AI Agent开发学习系列 - langchain之LCEL(1)：LangChain LCEL链式编排与RAG增强实践

什么是 LCEL？

LCEL（LangChain Expression Language）是 LangChain 框架中用于构建、组合和编排链式调用的表达式语言。它让你可以像搭积木一样，将 Prompt、模型、解析器、检索器等组件灵活组合，形成强大的链式推理和问答流程。

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import os 
from pydantic import SecretStr
from dotenv import load_dotenvload_dotenv()prompt = ChatPromptTemplate.from_template("请讲一个关于{topic}的故事。")
model = ChatOpenAI(api_key=SecretStr(os.environ.get("HUNYUAN_API_KEY", "")),  # 混元 APIKeybase_url="https://api.hunyuan.cloud.tencent.com/v1",  # 混元 endpointmodel="hunyuan-lite",  # 模型名称temperature=0,
)
output_parser = StrOutputParser()
chain = prompt | model | output_parserchain.invoke({"topic": "月亮"})

结果：

'从前，有一个古老的村庄，村子里的居民们相信月亮是他们的守护神。每个月圆之夜，月亮都会准时升起，照亮了整个天空，给村民们带来无尽的遐想和希望。\n\n在这个村子里，有一个聪明勇敢的小男孩，名叫小明。他非常喜欢观察天象，尤其是月亮的变化。每个月圆之夜，他都会爬上自家院子的屋顶，仰望那轮明亮的月亮，陷入深深的思索。\n\n有一天晚上，小明发现月亮似乎有些异样。它比往常更加明亮、更加圆润，仿佛在向人们传递着什么信息。小明决定去问问村里的长者，看看他们是否知道这是什么意思。\n\n长者们告诉小明，这是一个古老的传说。相传，在很久以前，月亮曾经被一位邪恶的巫师诅咒，变得暗淡无光。后来，一位善良的仙女为了拯救月亮，历经千辛万苦，终于找到了解除诅咒的方法。在月圆之夜，她会在月光下祈祷，用她的爱心驱散巫师的黑暗力量，让月亮重新焕发生机。\n\n小明听完这个故事，心中充满了好奇和敬佩。他决定在月圆之夜，也像仙女一样，为月亮祈祷，希望能够驱散巫师的黑暗力量，让月亮重新焕发出美丽的光芒。\n\n月圆之夜到了，小明爬上屋顶，仰望那轮明亮的月亮，心中默默祈祷。就在这时，他突然感觉到一股温暖的力量从心底涌出，顺着他的手臂，流向了月亮。他惊讶地发现，月亮的光芒变得更加明亮，仿佛在回应着他的祝福。\n\n从此以后，每当月圆之夜，小明都会为月亮祈祷，而月亮也总是准时升起，照亮了整个天空。村民们看到了这一幕，都纷纷感叹小明的善良和勇气，感激他为村子带来的光明和希望。'

LCEL 用法说明

1. 组件定义

• prompt：用 ChatPromptTemplate 创建了一个带变量的提示词模板，支持后续动态填充 topic。
• model：用 ChatOpenAI 封装了腾讯混元的对话模型，负责生成故事内容。
• output_parser：用 StrOutputParser 把模型的输出解析为纯字符串。

2. LCEL 链式组合

• 关键语法：chain = prompt | model | output_parser
• 这就是 LCEL 的典型“管道”式写法，把多个组件用 | 串联起来，形成一个“链”。
• 这条链的执行流程是：
  1. 先用 prompt 处理输入参数（如 {“topic”: “月亮”}），生成完整的提示词。
  2. 然后把提示词传给 model，由大模型生成故事内容。
  3. 最后用 output_parser 解析模型输出，得到最终的字符串结果。

3. 链的调用

• chain.invoke({“topic”: “月亮”})
• 这一步会把 {“topic”: “月亮”} 作为输入，自动流经上面定义的链条，得到最终的故事文本。

LCEL 的优势

• 简洁：用 | 连接各个环节，代码非常直观。
• 灵活：可以随意组合、插拔不同的 prompt、模型、解析器等组件。
• 可扩展：后续可以很方便地在链中插入更多处理环节（如检索器、过滤器等）。

这段代码用 LCEL 把“提示词模板 → 大模型 → 输出解析”三个环节无缝串联，形成了一个可复用、可扩展的链式推理流程。只需一行 chain.invoke(…)，即可完成从输入到输出的全流程，非常适合快速搭建和调试各类 LLM 应用。如需更复杂的链式编排（如加入检索、工具调用等），LCEL 也能用同样的方式扩展。

