本章将详细讲解大模型分类，langchain1.0中如何调用各类大模型。

一.模型分类

大模型可以从多个维度进行分类，以下是最常见和最重要的几种分类方式：

1. 按模态分类（处理的信息类型）

这是最直观的分类方式，关注模型能处理和生成什么类型的数据。

纯文本模型

特点：专门处理和理解文本。它们是当前最成熟、应用最广泛的大模型。
典型代表：GPT系列、LLaMA、ChatGLM、文心一言（基础版）、通义千问（基础版）。
主要应用：对话聊天、内容创作、翻译、代码生成、文本摘要等。

视觉模型

特点：专门处理和理解图像和视频。
典型代表：DALL-E、Stable Diffusion、Midjourney（图像生成）；SAM（分割一切模型）。
主要应用：图像生成、图像编辑、视觉问答、目标检测。

多模态模型

特点：能够同时理解和处理多种类型的信息（如文本、图像、音频、视频），并能在这些模态之间建立联系和进行转换。
典型代表：GPT-4V（具有视觉能力的GPT-4）、Gemini（原生多模态）、Claude 3、李飞飞家的VLM、腾讯的混元。
主要应用：根据图片回答问题、生成图文并茂的报告、视频内容理解、跨模态搜索。

音频模型

特点：专门处理语音和音频。
典型代表：Whisper（语音识别）、VALL-E（语音合成）、Suno AI（音乐生成）。
主要应用：语音转文字、文字转语音、音乐创作、声音克隆。

2. 按功能和应用领域分类

这个维度关注模型被设计用来解决什么类型的问题。

对话/聊天模型

特点：专注于流畅、连贯的多轮对话，理解上下文。
典型代表：ChatGPT、Claude、文心一言、通义千问。

代码模型

特点：在大量代码数据上训练，精通编程语言，擅长代码生成、补全、解释和调试。
典型代表：GitHub Copilot（基于Codex）、CodeLlama、DeepSeek-Coder。

科学计算模型

特点：在科学文献和数据上训练，用于辅助科学研究。
典型代表：AlphaFold（蛋白质结构预测）、Galactica（科学知识）、MatBERT（材料科学）。

专业领域模型

特点：在特定领域（如法律、金融、医疗）的精加工数据上训练，具备深厚的领域知识。
典型代表：BloombergGPT（金融）、Med-PaLM（医疗）。

二. LangchainV1.0大模型调用

学习使用大模型之前，先分享一个国内免费使用模型的网站：魔搭社区ModelScope，每天免费2000次调用，里面有丰富的模型以及开源项目，涵盖了所有开源大模型，可在线调用，可下载本地运行。

获取KEY

登录魔搭社区，获取KEY，后面调用模型使用，点击头像=》账号设置=》选择左边访问令牌=》新建令牌，如图：

1.大语言模型

调用DeepSeek-V3.1为例

from langchain_openai import ChatOpenAI
import os
# 1. 创建 LLM 并应用结构化输出
llm = ChatOpenAI(
    model='deepseek-ai/DeepSeek-V3.1',
    api_key=os.getenv("MODELSCOPE_API_KEY"),
    max_tokens=10,
    base_url="https://api-inference.modelscope.cn/v1")
# 2. 创建提示模板
prompt = """你是专业的写诗助手，模仿李白的静夜思，根据酒吧放松情景写一首诗。"""
# 3. 调用大模型
result = llm.invoke(prompt)
# 4. 打印结果
print(result)

输出结果如下：

content='《酒肆闲吟》\n霓虹如月映琼杯，玉液摇光入夜扉\n醉倚阑珊星欲语，心随云影共翩飞\n吧台不问尘间事，笑看浮生梦几回\n且尽樽前逍遥趣，清风伴我踏歌归\n\n注：作品借鉴李白《静夜思》的静夜感怀与物我相融笔法，首联以“霓虹拟月”勾连古今饮宴场景，颔联用醉眼看星云的迷离感延续太白醉态思维。尾联“不问尘间事”暗合原诗超脱意境，最终以清风踏歌收束，传递现代酒吧中亦能寻得的诗性逍遥。' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 159, 'prompt_tokens': 24, 'total_tokens': 183, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_provider': 'openai', 'model_name': 'deepseek-ai/DeepSeek-V3.1', 'system_fingerprint': '', 'id': 'chatcmpl-ef65e8c5-ea85-4be2-a2e3-c6a3fa03b057', 'finish_reason': 'stop', 'logprobs': None} id='lc_run--33445256-2a35-4eae-ba3d-9545c87dabf4-0' usage_metadata={'input_tokens': 24, 'output_tokens': 159, 'total_tokens': 183, 'input_token_details': {}, 'output_token_details': {}}

