└── MultiModal_RAG_Swin.ipynb /MultiModal_RAG_Swin.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "id": "886a6476", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "# !pip install langchain unstructured[all-docs] pydantic lxml openai chromadb tiktoken python-poppler opencv-python" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "id": "e09d1e48", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# !sudo apt install tesseract-ocr -y\n", 21 | "# !sudo apt install libtesseract-dev -y\n", 22 | "# !sudo apt-get install poppler-utils -y" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 12, 28 | "id": "d37060ee", 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "import os\n", 33 | "import uuid\n", 34 | "import base64\n", 35 | "from IPython import display\n", 36 | "from unstructured.partition.pdf import partition_pdf\n", 37 | "from langchain.chat_models import ChatOpenAI\n", 38 | "from langchain.embeddings import OpenAIEmbeddings\n", 39 | "from langchain.chains import LLMChain\n", 40 | "from langchain.prompts import PromptTemplate\n", 41 | "from langchain.schema.messages import HumanMessage, SystemMessage\n", 42 | "from langchain.schema.document import Document\n", 43 | "from langchain.vectorstores import FAISS\n", 44 | "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", 45 | "\n", 46 | "os.environ['OPENAI_API_KEY'] = \"sk-*****\"\n", 47 | "output_path = \"./images/Swin\"\n", 48 | "\n", 49 | "# Get elements\n", 50 | "raw_pdf_elements = partition_pdf(\n", 51 | " filename=\"./PDFs/Swin.pdf\",\n", 52 | " extract_images_in_pdf=True,\n", 53 | " infer_table_structure=True,\n", 54 | " chunking_strategy=\"by_title\",\n", 55 | " max_characters=4000,\n", 56 | " new_after_n_chars=3800,\n", 57 | " combine_text_under_n_chars=2000,\n", 58 | " extract_image_block_output_dir=output_path,\n", 59 | ")" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": null, 65 | "id": "2681702c", 66 | "metadata": {}, 67 | "outputs": [], 68 | "source": [ 69 | "# Get text summaries and table summaries\n", 70 | "text_elements = []\n", 71 | "table_elements = []\n", 72 | "\n", 73 | "text_summaries = []\n", 74 | "table_summaries = []\n", 75 | "\n", 76 | "summary_prompt = \"\"\"\n", 77 | "Summarize the following {element_type}: \n", 78 | "{element}\n", 79 | "\"\"\"\n", 80 | "summary_chain = LLMChain(\n", 81 | " llm=ChatOpenAI(model=\"gpt-3.5-turbo\", max_tokens=1024), \n", 82 | " prompt=PromptTemplate.from_template(summary_prompt)\n", 83 | ")\n", 84 | "\n", 85 | "for e in raw_pdf_elements:\n", 86 | " if 'CompositeElement' in repr(e):\n", 87 | " text_elements.append(e.text)\n", 88 | " summary = summary_chain.run({'element_type': 'text', 'element': e})\n", 89 | " text_summaries.append(summary)\n", 90 | "\n", 91 | " elif 'Table' in repr(e):\n", 92 | " table_elements.append(e.text)\n", 93 | " summary = summary_chain.run({'element_type': 'table', 'element': e})\n", 94 | " table_summaries.append(summary)" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 5, 100 | "id": "900abea6", 101 | "metadata": {}, 102 | "outputs": [], 103 | "source": [ 104 | "# Get image summaries\n", 105 | "image_elements = []\n", 106 | "image_summaries = []\n", 107 | "\n", 108 | "def encode_image(image_path):\n", 109 | " with open(image_path, \"rb\") as f:\n", 110 | " return base64.b64encode(f.read()).decode('utf-8')\n", 111 | " \n", 112 | "def summarize_image(encoded_image):\n", 113 | " prompt = [\n", 114 | " SystemMessage(content=\"You are a bot that is good at analyzing images.\"),\n", 115 | " HumanMessage(content=[\n", 116 | " {\n", 117 | " \"type\": \"text\", \n", 118 | " \"text\": \"Describe the contents of this image.\"\n", 119 | " },\n", 120 | " {\n", 121 | " \"type\": \"image_url\",\n", 122 | " \"image_url\": {\n", 123 | " \"url\": f\"data:image/jpeg;base64,{encoded_image}\"\n", 124 | " },\n", 125 | " },\n", 126 | " ])\n", 127 | " ]\n", 128 | " response = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=1024).invoke(prompt)\n", 129 | " return response.content\n", 130 | " \n", 131 | "for i in os.listdir(output_path):\n", 132 | " if i.endswith(('.png', '.jpg', '.jpeg')):\n", 133 | " image_path = os.path.join(output_path, i)\n", 134 | " encoded_image = encode_image(image_path)\n", 135 | " image_elements.append(encoded_image)\n", 136 | " summary = summarize_image(encoded_image)\n", 137 | " image_summaries.append(summary)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 18, 143 | "id": "190a7ddb", 144 | "metadata": {}, 145 | "outputs": [], 146 | "source": [ 147 | "# Create Documents and Vectorstore\n", 148 | "documents = []\n", 149 | "retrieve_contents = []\n", 150 | "\n", 151 | "for e, s in zip(text_elements, text_summaries):\n", 152 | " i = str(uuid.uuid4())\n", 153 | " doc = Document(\n", 154 | " page_content = s,\n", 155 | " metadata = {\n", 156 | " 'id': i,\n", 157 | " 'type': 'text',\n", 158 | " 'original_content': e\n", 159 | " }\n", 160 | " )\n", 161 | " retrieve_contents.append((i, e))\n", 162 | " documents.append(doc)\n", 163 | " \n", 164 | "for e, s in zip(table_elements, table_summaries):\n", 165 | " doc = Document(\n", 166 | " page_content = s,\n", 167 | " metadata = {\n", 168 | " 'id': i,\n", 169 | " 'type': 'table',\n", 170 | " 'original_content': e\n", 171 | " }\n", 172 | " )\n", 173 | " retrieve_contents.append((i, e))\n", 174 | " documents.append(doc)\n", 175 | " \n", 176 | "for e, s in zip(image_elements, image_summaries):\n", 177 | " doc = Document(\n", 178 | " page_content = s,\n", 179 | " metadata = {\n", 180 | " 'id': i,\n", 181 | " 'type': 'image',\n", 182 | " 'original_content': e\n", 183 | " }\n", 184 | " )\n", 185 | " retrieve_contents.append((i, s))\n", 186 | " documents.append(doc)\n", 187 | "\n", 188 | "vectorstore = FAISS.from_documents(documents=documents, embedding=OpenAIEmbeddings())" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": 63, 194 | "id": "3fce7c9a", 195 | "metadata": {}, 196 | "outputs": [], 197 | "source": [ 198 | "answer_template = \"\"\"\n", 199 | "Answer the question based only on the following context, which can include text, images and tables:\n", 200 | "{context}\n", 201 | "Question: {question} \n", 202 | "\"\"\"\n", 203 | "answer_chain = LLMChain(llm=ChatOpenAI(model=\"gpt-3.5-turbo\", max_tokens=1024), prompt=PromptTemplate.from_template(answer_template))\n", 204 | "\n", 205 | "def answer(question):\n", 206 | " relevant_docs = vectorstore.similarity_search(question)\n", 207 | " context = \"\"\n", 208 | " relevant_images = []\n", 209 | " for d in relevant_docs:\n", 210 | " if d.metadata['type'] == 'text':\n", 211 | " context += '[text]' + d.metadata['original_content']\n", 212 | " elif d.metadata['type'] == 'table':\n", 213 | " context += '[table]' + d.metadata['original_content']\n", 214 | " elif d.metadata['type'] == 'image':\n", 215 | " context += '[image]' + d.page_content\n", 216 | " relevant_images.append(d.metadata['original_content'])\n", 217 | " result = answer_chain.run({'context': context, 'question': question})\n", 218 | " return result, relevant_images" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 64, 224 | "id": "02464d28", 225 | "metadata": {}, 226 | "outputs": [ 227 | { 228 | "name": "stdout", 229 | "output_type": "stream", 230 | "text": [ 231 | "The main difference between Swin Transformer and ViT is the way they process visual data. Swin Transformer uses a hierarchical approach with multiple layers of grids at different scales of analysis, allowing for analysis at both zoomed-in and zoomed-out perspectives. On the other hand, ViT processes the image at a single scale without changing the granularity. Swin Transformer's approach of processing images at multiple scales could potentially be advantageous for various vision tasks compared to ViT's constant scale processing.\n" 232 | ] 233 | }, 234 | { 235 | "data": { 236 | "image/jpeg": 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237 | "text/plain": [ 238 | "" 239 | ] 240 | }, 241 | "metadata": {}, 242 | "output_type": "display_data" 243 | } 244 | ], 245 | "source": [ 246 | "# Display result\n", 247 | "result, relevant_images = answer(\"What is the difference between Swin Transformer and ViT?\")\n", 248 | "print(result)\n", 249 | "for e in relevant_images:\n", 250 | " display.display(display.Image(b64decode(e)))" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "id": "830d229d", 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [] 260 | } 261 | ], 262 | "metadata": { 263 | "kernelspec": { 264 | "display_name": "Python 3 (ipykernel)", 265 | "language": "python", 266 | "name": "python3" 267 | }, 268 | "language_info": { 269 | "codemirror_mode": { 270 | "name": "ipython", 271 | "version": 3 272 | }, 273 | "file_extension": ".py", 274 | "mimetype": "text/x-python", 275 | "name": "python", 276 | "nbconvert_exporter": "python", 277 | "pygments_lexer": "ipython3", 278 | "version": "3.10.12" 279 | } 280 | }, 281 | "nbformat": 4, 282 | "nbformat_minor": 5 283 | } 284 | --------------------------------------------------------------------------------