langchain/cookbook/multi_modal_RAG_vdms.ipynb

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    "## VDMS multi-modal RAG\n",
    "\n",
    "Many documents contain a mixture of content types, including text and images. \n",
    "\n",
    "Yet, information captured in images is lost in most RAG applications.\n",
    "\n",
    "With the emergence of multimodal LLMs, like [GPT-4V](https://openai.com/research/gpt-4v-system-card), it is worth considering how to utilize images in RAG. \n",
    "\n",
    "This cookbook highlights: \n",
    "* Use of [Unstructured](https://unstructured.io/) to parse images, text, and tables from documents (PDFs).\n",
    "* Use of multimodal embeddings (such as [CLIP](https://openai.com/research/clip)) to embed images and text\n",
    "* Use of [VDMS](https://github.com/IntelLabs/vdms/blob/master/README.md) as a vector store with support for multi-modal\n",
    "* Retrieval of both images and text using similarity search\n",
    "* Passing raw images and text chunks to a multimodal LLM for answer synthesis "
   ]
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   "source": [
    "## Start VDMS Server\n",
    "\n",
    "Let's start a VDMS docker using port 55559 instead of default 55555. \n",
    "Keep note of the port and hostname as this is needed for the vector store as it uses the VDMS Python client to connect to the server."
   ]
  },
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     "text": [
      "a1b9206b08ef626e15b356bf9e031171f7c7eb8f956a2733f196f0109246fe2b\n"
     ]
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   "source": [
    "! docker run --rm -d -p 55559:55555 --name vdms_rag_nb intellabs/vdms:latest\n",
    "\n",
    "# Connect to VDMS Vector Store\n",
    "from langchain_community.vectorstores.vdms import VDMS_Client\n",
    "\n",
    "vdms_client = VDMS_Client(port=55559)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2498a0a1",
   "metadata": {},
   "source": [
    "## Packages\n",
    "\n",
    "For `unstructured`, you will also need `poppler` ([installation instructions](https://pdf2image.readthedocs.io/en/latest/installation.html)) and `tesseract` ([installation instructions](https://tesseract-ocr.github.io/tessdoc/Installation.html)) in your system."
   ]
  },
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   "source": [
    "! pip install --quiet -U vdms langchain-experimental\n",
    "\n",
    "# lock to 0.10.19 due to a persistent bug in more recent versions\n",
    "! pip install --quiet pdf2image \"unstructured[all-docs]==0.10.19\" pillow pydantic lxml open_clip_torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "78ac6543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from dotenv import load_dotenv, find_dotenv\n",
    "# load_dotenv(find_dotenv(), override=True);"
   ]
  },
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   "source": [
    "## Data Loading\n",
    "\n",
    "### Partition PDF text and images\n",
    "  \n",
    "Let's use famous photographs from the PDF version of Library of Congress Magazine in this example.\n",
    "\n",
    "We can use `partition_pdf` from [Unstructured](https://unstructured-io.github.io/unstructured/introduction.html#key-concepts) to extract text and images."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from pathlib import Path\n",
    "\n",
    "import requests\n",
    "\n",
    "# Folder to store pdf and extracted images\n",
    "datapath = Path(\"./data/multimodal_files\").resolve()\n",
    "datapath.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "pdf_url = \"https://www.loc.gov/lcm/pdf/LCM_2020_1112.pdf\"\n",
    "pdf_path = str(datapath / pdf_url.split(\"/\")[-1])\n",
    "with open(pdf_path, \"wb\") as f:\n",
    "    f.write(requests.get(pdf_url).content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "# Extract images, tables, and chunk text\n",
    "from unstructured.partition.pdf import partition_pdf\n",
    "\n",
    "raw_pdf_elements = partition_pdf(\n",
    "    filename=pdf_path,\n",
    "    extract_images_in_pdf=True,\n",
    "    infer_table_structure=True,\n",
    "    chunking_strategy=\"by_title\",\n",
    "    max_characters=4000,\n",
    "    new_after_n_chars=3800,\n",
    "    combine_text_under_n_chars=2000,\n",
    "    image_output_dir_path=datapath,\n",
    ")\n",
    "\n",
    "datapath = str(datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "969545ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categorize text elements by type\n",
    "tables = []\n",
    "texts = []\n",
    "for element in raw_pdf_elements:\n",
    "    if \"unstructured.documents.elements.Table\" in str(type(element)):\n",
    "        tables.append(str(element))\n",
    "    elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n",
