AI大模型应用开发：LangChain核心组件-模型（Models）

文章目录

• • • 1、初始化模型
    • • 1.1、init_chat_model
      • 1.2、自定义模型及参数
      • [1.3、 使用Model类](#1.3、 使用Model类)
    • [2 、访问模型](#2 、访问模型)
    • • [2.1、 invoke](#2.1、 invoke)
      • [2.2、 stream](#2.2、 stream)
    • 3、模型示例
    • • 3.1、大语言模型
      • 3.2、聊天模型
      • 3.3、文本嵌入模型
    • 4、在Agent中使用模型
    • • [4.1、 创建智能体](#4.1、 创建智能体)
      • [4.2、 调用智能体](#4.2、 调用智能体)

这里说的模型，完整叫法是大语言模型（LLM）。它能够理解人类语言，使用人类语言生成内容、翻译、提取摘要、回答问题等。

不仅如此，现在大多数的模型还有一些特别能力：

• Tool calling - 调用外部工具（例如查询数据库或调用 API），并在其回复中使用这些工具返回的结果。
• Structured output - 将模型的响应结果约束为遵循已定义的格式，例如：json
• Multimodality - 可以处理和返回文本以外的数据，如图像、音频和视频。
• Reasoning - 模型可以执行多步推理来得出结论。

可以说LLM就是Agent的大脑，是Agent的推理引擎。它驱动Agent做出每个决定：何时调用工具、调用哪个工具、如何解释结果，以及何时提供最终答案。

LangChain支持现在市面上大部分的大语言模型（LLM），并且提供了统一的模型调用接口。使您可以轻松访问许多不同的模型提供者，并且在模型之间进行试验和切换也变得很容易。

有关模型提供者（Model Providers）的信息和功能，请参阅langchain官网的 chat model page.

1、初始化模型

langchain提供了两种常见方法用来初始化模型：

• 使用init_chat_model函数，由langchain自动创建模型对象
• 使用不同模型对应的Model类，手动创建模型对象

1.1、init_chat_model

在LangChain中开始使用独立模型的最简单方法是使用init_chat_model函数。

调用init_chat_model函数时，你需要从langchain支持的模型提供者（Model Provider）中选择一个模型，而langchain会自动初始化这个模型，非常方便。

例如，我们要使用Deepseek这个模型。

• 首先，我们需要安装模型依赖：

uv add langchain-deepseek

• 然后，我们要确保在项目的.env环境中配置好api_key:

• 最后，就可以直接使用init_chat_model初始化模型了：

# 导入Langchain的初始化模型的函数
from langchain.chat_models import init_chat_model
# 加载环境变量
from dotenv import load_dotenv
load_dotenv()

# 调用init_chat_model函数初始化模型，参数model用来指定模型名称，Langchain会根据模型名字自动设定base_url，并从环境变量中获取api_key
model = init_chat_model(model="deepseek-chat")

• 测试，我们可以通过打印model的类型，查看生成的结果：

print(type(model)) # <class 'langchain_deepseek.chat_models.ChatDeepSeek'>

可见，采用init_chat_model自动初始化模型时，模型的类型由LangChain通过模型名称自动推断。

如果要切换其它模型，我们只需要安装其它模型依赖，然后配置API_KEY，改变模型名称即可，其它代码不用动。

1.2、自定义模型及参数

init_chat_model默认会根据模型名称自动确定模型的提供者、其base_url，并从env读取api_key，但前提是必须是langchain支持的模型提供者(支持模型参考链接)，例如：

• Openai
• Deepseek
• Google
• Anthropic
• ...
  对于其它不支持的模型，我们必须自定义模型参数来访问。

例如，我们要访问阿里云百炼的qwen-max，它就是不被langchain支持的模型，我们必须自定义模型参数来访问。

• 我们需要在环境变量中定义api_key和base_url
• 然后在init_chat_model中指定model、model_provider、base_url和api_key

具体步骤如下：

• 首先，在.env中配置好api_key和base_url：

  

• 然后，手动读取环境变量中的api_key和base_url：

# 非支持模型无法自动加载环境遍历，我们需要自己加载环境变量中的base_url和api_key
import os

base_url = os.getenv("DASHSCOPE_BASE_URL")
api_key = os.getenv("DASHSCOPE_API_KEY")

• 最后，调用init_chat_model，初始化模型：

# 初始化模型
model = init_chat_model(
    model="qwen-max", # 模型名称，这里可以自定义，我们用的是阿里的qwen-max
    model_provider="openai", # 如果是Langchain不支持的模型，需要指定模型提供者（虽然我们用的是阿里，但是阿里兼容openai，所以这里用openai，就是默认采用openai的API规范）
    base_url=base_url,
    api_key=api_key
)

