使用DashScope qwen-vl模型进行图像推理的多模态LLM¶

在本notebook中，我们展示如何使用DashScope qwen-vl多模态LLM类/抽象进行图像理解/推理。目前不支持异步功能

我们还将展示目前为DashScope LLM支持的几个函数

• complete (同步): 用于单个提示和图像列表
• chat (同步): 用于多个聊天消息
• stream complete (同步): 用于流式输出complete的结果
• stream chat (同步): 用于流式输出chat的结果
• 多轮对话。

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!pip install -U llama-index-multi-modal-llms-dashscope

!pip install -U llama-index-multi-modal-llms-dashscope

使用DashScope理解来自URL的图像¶

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# Set API key
%env DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY

# Set API key %env DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY

初始化 DashScopeMultiModal 并从URL加载图像¶

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from llama_index.multi_modal_llms.dashscope import (
    DashScopeMultiModal,
    DashScopeMultiModalModels,
)

from llama_index.core.multi_modal_llms.generic_utils import load_image_urls


image_urls = [
    "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg",
]

image_documents = load_image_urls(image_urls)

dashscope_multi_modal_llm = DashScopeMultiModal(
    model_name=DashScopeMultiModalModels.QWEN_VL_MAX,
)

from llama_index.multi_modal_llms.dashscope import ( DashScopeMultiModal, DashScopeMultiModalModels, ) from llama_index.core.multi_modal_llms.generic_utils import load_image_urls image_urls = [ "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg", ] image_documents = load_image_urls(image_urls) dashscope_multi_modal_llm = DashScopeMultiModal( model_name=DashScopeMultiModalModels.QWEN_VL_MAX, )

使用图像完成提示¶

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complete_response = dashscope_multi_modal_llm.complete(
    prompt="What's in the image?",
    image_documents=image_documents,
)
print(complete_response)

complete_response = dashscope_multi_modal_llm.complete( prompt="What's in the image?", image_documents=image_documents, ) print(complete_response)

The image captures a serene moment on a sandy beach at sunset. A woman, dressed in a blue and white plaid shirt, is seated on the ground. She is holding a treat in her hand, which is being gently taken by a dog. The dog, wearing a blue harness, is sitting next to the woman, its paw resting on her leg. The backdrop of this heartwarming scene is the vast ocean, with the sun setting in the distance, casting a warm glow over the entire landscape. The image beautifully encapsulates the bond between the woman and her dog, set against the tranquil beauty of nature.

In [ ]

### Complete a prompt with multi images
multi_image_urls = [
    "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg",
    "https://dashscope.oss-cn-beijing.aliyuncs.com/images/panda.jpeg",
]

multi_image_documents = load_image_urls(multi_image_urls)
complete_response = dashscope_multi_modal_llm.complete(
    prompt="What animals are in the pictures?",
    image_documents=multi_image_documents,
)
print(complete_response)

### 使用多张图像完成提示 multi_image_urls = [ "https://dashscope.oss-cn-beijing.aliyuncs.com/images/dog_and_girl.jpeg", "https://dashscope.oss-cn-beijing.aliyuncs.com/images/panda.jpeg", ] multi_image_documents = load_image_urls(multi_image_urls) complete_response = dashscope_multi_modal_llm.complete( prompt="What animals are in the pictures?", image_documents=multi_image_documents, ) print(complete_response)

There is a dog in Picture 1, and there is a panda in Picture 2.

使用多张图像流式完成提示¶

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stream_complete_response = dashscope_multi_modal_llm.stream_complete(
    prompt="What's in the image?",
    image_documents=image_documents,
)

for r in stream_complete_response:
    print(r.delta, end="")

stream_complete_response = dashscope_multi_modal_llm.stream_complete( prompt="What's in the image?", image_documents=image_documents, ) for r in stream_complete_response: print(r.delta, end="")

The image captures a serene moment on a sandy beach at sunset. A woman, dressed in a blue and white plaid shirt, is seated on the ground. She is holding a treat in her hand, which is being gently taken by a dog. The dog, wearing a blue harness, is sitting next to the woman, its paw resting on her leg. The backdrop of this heartwarming scene is the vast ocean, with the sun setting in the distance, casting a warm glow over the entire landscape. The image beautifully encapsulates the bond between the woman and her dog, set against the tranquil beauty of nature.

