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Multi-Modal RAG\n", "\n", "The previous tutorial [01. RAG on Semi-structured data](https://github.com/sugarforever/Advanced-RAG/blob/main/01_semi_structured_data.ipynb) introduced RAG development on semi-structured data, for example texts and tables in PDF documents.\n", "\n", "BUT, it still can't read images.\n", "\n", "Let's learn how to enable image recognition in RAG by employing multi-modal models.\n", "\n", "**What is Multi-Modal model?**\n", "\n", "Multi-Modal model can process and analyze data from multiple modalities and provide a more complete and accurate understanding of the underlying data.\n", "\n", "**GPT-4V**\n", "\n", "GPT-4V is a multi-modal model that takes in both text and images, and responds with text output. Please refer to [GPT-4 Vision](https://platform.openai.com/docs/guides/vision) for introduction and API guide.\n", "\n", "In this tutorial, let's use GPT-4V model to implement multi-modal RAG application that can understand the images embedded in the PDF document and answer relevant questions.\n", "\n", "The PDF document we use in this example is the [JP Morgan - Weekly Market Recap](https://am.jpmorgan.com/content/dam/jpm-am-aem/americas/us/en/insights/market-insights/wmr/weekly_market_recap.pdf). It's a small PDF file containing several tables which is a good example for quick data processing and clear demonstration." ], "metadata": { "id": "AeO6Vg59nrPP" } }, { "cell_type": "markdown", "source": [ "### Prepare Environment" ], "metadata": { "id": "K-e_9LiVuJeQ" } }, { "cell_type": "markdown", "source": [ "Let's install the necessary Python packages." ], "metadata": { "id": "wVP2xyg3uh1r" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kaIyvvQuB4uu", "outputId": "38b15f39-722f-48c7-a865-ca652fd61bcd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m29.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K 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\u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for olefile (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for iopath (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "lida 0.0.10 requires kaleido, which is not installed.\n", "llmx 0.0.15a0 requires cohere, which is not installed.\n", "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ "!pip install langchain unstructured[all-docs] pydantic lxml openai chromadb tiktoken -q -U" ] }, { "cell_type": "markdown", "source": [ "Download the PDF file and name it as `weekly_market_recap.pdf`." ], "metadata": { "id": "O7276PRIuq4k" } }, { "cell_type": "code", "source": [ "!wget -O weekly_market_recap.pdf https://am.jpmorgan.com/content/dam/jpm-am-aem/americas/us/en/insights/market-insights/wmr/weekly_market_recap.pdf" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BInAbAKNDP55", "outputId": "eae3e613-c67e-409f-da10-ee5efbcd2262" }, "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2023-11-22 22:20:14-- https://am.jpmorgan.com/content/dam/jpm-am-aem/americas/us/en/insights/market-insights/wmr/weekly_market_recap.pdf\n", "Resolving am.jpmorgan.com (am.jpmorgan.com)... 170.148.208.53\n", "Connecting to am.jpmorgan.com (am.jpmorgan.com)|170.148.208.53|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 191565 (187K) [application/pdf]\n", "Saving to: ‘weekly_market_recap.pdf’\n", "\n", "weekly_market_recap 100%[===================>] 187.08K --.-KB/s in 0.1s \n", "\n", "2023-11-22 22:20:14 (1.92 MB/s) - ‘weekly_market_recap.pdf’ saved [191565/191565]\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "Install required platform packages:\n", "\n", "- poppler-utils\n", " \n", " A collection of command-line utilities built on Poppler's library API, to manage PDF and extract contents\n", "\n", "- tesseract-ocr\n", "\n", " Optical character recognition engine" ], "metadata": { "id": "SOSkdNt9ux_Z" } }, { "cell_type": "code", "source": [ "!apt-get install poppler-utils tesseract-ocr" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ER_h_kAPatZO", "outputId": "50b4febf-899c-4ed6-b5ad-34f43961296a" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", "The following additional packages will be installed:\n", " tesseract-ocr-eng tesseract-ocr-osd\n", "The following NEW packages will be installed:\n", " poppler-utils tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n", "0 upgraded, 4 newly installed, 0 to remove and 10 not upgraded.