examples/notebooks/langchain/Langchain-Milvus-Ingest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3420575b-4d00-458b-aa0e-7030008ccd53",
   "metadata": {},
   "source": [
    "## Creating an index and populating it with documents using Milvus\n",
    "\n",
    "Simple example on how to ingest PDF documents, then web pages content into a Milvus VectorStore.\n",
    "\n",
    "Requirements:\n",
    "- A Milvus instance, either standalone or cluster.\n",
    "- Connection credentials to Milvus must be available as environment variables: MILVUS_USERNAME and MILVUS_PASSWORD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8308b229-b520-4e82-a783-eb921bb955e7",
   "metadata": {},
   "source": [
    "### Needed packages and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91e41b41-f60a-4b0f-91a1-cd273b60f21b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -q einops==0.7.0 langchain==0.1.9 pypdf==4.0.2 pymilvus==2.3.6 sentence-transformers==2.4.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "600cd763-6ecc-4c77-89c0-47108c31c44e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import os\n",
    "from langchain.document_loaders import PyPDFDirectoryLoader, WebBaseLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
    "from langchain_community.vectorstores import Milvus"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a82063d-6153-4812-8977-042241736b53",
   "metadata": {},
   "source": [
    "### Base parameters, the Milvus connection info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "417ed4a4-9418-4f48-bebd-ef0ea11ae434",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Replace values according to your Milvus deployment\n",
    "MILVUS_HOST = \"vectordb-milvus.milvus.svc.cluster.local\"\n",
    "MILVUS_PORT = 19530\n",
    "MILVUS_USERNAME = os.getenv('MILVUS_USERNAME')\n",
    "MILVUS_PASSWORD = os.getenv('MILVUS_PASSWORD')\n",
    "MILVUS_COLLECTION = \"demo_collection\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68f6785-480e-4519-be4f-8e1738dba4ca",
   "metadata": {},
   "source": [
    "## Initial index creation and document ingestion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8cff5f7-c509-48db-90b5-e15815b8b530",
   "metadata": {},
   "source": [
    "#### Download and load pdfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc4fe0db-f494-4cbd-9e97-8b6359a78cb7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "product_version = \"2.13\"\n",
    "documents = [\n",
    "    \"release_notes\",\n",
    "    \"introduction_to_red_hat_openshift_ai\",\n",
    "    \"getting_started_with_red_hat_openshift_ai_self-managed\",\n",
    "    \"openshift_ai_tutorial_-_fraud_detection_example\",\n",
    "    \"developing_a_model\",\n",
    "    \"integrating_data_from_amazon_s3\",\n",
    "    \"working_on_data_science_projects\",\n",
    "    \"serving_models\",\n",
    "    \"monitoring_data_science_models\",\n",
    "    \"managing_users\",\n",
    "    \"managing_resources\",\n",
    "    \"installing_and_uninstalling_openshift_ai_self-managed\",\n",
    "    \"installing_and_uninstalling_openshift_ai_self-managed_in_a_disconnected_environment\",\n",
    "    \"upgrading_openshift_ai_self-managed\",\n",
    "    \"upgrading_openshift_ai_self-managed_in_a_disconnected_environment\",   \n",
    "]\n",
    "\n",
    "pdfs = [f\"https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/{product_version}/pdf/{doc}/red_hat_openshift_ai_self-managed-{product_version}-{doc}-en-us.pdf\" for doc in documents]\n",
    "pdfs_to_urls = {f\"red_hat_openshift_ai_self-managed-{product_version}-{doc}-en-us\": f\"https://access.redhat.com/documentation/en-us/red_hat_openshift_ai_self-managed/{product_version}/html-single/{doc}/index\" for doc in documents}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3eea5acc-49df-41c9-a01a-0cdbca96e8e2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs_dir = f\"rhoai-doc-{product_version}\"\n",
    "\n",
    "if not os.path.exists(docs_dir):\n",
    "    os.mkdir(docs_dir)\n",
    "\n",
    "for pdf in pdfs:\n",
    "    try:\n",
    "        response = requests.get(pdf)\n",
    "    except:\n",
    "        print(f\"Skipped {pdf}\")\n",
    "        continue\n",
    "    if response.status_code!=200:\n",
    "        print(f\"Skipped {pdf}\")\n",
    "        continue  \n",
    "    with open(f\"{docs_dir}/{pdf.split('/')[-1]}\", 'wb') as f:\n",
    "        f.write(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4074d4-eff4-45b2-902d-ec8c075a83ef",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pdf_folder_path = f\"./rhoai-doc-{product_version}\"\n",
    "\n",
    "pdf_loader = PyPDFDirectoryLoader(pdf_folder_path)\n",
    "pdf_docs = pdf_loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde7ed3a-0530-47a1-95c2-22db6c782a95",
   "metadata": {},
   "source": [
    "#### Inject metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "702230f6-e6d3-44c7-a643-4996387606ff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "for doc in pdf_docs:\n",
    "    doc.metadata[\"source\"] = pdfs_to_urls[Path(doc.metadata[\"source\"]).stem]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd511d44-2d92-47a0-9163-b25576c9557b",
