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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Personal Assistant\n",
+        "A Personal Assistant leveraging Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM). This tool is designed to revolutionize PDF document analysis tasks by combining machine learning with retrieval-based systems."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Origin of the RAG Architecture"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Retrieval-Augmented Generation (RAG) is a powerful technique in natural language processing (NLP) that combines retrieval-based methods with generative models to produce more accurate and contextually relevant outputs. This approach was introduced in the 2020 paper \"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks\" by Facebook AI Research (FAIR)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "For further reading and a deeper understanding of RAG, refer to the original paper by Facebook AI Research: [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401). "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Overview of the RAG Architecture\n",
+        "\n",
+        "The RAG model consists of two main components:\n",
+        "1. **Retriever**: This component retrieves relevant documents from a large corpus based on the input query.\n",
+        "2. **Generator**: This component generates responses conditioned on the retrieved documents.\n",
+        "\n",
+        "## Mathematical Formulation\n",
+        "\n",
+        "### Retriever\n",
+        "\n",
+        "The retriever selects the top $k$ documents from a corpus $\\mathcal{D}$ based on their relevance to the input query $q$.\n",
+        "\n",
+        "### Generator\n",
+        "\n",
+        "The generator produces a response $r$ based on the input query $q$ and the retrieved documents $\\{d_1, d_2, \\ldots, d_k\\}$.\n",
+        "\n",
+        "### Combining Retriever and Generator\n",
+        "\n",
+        "The final probability of generating a response $r$ given the query $q$ is obtained by marginalizing over the top $k$ retrieved documents:\n",
+        "\n",
+        "$$\n",
+        "P(r \\mid q) = \\sum_{i=1}^{k} P(d_i \\mid q) P(r \\mid q, d_i)\n",
+        "$$\n",
+        "\n",
+        "Here, $P(d_i \\mid q)$ is the normalized relevance score of document $d_i$ given the query $q$, and $P(r \\mid q, d_i)$ is the probability of generating response $r$ given the query $q$ and document $d_i$.\n",
+        "\n",
+        "## Implementation Details\n",
+        "\n",
+        "### Training\n",
+        "\n",
+        "The RAG model is trained in two stages:\n",
+        "1. **Retriever Training**: The retriever is trained to maximize the relevance score $s(q, d_i)$ for relevant documents.\n",
+        "2. **Generator Training**: The generator is trained to maximize the probability $P(r \\mid q, d_i)$ for the ground-truth responses.\n",
+        "\n",
+        "### Inference\n",
+        "\n",
+        "During inference, the RAG model retrieves the top $k$ documents for a given query and generates a response conditioned on these documents. The final response is obtained by marginalizing over the retrieved documents as described above.\n",
+        "\n",
+        "## Conclusion\n",
+        "\n",
+        "RAG leverages the strengths of both retrieval-based and generation-based models to produce more accurate and informative responses. By conditioning the generation on retrieved documents, RAG can incorporate external knowledge from large corpora, leading to better performance on various tasks.\n",
+        "\n",
+        "The combination of retriever and generator in the RAG model makes it a powerful approach for tasks that require access to external knowledge and the ability to generate coherent and contextually appropriate responses."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Install Conda Environment\n",
+        "1. To select a Conda environment in Visual Studio Code, press the play button in the next cell which will open up a command prompt then select `Python Environments...`\n",
+        "2. A new command prompt will pop up and select `+ Create Python Environment`.\n",
+        "3. A new command prompt will again pop up and select `Conda Creates a .conda Conda environment in the current workspace`.\n",
+        "4. A new command prompt will again pop up and select `* Python 3.11`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "!conda create -n pa python=3.11 -y"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### !!! ACTION ITEM !!!\n",
+        "In order for the Conda environment to be available, you need to close down VSCode and reload it and select `rea` in the Kernel area in the top-right of VSCode.\n",
+        "1. In the VSCode pop-up command window select `Select Another Kernel...`.\n",
+        "2. In the next command window select `Python Environments...`.\n",
+        "3. In the next command window select `pa (Python 3.11.9)`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DfRE18X_mqvI"
+      },
+      "source": [
+        "### Install Packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qfBKaUU2mqvK",
+        "outputId": "b1f5db4e-9d91-4f9f-e2ba-15e75edb0caa"
+      },
+      "outputs": [],
+      "source": [
+        "!conda install -n pa \\\n",
+        "    pytorch \\\n",
+        "    torchvision \\\n",
+        "    torchaudio \\\n",
+        "    cpuonly \\\n",
+        "    -c pytorch \\\n",
+        "    -c conda-forge \\\n",
+        "    --yes\n",
+        "%pip install -U ipywidgets\n",
+        "%pip install -U requests\n",
+        "%pip install -U llama-index\n",
+        "%pip install -U llama-index-embeddings-huggingface\n",
+        "%pip install -U llama-index-llms-groq\n",