\n", "Need to get 5,002 kB of archives.\n", "After this operation, 16.3 MB of additional disk space will be used.\n", "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 poppler-utils amd64 22.02.0-2ubuntu0.2 [186 kB]\n", "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n", "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n", "Get:4 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n", "Fetched 5,002 kB in 1s (4,952 kB/s)\n", "Selecting previously unselected package poppler-utils.\n", "(Reading database ... 120880 files and directories currently installed.)\n", "Preparing to unpack .../poppler-utils_22.02.0-2ubuntu0.2_amd64.deb ...\n", "Unpacking poppler-utils (22.02.0-2ubuntu0.2) ...\n", "Selecting previously unselected package tesseract-ocr-eng.\n", "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n", "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n", "Selecting previously unselected package tesseract-ocr-osd.\n", "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n", "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n", "Selecting previously unselected package tesseract-ocr.\n", "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n", "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n", "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n", "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n", "Setting up poppler-utils (22.02.0-2ubuntu0.2) ...\n", "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n", "Processing triggers for man-db (2.10.2-1) ...\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API Key with access to GPT-4 vision\"" ], "metadata": { "id": "o2lSAfBCxt4w" }, "execution_count": 5, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Coding" ], "metadata": { "id": "S336PokNwlE-" } }, { "cell_type": "markdown", "source": [ "#### Use `unstructured` library to partition the PDF document into different type of elements." ], "metadata": { "id": "sGONjGYXw0ot" } }, { "cell_type": "code", "source": [ "!mkdir images" ], "metadata": { "id": "sU9RxGtkKsRi" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "from typing import Any\n", "\n", "from pydantic import BaseModel\n", "from unstructured.partition.pdf import partition_pdf\n", "\n", "images_path = \"./images\"\n", "raw_pdf_elements = partition_pdf(\n", " filename=\"weekly_market_recap.pdf\",\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=images_path,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 312, "referenced_widgets": [ "fda96a1561d248bc8bbd3e2b5337ad8a", "463a89ff1a5b48c1832e90f800e474cf", "4da3006f851a4f4ebdbf8295204cca9a", "a4a9847a44f9406e91cdca9409b1655c", "5822c19d3d994299bbec5282dd3db4e9", "d9645f784e6a474fb1cd76f340aecea8", "b9686eae116d444ba3132bd277459cb9", "d04668cb9cfe41f197ef04edde67bd0d", "8eb4525550e74f9ab2bc9ae8e43c996f", "0bd0b128c02b4688a32ad968b44d18eb", "f9125d99536a405fa778edf39678bbd6", "29c67d94043047cfb62ae1c134859ad0", "04284fba3a1846cfacf0b1056e179132", "30b24398f6c04aa6813f8037a7732094", "9bf6e96d9ca84b7ebbe27f3b23484a94", "0f93fd01cd9b4066aaea1d8b844fcb0f", "aef22501d30d49248afbe073f4004c8c", "9c757d31c2de4678b44bc391b222f6f1", "55b119b0076d44d6ae9ec7bbb6e80d11", "099f03b0b1a542ccacfb0f07b0af040a", "67d839ee0023420483eec3d8a048c895", "e5214a17c56640cdbe031220edb8bd82", "eac2cb78b6504414921b28b92a27fb6a", "154a8a5f10614720b3225eec4804e9c7", "0e2b30d7002144738b515d1fbe56ef3f", "3398cf5fe2d94805912ea0a1fefc4f0b", "49b1b4cb880846d487232d2023d3f3d2", "46fd6829d1d341fe94b2b2d26d635a16", "1a982c92a5d24736b35f9b3cf645fabb", "6919913e8edb498a85b45615b27d41df", "2fba4e4b3e0b481d87212b8bf9291528", "9d7ea75f49c941f9bca767504a8fbf97", "24caacfd238745abae3c733b6d4505d6", "afcfad994b4b44e19aa781237b8087f7", "50a9d0767e0c4db9bcde549fae2999ef", "94c0c8242be148eca64415953b0fd541", "026df9b80d2e4b818a9fec89250c90a7", "933c599018b644be8731ab694ea76cf4", "da40b2b2ae1b4bd19cffbe55e859a08b", "9501af575d9b4779abf2e4d741c80a8a", "55d5c971acd54db597e791b138a34b4c", "34b2ccf2f8e84bca9a1dfd465a1b50c5", "76c5487ce18c416bbc1241b3e3c304df", "a3a57c00f0e5413d99330e2376c1558a" ] }, "id": "nFpMgbR6Csj6", "outputId": "3c1ac08d-db3b-413d-87ec-76ac04ea107d" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n", "[nltk_data] Downloading package averaged_perceptron_tagger to\n", "[nltk_data] /root/nltk_data...