许多文件包含包括图像和文本在内的多种内容类型。然而，在大多数RAG（可检索的问答生成）应用中，图像中捕获的信息往往会丢失。随着多模态大型语言模型（如GPT4-V、LLaVA或FUYU-8b）的出现，考虑如何在RAG管道中利用图像。

在RAG中利用多模态模型有几种选项：

选项1：

• 使用多模态嵌入（例如CLIP）对图像和文本进行嵌入
• 通过相似性搜索检索两者
• 将原始图像和文本块传递给多模态大型语言模型以合成回答

选项2：

• 使用多模态大型语言模型（例如GPT-4V、LLaVA或FUYU-8b）从图像生成文本摘要
• 嵌入并检索文本
• 将文本块传递给大型语言模型以合成回答

选项3

• 使用多模态大型语言模型（例如GPT-4V、LLaVA或FUYU-8b）从图像生成文本摘要
  
• 嵌入并检索带有原始图像参考的图像摘要
• 将原始图像和文本块传递给多模态大型语言模型以合成回答

在这里，我们将实施选项3

涉及的步骤

• 使用多模态嵌入（例如CLIP）对图像和文本进行嵌入
• 通过相似性搜索检索两者
• 将原始图像和文本块传递给多模态大型语言模型（GPT4-V）以合成回答

使用的技术栈

• 数据加载：非结构化。它是一个出色的ETL（提取、转化、加载）工具，非常适合于它的分区文档能力（文本、图像、表格等）
• Tesseract：Tesseract是一个适用于各种操作系统的光学字符识别引擎。它是免费软件，根据Apache许可证发布。
• Langchain：它是一个基于大型语言模型（LLMs）构建应用程序的开源框架。
• LLM：GPT-4带视觉功能，有时被称为GPT-4V或在API中被称为gpt-4-vision-preview，允许模型接收图像并回答关于它们的问题。

代码实现

安装所需的依赖项

对于非结构化数据，你还需要在系统中有poppler和tesseract。

非结构化将通过首先移除所有嵌入图像块来分区PDF文件。然后它将使用布局模型（YOLOX）获取边界框（用于表格）以及标题，这些标题是文档的候选子部分（例如，引言等）。之后，它将进行后处理来聚合每个标题下的文本，并基于用户特定的标志（例如，最小块大小等）进一步划分为下游处理的文本块。

! pip install pdf2image
! pip install pytesseract
! apt install poppler-utils
! apt install tesseract-ocr
#
! pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)
#
# lock to 0.10.19 due to a persistent bug in more recent versions
! pip install "unstructured[all-docs]==0.10.19" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch

数据加载

import os
import shutil
#os.mkdir("Data")
! wget "https://www.getty.edu/publications/resources/virtuallibrary/0892360224.pdf"
shutil.move("0892360224.pdf","Data")

提取图像并将其保存在所需路径中

path = "/content/Data/"
#
file_name = os.listdir(path)

使用下面来自 Unstructured 的 partition_pdf 方法来提取文本和图片

# Extract images, tables, and chunk text
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
    filename=path + file_name[0],
    extract_images_in_pdf=True,
    infer_table_structure=True,
    chunking_strategy="by_title",
    max_characters=4000,
    new_after_n_chars=3800,
    combine_text_under_n_chars=2000,
    image_output_dir_path=path,

将文本元素按类型分类

tables = []
texts = []
for element in raw_pdf_elements:
    if "unstructured.documents.elements.Table" in str(type(element)):
        tables.append(str(element))
    elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
        texts.append(str(element))
#
print(len(tables)
print(len(texts))
#### Response
2
194

图片存储在文件路径中

from PIL import Image
Image.open("/content/data/figure-26-1.jpg")

我们在文档中使用了Multi-modal嵌入

这里我们已经使用了OpenClip多模态嵌入。

• 我们使用更大的模型以获得更好的性能（在 langchain_experimental.open_clip.py 中设置）。
• model_name = “ViT-g-14” 检查点 = “laion2b_s34b_b88k”

import os
import uuid
import chromadb
import numpy as np
from langchain.vectorstores import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
from PIL import Image as _PILImage
# Create chroma
vectorstore = Chroma(
    collection_name="mm_rag_clip_photos", embedding_function=OpenCLIPEmbeddings()
)
# Get image URIs with .jpg extension only
image_uris = sorted(
    [
        os.path.join(path, image_name)
        for image_name in os.listdir(path)
        if image_name.endswith(".jpg")
    ]
)
# Add images
vectorstore.add_images(uris=image_uris)
# Add documents
vectorstore.add_texts(texts=texts)
# Make retriever
retriever = vectorstore.as_retriever()

