{ "cells": [ { "cell_type": "markdown", "id": "SzvBjdID1V3m", "metadata": { "id": "SzvBjdID1V3m" }, "source": [ "# Multi-modal RAG with Google Cloud" ] }, { "cell_type": "markdown", "id": "4tfidrmE1Zlo", "metadata": { "id": "4tfidrmE1Zlo" }, "source": [ "This tutorial demonstrates how to implement the Option 2 described [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb) with Generative API on Google Cloud." ] }, { "cell_type": "markdown", "id": "84fcd59f-2eaf-4a76-ad1a-96d6db70bf42", "metadata": {}, "source": [ "## Setup\n", "\n", "Install the required dependencies, and create an API key for your Google service." ] }, { "cell_type": "code", "execution_count": null, "id": "6b1e10dd-25de-4c0a-9577-f36e72518f89", "metadata": {}, "outputs": [], "source": [ "%pip install -U --quiet langchain langchain_community openai chromadb langchain-experimental\n", "%pip install --quiet \"unstructured[all-docs]\" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken" ] }, { "cell_type": "markdown", "id": "pSInKtCZ32mt", "metadata": { "id": "pSInKtCZ32mt" }, "source": [ "## Data loading" ] }, { "cell_type": "markdown", "id": "Iv2R8-lJ37dG", "metadata": { "id": "Iv2R8-lJ37dG" }, "source": [ "We use a zip file with a sub-set of the extracted images and pdf from [this](https://cloudedjudgement.substack.com/p/clouded-judgement-111023) blog post. If you want to follow the full flow, please, use the original [example](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "id": "d999f3fe-c165-4772-b63e-ffe4dd5b03cf", "metadata": {}, "outputs": [], "source": [ "# First download\n", "import logging\n", "import zipfile\n", "\n", "import requests\n", "\n", "logging.basicConfig(level=logging.INFO)\n", "\n", "data_url = \"https://storage.googleapis.com/benchmarks-artifacts/langchain-docs-benchmarking/cj.zip\"\n", "result = requests.get(data_url)\n", "filename = \"cj.zip\"\n", "with open(filename, \"wb\") as file:\n", " file.write(result.content)\n", "\n", "with zipfile.ZipFile(filename, \"r\") as zip_ref:\n", " zip_ref.extractall()" ] }, { "cell_type": "code", "execution_count": 2, "id": "eGUfuevMUA6R", "metadata": {}, "outputs": [], "source": [ "from langchain_community.document_loaders import PyPDFLoader\n", "\n", "loader = PyPDFLoader(\"./cj/cj.pdf\")\n", "docs = loader.load()\n", "tables = []\n", "texts = [d.page_content for d in docs]" ] }, { "cell_type": "code", "execution_count": 3, "id": "Fst17fNHWYcq", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(texts)" ] }, { "cell_type": "markdown", "id": "vjfcg_Vn3_1C", "metadata": { "id": "vjfcg_Vn3_1C" }, "source": [ "## Multi-vector retriever" ] }, { "cell_type": "markdown", "id": "1ynRqJn04BFG", "metadata": { "id": "1ynRqJn04BFG" }, "source": [ "Let's generate text and image summaries and save them to a ChromaDB vectorstore." ] }, { "cell_type": "code", "execution_count": 4, "id": "kWDWfSDBMPl8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" ] } ], "source": [ "from langchain.prompts import PromptTemplate\n", "from langchain_community.chat_models import ChatVertexAI\n", "from langchain_community.llms import VertexAI\n", "from langchain_core.messages import AIMessage\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnableLambda\n", "\n", "\n", "# Generate summaries of text elements\n", "def generate_text_summaries(texts, tables, summarize_texts=False):\n", " \"\"\"\n", " Summarize text elements\n", " texts: List of str\n", " tables: List of str\n", " summarize_texts: Bool to summarize texts\n", " \"\"\"\n", "\n", " # Prompt\n", " prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n", " These summaries will be embedded and used to retrieve the raw text or table elements. \\\n", " Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} \"\"\"\n", " prompt = PromptTemplate.from_template(prompt_text)\n", " empty_response = RunnableLambda(\n", " lambda x: AIMessage(content=\"Error processing document\")\n", " )\n", " # Text summary chain\n", " model = VertexAI(\n", " temperature=0, model_name=\"gemini-pro\", max_output_tokens=1024\n", " ).with_fallbacks([empty_response])\n", " summarize_chain = {\"element\": lambda x: x} | prompt | model | StrOutputParser()\n", "\n", " # Initialize empty summaries\n", " text_summaries = []\n", " table_summaries = []\n", "\n", " # Apply to text if texts are provided and summarization is requested\n", " if texts and summarize_texts:\n", " text_summaries = summarize_chain.batch(texts, {\"max_concurrency\": 1})\n", " elif texts:\n", " text_summaries = texts\n", "\n", " # Apply to tables if tables are provided\n", " if tables:\n", " table_summaries = summarize_chain.batch(tables, {\"max_concurrency\": 