\n", "[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "yolox_l0.05.onnx: 0%| | 0.00/217M [00:00" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "markdown", "source": [ "#### Summarize the extracted images" ], "metadata": { "id": "JTWPzTEv9B-d" } }, { "cell_type": "code", "source": [ "# Image summarizer\n", "\n", "import base64\n", "import os\n", "\n", "from langchain.chat_models import ChatOpenAI\n", "from langchain.schema.messages import HumanMessage\n", "\n", "class ImageSummarizer:\n", "\n", " def __init__(self, image_path) -> None:\n", " self.image_path = image_path\n", " self.prompt = \"\"\"\n", "You are an assistant tasked with summarizing images for retrieval.\n", "These summaries will be embedded and used to retrieve the raw image.\n", "Give a concise summary of the image that is well optimized for retrieval.\n", "\"\"\"\n", "\n", " def base64_encode_image(self):\n", " with open(self.image_path, \"rb\") as image_file:\n", " return base64.b64encode(image_file.read()).decode(\"utf-8\")\n", "\n", " def summarize(self, prompt = None):\n", " base64_image_data = self.base64_encode_image()\n", " chat = ChatOpenAI(model=\"gpt-4-vision-preview\", max_tokens=1000)\n", "\n", " # gpt4 vision api doc - https://platform.openai.com/docs/guides/vision\n", " response = chat.invoke(\n", " [\n", " HumanMessage(\n", " content=[\n", " {\n", " \"type\": \"text\",\n", " \"text\": prompt if prompt else self.prompt\n", " },\n", " {\n", " \"type\": \"image_url\",\n", " \"image_url\": {\"url\": f\"data:image/jpeg;base64,{base64_image_data}\"},\n", " },\n", " ]\n", " )\n", " ]\n", " )\n", " return base64_image_data, response.content" ], "metadata": { "id": "4T7oY2u-rl_i" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "image_data_list = []\n", "image_summary_list = []\n", "\n", "for img_file in sorted(os.listdir(images_path)):\n", " if img_file.endswith(\".jpg\"):\n", " summarizer = ImageSummarizer(os.path.join(images_path, img_file))\n", " data, summary = summarizer.summarize()\n", " image_data_list.append(data)\n", " image_summary_list.append(summary)" ], "metadata": { "id": "F56H0Uf4sDlu" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "image_summary_list" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gP7buEXGw4IF", "outputId": "7fb5542f-7d08-4e7a-f4fb-213545479bab" }, "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['This image contains three separate charts related to economic and market data. The first chart on the left is a bar graph showing holiday sales growth from 2010 to 2023, comparing actual and forecasted year-over-year growth, with annotations for the 2010-2019 average and the 2020-2022 average. The middle section of the image shows two sets of matrices labeled \"V,\" \"B,\" and \"G,\" representing weekly and year-to-date (YTD) values for small (S), mid (M), and large (L) cap segments of the market. The third chart on the right is a bar graph presenting one-week and YTD performance of different sectors such as real estate, technology, and health care. The bars show positive and negative percentage changes. The image is a composite of financial data visualizations useful for market analysis and forecasting.']" ] }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "markdown", "source": [ "#### Categorize the elements into tables and texts" ], "metadata": { "id": "cv8Tw7ETw90G" } }, { "cell_type": "code", "source": [ "class Element(BaseModel):\n", " type: str\n", " text: Any\n", "\n", "table_elements = []\n", "text_elements = []\n", "for element in raw_pdf_elements:\n", " if \"unstructured.documents.elements.Table\" in str(type(element)):\n", " table_elements.append(Element(type=\"table\", text=str(element)))\n", " elif \"unstructured.documents.elements.CompositeElement\" in