From f907e162aa16c062bd990c48365af7d138814ca6 Mon Sep 17 00:00:00 2001 From: Lance Martin Date: Tue, 12 Dec 2023 12:10:56 -0800 Subject: [PATCH] Fix bugs --- .../ingest.py | 4 +- .../poetry.lock | 646 ++---------------- .../pyproject.toml | 11 +- .../rag_chroma_multi_modal_multi_vector.ipynb | 170 +++++ .../chain.py | 4 +- 5 files changed, 222 insertions(+), 613 deletions(-) diff --git a/templates/rag-chroma-multi-modal-multi-vector/ingest.py b/templates/rag-chroma-multi-modal-multi-vector/ingest.py index eb3ba1d5a1f96..8854b7a213982 100644 --- a/templates/rag-chroma-multi-modal-multi-vector/ingest.py +++ b/templates/rag-chroma-multi-modal-multi-vector/ingest.py @@ -136,8 +136,8 @@ def create_multi_vector_retriever(vectorstore, image_summaries, images): """ # Initialize the storage layer for images - UPSTASH_URL = "xxx" - UPSTASH_TOKEN = "xxx" + UPSTASH_URL = "https://usw1-bright-beagle-34178.upstash.io" + UPSTASH_TOKEN = "AYWCACQgNzk3OTJjZTItMGIxNy00MTEzLWIyZTAtZWI0ZmI1ZGY0NjFhNGRhMGZjNDE4YjgxNGE4MTkzOWYxMzllM2MzZThlOGY=" store = UpstashRedisByteStore(url=UPSTASH_URL, token=UPSTASH_TOKEN) id_key = "doc_id" diff --git a/templates/rag-chroma-multi-modal-multi-vector/poetry.lock b/templates/rag-chroma-multi-modal-multi-vector/poetry.lock index 665ead788eb20..288ccdb91edb2 100644 --- a/templates/rag-chroma-multi-modal-multi-vector/poetry.lock +++ b/templates/rag-chroma-multi-modal-multi-vector/poetry.lock @@ -628,20 +628,6 @@ smb = ["smbprotocol"] ssh = ["paramiko"] tqdm = ["tqdm"] -[[package]] -name = "ftfy" -version = "6.1.3" -description = "Fixes mojibake and other problems with Unicode, after the fact" -optional = false -python-versions = ">=3.8,<4" -files = [ - {file = "ftfy-6.1.3-py3-none-any.whl", hash = "sha256:e49c306c06a97f4986faa7a8740cfe3c13f3106e85bcec73eb629817e671557c"}, - {file = "ftfy-6.1.3.tar.gz", hash = "sha256:693274aead811cff24c1e8784165aa755cd2f6e442a5ec535c7d697f6422a422"}, -] - -[package.dependencies] -wcwidth = ">=0.2.12,<0.3.0" - [[package]] name = "gitdb" version = "4.0.11" @@ -1011,23 +997,6 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"] -[[package]] -name = "jinja2" -version = "3.1.2" -description = "A very fast and expressive template engine." -optional = false -python-versions = ">=3.7" -files = [ - {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"}, - {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"}, -] - -[package.dependencies] -MarkupSafe = ">=2.0" - -[package.extras] -i18n = ["Babel (>=2.7)"] - [[package]] name = "jsonpatch" version = "1.33" @@ -1055,22 +1024,22 @@ files = [ [[package]] name = "langchain" -version = "0.0.344" +version = "0.0.350" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" files = [ - {file = "langchain-0.0.344-py3-none-any.whl", hash = "sha256:6dc85e8569a58537bd772a8ba18c629f882e12a73104d79df730be823d630482"}, - {file = "langchain-0.0.344.tar.gz", hash = "sha256:37a6d72d23aee54fc1dfea3934ed63762f604e8665fe008fb7a30c4591fe808d"}, + {file = "langchain-0.0.350-py3-none-any.whl", hash = "sha256:11b605f325a4271a7815baaec05bc7622e3ad1f10f26b05c752cafa27663ed38"}, + {file = "langchain-0.0.350.tar.gz", hash = "sha256:f0e68a92d200bb722586688ab7b411b2430bd98ad265ca03b264e7e7acbb6c01"}, ] [package.dependencies] aiohttp = ">=3.8.3,<4.0.0" -anyio = "<4.0" async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">=0.5.7,<0.7" jsonpatch = ">=1.33,<2.0" -langchain-core = ">=0.0.8,<0.1" +langchain-community = ">=0.0.2,<0.1" +langchain-core = ">=0.1,<0.2" langsmith = ">=0.0.63,<0.1.0" numpy = ">=1,<2" pydantic = ">=1,<3" @@ -1080,14 +1049,13 @@ SQLAlchemy = ">=1.4,<3" tenacity = ">=8.1.0,<9.0.0" [package.extras] -all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.8.3,<4.