1})\n", "\n", " return text_summaries, table_summaries\n", "\n", "\n", "# Get text, table summaries\n", "text_summaries, table_summaries = generate_text_summaries(\n", " texts, tables, summarize_texts=True\n", ")" ] }, { "cell_type": "code", "execution_count": 5, "id": "F0NnyUl48yYb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "21" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(text_summaries)" ] }, { "cell_type": "code", "execution_count": 6, "id": "PeK9bzXv3olF", "metadata": {}, "outputs": [], "source": [ "import base64\n", "import os\n", "\n", "from langchain_core.messages import HumanMessage\n", "\n", "\n", "def encode_image(image_path):\n", " \"\"\"Getting the base64 string\"\"\"\n", " with open(image_path, \"rb\") as image_file:\n", " return base64.b64encode(image_file.read()).decode(\"utf-8\")\n", "\n", "\n", "def image_summarize(img_base64, prompt):\n", " \"\"\"Make image summary\"\"\"\n", " model = ChatVertexAI(model_name=\"gemini-pro-vision\", max_output_tokens=1024)\n", "\n", " msg = model(\n", " [\n", " HumanMessage(\n", " content=[\n", " {\"type\": \"text\", \"text\": prompt},\n", " {\n", " \"type\": \"image_url\",\n", " \"image_url\": {\"url\": f\"data:image/jpeg;base64,{img_base64}\"},\n", " },\n", " ]\n", " )\n", " ]\n", " )\n", " return msg.content\n", "\n", "\n", "def generate_img_summaries(path):\n", " \"\"\"\n", " Generate summaries and base64 encoded strings for images\n", " path: Path to list of .jpg files extracted by Unstructured\n", " \"\"\"\n", "\n", " # Store base64 encoded images\n", " img_base64_list = []\n", "\n", " # Store image summaries\n", " image_summaries = []\n", "\n", " # Prompt\n", " prompt = \"\"\"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", " # Apply to images\n", " for img_file in sorted(os.listdir(path)):\n", " if img_file.endswith(\".jpg\"):\n", " img_path = os.path.join(path, img_file)\n", " base64_image = encode_image(img_path)\n", " img_base64_list.append(base64_image)\n", " image_summaries.append(image_summarize(base64_image, prompt))\n", "\n", " return img_base64_list, image_summaries\n", "\n", "\n", "# Image summaries\n", "img_base64_list, image_summaries = generate_img_summaries(\"./cj\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "6WDYpDFzjocl", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(image_summaries)" ] }, { "cell_type": "code", "execution_count": 8, "id": "cWyWfZ-XB6cS", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:chromadb.telemetry.product.posthog:Anonymized telemetry enabled. See https://docs.trychroma.com/telemetry for more information.\n" ] } ], "source": [ "import uuid\n", "\n", "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", "from langchain.storage import InMemoryStore\n", "from langchain_community.embeddings import VertexAIEmbeddings\n", "from langchain_community.vectorstores import Chroma\n", "from langchain_core.documents import Document\n", "\n", "\n", "def create_multi_vector_retriever(\n", " vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images\n", "):\n", " \"\"\"\n", " Create retriever that indexes summaries, but returns raw images or texts\n", " \"\"\"\n", "\n", " # Initialize the storage layer\n", " store = InMemoryStore()\n", " id_key = \"doc_id\"\n", "\n", " # Create the multi-vector retriever\n", " retriever = MultiVectorRetriever(\n", " vectorstore=vectorstore,\n", " docstore=store,\n", " id_key=id_key,\n", " )\n", "\n", " # Helper function to add documents to the vectorstore and docstore\n", " def add_documents(retriever, doc_summaries, doc_contents):\n", " doc_ids = [str(uuid.uuid4()) for _ in doc_contents]\n", " summary_docs = [\n", " Document(page_content=s, metadata={id_key: doc_ids[i]})\n", " for i, s in enumerate(doc_summaries)\n", " ]\n", " retriever.vectorstore.add_documents(summary_docs)\n", " retriever.docstore.mset(list(zip(doc_ids, doc_contents)))\n", "\n", " # Add texts, tables, and images\n", " # Check that text_summaries is not empty before adding\n", " if text_summaries:\n", " add_documents(retriever, text_summaries, texts)\n", " # Check that table_summaries is not empty before adding\n", " if table_summaries:\n", " add_documents(retriever, table_summaries, tables)\n", " # Check that image_summaries is not empty before adding\n", " if image_summaries:\n", " add_documents(retriever, image_summaries, images)\n", "\n", " return retriever\n", "\n", "\n", "# The vectorstore to use to index the summaries\n", "vectorstore = Chroma(\n", " collection_name=\"mm_rag_cj_blog\",\n", " embedding_function=VertexAIEmbeddings(model_name=\"textembedding-gecko@latest\"),\n", ")\n", "\n", "# Create retriever\n", "retriever_multi_vector_img = create_multi_vector_retriever(\n", " vectorstore,\n", " text_summaries,\n", " texts,\n", " table_summaries,\n", " tables,\n", " image_summaries,\n", " img_base64_list,\n", ")" ] }, { "cell_type": "markdown", "id": "NGDkkMFfCg4j", "metadata": { "id": "NGDkkMFfCg4j" }, "source": [ "## Building a RAG" ] }, { "cell_type": "markdown", "id": "8TzOcHVsCmBc", "metadata": { "id": "8TzOcHVsCmBc" }, "source": [ "Let's build a retriever:" ] }, { "cell_type": "code", "execution_count": 9, "id": "GlwCErBaCKQW", "metadata": {}, "outputs": [], "source": [ "import io\n", "import re\n", "\n", "from IPython.display import HTML, display\n", "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n", "from PIL import Image\n", "\n", "\n", "def plt_img_base64(img_base64):\n", " \"\"\"Disply base64 encoded string as image\"\"\"\n", " # Create an HTML img tag with the base64 string as the source\n", " image_html = f''\n", " # Display the image by rendering the HTML\n", " display(HTML(image_html))\n", "\n", "\n", "def looks_like_base64(sb):\n", " \"\"\"Check if the string looks like base64\"\"\"\n", " return re.match(\"^[A-Za-z0-9+/]+[=]{0,2}$\", sb) is not None\n", "\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", "\n", "def resize_base64_image(base64_string, size=(128, 128)):\n", " \"\"\"\n", " Resize an image encoded as a Base64 string\n", " \"\"\"\n", " # Decode the Base64 string\n", " img_data = base64.b64decode(base64_string)\n", " img = Image.open(io.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 = io.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 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", " if looks_like_base64(doc) and is_image_data(doc):\n", " doc = resize_base64_image(doc, size=(1300, 600))\n", " b64_images.append(doc)\n", " else:\n", " texts.append(doc)\n", " if len(b64_images) > 0:\n", " return {\"images\": b64_images[:1], \"texts\": []}\n", " return {\"images\": b64_images, \"texts\": texts}\n", "\n", "\n", "def img_prompt_func(data_dict):\n", " \"\"\"\n", " Join the context into a single string\n", " \"\"\"\n", " formatted_texts = \"\\n\".join(data_dict[\"context\"][\"texts\"])\n", " messages = []\n", "\n", " # Adding the text for analysis\n", " text_message = {\n", " \"type\": \"text\",\n", " \"text\": (\n", " \"You are financial analyst tasking with providing investment advice.\\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 provide investment advice related to the user question. \\n\"\n", " f\"User-provided question: {data_dict['question']}\\n\\n\"\n", " \"Text and / or tables:\\n\"\n", " f\"{formatted_texts}\"\n", " ),\n", " }\n", " messages.append(text_message)\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", " return [HumanMessage(content=messages)]\n", "\n", "\n", "def multi_modal_rag_chain(retriever):\n", " \"\"\"\n", " Multi-modal RAG chain\n", " \"\"\"\n", "\n", " # Multi-modal LLM\n", " model = ChatVertexAI(\n", " temperature=0, model_name=\"gemini-pro-vision\", max_output_tokens=1024\n", " )\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", " )\n", "\n", " return chain\n", "\n", "\n", "# Create RAG chain\n", "chain_multimodal_rag = multi_modal_rag_chain(retriever_multi_vector_img)" ] }, { "cell_type": "markdown", "id": "BS4hNKqCCp8u", "metadata": { "id": "BS4hNKqCCp8u" }, "source": [ "Let's check that we get images as documents:" ] }, { "cell_type": "code", "execution_count": 10, "id": "Q7GrwFC_FGwr", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"What are the EV / NTM and NTM rev growth for MongoDB, Cloudflare, and Datadog?\"\n", "docs = retriever_multi_vector_img.get_relevant_documents(query, limit=1)\n", "\n", "# We get 2 docs\n", "len(docs)" ] }, { "cell_type": "code", "execution_count": 11, "id": "unnxB5M_FLCD", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt_img_base64(docs[0])" ] }, { "cell_type": "markdown", "id": "YUkGZXqsCtF6", "metadata": { "id": "YUkGZXqsCtF6" }, "source": [ "And let's run our RAG on the same query:" ] }, { "cell_type": "code", "execution_count": 12, "id": "LsPTehdK-T-_", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "' | Company | EV / NTM Rev | NTM Rev Growth |\\n|---|---|---|\\n| MongoDB | 14.6x | 17% |\\n| Cloudflare | 13.4x | 28% |\\n| Datadog | 13.1x | 19% |'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chain_multimodal_rag.invoke(query)" ] }, { "cell_type": "markdown", "id": "XpLQB6dEfQX-", "metadata": { "id": "XpLQB6dEfQX-" }, "source": [ "As we can see, the model was able to figure out the the right values that are relevant to answer the question." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.2" } }, "nbformat": 4, "nbformat_minor": 5 }