• 测试，查看生成的模型类型：

print(type(model)) # <class 'langchain_openai.chat_models.base.ChatOpenAI'>

可见，通过参数自定义模型时，模型的类型由model_provider参数类决定。

除了修改模型提供者以外，init_chat_model方法允许我们调整模型参数，例如：

• temperature: 控制生成文本的随机性，值越小越确定，值越大越随机
• max_tokens: 控制生成文本的最大长度
• top_p: 控制生成文本的多样性，值越小越多样，值越大越确定
• timeout: 控制生成文本的超时时间
• max_retries: 控制生成文本的最大重试次数
• ...

示例：

# 调用init_chat_model函数初始化模型，并设定模型参数
model = init_chat_model(
    model="qwen-max", 
    model_provider="openai",
    base_url=base_url,
    api_key=api_key,
    temperature=1.5,
)

1.3、 使用Model类

其实init_chat_model方法底层就是帮我们利用Model类创建对象。但只支持有限的模型。而在langchain的社区，除了langchain官方提供的Model，还有些类是社区提供，更丰富多样。

具体支持的模型，可以查看官网地址：https://docs.langchain.com/oss/python/integrations/chat

例如，我们使用社区版本的Model类来访问阿里云百炼的通义千问模型：

1. 首先，我们需要安装依赖
2. LangChain社区依赖：

 uv add langchain-community

2. 阿里云百炼依赖：

 uv add dashscope

2. 然后，我们就可以使用Model类初始化模型了

from langchain_community.chat_models.tongyi import ChatTongyi

# 使用Model类初始化模型
model = ChatTongyi(
    model="qwen-plus"
    # 其它模型参数...
)

3. 测试，查看生成的模型类型：

print(type(model)) # <class 'langchain_community.chat_models.tongyi.ChatTongyi'>

2 、访问模型

LangChain提供了两个不同的方法来访问模型：

• invoke：阻塞式访问
• stream：流式访问

2.1、 invoke

invoke方法是阻塞式调用，需要等待模型生成全部结果才会返回，等待时间较长。

# 调用invoke方法
response = model.invoke("月亮的首都是哪里？")

# 查看响应结果
print(response)

2.2、 stream

阻塞式调用需要等待较长时间才能看到AI返回的结果，而流式调用则可以实时看到AI返回的一个个词。

示例：

# 通过.stream方法实现流式访问
stream = model.stream("月亮的首都是哪里？")

# stream调用返回的结果是一个generator，方便我们循环获取结果
print(type(stream))

# 遍历stream结果，实时打印AI的回复
for chunk in stream:
    print(chunk.content, end="", flush=True)

3、模型示例

3.1、大语言模型

示例代码

from langchain_community.llms.tongyi import Tongyi

llm = Tongyi(model="qwen-plus")

res = llm.invoke(input="帮我讲个睡前小故事吧")

print(res)

输出结果

🌙 睡前小故事：《星星的毛线球》

小刺猬阿绒总睡不着。  
每到夜晚，它就把小爪子蜷在胸口，数着窗外的树叶沙沙声------可数到第七片，就又清醒了。

这天晚上，月亮特别圆，像一枚温润的银币挂在树梢。阿绒刚叹气，忽然看见一缕微光"啪"地掉在鼻尖上------是颗小小的、毛茸茸的星星！它没掉进泥土，而是轻轻弹跳两下，滚进阿绒怀里，暖暖的，像一团会呼吸的云。

"我迷路啦，"星星软软地说，"我的光绳断了，回不了天上......你能帮我织一条新绳子吗？"

阿绒翻出妈妈留下的旧毛线团（淡蓝色的，还带着阳光的味道），用小爪子笨拙地缠啊绕啊。星星就趴在它背上，一闪一闪，把光洒在毛线上------那线渐渐亮起来，浮起细碎的银点，像银河的碎屑。

当最后一针绕好，阿绒轻轻把光绳一抛------  
咻～星星顺着光绳，悠悠升空，越飞越高，最后停在夜幕里，变成一颗崭新的、温柔的小星。

而阿绒低头发现：自己爪心里，悄悄留下了一小团发光的毛线球，暖暖的，像一小块凝固的月光。

它把它放在枕边，闭上眼。  
这一次，它梦见自己躺在云朵上，数着星星织的梦......  
数着数着，就沉进了甜甜的黑甜乡里。

✨ 晚安，愿你枕边也有一颗不灭的小星光。  
（轻轻盖好被子，呼------慢慢睡吧...）

进程已结束，退出代码为 0

3.2、聊天模型

示例代码

from langchain_community.chat_models.tongyi import ChatTongyi

llm = ChatTongyi(model="qwen-plus")

res = llm.invoke(input="你是谁呀？")

print(res.content)