str(type(element)):\n", " text_elements.append(Element(type=\"text\", text=str(element)))" ], "metadata": { "id": "alMtzOivbJXo" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "print(len(table_elements))\n", "print(len(text_elements))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4rGCd4O5xlSh", "outputId": "27b1b3b7-8cae-479e-b71d-b465efd21a1f" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1\n", "6\n" ] } ] }, { "cell_type": "code", "source": [ "table_elements[0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ld91YQP3nKTd", "outputId": "1b936936-acd5-4308-ca39-eae31080cca7" }, "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Element(type='table', text='MSCI EM 977 2.99 2.62 4.84 6.86 -11.35 1.68 1.55 3.18 6687 NASDAQ 14125 242 6.98 35.98 27.83 21.52 26.20 5.42 0.77 22940 Levels Fixed Income Yield 1 week QTD YTD 1year 3-yr.Cum. Currencies 11/17/23 12/30/22 11/17/22 U.S. Aggregate 5.21 1.37 41.77 0.54. 1.19 -13.40 $per€ 1.09 1.07 1.03 U.S. Corporates 5.83 1.75 2.32 2.33 3.41 13.37. $perf 1.24 1.20 1.18 Municipals (10yr) 3.54 1.25 2.92 1.30 3.25 -3.37 ¥per$ 149.58 131.95 140.58 High Yield 8.82 0.88 1.85 7.81 8.40 3.61 Levels (%) Levels Key Rates 11/17/23 11/10/23 9/29/23 12/30/22 11/17/22 11/17/20 Commod. 11/17/23 12/30/22 11/17/22 2-yr U.S. Treasuries 4.88 5.04 5.03 441 4.43 0.18 — Oil (WTI) 72.92 80.16 81.69 10-yr U.S. Treasuries 4.44 4.61 4.59 3.88 3.77 0.87. Gasoline 3.35 3.09 3.76 30-yr U.S. Treasuries 4.59 4.73 4.73 3.97 3.89 1.62 Natural Gas 3.07 3.52 6.20 10-yr German Bund 2.58 2.72 2.82 2.53 2.02 -0.56 Gold 1981 1814 1159 3-mo. LIBOR 5.63 5.64 5.66 477 4.68 0.23 Silver 24.00 23.95, 21.08 3-mo. EURIBOR 3.98 3.99 3.95 2.13 1.80 -0.52 Copper 8141 8387 8155 6-mo. CD rate 2.18 2.19 2.21 1.80 1.42 0.28 Corn 6.10 6.14 6.48 30-yr fixed mortgage 7.61 7.61 153 6.58 6.90 2.99 BBG Idx 232.50 245.89 249.65 Prime Rate 8.50 8.50 8.50 7.50 7.00 3.25')" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "from langchain.chat_models import ChatOpenAI\n", "from langchain.prompts import ChatPromptTemplate\n", "from langchain.schema.output_parser import StrOutputParser" ], "metadata": { "id": "he7fQp5qbLnu" }, "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "source": [ "#### Build up summarization chain with LangChain framework" ], "metadata": { "id": "1hBf_lftySxt" } }, { "cell_type": "code", "source": [ "from langchain.prompts import ChatPromptTemplate\n", "from langchain.schema.output_parser import StrOutputParser\n", "\n", "prompt_text = \"\"\"\n", " You are responsible for concisely summarizing table or text chunk:\n", "\n", " {element}\n", "\"\"\"\n", "prompt = ChatPromptTemplate.from_template(prompt_text)\n", "summarize_chain = {\"element\": lambda x: x} | prompt | ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo\") | StrOutputParser()" ], "metadata": { "id": "uDQYbnKDbM7C" }, "execution_count": 20, "outputs": [] }, { "cell_type": "markdown", "source": [ "#### Summarize each text and table element" ], "metadata": { "id": "TikuTLiKyXy3" } }, { "cell_type": "code", "source": [ "tables = [i.text for i in table_elements]\n", "table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 5})\n", "\n", "texts = [i.text for i in text_elements]\n", "text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 5})" ], "metadata": { "id": "SMqqogGDbOk_" }, "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "source": [ "#### Use LangChain MultiVectorRetriever to associate summaries of tables, texts and images with original data chunks in parent-child relationship." ], "metadata": { "id": "PskDZuy7ydfV" } }, { "cell_type": "code", "source": [ "import uuid\n", "\n", "from langchain.embeddings import OpenAIEmbeddings\n", "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", "from langchain.schema.document import Document\n", "from langchain.storage import InMemoryStore\n", "from langchain.vectorstores import Chroma\n", "\n", "id_key = \"doc_id\"\n", "\n", "# The retriever (empty to start)\n", "retriever = MultiVectorRetriever(\n", " vectorstore=Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings()),\n", " docstore=InMemoryStore(),\n", " id_key=id_key,\n", ")\n", "\n", "# Add