消息

from langchain.chat_models import init_chat_model
from langchain.messages import HumanMessage, AIMessage, SystemMessage

model = init_chat_model("gpt-5-nano")

system_msg = SystemMessage("You are a helpful assistant.")
human_msg = HumanMessage("Hello, how are you?")

# Use with chat models
messages = [system_msg, human_msg]
response = model.invoke(messages)  # Returns AIMessage

response = model.invoke("Write a haiku about spring")

from langchain.messages import SystemMessage, HumanMessage, AIMessage

messages = [
    SystemMessage("You are a poetry expert"),
    HumanMessage("Write a haiku about spring"),
    AIMessage("Cherry blossoms bloom...")
]
response = model.invoke(messages)

messages = [
    {"role": "system", "content": "You are a poetry expert"},
    {"role": "user", "content": "Write a haiku about spring"},
    {"role": "assistant", "content": "Cherry blossoms bloom..."}
]
response = model.invoke(messages)

response = model.invoke("Explain AI")
print(type(response))  # <class 'langchain.messages.AIMessage'>

from langchain.messages import AIMessage, SystemMessage, HumanMessage

# 手动创建 AI 消息（例如，用于对话历史）
ai_msg = AIMessage("I'd be happy to help you with that question!")

# 添加到对话历史
messages = [
    SystemMessage("You are a helpful assistant"),
    HumanMessage("Can you help me?"),
    ai_msg,  # 插入为好像来自模型
    HumanMessage("Great! What's 2+2?")
]

response = model.invoke(messages)

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano")

def get_weather(location: str) -> str:
    """Get the weather at a location."""
    ...

model_with_tools = model.bind_tools([get_weather])
response = model_with_tools.invoke("What's the weather in Paris?")

for tool_call in response.tool_calls:
    print(f"Tool: {tool_call['name']}")
    print(f"Args: {tool_call['args']}")
    print(f"ID: {tool_call['id']}")

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-5-nano")

response = model.invoke("Hello!")
response.usage_metadata

{'input_tokens': 8,
 'output_tokens': 304,
 'total_tokens': 312,
 'input_token_details': {'audio': 0, 'cache_read': 0},
 'output_token_details': {'audio': 0, 'reasoning': 256}}

chunks = []
full_message = None
for chunk in model.stream("Hi"):
    chunks.append(chunk)
    print(chunk.text)
    full_message = chunk if full_message is None else full_message + chunk

from langchain.messages import AIMessage
from langchain.messages import ToolMessage

# 在模型进行工具调用后
# （这里，我们手动创建消息以简洁起见）
ai_message = AIMessage(
    content=[],
    tool_calls=[{
        "name": "get_weather",
        "args": {"location": "San Francisco"},
        "id": "call_123"
    }]
)

# 执行工具并创建结果消息
weather_result = "Sunny, 72°F"
tool_message = ToolMessage(
    content=weather_result,
    tool_call_id="call_123"  # 必须与调用 ID 匹配
)

# 继续对话
messages = [
    HumanMessage("What's the weather in San Francisco?"),
    ai_message,  # 模型的工具调用
    tool_message,  # 工具执行结果
]
response = model.invoke(messages)  # 模型处理结果

from langchain.messages import HumanMessage

# 字符串内容
human_message = HumanMessage("Hello, how are you?")

# 提供商原生格式（例如，OpenAI）
human_message = HumanMessage(content=[
    {"type": "text", "text": "Hello, how are you?"},
    {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}
])

# 标准内容块列表
human_message = HumanMessage(content_blocks=[
    {"type": "text", "text": "Hello, how are you?"},
    {"type": "image", "url": "https://example.com/image.jpg"},
])