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核心模块（Modules)
模型 I/O（ModelIO）
聊天（Chat）
快速开始（QuickStart）

快速入门

聊天模型是语言模型的一种变体。 虽然聊天模型底层使用的是语言模型，但它们使用的接口略有不同。 它们不是使用“文本输入、文本输出”的 API，而是使用一种“聊天消息”作为输入和输出的接口。

设置

默认情况下，我们使用 OpenAI 的聊天模型。你也可以选择其它聊天模型提供方。

from langchain_openai import ChatOpenAI
 
chat = ChatOpenAI()
export OPENAI_API_KEY="..."

如果你不想设置环境变量，可以在初始化聊天模型类时，通过名为 api_key 的参数直接传入密钥：

from langchain_openai import ChatOpenAI
 
chat = ChatOpenAI(api_key="...")

消息

聊天模型接口围绕的是消息而不是原始文本。 LangChain 目前支持的消息类型有 AIMessage、HumanMessage、SystemMessage、FunctionMessage 和 ChatMessage——ChatMessage 接受一个任意角色参数。大多数情况下，你只会用到 HumanMessage、AIMessage 和 SystemMessage。

LCEL

聊天模型实现了 Runnable 接口，它是 LangChain 表达式语言（LCEL） 的基本构建块。这意味着它们支持 invoke、ainvoke、stream、astream、batch、abatch、astream_log 调用。

聊天模型接受 List[BaseMessage] 作为输入，或接受可被强制转换为消息的对象，包括 str（转换为 HumanMessage）和 PromptValue。

from langchain_core.messages import HumanMessage, SystemMessage
 
messages = [
    SystemMessage(content="You're a helpful assistant"),
    HumanMessage(content="What is the purpose of model regularization?"),
]
# | output: false
# | echo: false
 
from langchain_openai import ChatOpenAI
 
chat = ChatOpenAI()
chat.invoke(messages)
AIMessage(content="The purpose of model regularization is to prevent overfitting in machine learning models. Overfitting occurs when a model becomes too complex and starts to fit the noise in the training data, leading to poor generalization on unseen data. Regularization techniques introduce additional constraints or penalties to the model's objective function, discouraging it from becoming overly complex and promoting simpler and more generalizable models. Regularization helps to strike a balance between fitting the training data well and avoiding overfitting, leading to better performance on new, unseen data.")
for chunk in chat.stream(messages):
    print(chunk.content, end="", flush=True)
The purpose of model regularization is to prevent overfitting and improve the generalization of a machine learning model. Overfitting occurs when a model is too complex and learns the noise or random variations in the training data, which leads to poor performance on new, unseen data. Regularization techniques introduce additional constraints or penalties to the model's learning process, discouraging it from fitting the noise and reducing the complexity of the model. This helps to improve the model's ability to generalize well and make accurate predictions on unseen data.

chat.batch([messages])
[AIMessage(content="The purpose of model regularization is to prevent overfitting in machine learning models. Overfitting occurs when a model becomes too complex and starts to learn the noise or random fluctuations in the training data, rather than the underlying patterns or relationships. Regularization techniques add a penalty term to the model's objective function, which discourages the model from becoming too complex and helps it generalize better to new, unseen data. This improves the model's ability to make accurate predictions on new data by reducing the variance and increasing the model's overall performance.")]
await chat.ainvoke(messages)
AIMessage(content='The purpose of model regularization is to prevent overfitting in machine learning models. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning general patterns and relationships. This leads to poor performance on new, unseen data.\n\nRegularization techniques introduce additional constraints or penalties to the model during training, discouraging it from becoming overly complex. This helps to strike a balance between fitting the training data well and generalizing to new data. Regularization techniques can include adding a penalty term to the loss function, such as L1 or L2 regularization, or using techniques like dropout or early stopping. By regularizing the model, it encourages it to learn the most relevant features and reduce the impact of noise or outliers in the data.')
async for chunk in chat.astream(messages):
    print(chunk.content, end="", flush=True)
The purpose of model regularization is to prevent overfitting in machine learning models. Overfitting occurs when a model becomes too complex and starts to memorize the training data instead of learning the underlying patterns. Regularization techniques help in reducing the complexity of the model by adding a penalty to the loss function. This penalty encourages the model to have smaller weights or fewer features, making it more generalized and less prone to overfitting. The goal is to find the right balance between fitting the training data well and being able to generalize well to unseen data.