texts\n", "doc_ids = [str(uuid.uuid4()) for _ in texts]\n", "summary_texts = [\n", " Document(page_content=s, metadata={id_key: doc_ids[i]})\n", " for i, s in enumerate(text_summaries)\n", "]\n", "retriever.vectorstore.add_documents(summary_texts)\n", "retriever.docstore.mset(list(zip(doc_ids, texts)))\n", "\n", "# Add tables\n", "table_ids = [str(uuid.uuid4()) for _ in tables]\n", "summary_tables = [\n", " Document(page_content=s, metadata={id_key: table_ids[i]})\n", " for i, s in enumerate(table_summaries)\n", "]\n", "retriever.vectorstore.add_documents(summary_tables)\n", "retriever.docstore.mset(list(zip(table_ids, tables)))\n", "\n", "# Add images\n", "# image_data_list = []\n", "# image_summary_list = []\n", "doc_ids = [str(uuid.uuid4()) for _ in image_data_list]\n", "summary_images = [\n", " Document(page_content=s, metadata={id_key: doc_ids[i]})\n", " for i, s in enumerate(image_summary_list)\n", "]\n", "retriever.vectorstore.add_documents(summary_images)\n", "retriever.docstore.mset(list(zip(doc_ids, image_data_list)))" ], "metadata": { "id": "-deJeuO4bRSZ" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "#### Image helper functions" ], "metadata": { "id": "9Nk0udht9eHj" } }, { "cell_type": "code", "source": [ "from PIL import Image\n", "from IPython.display import HTML, display\n", "import io\n", "import re\n", "\n", "\n", "def plt_img_base64(img_base64):\n", " display(HTML(f''))\n", "\n", "def is_image_data(b64data):\n", " \"\"\"\n", " Check if the base64 data is an image by looking at the start of the data\n", " \"\"\"\n", " image_signatures = {\n", " b\"\\xFF\\xD8\\xFF\": \"jpg\",\n", " b\"\\x89\\x50\\x4E\\x47\\x0D\\x0A\\x1A\\x0A\": \"png\",\n", " b\"\\x47\\x49\\x46\\x38\": \"gif\",\n", " b\"\\x52\\x49\\x46\\x46\": \"webp\",\n", " }\n", " try:\n", " header = base64.b64decode(b64data)[:8] # Decode and get the first 8 bytes\n", " for sig, format in image_signatures.items():\n", " if header.startswith(sig):\n", " return True\n", " return False\n", " except Exception:\n", " return False\n", "\n", "def split_image_text_types(docs):\n", " \"\"\"\n", " Split base64-encoded images and texts\n", " \"\"\"\n", " b64_images = []\n", " texts = []\n", " for doc in docs:\n", " # Check if the document is of type Document and extract page_content if so\n", " if isinstance(doc, Document):\n", " doc = doc.page_content\n", "\n", " if is_image_data(doc):\n", " b64_images.append(doc)\n", " else:\n", " texts.append(doc)\n", " return {\"images\": b64_images, \"texts\": texts}\n", "\n", "\n", "def img_prompt_func(data_dict):\n", " messages = []\n", "\n", " # Adding image(s) to the messages if present\n", " if data_dict[\"context\"][\"images\"]:\n", " for image in data_dict[\"context\"][\"images\"]:\n", " image_message = {\n", " \"type\": \"image_url\",\n", " \"image_url\": {\"url\": f\"data:image/jpeg;base64,{image}\"},\n", " }\n", " messages.append(image_message)\n", "\n", " # Adding texts to the messages\n", " formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n", " text_message = {\n", " \"type\": \"text\",\n", " \"text\": (\n", " \"You are financial analyst.\\n\"\n", " \"You will be given a mixed of text, tables, and image(s) usually of charts or graphs.\\n\"\n", " \"Use this information to answer the user question in the finance. \\n\"\n", " f\"Question: {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)]" ], "metadata": { "id": "dr0iTr5481kI" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n", "\n", "model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n", "\n", "# RAG pipeline\n", "chain = (\n", " {\n", " \"context\": retriever | RunnableLambda(split_image_text_types),\n", " \"question\": RunnablePassthrough(),\n", " }\n", " | RunnableLambda(img_prompt_func)\n", " | model\n", " | StrOutputParser()\n", ")" ], "metadata": { "id": "9LXOzR8GO_NN" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [ "query = \"Which year had the highest holiday sales growth?