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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b65a7e72",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Load Data into Milvus for RAG\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15258f81",
+   "metadata": {},
+   "source": [
+    " \n",
+    "\n",
+    "\n",
+    "<a class=\"anchor\" id=\"setup\"></a>\n",
+    "## 1. Set up the environment \n",
+    "\n",
+    "### Install Libraries\n",
+    "\n",
+    "We need to install the pymilvus package to the watsonx.ai Python environment."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51677357",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install grpcio==1.60.0 \n",
+    "!pip install pymilvus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "579e78ba",
+   "metadata": {},
+   "source": [
+    "## !!RESTART THE KERNAL AFTER pymilvus install!!\n",
+    "\n",
+    "Certain dependencies need to be persisted. Restarting the kernal allows this to occur. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "694eff33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install ipython-sql==0.4.1\n",
+    "!pip install sqlalchemy==1.4.46\n",
+    "!pip install sqlalchemy==1.4.46 \"pyhive[presto]\"\n",
+    "!pip install python-dotenv\n",
+    "!pip install wikipedia\n",
+    "!pip install sentence_transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "356cf465",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create environment variables\n",
+    "\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from ibm_cloud_sdk_core import IAMTokenManager\n",
+    "from ibm_watson_studio_lib import access_project_or_space\n",
+    "\n",
+    "wslib = access_project_or_space({\n",
+    "        'token': '<INSERT_PROJECT_TOKEN>',\n",
+    "        'project_id': '<INSERT_PROJECT_ID>'\n",
+    "})\n",
+    "\n",
+    "wslib.download_file('config.env')\n",
+    "load_dotenv('config.env')\n",
+    "\n",
+    "\n",
+    "# Connection variables\n",
+    "api_key = os.getenv(\"API_KEY\", None)\n",
+    "ibm_cloud_url = os.getenv(\"IBM_CLOUD_URL\", None) \n",
+    "project_id = os.getenv(\"PROJECT_ID\", None)\n",
+    "\n",
+    "creds = {\n",
+    "    \"url\": ibm_cloud_url,\n",
+    "    \"apikey\": api_key \n",
+    "}\n",
+    "access_token = IAMTokenManager(\n",
+    "    apikey = api_key,\n",
+    "    url = \"https://iam.cloud.ibm.com/identity/token\"\n",
+    ").get_token()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b747755a",
+   "metadata": {},
+   "source": [
+    "## Wikipedia Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32f2b28a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wikipedia\n",
+    "\n",
+    "# search\n",
+    "search_results = wikipedia.search(\"<PICK_A_TOPIC>\")\n",
+    "search_results\n",
+    "\n",
+    "print(search_results)\n",
+    "\n",
+    "# view article summary\n",
+    "article_summary = wikipedia.summary(search_results[0])\n",
+    "article_summary\n",
+    "\n",
+    "print(article_summary)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d89314fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import wikipedia\n",
+    "\n",
+    "# fetch wikipedia articles\n",
+    "articles = {\n",
+    "    #'IBM': None, \n",
+    "    '<INSERT_WIKIPEDIA_TITLE>': None\n",
+    "}\n",
+    "\n",
+    "for k,v in articles.items():\n",
+    "    article = wikipedia.page(k)\n",
+    "    articles[k] = article.content\n",
+    "    print(f\"Successfully fetched {k}\")\n",
+    "\n",
+    "print(f\"Successfully fetched {len(articles)} articles \")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5f350d8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4353d5b0",
+   "metadata": {},
+   "source": [
+    "### Split Wikipedia Data into Chunks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18894c13",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chunk data\n",
+    "def split_into_chunks(text, chunk_size):\n",
+    "    words = text.split()\n",
+    "    return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]\n",
+    "\n",
+    "split_articles = {}\n",
+    "for k,v in articles.items():\n",
+    "    split_articles[k] = split_into_chunks(v, 225)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb6dc64d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "article_titles = list(split_articles.keys())\n",