   "metadata": {},
   "source": [
    "#### Load websites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8aebf003-d7ec-43ba-8e04-1931bcff2866",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "websites = [\n",
    "    \"https://ai-on-openshift.io/getting-started/openshift/\",\n",
    "    \"https://ai-on-openshift.io/getting-started/opendatahub/\",\n",
    "    \"https://ai-on-openshift.io/getting-started/openshift-ai/\",\n",
    "    \"https://ai-on-openshift.io/odh-rhoai/configuration/\",\n",
    "    \"https://ai-on-openshift.io/odh-rhoai/custom-notebooks/\",\n",
    "    \"https://ai-on-openshift.io/odh-rhoai/nvidia-gpus/\",\n",
    "    \"https://ai-on-openshift.io/odh-rhoai/custom-runtime-triton/\",\n",
    "    \"https://ai-on-openshift.io/odh-rhoai/openshift-group-management/\",\n",
    "    \"https://ai-on-openshift.io/tools-and-applications/minio/minio/\",\n",
    "    \"https://access.redhat.com/articles/7047935\",\n",
    "    \"https://access.redhat.com/articles/rhoai-supported-configs\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99f41110-8ca7-4d90-93b2-3b5021c894b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "website_loader = WebBaseLoader(websites)\n",
    "website_docs = website_loader.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157ddd29-54b3-474a-9b10-2d274bc3254f",
   "metadata": {},
   "source": [
    "#### Merge both types of docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d361094-8b43-4351-8495-37628c35c42d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "docs = pdf_docs + website_docs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4198fe0a-38bf-4cd4-af7d-35b41c645edd",
   "metadata": {},
   "source": [
    "#### Split documents into chunks with some overlap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edba4a08-2194-4df1-9091-6f2b596757a1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024,\n",
    "                                               chunk_overlap=128)\n",
    "all_splits = text_splitter.split_documents(docs)\n",
    "all_splits[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae7eae2-c670-4eb5-803b-b4d591fa83db",
   "metadata": {},
   "source": [
    "#### Create the index and ingest the documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbb6a3e3-5ccd-441e-b80d-427555d9e9f6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# If you don't want to use a GPU, you can remove the 'device': 'cuda' argument\n",
    "model_kwargs = {'device': 'cuda'}\n",
    "embeddings = HuggingFaceEmbeddings(\n",
    "    model_kwargs=model_kwargs,\n",
    "    show_progress=True\n",
    ")\n",
    "\n",
    "# BEWARE: `drop_old` is set to True, so if the collection already existed it will deleted first.\n",
    "db = Milvus(\n",
    "    embedding_function=embeddings,\n",
    "    connection_args={\"host\": MILVUS_HOST, \"port\": MILVUS_PORT, \"user\": MILVUS_USERNAME, \"password\": MILVUS_PASSWORD},\n",
    "    collection_name=MILVUS_COLLECTION,\n",
    "    metadata_field=\"metadata\",\n",
    "    text_field=\"page_content\",\n",
    "    auto_id=True,\n",
    "    drop_old=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6bf425b-dffd-4f42-9537-49d41383182d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db.add_documents(all_splits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a3d987b-8ebc-46ce-a206-48c1339b7a5b",
   "metadata": {},
   "source": [
    "#### Alternatively, add new documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c37f227d-a13d-456c-b91b-3c203e62fc0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you don't want to use a GPU, you can remove the 'device': 'cuda' argument\n",
    "# model_kwargs = {'device': 'cuda'}\n",
    "# embeddings = HuggingFaceEmbeddings(\n",
    "#     model_kwargs=model_kwargs,\n",
    "#     show_progress=True\n",
    "# )\n",
    "\n",
    "# db = Milvus(\n",
    "#     embedding_function=embeddings,\n",
    "#     connection_args={\"host\": MILVUS_HOST, \"port\": MILVUS_PORT, \"user\": MILVUS_USERNAME, \"password\": MILVUS_PASSWORD},\n",
    "#     collection_name=MILVUS_COLLECTION,\n",
    "#     metadata_field=\"metadata\",\n",
    "#     text_field=\"page_content\",\n",
    "#     auto_id=True,\n",
    "#     drop_old=False\n",
    "#     )\n",
    "\n",
    "# db.add_documents(all_splits)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae3b458-4979-46df-8493-7496764a2568",
   "metadata": {},
   "source": [
    "#### Test query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "489c6e6d-c42c-4de4-87cf-8edfd0e63da3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"How can I work with GPU and taints in OpenShift AI?\"\n",
    "docs_with_score = db.similarity_search_with_score(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90feeb37-7888-4c5f-a5cb-5f82637cec16",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for doc, score in docs_with_score:\n",
    "    print(\"-\" * 80)\n",
    "    print(\"Score: \", score)\n",
    "    print(doc.page_content)\n",
    "    print(\"-\" * 80)"
   ]
  }
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