检索增强型生成

• vectorstore.add_images 将存储/检索图像作为 base64 编码的字符串。
• 然后，这些信息可以传递给 GPT-4V。

import base64
import io
from io import BytesIO
import numpy as np
from PIL import Image

def resize_base64_image(base64_string, size=(128, 128)):
    """
    Resize an image encoded as a Base64 string.
    Args:
    base64_string (str): Base64 string of the original image.
    size (tuple): Desired size of the image as (width, height).
    Returns:
    str: Base64 string of the resized image.
    """
    # Decode the Base64 string
    img_data = base64.b64decode(base64_string)
    img = Image.open(io.BytesIO(img_data))
    # Resize the image
    resized_img = img.resize(size, Image.LANCZOS)
    # Save the resized image to a bytes buffer
    buffered = io.BytesIO()
    resized_img.save(buffered, format=img.format)
    # Encode the resized image to Base64
    return base64.b64encode(buffered.getvalue()).decode("utf-8")

def is_base64(s):
    """Check if a string is Base64 encoded"""
    try:
        return base64.b64encode(base64.b64decode(s)) == s.encode()
    except Exception:
        return False

def split_image_text_types(docs):
    """Split numpy array images and texts"""
    images = []
    text = []
    for doc in docs:
        doc = doc.page_content  # Extract Document contents
        if is_base64(doc):
            # Resize image to avoid OAI server error
            images.append(
                resize_base64_image(doc, size=(250, 250))
            )  # base64 encoded str
        else:
            text.append(doc)
    return {"images": images, "texts": text}

为什么采用Lanchain表达语言？

LCEL使构建基本组件的复杂链变得简单。 它通过提供以下方式实现这一点：

1. 统一接口：每个LCEL对象都实现了Runnable接口，该接口定义了一套公共的调用方法（invoke, batch, stream, ainvoke, …）。 这使得LCEL对象链也能自动支持这些调用。 也就是说，每个LCEL对象链本身就是一个LCEL对象。
2. 构建原语：LCEL提供了许多原语，它们便于组合链，使组件并行化，添加回退机制，动态配置链内部等等。

在这里，我们使用了RunnableParallel来格式化输入，同时为ChatPromptTemplates添加图像支持。

步骤：

• 首先，我们计算上下文（在这种情况下既包括“文本”也包括“图片”）和问题（这里仅是一个RunnablePassthrough）
• 然后，我们将这些信息传入我们的提示模板，这是一个自定义函数，用于为gpt-4-vision-preview模型格式化消息。
• 最后，我们将输出解析为一个字符串。
• 我们将响应以及源上下文传递出去。

from operator import itemgetter
from langchain.chat_models import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, RunnablePassthrough,RunnableParallel

def prompt_func(data_dict):
    # Joining the context texts into a single string
    formatted_texts = "\n".join(data_dict["context"]["texts"])
    messages = []
    # Adding image(s) to the messages if present
    if data_dict["context"]["images"]:
        image_message = {
            "type": "image_url",
            "image_url": {
                "url": f_"data:image/jpeg;base64,{data_dict['context']['images'][0]}"
            },
        }
        messages.append(image_message)
    # Adding the text message for analysis
    text_message = {
        "type": "text",
        "text": (
            "As an expert art critic and historian, your task is to analyze and interpret images, "
            "considering their historical and cultural significance. Alongside the images, you will be "
            "provided with related text to offer context. Both will be retrieved from a vectorstore based "
            "on user-input keywords. Please use your extensive knowledge and analytical skills to provide a "
            "comprehensive summary that includes:\n"
            "- A detailed description of the visual elements in the image.\n"
            "- The historical and cultural context of the image.\n"
            "- An interpretation of the image's symbolism and meaning.\n"
            "- Connections between the image and the related text.\n\n"
            f"User-provided keywords: {data_dict['question']}\n\n"
            "Text and / or tables:\n"
            f"{formatted_texts}"
        ),
    }
    messages.append(text_message)
    return [HumanMessage(content=messages)]

返回源文件的代码

from google.colab import userdata

openai_api_key = userdata.get('OPENAI_API_KEY')
model = ChatOpenAI(temperature=0,
                   openai_api_key=openai_api_key,
                   model="gpt-4-vision-preview",
                   max_tokens=1024)
# RAG pipeline
chain = (
    {
        "context": retriever | RunnableLambda(split_image_text_types),
        "question": RunnablePassthrough(),
    }
    | RunnableParallel({"response":prompt_func| model| StrOutputParser(),
                      "context": itemgetter("context"),})
)

启动RAG问题解答链

问题1

response = chain.invoke("hunting on the lagoon")
#
print(response['response'])
print(response['context'])

