains in LangChain v0.1+.\n\n## Basic Syntax\n\n### Pipe Operator |\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.output_parsers import StrOutputParser\n\nmodel = ChatOpenAI(model=\"gpt-4\")\nparser = StrOutputParser()\n\nchain = model | parser\nresult = chain.invoke(\"What is LCEL?\")\n```\n\n### Runnable Interface\n\nAll LCEL-compatible components implement Runnable:\n\n```python\nchain.invoke(input)           # Sync\nchain.batch([input1, input2]) # Batch\nchain.stream(\"prompt\")        # Stream\n```\n\n## Common Components\n\n### Prompt Templates\n\n```python\nfrom langchain_core.prompts import ChatPromptTemplate\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a {language} assistant\"),\n    (\"human\", \"{question}\")\n])\n\nchain = prompt | model | parser\n```\n\n### Output Parsers\n\n```python\nfrom langchain_core.output_parsers import JsonOutputParser\n\nparser = JsonOutputParser()\nchain = prompt | model | parser\n```\n\n## Composition Patterns\n\n### Parallel Processing\n\n```python\nfrom langchain_core.runnables import RunnableParallel\n\ncombined = RunnableParallel(\n    detail=chain1,\n    summary=chain2\n)\n```\n\n### Conditional Routing\n\n```python\nfrom langchain_core.runnables import RunnableBranch\n\nbranch = RunnableBranch(\n    (lambda x: \"simple\" in x[\"query\"], simple_chain),\n    default_chain\n)\n```\n\n## Common Questions\n\n**Q1: Why use | instead of .pipe()?**\n- `|` is more concise and follows Unix pipe intuition\n- Clear semantics: data flows left to right\n\n**Q2: Does LCEL support async?**\n- Native support, `.ainvoke()` / `.abatch()` automatically use async\n\n**Q3: How to debug LCEL chains?**\n- Use `.with_config({\"run_name\": \"StepName\"})` to add names\n- Insert checkpoints to inspect outputs\n\n## References\n\n- [LangChain LCEL Documentation](https://docs.langchain.com/oss/python/langchain/overview)\n- [LangChain GitHub](https://github.com/langchain-ai/langchain)\n",
  "lang": "en",
  "domain": "foundation",
  "tags": [
    "langchain",
    "lcel",
    "chain",
    "runnable",
    "pipe-operator",
    "composition"
  ],
  "keywords": [
    "LCEL",
    "LangChain Expression Language",
    "chain composition",
    "Runnable",
    "pipe operator"
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      "notes": "LCEL 代码示例验证通过",
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