Prompt

在上面的代码基础上，继续执行。这几行代码的目的是用 LCEL prompt 模板生成带变量的 prompt，并以不同格式（消息/字符串）输出，方便后续传递给大模型或调试查看。
这种写法常用于 LangChain 新版链式表达和调试 prompt 渲染效果。

prompt_value = prompt.invoke({"topic":"熊猫"})
prompt_value

ChatPromptValue(messages=[HumanMessage(content='请讲一个关于熊猫的故事。', additional_kwargs={}, response_metadata={})])

prompt_value.to_messages()

[HumanMessage(content='请讲一个关于熊猫的故事。', additional_kwargs={}, response_metadata={})]

prompt_value.to_string()

'Human: 请讲一个关于熊猫的故事。'

Model

展示如何用 LCEL 风格链式调用，将 prompt 渲染后直接传给大模型，获取并展示模型的实际回复结果。

message = model.invoke(prompt_value)
message

AIMessage(content='从前，在中国的一个遥远山区，有一个美丽的竹林。竹林里住着一只可爱的熊猫，名叫悠悠。悠悠非常喜欢在竹林里玩耍，攀爬高高的竹子，吃着鲜嫩的竹叶。\n\n有一天，悠悠在竹林里发现了一个神秘的小洞。他很好奇，决定去探险。悠悠小心翼翼地走进小洞，发现里面有一棵结满美丽果实的树。这些果实散发着淡淡的光芒，看起来非常诱人。\n\n悠悠忍不住摘下了一个果实，突然，果实发出了一阵美妙的音乐。接着，一只会说话的鹦鹉从果实中飞了出来，原来是这棵树的守护者——鹦鹉精灵。\n\n鹦鹉精灵告诉悠悠，这些果实叫做“熊猫果”，吃了它们可以让人变得更加聪明、快乐。悠悠非常想试试，于是他吃了一个果实。\n\n果然，悠悠的感觉焕然一新，他变得更加聪明，也更加快乐。他开始用新获得的知识帮助竹林里的动物们解决问题，大家都非常感激他。\n\n从此，悠悠成了竹林里的英雄，大家都欢迎他常来玩。而悠悠也一直珍惜这个果实，每天都会分享给朋友们，让大家一起分享这份快乐和智慧。\n\n这个故事告诉我们，友谊和分享是非常重要的，只有互相帮助，才能让我们生活得更加美好。', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 270, 'prompt_tokens': 9, 'total_tokens': 279, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'hunyuan-lite', 'system_fingerprint': '', 'id': '6a8031dfe7f635f97cb4ed0a04428f77', 'service_tier': None, 'finish_reason': 'stop', 'logprobs': None}, id='run--afac90c3-893c-4789-8b25-a6e2da08501e-0', usage_metadata={'input_tokens': 9, 'output_tokens': 270, 'total_tokens': 279, 'input_token_details': {}, 'output_token_details': {}})

Output Parser

展示如何用 output_parser 对模型回复进行后处理，把原始消息转为更易用的格式，便于后续业务逻辑处理或直接展示给用户。

output_parser.invoke(message)

'从前，在中国的一个遥远山区，有一个美丽的竹林。竹林里住着一只可爱的熊猫，名叫悠悠。悠悠非常喜欢在竹林里玩耍，攀爬高高的竹子，吃着鲜嫩的竹叶。\n\n有一天，悠悠在竹林里发现了一个神秘的小洞。他很好奇，决定去探险。悠悠小心翼翼地走进小洞，发现里面有一棵结满美丽果实的树。这些果实散发着淡淡的光芒，看起来非常诱人。\n\n悠悠忍不住摘下了一个果实，突然，果实发出了一阵美妙的音乐。接着，一只会说话的鹦鹉从果实中飞了出来，原来是这棵树的守护者——鹦鹉精灵。\n\n鹦鹉精灵告诉悠悠，这些果实叫做“熊猫果”，吃了它们可以让人变得更加聪明、快乐。悠悠非常想试试，于是他吃了一个果实。\n\n果然，悠悠的感觉焕然一新，他变得更加聪明，也更加快乐。他开始用新获得的知识帮助竹林里的动物们解决问题，大家都非常感激他。\n\n从此，悠悠成了竹林里的英雄，大家都欢迎他常来玩。而悠悠也一直珍惜这个果实，每天都会分享给朋友们，让大家一起分享这份快乐和智慧。\n\n这个故事告诉我们，友谊和分享是非常重要的，只有互相帮助，才能让我们生活得更加美好。'