+        "%pip install -U groq\n",
+        "%pip install -U gradio"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Install Tesseract"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import platform\n",
+        "import subprocess\n",
+        "import requests\n",
+        "\n",
+        "\n",
+        "def install_tesseract():\n",
+        "    \"\"\"\n",
+        "    Installs Tesseract OCR based on the operating system.\n",
+        "    \"\"\"\n",
+        "    os_name = platform.system()\n",
+        "    if os_name == \"Linux\":\n",
+        "        print(\"Detected Linux. Installing Tesseract using apt-get...\")\n",
+        "        subprocess.run([\"sudo\", \"apt-get\", \"update\"], check=True)\n",
+        "        subprocess.run([\"sudo\", \"apt-get\", \"install\", \"-y\", \"tesseract-ocr\"], check=True)\n",
+        "    elif os_name == \"Darwin\":\n",
+        "        print(\"Detected macOS. Installing Tesseract using Homebrew...\")\n",
+        "        subprocess.run([\"brew\", \"install\", \"tesseract\"], check=True)\n",
+        "    elif os_name == \"Windows\":\n",
+        "        tesseract_installer_url = \"https://github.com/UB-Mannheim/tesseract/releases/download/v5.4.0.20240606/tesseract-ocr-w64-setup-5.4.0.20240606.exe\"\n",
+        "        installer_path = \"tesseract-ocr-w64-setup-5.4.0.20240606.exe\"\n",
+        "        response = requests.get(tesseract_installer_url)\n",
+        "        with open(installer_path, \"wb\") as file:\n",
+        "            file.write(response.content)\n",
+        "        tesseract_path = r\"C:\\Program Files\\Tesseract-OCR\"\n",
+        "        os.environ[\"PATH\"] += os.pathsep + tesseract_path\n",
+        "        try:\n",
+        "            result = subprocess.run([\"tesseract\", \"--version\"], check=True, capture_output=True, text=True)\n",
+        "            print(result.stdout)\n",
+        "        except subprocess.CalledProcessError as e:\n",
+        "            print(f\"Error running Tesseract: {e}\")\n",
+        "    else:\n",
+        "        print(f\"Unsupported OS: {os_name}\")\n",
+        "\n",
+        "\n",
+        "install_tesseract()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Convert PDF to OCR"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import webbrowser\n",
+        "\n",
+        "url = \"https://www.ilovepdf.com/ocr-pdf\"\n",
+        "webbrowser.open_new(url)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dO7FZrPBmqvK"
+      },
+      "source": [
+        "### Import Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "WDBGD1-EmqvK"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "from llama_index.core import (\n",
+        "    Settings,\n",
+        "    VectorStoreIndex,\n",
+        "    SimpleDirectoryReader,\n",
+        "    StorageContext,\n",
+        "    load_index_from_storage\n",
+        ")\n",
+        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
+        "from llama_index.core.node_parser import SentenceSplitter\n",
+        "from llama_index.llms.groq import Groq\n",
+        "import gradio as gr"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### !!! ACTION ITEM !!!\n",
+        "Visit https://console.groq.com/keys and set up an API Key then replace `<GROQ_API_KEY>` below with the newly generated key."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "os.environ[\"GROQ_API_KEY\"] = \"<GROQ_API_KEY>\"\n",
+        "GROQ_API_KEY = os.getenv(\"GROQ_API_KEY\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Disable Tokenizer Parallelism Globally"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7nsQejq6mqvL"
+      },
+      "source": [
+        "### Data Ingestion"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "DLZlByiMmqvL"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Successfully loaded 2 pages(s).\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def load_single_pdf_from_directory(directory_path):\n",
+        "    \"\"\"\n",
+        "    Load a single PDF file from the specified directory.\n",
+        "\n",
+        "    Args:\n",
+        "        directory_path (str): The path to the directory containing the PDF files.\n",
+        "\n",
+        "    Returns:\n",
+        "        documents (list): The loaded documents if exactly one PDF is found.\n",
+        "    \"\"\"\n",
+        "    pdf_files = [file for file in os.listdir(directory_path) if file.lower().endswith(\".pdf\")]\n",
+        "    if len(pdf_files) != 1:\n",
+        "        print(\"Error: There must be exactly one PDF file in the directory.\")\n",
+        "        return None\n",
+        "    file_path = os.path.join(directory_path, pdf_files[0])\n",
+        "    reader = SimpleDirectoryReader(input_files=[file_path])\n",
+        "    documents = reader.load_data()\n",
+        "    return documents\n",
+        "\n",
+        "\n",
+        "directory_path = \"files\"\n",
+        "documents = load_single_pdf_from_directory(directory_path)\n",
+        "if documents is not None:\n",
+        "    print(f\"Successfully loaded {len(documents)} pages(s).\")\n",
+        "else:\n",
+        "    print(\"ABORTING NOTEBOOK!\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kK3fmpYxmqvM"
+      },
+      "source": [
+        "### Chunking"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "db7e1834b8b642f8827fe201729b22d3",
+            "d20f55b4c6ca49d082d57d80c08a41a4",
+            "b716d1162418484593dc4d82606bc02c",
+            "3e6fe7b74860450995a922147b407e88",
+            "d02f365d3b3744b8b6e887792d031cba",
+            "7fc5d81d00ac482692c366bf3d91acf3",
+            "3fe00422cb0346af81545c075ef3e1cd",
+            "f41af0074acb4ffebc07c070ddd3866b",
+            "2bc5cbf617cf406888bfd5088276a575",