2.多模态大模型

以Qwen3-VL-235B-A22B-Instruct为例：

from openai import OpenAI
import os
import base64
# 1. 创建 LLM
llm=OpenAI(
    base_url="https://api-inference.modelscope.cn/v1/",
    api_key=os.getenv("MODELSCOPE_API_KEY")
)
def load_png_as_base64(file_path):
    with open(file_path, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
        return encoded_string.decode("utf-8")
test2_base64=load_png_as_base64("images/test2.png")
# 2. 构造更清晰的问题描述
messages=[
    {
        "role":"user",
        "content":[{
            'type': 'text',
            'text': '你是专业图表分析助手，请分析这张图。',
        }, {
            'type': 'image_url',
            'image_url': {
                'url':f"data:image/png;base64,{test2_base64}",
            },
        }],
    }
]
try:
    # 3. 调用大模型
    response = llm.chat.completions.create(
        model='Qwen/Qwen3-VL-235B-A22B-Instruct', # ModelScope Model-Id, required
        messages=[{
            'role': 'user',
            'content': [{
                'type': 'text',
                'text': '你是专业图表分析助手，请提取这张图，不要做阐述。',
            }, {
                'type': 'image_url',
                'image_url': {
                    'url':f"data:image/png;base64,{test2_base64}",
                },
            }],
        }],
        stream=False,
        temperature=0.0
    )
    # 4. 打印结果
    print("=== 模型回答 ===")
    print(response)
except Exception as e:
    print(f"调用模型时出错: {e}")

图片：

运行输出效果：

=== 模型回答 ===
ChatCompletion(id='chatcmpl-b9fcd80e-77bc-482d-b797-a35e729f598f', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='| TYPE           | ASCII                          | HTML                           |\n|----------------|--------------------------------|--------------------------------|\n| Single backticks | \'Isn\'t this fun?\'              | ‘Isn’t this fun?’              |\n| Quotes         | "Isn\'t this fun?"              | “Isn’t this fun?”              |\n| Dashes         | -- is en-dash, --- is em-dash  | – is en-dash, — is em-dash     |', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None, function_calls=None, reasoning_content=''), delta={'role': None, 'content': '', 'tool_calls': None, 'function_calls': None, 'reasoning_content': ''})], created=1764604248, model='Qwen/Qwen3-VL-235B-A22B-Instruct', object='chat.completion', service_tier=None, system_fingerprint='', usage=CompletionUsage(completion_tokens=90, prompt_tokens=164, total_tokens=254, completion_tokens_details=None, prompt_tokens_details=None))

3.向量模型

以Qwen3-Embedding-8B为例：

from openai import OpenAI
import os
client = OpenAI(
    base_url='https://api-inference.modelscope.cn/v1',
    api_key=os.getenv("MODELSCOPE_API_KEY"), # ModelScope Token
)

response = client.embeddings.create(
    model='Qwen/Qwen3-Embedding-8B', # ModelScope Model-Id, required
    input='你好，你吃饭了吗？',
    encoding_format="float"
)

print(response.data)

运行效果如下：

[Embedding(embedding=[0.00623321533203125, -0.0176544189453125, 0.0178985595703125, -0.050018310546875, 0.0098114013671875, -0.0282440185546875, 0.007476806640625, 0.0030231475830078125, -0.0225677490234375, 0.03558349609375, 0.0007276535034179688, 0.0106964111328125, 0.044586181640625, -0.0241546630859375, -0.0071258544921875, 0.01436614990234375, -0.0282745361328125, -0.032470703125, 0.0238189697265625, 0.013458251953125, 0.013824462890625, -0.00408935546875, 0.0237884521484375, -0.029022216796875, 0.0251617431640625, 0.00965118408203125, -0.01288604736328125, -0.0143890380859375, -0.0167388916015625, 0.03057861328125, -0.04388427734375, -0.0229949951171875, -0.004291534423828125, 0.00836181640625, -0.0137786865234375, 0.0172576904296875, 0.00986480712890625, -0.0138702392578125, 0.0139312744140625, -0.016448974609375, 0.00394439697265625, 0.00157928466796875, -0.01447296142578125, 0.0020580291748046875, -0.0210418701171875, -0.0220794677734375, -0.007293701171875, 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更多参考：https://docs.langchain.com/oss/python/integrations/chat