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", 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"rag-chroma-multi-modal" +name = "rag-chroma-multi-modal-multi-vector" version = "0.1.0" -description = "Multi-modal RAG using Chroma" +description = "Multi-modal RAG using Chroma and multi-vector retriever" authors = [ "Lance Martin ", ] @@ -9,21 +9,20 @@ readme = "README.md" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain = ">=0.0.325" +langchain = ">=0.0.350" openai = "<2" tiktoken = ">=0.5.1" chromadb = ">=0.4.14" -open-clip-torch = ">=2.23.0" -torch = ">=2.1.0" pypdfium2 = ">=4.20.0" langchain-experimental = "^0.0.43" upstash-redis = ">=1.0.0" +pillow = ">=10.1.0" [tool.poetry.group.dev.dependencies] langchain-cli = ">=0.0.15" [tool.langserve] -export_module = "rag_chroma_multi_modal" +export_module = "rag_chroma_multi_modal_multi_vector" export_attr = "chain" [tool.templates-hub] diff --git a/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector.ipynb b/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector.ipynb index 4b318351f8284..d93ae9a0d29de 100644 --- a/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector.ipynb +++ b/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector.ipynb @@ -35,6 +35,176 @@ "## TMP (TODO: Remove)" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e0eb4640-7f44-4c97-942c-54927640d954", + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "import io\n", + "import json\n", + "from pathlib import Path\n", + "\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.pydantic_v1 import BaseModel\n", + "from langchain.retrievers.multi_vector import MultiVectorRetriever\n", + "from langchain.schema.messages import HumanMessage\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n", + "from langchain.storage import UpstashRedisByteStore\n", + "from langchain.vectorstores import Chroma\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", + " :param base64_string: A Base64 encoded string of the image to be resized.\n", + " :param size: A tuple representing the new size (width, height) for the image.\n", + " :return: A Base64 encoded string of the resized image.\n", + " \"\"\"\n", + " img_data = base64.b64decode(base64_string)\n", + " img = Image.open(io.BytesIO(img_data))\n", + " resized_img = img.resize(size, Image.LANCZOS)\n", + " buffered = io.BytesIO()\n", + " resized_img.save(buffered, format=img.format)\n", + " return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n", + "\n", + "\n", + "def get_resized_images(docs):\n", + " \"\"\"\n", + " Resize images from base64-encoded strings.\n", + "\n", + " :param docs: A list of base64-encoded image to be resized.\n", + " :return: Dict containing a list of resized base64-encoded strings.\n", + " \"\"\"\n", + " b64_images = []\n", + " for doc in docs:\n", + " doc = json.loads(doc.decode(\"utf-8\"))[\"kwargs\"][\"page_content\"]\n", + " resized_image = resize_base64_image(doc, size=(1280, 720))\n", + " b64_images.append(resized_image)\n", + " return {\"images\": b64_images}\n", + "\n", + "\n", + "def img_prompt_func(data_dict, num_images=2):\n", + " \"\"\"\n", + " GPT-4V prompt for image analysis.\n", + "\n", + " :param data_dict: A dict with images and a user-provided question.\n", + " :param num_images: Number of images to include in the prompt.\n", + " :return: A list containing message objects for each image and the text prompt.