\"" ], "metadata": { "id": "zhYve-VU1IZX" }, "execution_count": 37, "outputs": [] }, { "cell_type": "code", "source": [ "chain.invoke(query)" ], "metadata": { "id": "wAEyfvmabU3u", "colab": { "base_uri": "https://localhost:8080/", "height": 54 }, "outputId": "cffa17e4-2658-45e8-cfde-9e050bfbb840" }, "execution_count": 38, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'The year with the highest holiday sales growth, as shown in the provided image, is 2021 with a growth rate of 12.7%. This is indicated by the tallest bar in the chart, which represents actual sales growth for November and December of that year.'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "code", "source": [ "docs = retriever.get_relevant_documents(query)\n", "\n", "len(docs)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x38lN6N51GcP", "outputId": "feb223a4-a08c-40d2-8de5-dc64cbee2a55" }, "execution_count": 39, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "4" ] }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "code", "source": [ "docs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HwjkcIX62JAm", "outputId": "40a2e8fa-c5cd-49a9-a1c6-2d42333e6a81" }, "execution_count": 40, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[\"Chart of the Week\\n\\nStyle Returns\\n\\nS&P 500 Sector Returns\\n\\nHoliday sales growth is set to return to its pre-pandemic pace G e713 Noy November & December sales, % y/y growth, nsa 65 3 3 2 14% Actual 12% a SS Forecast 10% 2020-22 average 9.1% 8% 2010-19 average 6% 5.2% 46% 4.8% 5.0% 4% 3.8% Fox 2.7% [2.00 2% 1.7% 0% ‘10 ‘11 ‘12 ‘13 ‘14 ‘15 ‘16 ‘17 ‘18 ‘19 ‘20 ‘21 ‘22 '23 5.4% B » a = iti 1Week 23 23 © 0 © 2@ey 8 @H8 sO Fe BE ao 2S5m5 25 FS BR BQ # fesse 8 § 8 §Eo 2 9 30 Be@2ae0 c 39 w Bats OB cs Or 3845 82 cs 5 5 € RB £ EH BH €2s £ Be ES 6 e« 9° = 8 er ° Oo g , © 5 8 £ Ea? & B3e8e ao @ FESR S$ £52 YO ¢ GOST > sheo oo oN F25sS gana Ssesse ego @ 2300 % & 2 £e2 438 8 S ES vos ge §o £ ic 8 1Week\\n\\nNot FDIC Insured | No Bank Guarantee | May Lose Value\\n\\nMarket Insights\",\n", " 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" 'Market Insights\\n\\nWeekly Market Recap\\n\\nThe weekin review\\n\\ne Headline CPI was unchanged m/m and rose 3.2% y/y in Oct.\\n\\ne@ Retail sales fell 0.1% m/m in Oct.\\n\\ne Industrial production declined 0.6% m/m in Oct.\\n\\nThe week ahead\\n\\n@ FOMC minutes\\n\\n@ Prelim. Markit PMIs\\n\\nThought of the week\\n\\nIt’s not even Thanksgiving yet, but the holiday spending rush has already begun. The National Retail Federation is forecasting holiday spending growth of 3-4% from 2022, totaling between $957.3-$966.6 billion. This represents a normalization in holiday consumption growth to the pre- pandemic average of 3.6% y/y from 2010-19 after the last three years that were impacted by government stimulus. Higher prices and dwindling savings are putting pressure on consumers, but household balance sheets and debt service ratios remain healthy overall. However, holiday spending plans vary significantly between income groups. High-income households still have considerable savings, and those earning +$200K annually are expected to spend 22% more than last year according to Deloitte. Lower- income shoppers may have already depleted their savings but will likely hunt for discounts, use more “buy now, pay later” options and rely on credit to continue spending.\\n\\nDespite expectations for solid holiday spending growth, consumer companies need to be strategic given building jeadwinds such as increased difficulty in obtaining credit and lower consumer confidence. Those offering discounts and adapting to changes in customer preferences will likely see strong earnings results in 4Q.\\n\\nLooking ahead, consumption is likely to soften further fol- lowing October’s retail sales decline of 0.1% m/m. More “normal” consumer spending activity in November and December could be favorable for markets and allow the Fed to cut rates earlier in 2024. While investors may want to wait for more data on how the economy is performing, slowly adding exposure to longer duration fixed income and equi- ties is still prudent.\\n\\nPlease see important disclosures on next page.