    "        texts.append(str(element))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8e6349-1547-4cbf-9c6f-491d8610ec10",
   "metadata": {},
   "source": [
    "## Multi-modal embeddings with our document\n",
    "\n",
    "In this section, we initialize the VDMS vector store for both text and images. For better performance, we use model `ViT-g-14` from [OpenClip multimodal embeddings](https://python.langchain.com/docs/integrations/text_embedding/open_clip).\n",
    "The images are stored as base64 encoded strings with `vectorstore.add_images`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "import os\n",
    "\n",
    "from langchain_community.vectorstores import VDMS\n",
    "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
    "\n",
    "# Create VDMS\n",
    "vectorstore = VDMS(\n",
    "    client=vdms_client,\n",
    "    collection_name=\"mm_rag_clip_photos\",\n",
    "    embedding=OpenCLIPEmbeddings(model_name=\"ViT-g-14\", checkpoint=\"laion2b_s34b_b88k\"),\n",
    ")\n",
    "\n",
    "# Get image URIs with .jpg extension only\n",
    "image_uris = sorted(\n",
    "    [\n",
    "        os.path.join(datapath, image_name)\n",
    "        for image_name in os.listdir(datapath)\n",
    "        if image_name.endswith(\".jpg\")\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Add images\n",
    "if image_uris:\n",
    "    vectorstore.add_images(uris=image_uris)\n",
    "\n",
    "# Add documents\n",
    "if texts:\n",
    "    vectorstore.add_texts(texts=texts)\n",
    "\n",
    "# Make retriever\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02a186d0-27e0-4820-8092-63b5349dd25d",
   "metadata": {},
   "source": [
    "## RAG\n",
    "\n",
    "Here we define helper functions for image results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "344f56a8-0dc3-433e-851c-3f7600c7a72b",
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   "source": [
    "import base64\n",
    "from io import BytesIO\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "def resize_base64_image(base64_string, size=(128, 128)):\n",
    "    \"\"\"\n",
    "    Resize an image encoded as a Base64 string.\n",
    "\n",
    "    Args:\n",
    "    base64_string (str): Base64 string of the original image.\n",
    "    size (tuple): Desired size of the image as (width, height).\n",
    "\n",
    "    Returns:\n",
    "    str: Base64 string of the resized image.\n",
    "    \"\"\"\n",
    "    # Decode the Base64 string\n",
    "    img_data = base64.b64decode(base64_string)\n",
    "    img = Image.open(BytesIO(img_data))\n",
    "\n",
    "    # Resize the image\n",
    "    resized_img = img.resize(size, Image.LANCZOS)\n",
    "\n",
    "    # Save the resized image to a bytes buffer\n",
    "    buffered = BytesIO()\n",
    "    resized_img.save(buffered, format=img.format)\n",
    "\n",
    "    # Encode the resized image to Base64\n",
    "    return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
    "\n",
    "\n",
    "def is_base64(s):\n",
    "    \"\"\"Check if a string is Base64 encoded\"\"\"\n",
    "    try:\n",
    "        return base64.b64encode(base64.b64decode(s)) == s.encode()\n",
    "    except Exception:\n",
    "        return False\n",
    "\n",
    "\n",
    "def split_image_text_types(docs):\n",
    "    \"\"\"Split numpy array images and texts\"\"\"\n",
    "    images = []\n",
    "    text = []\n",
    "    for doc in docs:\n",
    "        doc = doc.page_content  # Extract Document contents\n",
    "        if is_base64(doc):\n",
    "            # Resize image to avoid OAI server error\n",
    "            images.append(\n",
    "                resize_base64_image(doc, size=(250, 250))\n",
    "            )  # base64 encoded str\n",
    "        else:\n",
    "            text.append(doc)\n",
    "    return {\"images\": images, \"texts\": text}"
   ]
  },
  {
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   "id": "23a2c1d8-fea6-4152-b184-3172dd46c735",
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   "source": [
    "Currently, we format the inputs using a `RunnableLambda` while we add image support to `ChatPromptTemplates`.\n",
    "\n",
    "Our runnable follows the classic RAG flow - \n",
    "\n",
    "* We first compute the context (both \"texts\" and \"images\" in this case) and the question (just a RunnablePassthrough here) \n",
    "* Then we pass this into our prompt template, which is a custom function that formats the message for the llava model. \n",
    "* And finally we parse the output as a string.\n",
    "\n",
    "Here we are using Ollama to serve the Llava model. Please see [Ollama](https://python.langchain.com/docs/integrations/llms/ollama) for setup instructions."