+            "d4f48ee8a6694f48a96626d461807e4d",
+            "7c24b18a5c1a4b0587285df0a3e1b8e1"
+          ]
+        },
+        "id": "dNksqlkTmqvM",
+        "outputId": "6395bb92-e831-49c7-b1fc-d9555815142b"
+      },
+      "outputs": [],
+      "source": [
+        "text_splitter = SentenceSplitter(chunk_size=2048, chunk_overlap=200)\n",
+        "nodes = text_splitter.get_nodes_from_documents(documents)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gtS3uKzemqvN"
+      },
+      "source": [
+        "### Embedding Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 369,
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+            "682644d739524a35be42202b4626e791"
+          ]
+        },
+        "id": "jEwRVbmumqvN",
+        "outputId": "eb7b083a-a607-40cc-d7e1-88514907f24c"
+      },
+      "outputs": [],
+      "source": [
+        "embed_model = HuggingFaceEmbedding(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WT-XDReFmqvN"
+      },
+      "source": [
+        "### Define LLM Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "bNR7HSSvmqvN"
+      },
+      "outputs": [],
+      "source": [
+        "llm = Groq(model=\"llama-3.1-8b-instant\", api_key=GROQ_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XpccqQs6mqvN"
+      },
+      "source": [
+        "### Configure Service Context"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "sikMtIffmqvN"
+      },
+      "outputs": [],
+      "source": [
+        "Settings.embed_model = embed_model\n",
+        "Settings.llm = llm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lqFfhVIbmqvN"
+      },
+      "source": [
+        "### Create Vector Store Index"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
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+        "id": "UxM18VGVmqvO",
+        "outputId": "b223375d-3b73-40a0-8d6a-1bf92dd39427"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "VectorStoreIndex initialization\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "8dca06ebfb474b8ea10ab10bc9870f7d",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Parsing nodes:   0%|          | 0/2 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "366370cd49cb40cd961853902aee2c6f",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Generating embeddings:   0%|          | 0/10 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Debug VectorStoreIndex\n",
+        "print(\"VectorStoreIndex initialization\")\n",
+        "vector_index = VectorStoreIndex.from_documents(\n",
+        "    documents, \n",
+        "    show_progress=True, \n",
+        "    node_parser=nodes\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "irKTccwAmqvO"
+      },
+      "source": [
+        "#### Persist/Save Index"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "4bGXXhWFmqvO"
+      },
+      "outputs": [],
+      "source": [
+        "vector_index.storage_context.persist(persist_dir=\"./storage_mini\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-bjvO0j2mqvO"
+      },
+      "source": [
+        "#### Define Storage Context"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "F4-ZEvtBmqvO"
+      },
+      "outputs": [],
+      "source": [
+        "storage_context = StorageContext.from_defaults(persist_dir=\"./storage_mini\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cncnv6tcmqvO"
+      },
+      "source": [
+        "#### Load Index"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "mxEw8WU2mqvO"
+      },
+      "outputs": [],
+      "source": [
+        "index = load_index_from_storage(storage_context)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7CtxxbIqmqvO"
+      },
+      "source": [
+        "### Define Query Engine"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "z_SHXU4QmqvO"
+      },
+      "outputs": [],
+      "source": [
+        "query_engine = index.as_query_engine()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h8li26pcmqvP"
+      },
+      "source": [
+        "#### Feed in user query"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running on local URL:  http://127.0.0.1:7860\n",
+            "\n",
+            "To create a public link, set `share=True` in `launch()`.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><iframe src=\"http://127.0.0.1:7860/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": []
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "def query_function(query):\n",
+        "    \"\"\"\n",
+        "    Processes a query using the query engine and returns the response.\n",
+        "\n",
+        "    Args:\n",
+        "        query (str): The query string to be processed by the query engine.\n",
+        "\n",
+        "    Returns:\n",
+        "        str: The response generated by the query engine based on the input query.\n",
+        "        \n",
+        "    Example:\n",
+        "        >>> query_function(\"What is Reverse Engineering?\")\n",
+        "        'Reverse engineering is the process of deconstructing an object to understand its design, architecture, and functionality.'\n",
+        "    \"\"\"\n",
+        "    response = query_engine.query(query)\n",
+        "    return response\n",
+        "\n",
+        "\n",
+        "iface = gr.Interface(\n",
+        "    fn=query_function,\n",
+        "    inputs=gr.Textbox(label=\"Query\"),\n",
+        "    outputs=gr.Textbox(label=\"Response\")\n",
+        ")\n",
+        "\n",
+        "iface.launch()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
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+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
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+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
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