############# RESPONSE ###############
The image depicts a serene scene of a lagoon with several groups of people engaged in bird hunting. The visual elements include calm waters, boats with hunters wearing red and white clothing, and birds both in flight and used as decoys. The hunters appear to be using long poles, possibly to navigate through the shallow waters or to assist in the hunting process. In the background, there are simple straw huts, suggesting temporary shelters for the hunters. The sky is painted with soft clouds, and the overall color palette is muted, with the reds of the hunters' clothing standing out against the blues and greens of the landscape.
The historical and cultural context of this image is rooted in the Italian Renaissance, specifically in Venice during the late 15th to early 16th century. Vittore Carpaccio, the artist, was known for his genre paintings, which depicted scenes from everyday life with great detail and realism. This painting, "Hunting on the Lagoon," is a testament to Carpaccio's keen observation of his environment and the activities of his contemporaries. The inclusion of diverse figures, such as some black individuals, reflects the cosmopolitan nature of Venetian society at the time.
Interpreting the symbolism and meaning of the image, one might consider the lagoon as a symbol of Venice itself—a city intertwined with water, where the boundary between land and sea is often blurred. The act of hunting could represent the human endeavor to harness and interact with nature, a common theme during the Renaissance as people sought to understand and depict the natural world with increasing accuracy. The presence of decoys suggests themes of illusion and reality, which were also explored in Renaissance art.
The connection between the image and the related text is clear. The text provides valuable insights into the painting's background, such as its use as a window cover, which adds a layer of functionality and interactivity to the artwork. The trompe l'oeil on the back with the illusionistic cornice and the real hinge further emphasizes the artist's interest in creating a sense of depth and reality. The mention of the lily blossom at the bottom indicates that the painting may have been altered from its original form, which could have included more symbolic elements or been part of a larger composition.
The text also notes that Carpaccio was famous as a landscape painter, which aligns with the detailed and atmospheric depiction of the lagoon setting. The discovery of the painting only a few years ago suggests that there is still much to learn about Carpaccio's work and the nuances of this particular piece. The lack of complete understanding of the subject matter invites further research and interpretation, allowing viewers to ponder the daily life and environment of Renaissance Venice.

{'images': 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'],
 'texts': ["VITTORE  CARPACCIO Venetian, 1455/56-1525/26 Hunting  on the  Lagoon oil on panel, 75.9x63.7cm 6 Carpaccio  is considered to be the first great genre painter of the Italian Renaissance, and it is ob- vious that he was a careful observer of his surroundings. The  subject of this unusual painting is not yet completely understood, but it apparently depicts groups of Venetians, including some blacks, hunting for birds on the Venetian lagoon. Some birds standing upright in the boats must be decoys. In the background are huts built of straw, which the hunters must have used as temporary lodging. The  back of the painting shows an illusionistic cornice with some letters and memoranda—still legible—fastened  to the wall. The presence of a real hinge on the back indicates the painting was used as a door to a cupboard or more probably a window cover. It is therefore possible that one had the illusion of looking into the lagoon when the window was shuttered. The presence of a lily blossom at the bottom implies that the painting has been cut down; originally it may have shown the lily in a vase or it may have been cut from  a still larger painting in which our fragment was only the background. Reperse:  Trompe  l'Oeil  ",
  '3\n\nThis painting was discovered only a few years ago. Unfortunately very little is known about its',
  '18\n\npersonality and artistic interests, but he was most famous as a landscape painter.']}

print(response['context']['images'])
####### RESPONSE ##################
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

如果作为生成响应的来源内容的一部分检索到任何图像，则使用辅助函数显示图像。

from IPython.display import HTML, display

def plt_img_base64(img_base64):
    # Create an HTML img tag with the base64 string as the source
    image_html = f'<img src=_"data:image/jpeg;base64,{img_base64}" />'
    # Display the image by rendering the HTML
    display(HTML(image_html))

显示与检索到的文本相关的图像

plt_img_base64(response['context']['images'][0])