+    "article_chunks = list(split_articles.values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "947812bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## create titles_list for associates chunks to be loaded into milvus \n",
+    "\n",
+    "i = 0\n",
+    "for title in article_titles:\n",
+    "    list_length = len(article_chunks[i])\n",
+    "    article_titles[i] = [title] * list_length\n",
+    "    i+=1\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a58926d6",
+   "metadata": {},
+   "source": [
+    "## Insert Chunks with Embeddings into Milvus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65714f49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pymilvus import(\n",
+    "    Milvus,\n",
+    "    IndexType,\n",
+    "    Status,\n",
+    "    connections,\n",
+    "    FieldSchema,\n",
+    "    DataType,\n",
+    "    Collection,\n",
+    "    CollectionSchema,\n",
+    ")\n",
+    "\n",
+    "import os \n",
+    "\n",
+    "host = os.getenv(\"MILVUS_HOST\", None)\n",
+    "port = os.getenv(\"MILVUS_PORT\", None)\n",
+    "password = os.getenv(\"MILVUS_PASSWORD\", None)\n",
+    "user = 'ibmlhapikey'\n",
+    "\n",
+    "\n",
+    "connections.connect(alias=\"default\", \n",
+    "                    host=url, \n",
+    "                    port=port, \n",
+    "                    user=apiuser, \n",
+    "                    password=apikey, \n",
+    "                    secure=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4d98158",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create collection - define fields + schema\n",
+    "\n",
+    "fields = [\n",
+    "    FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True, auto_id=True), # Primary key\n",
+    "    FieldSchema(name=\"article_text\", dtype=DataType.VARCHAR, max_length=2500,),\n",
+    "    FieldSchema(name=\"article_title\", dtype=DataType.VARCHAR, max_length=200,),\n",
+    "    FieldSchema(name=\"vector\", dtype=DataType.FLOAT_VECTOR, dim=384),\n",
+    "]\n",
+    "\n",
+    "schema = CollectionSchema(fields, \"<COLLECTION_DESCRIPTION>\")\n",
+    "\n",
+    "wiki_collection = Collection(\"<NAME_OF_COLLECTION>\", schema)\n",
+    "\n",
+    "# Create index\n",
+    "index_params = {\n",
+    "        'metric_type':'L2',\n",
+    "        'index_type':\"IVF_FLAT\",\n",
+    "        'params':{\"nlist\":2048}\n",
+    "}\n",
+    "\n",
+    "wiki_collection.create_index(field_name=\"vector\", index_params=index_params)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efbefcbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can run a check to see the collections in our milvus instance and we see the new collection has been created \n",
+    "\n",
+    "from pymilvus import utility\n",
+    "utility.list_collections()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e2b9046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load data into Milvus\n",
+    "import pandas as pd\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "from pymilvus import Collection, connections\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "\n",
+    "for i in range(len(article_titles)):\n",
+    "    # Create vector embeddings + data\n",
+    "    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') # 384 dim\n",
+    "    passage_embeddings = model.encode(article_chunks[i])\n",
+    "\n",
+    "    basic_collection = Collection(\"<NAME_OF_COLLECTION>\") \n",
+    "    data = [\n",
+    "        article_chunks[i],\n",
+    "        article_titles[i],\n",
+    "        passage_embeddings\n",
+    "    ]\n",
+    "               \n",
+    "    out = basic_collection.insert(data)\n",
+    "    basic_collection.flush()  # Ensures data persistence\n",
+    "\n",
+    "    \n",
+    "    print(\"Wikipedia Article: \\'\" + article_titles[i][0] + \"\\' has been loaded.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3547110e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## check to ensure entities have been loaded into the collection\n",
+    "\n",
+    "basic_collection = Collection(\"<NAME_OF_COLLECTION>\") \n",
+    "\n",
+    "basic_collection.num_entities "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f8253da",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34a915ff",
+   "metadata": {},
+   "source": [
+    "### Prompt LLM with Query Results\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77e075a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sentence_transformers import SentenceTransformer\n",
+    "from pymilvus import(\n",
+    "    Milvus,\n",
+    "    IndexType,\n",