一个 RAG Search 示例

这段代码实现了一个基于腾讯混元大模型和自定义 Embedding 的 RAG（检索增强生成）问答链。其流程为：首先自定义了 HunyuanEmbeddings 类，通过腾讯混元 API 实现文本向量化，并用 FAISS 构建本地向量数据库；然后将一条知识文本“小明在北京工作。”存入向量库，并通过 retriever 实现相似内容检索；接着定义了一个带上下文的 prompt 模板，将检索到的内容和用户问题拼接后输入给混元大模型（ChatOpenAI）；最后用 LCEL（LangChain Expression Language）方式将检索、拼接、生成、输出解析等步骤串联成一条链，实现了“给定问题，先检索相关知识，再让大模型基于上下文生成答案”的完整 RAG 问答流程。

from operator import itemgetter
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain.embeddings.base import Embeddings
from pydantic import SecretStr
import os
import requests
from dotenv import load_dotenvload_dotenv()# 设置API配置
api_base = "https://api.hunyuan.cloud.tencent.com/v1"  # 腾讯混元OpenAI兼容endpoint
api_key = os.environ.get("HUNYUAN_API_KEY")# 定义腾讯混元Embeddings类
class HunyuanEmbeddings(Embeddings):def __init__(self, api_key, api_base, model="hunyuan-embedding"):self.api_key = api_keyself.api_base = api_baseself.model = model# 批量文本向量化方法def embed_documents(self, texts):url = f"{self.api_base}/embeddings"headers = {"Authorization": f"Bearer {self.api_key}","Content-Type": "application/json"}data = {"model": self.model,"input": texts}response = requests.post(url, headers=headers, json=data)result = response.json()print("混元 embedding 返回：", result)return [item["embedding"] for item in result["data"]]# 单条文本向量化方法def embed_query(self, text):return self.embed_documents([text])[0]vectorstore = FAISS.from_texts(["小明在北京工作。"], embedding=HunyuanEmbeddings(api_key=api_key, api_base=api_base)
)retriever = vectorstore.as_retriever()template = """根据以下上下文回答问题：
{context}问题：{question}
"""prompt = ChatPromptTemplate.from_template(template)model = ChatOpenAI(api_key=SecretStr(os.environ.get("HUNYUAN_API_KEY", "")),  # 混元 APIKeybase_url="https://api.hunyuan.cloud.tencent.com/v1",  # 混元 endpointmodel="hunyuan-lite",  # 模型名称temperature=0
)chain = ({"context": retriever, "question": RunnablePassthrough()}| prompt| model| StrOutputParser()
)

混元 embedding 返回： {'object': 'list', 'data': [{'index': 0, 'embedding': [0.01209259033203125, -0.0193939208984375, -0.00030994415283203125, -0.0340576171875, -0.0189971923828125, 0.0203704833984375, -3.0517578125e-05, -0.0119781494140625, -0.021148681640625, 0.0030384063720703125, -0.04266357421875, 0.052947998046875, -0.42919921875, 0.0034275054931640625, -0.01324462890625, -0.051727294921875, 0.04412841796875, 0.0007004737854003906, 0.0291900634765625, -0.003261566162109375, -0.0004372596740722656, -0.01039886474609375, -0.0206756591796875, -0.0209503173828125, -0.02117919921875, -0.021575927734375, -0.044036865234375, 0.0161590576171875, 0.0379638671875, -0.0227508544921875, -0.01666259765625, -6.443262100219727e-05, 0.040252685546875, 0.01690673828125, 0.044525146484375, 0.031768798828125, 0.03704833984375, 0.052032470703125, -0.031951904296875, -0.0010919570922851562, -0.043853759765625, 0.036834716796875, -0.03363037109375, -0.01776123046875, 0.0284576416015625, 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chain.invoke("小明在哪里工作？")