输出结果

你好呀！😊 我是通义千问（Qwen），阿里巴巴集团旗下的超大规模语言模型。我可以陪你聊天、回答问题、创作文字（比如写故事、写公文、写邮件、写剧本、逻辑推理、编程等等），还能表达观点，甚至玩游戏～  
我支持多种语言，也努力做到真诚、友好、有帮助。虽然我是AI，没有真实的情感或身体，但我很乐意倾听你的想法，尽力为你提供有用的信息和温暖的交流体验。

你有什么想聊的、想问的，或者需要帮忙的吗？✨

进程已结束，退出代码为 0

3.3、文本嵌入模型

示例代码

from langchain_community.embeddings import DashScopeEmbeddings

# 创建模型对象，不传model默认使用的是text-embeddings-v1
model = DashScopeEmbeddings()

# 单次转换
e1=model.embed_query("我想买一台适合打游戏的笔记本")

# 批量转换
e2=model.embed_documents(["我喜欢苹果手机","我很喜欢吃苹果","我要去买一瓶苹果醋"])

print(e1)

print(e2)

输出结果

[-1.210546851158142, 7.14429235458374, -2.4969027042388916, 3.6912760734558105, -0.20753037929534912, 0.19825710356235504, 3.344675064086914, -0.07373589277267456, -4.5517144203186035, 2.8160319328308105, -3.161691665649414, 2.5066514015197754, -0.563037097454071, 1.834201693534851, -1.1933865547180176, 4.216969013214111, -0.6096842288970947, -1.953847050666809, 0.1912163645029068, 0.741015613079071, -3.451888084411621, 0.0389539934694767, 0.7930446863174438, -2.99157977104187, 0.7853949666023254, -0.8102782964706421, 2.052098274230957, -3.8362412452697754, 0.3990953266620636, 3.565134048461914, 0.1808268278837204, -3.7750027179718018, -1.4280924797058105, -2.8929200172424316, 1.6448581218719482, -1.6438151597976685, 0.3371486961841583, -0.0712551549077034, 0.23594021797180176, 0.635899543762207, 3.121680974960327, 1.0536125898361206, -2.05310320854187, -0.20381401479244232, 1.0015400648117065, 0.775477409362793, -0.12701043486595154, -1.2600579261779785, 4.548291206359863, 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进程已结束，退出代码为 0

4、在Agent中使用模型

Langchain提供了一个create_agent方法用来快速创建智能体。当我们创建Agent的时候，可以直接使用创建好的Model，也可以指定模型名，让Langchain自动初始化模型。

4.1、 创建智能体

1. 创建智能体，指定模型名，由Langchain初始化模型

from langchain.agents import create_agent

# 1.指定Model名称，由LangChain自动初始化模型
agent = create_agent(model="deepseek-chat")

2. 创建智能体，并使用创建好的model

from langchain.agents import create_agent
from langchain_community.chat_models.tongyi import ChatTongyi

# 1.使用Model类初始化模型
model = ChatTongyi(
    model="qwen-plus"
    # 其它模型参数...
)

# 2.使用初始化好的model创建智能体
agent = create_agent(model=model)

4.2、 调用智能体

智能体也分为阻塞调用和流式调用两种。

1. 阻塞式调用，使用invoke方法：

# 2.调用模型，需要传入一个消息列表
response = agent.invoke({
    "messages": [{"role": "user", "content": "月亮的首都是哪里？"}]
})

print(response)

2. 流式调用，只需要把调用方式改为stream：

for token, metadata in agent.stream(
    {"messages": [{"role": "user", "content": "月亮的首都是哪里？"}]},
    stream_mode="messages"
):
    if token.content:  # Check if there's actual content
        print(token.content, end="", flush=True)  # Print token

要注意，Agent的stream模式同样返回一个generator，但是其结构由stream_mode参数决定：

• messages: 返回LLM生成的每一个片段，是一个包含token和metadata的元组（Tuple）
• updates: 返回Agent运行过程中的每一次事件，例如与LLM的对话、工具的调用等
• custom: 返回通过stream writer记录的每一次自定义的输出

如果是为了流式输出AI返回的结果，使用messages模式即可。