使用聊天消息进行多轮对话¶

In [ ]

from llama_index.core.base.llms.types import MessageRole
from llama_index.multi_modal_llms.dashscope.utils import (
    create_dashscope_multi_modal_chat_message,
)

chat_message_user_1 = create_dashscope_multi_modal_chat_message(
    "What's in the image?", MessageRole.USER, image_documents
)
chat_response = dashscope_multi_modal_llm.chat([chat_message_user_1])
print(chat_response.message.content[0]["text"])
chat_message_assistent_1 = create_dashscope_multi_modal_chat_message(
    chat_response.message.content[0]["text"], MessageRole.ASSISTANT, None
)
chat_message_user_2 = create_dashscope_multi_modal_chat_message(
    "what are they doing?", MessageRole.USER, None
)
chat_response = dashscope_multi_modal_llm.chat(
    [chat_message_user_1, chat_message_assistent_1, chat_message_user_2]
)
print(chat_response.message.content[0]["text"])

from llama_index.core.base.llms.types import MessageRole from llama_index.multi_modal_llms.dashscope.utils import ( create_dashscope_multi_modal_chat_message, ) chat_message_user_1 = create_dashscope_multi_modal_chat_message( "What's in the image?", MessageRole.USER, image_documents ) chat_response = dashscope_multi_modal_llm.chat([chat_message_user_1]) print(chat_response.message.content[0]["text"]) chat_message_assistent_1 = create_dashscope_multi_modal_chat_message( chat_response.message.content[0]["text"], MessageRole.ASSISTANT, None ) chat_message_user_2 = create_dashscope_multi_modal_chat_message( "what are they doing?", MessageRole.USER, None ) chat_response = dashscope_multi_modal_llm.chat( [chat_message_user_1, chat_message_assistent_1, chat_message_user_2] ) print(chat_response.message.content[0]["text"])

The image shows two photos of a panda sitting on a wooden log in an enclosure. In the top photo, the panda is sitting upright with its front paws on the log, facing three crows that are perched on the log. The panda looks alert and curious, while the crows seem to be observing the panda. In the bottom photo, the panda is lying down on the log, its head resting on its front paws. One crow has landed on the ground next to the log, and it seems to be interacting with the panda. The background of the photo shows green plants and a wire fence, creating a natural and relaxed atmosphere.
The woman is sitting on the beach with her dog, and they are giving each other high fives. The panda and the crows are sitting together on a log, and the panda seems to be communicating with the crows.

通过聊天消息列表流式聊天¶

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stream_chat_response = dashscope_multi_modal_llm.stream_chat(
    [chat_message_user_1, chat_message_assistent_1, chat_message_user_2]
)
for r in stream_chat_response:
    print(r.delta, end="")

stream_chat_response = dashscope_multi_modal_llm.stream_chat( [chat_message_user_1, chat_message_assistent_1, chat_message_user_2] ) for r in stream_chat_response: print(r.delta, end="")

The woman is sitting on the beach, holding a treat in her hand, while the dog is sitting next to her, taking the treat from her hand.

使用本地文件中的图像¶

使用本地文件
Linux和mac文件模式: file:///home/images/test.png
Windows文件模式: file://D:/images/abc.png

In [ ]

from llama_index.multi_modal_llms.dashscope.utils import load_local_images

local_images = [
    "file://THE_FILE_PATH1",
    "file://THE_FILE_PATH2",
]

image_documents = load_local_images(local_images)
chat_message_local = create_dashscope_multi_modal_chat_message(
    "What animals are in the pictures?", MessageRole.USER, image_documents
)
chat_response = dashscope_multi_modal_llm.chat([chat_message_local])
print(chat_response.message.content[0]["text"])

from llama_index.multi_modal_llms.dashscope.utils import load_local_images local_images = [ "file://THE_FILE_PATH1", "file://THE_FILE_PATH2", ] image_documents = load_local_images(local_images) chat_message_local = create_dashscope_multi_modal_chat_message( "What animals are in the pictures?", MessageRole.USER, image_documents ) chat_response = dashscope_multi_modal_llm.chat([chat_message_local]) print(chat_response.message.content[0]["text"])

There is a dog in Picture 1, and there is a panda in Picture 2.