混元 embedding 返回： {'object': 'list', 'data': [{'index': 0, 'embedding': [0.0130767822265625, -0.0018110275268554688, 0.0224151611328125, -0.031494140625, -0.0210723876953125, 0.003986358642578125, 0.0019464492797851562, -0.07232666015625, -0.004791259765625, 0.023223876953125, -0.040435791015625, 0.042144775390625, -0.417236328125, 0.022186279296875, 0.005435943603515625, -0.056396484375, 0.036102294921875, -0.0006375312805175781, 0.0146026611328125, -0.023223876953125, -0.0294952392578125, -0.03717041015625, -0.020477294921875, -0.01287078857421875, -0.01087188720703125, -0.003582000732421875, -0.0304718017578125, -0.017364501953125, 0.0254974365234375, -0.00804901123046875, -0.0341796875, 0.0142669677734375, 0.030914306640625, 0.0228118896484375, 0.07012939453125, 0.0273895263671875, 0.0584716796875, 0.0546875, -0.01465606689453125, -0.007793426513671875, -0.020843505859375, 0.038482666015625, -0.042083740234375, -0.039581298828125, 0.043426513671875, -0.0156707763671875, 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自定义

这段代码同样实现了基于腾讯混元大模型和自定义 Embedding 的 RAG 问答链，但与上一段代码相比，主要区别在于链路输入和 prompt 模板的自定义更灵活。本段代码通过 LCEL 的 itemgetter，让链的输入参数支持 question 和 language，不仅能动态检索问题相关内容，还能让用户指定回答语言（如中英文）；prompt 模板也增加了 {language} 占位符，实现了“用指定语言回答”的自定义需求。此外，检索器的输入由 itemgetter("question") 显式指定，链式结构更清晰，便于扩展多输入场景。这种设计使得问答系统在多语言、个性化等实际应用中更具灵活性和可定制性。

from operator import itemgetter
from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain.embeddings.base import Embeddings
from pydantic import SecretStr
import os
import requests
from dotenv import load_dotenvload_dotenv()# 设置API配置
api_base = "https://api.hunyuan.cloud.tencent.com/v1"  # 腾讯混元OpenAI兼容endpoint
api_key = os.environ.get("HUNYUAN_API_KEY")# 定义腾讯混元Embeddings类
class HunyuanEmbeddings(Embeddings):def __init__(self, api_key, api_base, model="hunyuan-embedding"):self.api_key = api_keyself.api_base = api_baseself.model = model# 批量文本向量化方法def embed_documents(self, texts):url = f"{self.api_base}/embeddings"headers = {"Authorization": f"Bearer {self.api_key}","Content-Type": "application/json"}data = {"model": self.model,"input": texts}response = requests.post(url, headers=headers, json=data)result = response.json()print("混元 embedding 返回：", result)return [item["embedding"] for item in result["data"]]# 单条文本向量化方法def embed_query(self, text):return self.embed_documents([text])[0]vectorstore = FAISS.from_texts(["小明在北京工作。"], embedding=HunyuanEmbeddings(api_key=api_key, api_base=api_base)
)retriever = vectorstore.as_retriever()template = """根据以下上下文回答问题：
{context}问题：{question}用{language}回答。
"""prompt = ChatPromptTemplate.from_template(template)model = ChatOpenAI(api_key=SecretStr(os.environ.get("HUNYUAN_API_KEY", "")),  # 混元 APIKeybase_url="https://api.hunyuan.cloud.tencent.com/v1",  # 混元 endpointmodel="hunyuan-lite",  # 模型名称temperature=0
)chain = ({"context": itemgetter("question") | retriever, "question": itemgetter("question"),"language": itemgetter("language")}| prompt| model| StrOutputParser()
)

混元 embedding 返回： {'object': 'list', 'data': [{'index': 0, 'embedding': [0.01209259033203125, -0.0193939208984375, -0.00030994415283203125, -0.0340576171875, -0.0189971923828125, 0.0203704833984375, -3.0517578125e-05, -0.0119781494140625, -0.021148681640625, 0.0030384063720703125, -0.04266357421875, 0.052947998046875, -0.42919921875, 0.0034275054931640625, -0.01324462890625, -0.051727294921875, 0.04412841796875, 0.0007004737854003906, 0.0291900634765625, -0.003261566162109375, -0.0004372596740722656, -0.01039886474609375, -0.0206756591796875, -0.0209503173828125, -0.02117919921875, -0.021575927734375, -0.044036865234375, 0.0161590576171875, 0.0379638671875, -0.0227508544921875, -0.01666259765625, -6.443262100219727e-05, 0.040252685546875, 0.01690673828125, 0.044525146484375, 0.031768798828125, 0.03704833984375, 0.052032470703125, -0.031951904296875, -0.0010919570922851562, -0.043853759765625, 0.036834716796875, -0.03363037109375, -0.01776123046875, 0.0284576416015625, 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chain.invoke({"question": "小明在哪里工作？", "language": "英文"})

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