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from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
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from langchain.chat_models import init_chat_model
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from dotenv import load_dotenv
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import os
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load_dotenv(override=True)
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model = init_chat_model(
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    model="gpt-5.4-mini",
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    model_provider="openai",
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    api_key=os.getenv("CLOSEAI_API_KEY"),
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    base_url=os.getenv("CLOSEAI_BASE_URL"),
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)
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messages = [
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    SystemMessage(
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        "你是一个信息抽取器。你会收到多条来自不同发言者的 user 消息。每条消息可能带有 name 字段。"
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        "你的任务是：严格根据每条消息的 name 提取发言者及其观点，并输出 JSON。"
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        "禁止使用“第一个人/第二个人”这种相对称呼。若某条消息没有 name，则输出 unknown。"
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        '输出格式：{"speakers":[{"name":"...","claim":"..."}]}'
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    ),
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    HumanMessage(content="我认为 1+1=2", name="Bob"),
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    HumanMessage(content="我认为 1+1>2", name="Tom"),
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    HumanMessage(content="请列出谁说了什么，不要判断对错。", name="audience"),
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]
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response = model.invoke(messages)
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print(response.content)

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# 举例1：给出最终答案
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AIMessage(content="北京今天晴天，温度 15°C")
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# 举例2：调用工具（content 通常是空的）
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AIMessage(
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    content="",
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    tool_calls=[{
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        "name": "get_weather",
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        "args": {"city": "北京"},
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        "id": "call_xxx",
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    }],
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)
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# 举例3：手工构造工具调用后，交给模型接着算
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response = model.invoke([
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    HumanMessage(content="北京天气如何"),
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    AIMessage(content="", tool_calls=[{
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        "name": "get_weather", "args": {"city": "北京"}, "id": "call_00_..."}]),
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    ToolMessage(content="今天北京天气晴朗，万里无云~", tool_call_id="call_00_..."),
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])
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print(response.content)     # 今天北京天气晴朗，万里无云。

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import base64
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from langchain.chat_models import init_chat_model
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from langchain.messages import HumanMessage
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from dotenv import load_dotenv
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import os
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load_dotenv(override=True)
8

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model = init_chat_model(
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    model="gpt-5.4-mini",
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    model_provider="openai",
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    api_key=os.getenv("CLOSEAI_API_KEY"),
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    base_url=os.getenv("CLOSEAI_BASE_URL"),
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)
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def encode_image(img_path, img_type="jpeg"):
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    """将一张本地图片转换成 Base64 编码的 Data URI 字符串，方便在文本中嵌入图片数据"""
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    with open(img_path, "rb") as img_file:
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        return f"data:image/{img_type};base64,{base64.b64encode(img_file.read()).decode('utf-8')}"
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img_path = "image_test.png"
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base64_image = encode_image(img_path)          # 得到 "data:image/jpeg;base64,...."
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response = model.invoke(
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    [
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        HumanMessage(
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            content=[
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                {"type": "text", "text": "这张图里有什么？"},
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                {
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                    "type": "image_url",
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                    "image_url": base64_image,      # Data URI 字符串直接放进来
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                },
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            ]
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        )
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    ]
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)
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print(response.content)

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import base64
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from langchain.messages import HumanMessage
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from langchain.chat_models import init_chat_model
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from dotenv import load_dotenv
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import os
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load_dotenv(override=True)
8

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model = init_chat_model(
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    model="gpt-5.4-mini",
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    model_provider="openai",
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    api_key=os.getenv("CLOSEAI_API_KEY"),
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    base_url=os.getenv("CLOSEAI_BASE_URL"),
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)
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def encode_image(img_path):
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    """将一张本地图片转换成 Base64 字符串（这里不带 Data URI 前缀）"""
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    with open(img_path, "rb") as img_file:
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        return base64.b64encode(img_file.read()).decode("utf-8")
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base64_image = encode_image("image_test.png")
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response = model.invoke(
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    [
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        # 老写法（按供应商规范拼字典，能用但要记格式）
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        # HumanMessage(content=[
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        #     {"type": "text", "text": "这张图里有什么？"},
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        #     {"type": "image_url", "image_url": base64_image},
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        # ]),
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        # 推荐的统一写法
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        HumanMessage(
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            content_blocks=[
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                {"type": "text", "text": "这张图里有什么？"},
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                {
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                    "type": "image",
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                    "base64": base64_image,
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                    "mime_type": "image/png",
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                },
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            ]
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        )
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    ]
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)
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print(response.content)

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model = init_chat_model(
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    model="claude-haiku-4-5",
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    model_provider="openai",
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    api_key=os.getenv("CLOSEAI_API_KEY"),
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    base_url=os.getenv("CLOSEAI_BASE_URL"),
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)
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response = model.invoke(
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    [
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        HumanMessage(
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            content_blocks=[
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                {"type": "text", "text": "这张图里有什么？"},
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                {
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                    "type": "image",
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                    "base64": base64_image,
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                    "mime_type": "image/png",
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                },
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            ]
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        )
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    ]
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)