+    "    Status,\n",
+    "    connections,\n",
+    "    FieldSchema,\n",
+    "    DataType,\n",
+    "    Collection,\n",
+    "    CollectionSchema,\n",
+    ")\n",
+    "\n",
+    "import os \n",
+    "\n",
+    "host = os.getenv(\"MILVUS_HOST\", None)\n",
+    "port = os.getenv(\"MILVUS_PORT\", None)\n",
+    "password = os.getenv(\"MILVUS_PASSWORD\", None)\n",
+    "user = 'ibmlhapikey'\n",
+    "\n",
+    "\n",
+    "connections.connect(alias=\"default\", \n",
+    "                    host=url, \n",
+    "                    port=port, \n",
+    "                    user=apiuser, \n",
+    "                    password=apikey, \n",
+    "                    secure=True)\n",
+    "\n",
+    "\n",
+    "# Load collection\n",
+    "\n",
+    "basic_collection = Collection(\"<NAME_OF_COLLECTION>\")      \n",
+    "basic_collection.load()\n",
+    "\n",
+    "# Query function\n",
+    "def query_milvus(query, num_results):\n",
+    "    \n",
+    "    # Vectorize query\n",
+    "    model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2') # 384 dim\n",
+    "    query_embeddings = model.encode([query])\n",
+    "\n",
+    "    # Search\n",
+    "    search_params = {\n",
+    "        \"metric_type\": \"L2\", \n",
+    "        \"params\": {\"nprobe\": 5}\n",
+    "    }\n",
+    "    results = basic_collection.search(\n",
+    "        data=query_embeddings, \n",
+    "        anns_field=\"vector\", \n",
+    "        param=search_params,\n",
+    "        limit=num_results,\n",
+    "        expr=None, \n",
+    "        output_fields=['article_text'],\n",
+    "    )\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f62a489",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Consider some questions to ask regarding the topic you have chosen \n",
+    "\n",
+    "#question_text = \"How does IBM treat their employees?\"\n",
+    "\n",
+    "question_text = \"<INSERT_QUESTION_TO_ASK_LLM>\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8259b76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Query Milvus \n",
+    "\n",
+    "num_results = 3\n",
+    "results = query_milvus(question_text, num_results)\n",
+    "\n",
+    "relevant_chunks = []\n",
+    "for i in range(num_results):    \n",
+    "    #print(f\"id: {results[0].ids[i]}\")\n",
+    "    #print(f\"distance: {results[0].distances[i]}\")\n",
+    "    text = results[0][i].entity.get('article_text')\n",
+    "    relevant_chunks.append(text)\n",
+    "    \n",
+    "#print(relevant_chunks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c72ab9a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_prompt(context, question_text):\n",
+    "    return (f\"{context}\\n\\nPlease answer a question using this text. \"\n",
+    "          + f\"If the question is unanswerable, say \\\"unanswerable\\\".\"\n",
+    "          + f\"\\n\\nQuestion: {question_text}\")\n",
+    "\n",
+    "\n",
+    "# Build prompt w/ Milvus results\n",
+    "# Embed retrieved passages(context) and user question into into prompt text\n",
+    "\n",
+    "context = \"\\n\\n\".join(relevant_chunks)\n",
+    "prompt = make_prompt(context, question_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aef20205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(prompt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47852e4f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ibm_watson_machine_learning.foundation_models import Model\n",
+    "from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams\n",
+    "\n",
+    "# Model Parameters\n",
+    "params = {\n",
+    "        GenParams.DECODING_METHOD: \"greedy\",\n",
+    "        GenParams.MIN_NEW_TOKENS: 1,\n",
+    "        GenParams.MAX_NEW_TOKENS: 500,\n",
+    "        GenParams.TEMPERATURE: 0,\n",
+    "}\n",
+    "model = Model(\n",
+    "        model_id='meta-llama/llama-2-70b-chat', \n",
+    "        params=params, credentials=creds, \n",
+    "        project_id=project_id\n",
+    ")\n",
+    "\n",
+    "# Prompt LLM\n",
+    "response = model.generate_text(prompt)\n",
+    "print(f\"Question: {question_text}{response}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9503c73",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5faa6b69",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dedcfc9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10",
+   "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": "3.10.14"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}