问题2

response = chain.invoke("Woman with children")
print(response['response'])
print(response['context'])
########### RESPONSE ######################
The image in question appears to be a portrait of a woman with children, painted in oil on canvas and measuring 94.4x114.2 cm. The woman is likely the central figure in the painting, and the children are probably depicted around her, possibly playing with various instruments as suggested by the text. The woman's age is given as 21, and the painting is dated 1632, which places it in the early 17th century.
The historical and cultural context of this image is significant. The early 17th century was a time of great change and upheaval in Europe, with the Thirty Years' War raging and the rise of absolutist monarchies. In the art world, this was the era of the Baroque, characterized by dramatic, emotional, and often theatrical compositions. The fact that the woman is identified by her age suggests that this is a portrait of a specific individual, possibly a member of the nobility or upper class, as such portraits were often commissioned to commemorate important life events or to display wealth and status.
The symbolism and meaning of the image could be interpreted in several ways. The presence of children suggests themes of motherhood, family, and domesticity. The fact that they are playing instruments could symbolize harmony, creativity, and the importance of music and the arts in the family's life. The woman's age, 21, could also be significant, as it is often considered the age of adulthood and independence.
The related text mentions that the painting was discovered only a few years ago and that very little is known about it. This adds an element of mystery to the image and suggests that there may be more to uncover about its history and significance. The text also mentions a French artist, born in 1702 and died in 1766, which could indicate that the painting is French in origin, although the date of the painting does not align with the artist's lifetime. The mention of Marc de Villiers, born in 1671 and the subject of a painting dated 1747, suggests that the image may be part of a larger collection of portraits of notable individuals from this period.
Overall, this image of a woman with children is a rich and complex work that offers insights into the cultural and historical context of the early 17th century. Its symbolism and meaning are open to interpretation, and the connections between the image and the related text suggest that there is still much to learn about this painting and its place in art history.

{'images': [],
 'texts': ['31\n\nThis portrait is dated 1632 and gives the age of the sitter, 21. To our eyes she would appear to be',
  '3\n\nThis painting was discovered only a few years ago. Unfortunately very little is known about its',
  'oil on canvas, 94.4x114.2 cm\n\n4l\n\nat which they want to play their various instruments.',
  'French, 1702-1766\n\n46\n\nThe sitter, Marc de Villiers, was born in 1671, and since this painting is signed and dated in 1747,']}

问题3

response = chain.invoke("Moses and the Messengers from Canaan")
print(response['response'])
print(response['context'])

########### RESPONSE #############
The image you've provided appears to be a classical painting depicting a group of figures in a pastoral landscape. Unfortunately, the image does not directly correspond to the provided keywords "Moses and the Messengers from Canaan," nor does it seem to relate to the text snippets you've included. However, I will do my best to analyze the image based on its visual elements and provide a general interpretation that might align with the themes of historical and cultural significance.
Visual Elements:
- The painting shows a group of people gathered in a natural setting, which seems to be a forest clearing or the edge of a wooded area.
- The figures are dressed in what appears to be classical or ancient attire, suggesting a historical or mythological scene.
- The color palette is composed of earthy tones, with a contrast between the light and shadow that gives depth to the scene.
- The composition is balanced, with trees framing the scene on the left and the background opening up to a brighter, possibly sunlit area.
Historical and Cultural Context:
- The painting style and attire of the figures suggest it could be from the Renaissance or Baroque period, which were times of great interest in classical antiquity and biblical themes.
- The reference to "Arcadian shepherds discovering a tomb" and "Poussin" in the text indicates a connection to Nicolas Poussin, a French painter of the Baroque era known for his classical landscapes and historical scenes.
Interpretation and Symbolism:
- Without a direct connection to the story of Moses and the messengers from Canaan, it's challenging to provide a precise interpretation. However, the painting could be depicting a scene of discovery or revelation, common themes in Poussin's work.
- The pastoral setting might symbolize an idyllic, peaceful world, often associated with the concept of Arcadia in classical literature and art.
- The gathering of figures could represent a moment of communal storytelling or the sharing of important news, which could loosely tie into the idea of messengers or a significant event.
Connections to Related Text:
- The text mentions the theme of "Arcadian shepherds discovering a tomb," which is a motif Poussin famously depicted in his painting "Et in Arcadia ego." While the image does not show a tomb, the pastoral setting and classical attire could suggest a similar thematic exploration.
- The reference to Flemish art and the interaction with Italian Renaissance artists might imply a fusion of Northern European and Italian artistic styles, which could be reflected in the painting's technique and composition.
In conclusion, while the image does not directly depict the story of Moses and the messengers from Canaan, it does evoke the classical and pastoral themes prevalent in the work of artists like Poussin during the Baroque period. The painting may represent a general scene of classical antiquity or a mythological event, characterized by a serene landscape and a gathering of figures engaged in a significant moment. The historical and cultural significance of such a painting would lie in its representation of the values and aesthetics of the time, as well as its potential to blend different artistic traditions.


{'images': 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',
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'],
 'texts': ['16\n\nThe theme of Arcadian shepherds discovering a tomb originated in painting with Poussin in the',
  'Flemish, 1488-1541\n\n20\n\nWhen Italian artists of the Renaissance came into contact with paintings from the north, they']}

显示检索到的图像

for images in response['context']['images']:
  plt_img_base64(images)

结论

我们已经成功地利用多模态大型语言模型（LLM）和Langchain以及非结构化数据包，从非结构化数据中实现了RAG。通过使用这些工具，我们不仅利用了文档中嵌入的图像信息，而且还利用了文本信息。