\\n\\nJ.PMorgan ASSET MANAGEMENT\\n\\nU.S. | November 20, 2023',\n", " 'Chart of the Week: National Retail Federation (NRF), U.S. Census, J.P. Morgan Asset Management. NRF holiday spending is defined as the months of November and December. NRF\\'s holiday sales exclude automobile dealers, gasoline stations and restaurants. Thought of the week: 2023 Deloitte holiday retail survey, National Retail Federation (NRF), U.S. Census, J.P. Morgan Asset Management. Abbreviations: Cons. Sent.: University of Michigan Consumer Sentiment Index; CPI: Consumer Price Index; EIA: Energy Information Agency; FHFA HPI: - Federal Housing Finance Authority House Price Index; FOMC: Federal Open Market Committee; GDP: gross domestic product; HPI: Home Price Index; HMI: Housing Market Index; ISM Mfg. Index: Institute for Supply Management Manufacturing Index; PCE: Personal consumption expenditures; Philly Fed Survey: Philadelphia Fed Business Outlook Survey; PMI: Purchasing Managers\\' Manufacturing Index; PPI: Producer Price Index; SAAR: Seasonally Adjusted Annual Rate Equity Price Levels and Returns: All returns represent total return for stated period. Index: S&P 500; provided by: Standard & Poor’s. Index: Dow Jones Industrial 30 (The Dow Jones is a price-weighted index composing of 30 widely-traded blue chip stocks.) ; provided by: S&P Dow Jones Indices LLC. Index: Russell 2000; provided by: Russell Investments. Index: Russell 1000 Growth; provided by: Russell Investments. Index: Russell 1000 Value; provided by: Russell Investments. Index: MSCI – EAFE; provided by: MSCI – gross official pricing. Index: MSCI – EM; provided by: MSCI – gross official pricing. Index: Nasdaq Composite; provided by: NASDAQ OMX Group. MSCI EAFE is a Morgan Stanley Capital International Index that is designed to measure the performance of the developed stock markets of Europe, Australasia, and the Far East. Bond Returns: All returns represent total return. Index: Bloomberg US Aggregate; provided by: Bloomberg Capital. Index: Bloomberg Investment Grade Credit; provided by: Bloomberg Capital. Index: Bloomberg Municipal Bond 10 Yr; provided by: Blomberg Capital. Index: Bloomberg Capital High Yield Index; provided by: Bloomberg Capital. Key Interest Rates: 2 Year Treasury, FactSet; 10 Year Treasury, FactSet; 30 Year Treasury, FactSet; 10 Year German Bund, FactSet. 3 Month LIBOR, British Bankers’ Association; 3 Month EURIBOR, European Banking Federation; 6 Month CD, Federal Reserve; 30 Year Mortgage, Mortgage Bankers Association (MBA); Prime Rate: Federal Reserve. Commodities: Gold, FactSet; Crude Oil (WTI), FactSet; Gasoline, FactSet; Natural Gas, FactSet; Silver, FactSet; Copper, FactSet; Corn, FactSet. Bloomberg Commodity Index (BBG Idx), Bloomberg Finance L.P. Currency: Dollar per Pound, FactSet; Dollar per Euro, FactSet; Yen per Dollar, FactSet. S&P Index Characteristics: Dividend yield provided by FactSet Pricing database. Fwd. P/E is a bottom-up weighted harmonic average using First Call Mean estimates for the \"Next 12 Months\" (NTM) period. Market cap is a bottom-up weighted average based on share information from Compustat and price information from FactSet\\'s Pricing database as provided by Standard & Poor\\'s. MSCI Index Characteristics: Dividend yield provided by FactSet Pricing database. Fwd. P/E is a bottom-up weighted harmonic average for the \"Next 12 Months\" (NTM) period. Market cap is a bottom- up weighted average based on share information from MSCI and Price information from FactSet\\'s Pricing database as provided by MSCI. Russell 1000 Value Index, Russell 1000 Growth Index, and Russell 2000 Index Characteristics: Trailing P/E is provided directly by\\n\\nJ.PMorgan ASSET MANAGEMENT']" ] }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "code", "source": [ "is_image_data(docs[1])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PtVeXa8q1zM7", "outputId": "e174e070-9c3f-45c3-aeb8-5543002e0a63" }, "execution_count": 42, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "code", "source": [ "plt_img_base64(docs[1])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 552 }, "id": "8x7yce9O1kRb", "outputId": "fdd0abdf-3a65-47a6-fe27-9cd712874d9c" }, "execution_count": 43, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "" ] }, "metadata": {} } ] } ] }