   ]
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  {
   "cell_type": "code",
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   "source": [
    "from langchain_community.llms.ollama import Ollama\n",
    "from langchain_core.messages import HumanMessage\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
    "\n",
    "\n",
    "def prompt_func(data_dict):\n",
    "    # Joining the context texts into a single string\n",
    "    formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n",
    "    messages = []\n",
    "\n",
    "    # Adding image(s) to the messages if present\n",
    "    if data_dict[\"context\"][\"images\"]:\n",
    "        image_message = {\n",
    "            \"type\": \"image_url\",\n",
    "            \"image_url\": {\n",
    "                \"url\": f\"data:image/jpeg;base64,{data_dict['context']['images'][0]}\"\n",
    "            },\n",
    "        }\n",
    "        messages.append(image_message)\n",
    "\n",
    "    # Adding the text message for analysis\n",
    "    text_message = {\n",
    "        \"type\": \"text\",\n",
    "        \"text\": (\n",
    "            \"As an expert art critic and historian, your task is to analyze and interpret images, \"\n",
    "            \"considering their historical and cultural significance. Alongside the images, you will be \"\n",
    "            \"provided with related text to offer context. Both will be retrieved from a vectorstore based \"\n",
    "            \"on user-input keywords. Please convert answers to english and use your extensive knowledge \"\n",
    "            \"and analytical skills to provide a comprehensive summary that includes:\\n\"\n",
    "            \"- A detailed description of the visual elements in the image.\\n\"\n",
    "            \"- The historical and cultural context of the image.\\n\"\n",
    "            \"- An interpretation of the image's symbolism and meaning.\\n\"\n",
    "            \"- Connections between the image and the related text.\\n\\n\"\n",
    "            f\"User-provided keywords: {data_dict['question']}\\n\\n\"\n",
    "            \"Text and / or tables:\\n\"\n",
    "            f\"{formatted_texts}\"\n",
    "        ),\n",
    "    }\n",
    "    messages.append(text_message)\n",
    "    return [HumanMessage(content=messages)]\n",
    "\n",
    "\n",
    "def multi_modal_rag_chain(retriever):\n",
    "    \"\"\"Multi-modal RAG chain\"\"\"\n",
    "\n",
    "    # Multi-modal LLM\n",
    "    llm_model = Ollama(\n",
    "        verbose=True, temperature=0.5, model=\"llava\", base_url=\"http://localhost:11434\"\n",
    "    )\n",
    "\n",
    "    # RAG pipeline\n",
    "    chain = (\n",
    "        {\n",
    "            \"context\": retriever | RunnableLambda(split_image_text_types),\n",
    "            \"question\": RunnablePassthrough(),\n",
    "        }\n",
    "        | RunnableLambda(prompt_func)\n",
    "        | llm_model\n",
    "        | StrOutputParser()\n",
    "    )\n",
    "\n",
    "    return chain"
   ]
  },
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   "cell_type": "markdown",
   "id": "1566096d-97c2-4ddc-ba4a-6ef88c525e4e",
   "metadata": {},
   "source": [
    "## Test retrieval and run RAG\n",
    "Now let's query for a `woman with children` and retrieve the top results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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      "GREAT PHOTOGRAPHS\n",
      "The subject of the photo, Florence Owens Thompson, a Cherokee from Oklahoma, initially regretted that Lange ever made this photograph. “She was a very strong woman. She was a leader,” her daughter Katherine later said. “I think that's one of the reasons she resented the photo — because it didn't show her in that light.”\n",
      "\n",
      "DOROTHEA LANGE. “DESTITUTE PEA PICKERS IN CALIFORNIA. MOTHER OF SEVEN CHILDREN. AGE THIRTY-TWO. NIPOMO, CALIFORNIA.” MARCH 1936. NITRATE NEGATIVE. FARM SECURITY ADMINISTRATION-OFFICE OF WAR INFORMATION COLLECTION. PRINTS AND PHOTOGRAPHS DIVISION.\n",
      "\n",
      "—Helena Zinkham\n",
      "\n",
      "—Helena Zinkham\n",
      "\n",
      "NOVEMBER/DECEMBER 2020 LOC.GOV/LCM\n"
     ]
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import HTML, display\n",
    "\n",
    "\n",
    "def plt_img_base64(img_base64):\n",
    "    # Create an HTML img tag with the base64 string as the source\n",
    "    image_html = f'<img src=\"data:image/jpeg;base64,{img_base64}\" />'\n",
    "\n",
    "    # Display the image by rendering the HTML\n",
    "    display(HTML(image_html))\n",
    "\n",
    "\n",
    "query = \"Woman with children\"\n",
    "docs = retriever.invoke(query, k=10)\n",
    "\n",
    "for doc in docs:\n",
    "    if is_base64(doc.page_content):\n",
    "        plt_img_base64(doc.page_content)\n",
    "    else:\n",
    "        print(doc.page_content)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e9b54d",
   "metadata": {},
   "source": [
    "Now let's use the `multi_modal_rag_chain` to process the same query and display the response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69fb15fd-76fc-49b4-806d-c4db2990027d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The image depicts a woman with several children. The woman appears to be of Cherokee heritage, as suggested by the text provided. The image is described as having been initially regretted by the subject, Florence Owens Thompson, due to her feeling that it did not accurately represent her leadership qualities.\n",
      "The historical and cultural context of the image is tied to the Great Depression and the Dust Bowl, both of which affected the Cherokee people in Oklahoma. The photograph was taken during this period, and its subject, Florence Owens Thompson, was a leader within her community who worked tirelessly to help those affected by these crises.\n",
      "The image's symbolism and meaning can be interpreted as a representation of resilience and strength in the face of adversity. The woman is depicted with multiple children, which could signify her role as a caregiver and protector during difficult times.\n",
      "Connections between the image and the related text include Florence Owens Thompson's leadership qualities and her regretted feelings about the photograph. Additionally, the mention of Dorothea Lange, the photographer who took this photo, ties the image to its historical context and the broader narrative of the Great Depression and Dust Bowl in Oklahoma. \n"
     ]
    }
   ],
   "source": [
    "chain = multi_modal_rag_chain(retriever)\n",
    "response = chain.invoke(query)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ec2ea7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vdms_rag_nb\n"
     ]
    }
   ],
   "source": [
    "! docker kill vdms_rag_nb"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".langchain-venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}