async for chunk in chat.astream_log(messages):
    print(chunk)
RunLogPatch({'op': 'replace',
  'path': '',
  'value': {'final_output': None,
            'id': '754c4143-2348-46c4-ad2b-3095913084c6',
            'logs': {},
            'streamed_output': []}})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='The')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' purpose')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' of')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' regularization')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' is')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' to')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' prevent')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' a')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' machine')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' learning')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' from')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' over')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='fit')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ting')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' training')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' data')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' and')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' improve')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' its')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' general')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ization')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' ability')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='.')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' Over')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='fit')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ting')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' occurs')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' when')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' a')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' becomes')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' too')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' complex')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' and')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' learns')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' to')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' fit')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' noise')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' or')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' random')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' fluctuations')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' in')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' training')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' data')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=',')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' instead')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' of')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' capturing')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' underlying')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' patterns')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' and')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' relationships')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='.')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' Regular')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ization')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' techniques')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' introduce')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' a')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' penalty')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' term')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' to')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content="'s")})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' objective')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' function')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=',')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' which')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' discour')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ages')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' from')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' becoming')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' too')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' complex')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='.')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' This')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' helps')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' to')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' control')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' model')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content="'s")})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' complexity')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' and')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' reduces')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' the')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' risk')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' of')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' over')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='fit')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='ting')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=',')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' leading')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' to')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' better')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' performance')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' on')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' unseen')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content=' data')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='.')})
RunLogPatch({'op': 'add',
  'path': '/streamed_output/-',
  'value': AIMessageChunk(content='')})
RunLogPatch({'op': 'replace',
  'path': '/final_output',
  'value': {'generations': [[{'generation_info': {'finish_reason': 'stop'},
                              'message': AIMessageChunk(content="The purpose of model regularization is to prevent a machine learning model from overfitting the training data and improve its generalization ability. Overfitting occurs when a model becomes too complex and learns to fit the noise or random fluctuations in the training data, instead of capturing the underlying patterns and relationships. Regularization techniques introduce a penalty term to the model's objective function, which discourages the model from becoming too complex. This helps to control the model's complexity and reduces the risk of overfitting, leading to better performance on unseen data."),
                              'text': 'The purpose of model regularization is '
                                      'to prevent a machine learning model '
                                      'from overfitting the training data and '
                                      'improve its generalization ability. '
                                      'Overfitting occurs when a model becomes '
                                      'too complex and learns to fit the noise '
                                      'or random fluctuations in the training '
                                      'data, instead of capturing the '
                                      'underlying patterns and relationships. '
                                      'Regularization techniques introduce a '
                                      "penalty term to the model's objective "
                                      'function, which discourages the model '
                                      'from becoming too complex. This helps '
                                      "to control the model's complexity and "
                                      'reduces the risk of overfitting, '
                                      'leading to better performance on unseen '
                                      'data.'}]],
            'llm_output': None,
            'run': None}})

LangSmith

所有 ChatModel 都内置了 LangSmith 追踪。只需设置以下环境变量：

export LANGCHAIN_TRACING_V2="true"
export LANGCHAIN_API_KEY=<your-api-key>

任何 ChatModel 调用（无论是否嵌套在链中）都会被自动追踪。一次追踪将包含输入、输出、延迟、令牌用量、调用参数、环境参数等。参见这里的示例：https://smith.langchain.com/public/a54192ae-dd5c-4f7a-88d1-daa1eaba1af7/r。 (opens in a new tab)

之后你可以在 LangSmith 中为任何追踪提供反馈、为评估编译带标注的数据集、在 playground 中调试性能等等。

[旧版] __call__

消息输入 -> 消息输出

为了方便，你也可以把聊天模型当作可调用对象。你可以通过向聊天模型传入一条或多条消息来获得聊天补全。响应将是一条消息。

from langchain_core.messages import HumanMessage, SystemMessage
 
chat(
    [
        HumanMessage(
            content="Translate this sentence from English to French: I love programming."
        )
    ]
)
AIMessage(content="J'adore la programmation.")

OpenAI 的聊天模型支持多条消息作为输入。更多信息见 此处 (opens in a new tab)。下面是一个向聊天模型发送系统消息和用户消息的示例：

messages = [
    SystemMessage(
        content="You are a helpful assistant that translates English to French."
    ),
    HumanMessage(content="I love programming."),
]
chat(messages)
AIMessage(content="J'adore la programmation.")

[旧版] generate

批量调用，更丰富的输出

你可以更进一步，使用 generate 为多组消息生成补全。这会返回一个 LLMResult，其中带有一个额外的 message 参数。它将包含关于每次生成（例如结束原因）的额外信息，以及关于整个 API 调用（例如使用的总令牌数）的额外信息。

batch_messages = [
    [
        SystemMessage(
            content="You are a helpful assistant that translates English to French."
        ),
        HumanMessage(content="I love programming."),
    ],
    [
        SystemMessage(
            content="You are a helpful assistant that translates English to French."
        ),
        HumanMessage(content="I love artificial intelligence."),
    ],
]
result = chat.generate(batch_messages)
result
LLMResult(generations=[[ChatGeneration(text="J'adore programmer.", generation_info={'finish_reason': 'stop'}, message=AIMessage(content="J'adore programmer."))], [ChatGeneration(text="J'adore l'intelligence artificielle.", generation_info={'finish_reason': 'stop'}, message=AIMessage(content="J'adore l'intelligence artificielle."))]], llm_output={'token_usage': {'prompt_tokens': 53, 'completion_tokens': 18, 'total_tokens': 71}, 'model_name': 'gpt-3.5-turbo'}, run=[RunInfo(run_id=UUID('077917a9-026c-47c4-b308-77b37c3a3bfa')), RunInfo(run_id=UUID('0a70a0bf-c599-4f51-932a-c7d42202c984'))])

你可以从这个 LLMResult 中恢复令牌用量等信息：

result.llm_output
{'token_usage': {'prompt_tokens': 53,
  'completion_tokens': 18,
  'total_tokens': 71},
 'model_name': 'gpt-3.5-turbo'}
索引（Index）聊天模型缓存（ChatModelCaching）