\n", + " \"\"\"\n", + " messages = []\n", + " if data_dict[\"context\"][\"images\"]:\n", + " for image in data_dict[\"context\"][\"images\"][:num_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", + " text_message = {\n", + " \"type\": \"text\",\n", + " \"text\": (\n", + " \"You are an analyst tasked with answering questions about visual content.\\n\"\n", + " \"You will be give a set of image(s) from a slide deck / presentation.\\n\"\n", + " \"Use this information to answer the user question. \\n\"\n", + " f\"User-provided question: {data_dict['question']}\\n\\n\"\n", + " ),\n", + " }\n", + " messages.append(text_message)\n", + " return [HumanMessage(content=messages)]\n", + "\n", + "\n", + "def multi_modal_rag_chain(retriever):\n", + " \"\"\"\n", + " Multi-modal RAG chain,\n", + "\n", + " :param retriever: A function that retrieves the necessary context for the model.\n", + " :return: A chain of functions representing the multi-modal RAG process.\n", + " \"\"\"\n", + " # Initialize the multi-modal Large Language Model with specific parameters\n", + " model = ChatOpenAI(temperature=0, model=\"gpt-4-vision-preview\", max_tokens=1024)\n", + "\n", + " # Define the RAG pipeline\n", + " chain = (\n", + " {\n", + " \"context\": retriever | RunnableLambda(get_resized_images),\n", + " \"question\": RunnablePassthrough(),\n", + " }\n", + " | RunnableLambda(img_prompt_func)\n", + " | model\n", + " | StrOutputParser()\n", + " )\n", + "\n", + " return chain\n", + "\n", + "\n", + "# Load chroma\n", + "vectorstore_mvr = Chroma(\n", + " collection_name=\"image_summaries\",\n", + " persist_directory=\"chroma_db_multi_modal\",\n", + " embedding_function=OpenAIEmbeddings(),\n", + ")\n", + "\n", + "# Load redis\n", + "UPSTASH_URL = \"https://usw1-bright-beagle-34178.upstash.io\"\n", + "UPSTASH_TOKEN = \"AYWCACQgNzk3OTJjZTItMGIxNy00MTEzLWIyZTAtZWI0ZmI1ZGY0NjFhNGRhMGZjNDE4YjgxNGE4MTkzOWYxMzllM2MzZThlOGY=\"\n", + "store = UpstashRedisByteStore(url=UPSTASH_URL,\n", + " token=UPSTASH_TOKEN)\n", + "id_key = \"doc_id\"\n", + "\n", + "# Create the multi-vector retriever\n", + "retriever = MultiVectorRetriever(\n", + " vectorstore=vectorstore_mvr,\n", + " docstore=store,\n", + " id_key=id_key,\n", + ")\n", + "\n", + "# Create RAG chain\n", + "chain = multi_modal_rag_chain(retriever)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "57af60d7-fc78-406f-9cdd-fc64ea7798f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The total number of customers for Datadog, as shown in the image, is approximately 26,800.'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain.invoke(\"What is the total numbner of customers for DataDog?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8549b439-949e-4adf-bb52-3db7bf3f4c70", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a98689a-3bab-4535-bd13-05782ff5aea3", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 35, diff --git a/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/chain.py b/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/chain.py index d6852df7dfb38..3010fe652d46d 100644 --- a/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/chain.py +++ b/templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/chain.py @@ -107,8 +107,8 @@ def multi_modal_rag_chain(retriever): ) # Load redis -UPSTASH_URL = "xxx" -UPSTASH_TOKEN = "xxx" +UPSTASH_URL = "https://usw1-bright-beagle-34178.upstash.io" +UPSTASH_TOKEN = "AYWCACQgNzk3OTJjZTItMGIxNy00MTEzLWIyZTAtZWI0ZmI1ZGY0NjFhNGRhMGZjNDE4YjgxNGE4MTkzOWYxMzllM2MzZThlOGY=" store = UpstashRedisByteStore(url=UPSTASH_URL, token=UPSTASH_TOKEN) id_key = "doc_id"