Intro_to_RAG_Gemma_2.ipynb

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  "cells": [
    {
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nathalyAlarconT/GenAI_Workshops/blob/main/Intro_to_RAG_Gemma_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup del Notebook"
      ],
      "metadata": {
        "id": "eBpCroEr6JVq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instalando las librerías Requeridas"
      ],
      "metadata": {
        "id": "hdd6n79L6OG4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "colab": {
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        },
        "outputId": "f694b608-f86f-482b-9269-8aba70355448"
      },
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m273.8/273.8 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.6/94.6 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m70.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.2/93.2 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.2/13.2 MB\u001b[0m \u001b[31m103.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.0/64.0 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m52.5/52.5 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m13.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.4/341.4 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m96.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m425.7/425.7 kB\u001b[0m \u001b[31m34.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m164.1/164.1 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for durationpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m292.8/292.8 kB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.3/49.3 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m245.3/245.3 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade -q langchain --quiet\n",
        "!pip install google-generativeai langchain-google-genai --quiet\n",
        "!pip install chromadb pypdf2 python-dotenv --quiet\n",
        "!pip install PyPDF --quiet\n",
        "!pip install -U langchain-community --quiet\n",
        "!pip install sentence-transformers --quiet\n",
        "!pip install langchainhub --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade keras-nlp\n",
        "!pip install -q --upgrade keras>=3\n",
        "!pip install -q langchain langchain-google-vertexai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DeIt8DMHVyp",
        "outputId": "54f54160-6833-466d-d0f3-543e31ac0e86"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/548.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m548.4/548.4 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m96.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.9/86.9 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.5/130.5 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importando librerías"
      ],
      "metadata": {
        "id": "Vt4wMCO06lez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "import os\n",
        "from IPython.display import Markdown"
      ],
      "metadata": {
        "id": "n9aR9wbcJajt"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si la siguiente celda genera un error, reiniciar el notebook"
      ],
      "metadata": {
        "id": "absInLjcP2iP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Gemma using LangChain library\n",
        "from langchain_google_vertexai import GemmaChatLocalKaggle\n",
        "# If Error on this command, restart the session and run again."
      ],
      "metadata": {
        "id": "5sTLMXZAHmW0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configurando credenciales"
      ],
      "metadata": {
        "id": "4XZPHVVJ6v4k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si quieres poder loggear y visualizar los prompts, puedes configurar tu API Key de LangSmith/LangChain"
      ],
      "metadata": {
        "id": "hkd5mlyXTKiD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
        "os.environ[\"LANGCHAIN_API_KEY\"] = userdata.get('SMITH_APIKEY')\n",
        "GOOGLE_API_KEY = userdata.get('GoogleAIStudio')"
      ],
      "metadata": {
        "id": "U6DoVr1AIi5b"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos acceder a Gemma 2, desde Kaggle, Hugging Face ó Vertex AI. En este caso usaremos Kaggle.\n",
        "\n",
        "Recuerda que previamente debes aceptar los términos y condiciones en: https://www.kaggle.com/models/google/gemma-2\n",
        "\n",
        "Para autenticarnos a Kaggle desde el notebook, puedes descargar tus credenciales (KAGGLE_USERNAME, KAGGLE_KEY) en: https://www.kaggle.com/settings desde la sección API Create New Token"
      ],
      "metadata": {
        "id": "REJnTZT6QuIt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "# Note: `userdata.get` is a Colab API. If you're not using Colab, set the env\n",
        "# vars as appropriate for your system.\n",
        "os.environ[\"KAGGLE_USERNAME\"] = userdata.get(\"KAGGLE_USERNAME\")\n",
        "os.environ[\"KAGGLE_KEY\"] = userdata.get(\"KAGGLE_KEY\")\n",
        "os.environ[\"KERAS_BACKEND\"] = \"jax\"  # \"jax\" Or \"tensorflow\" or \"torch\".\n",
        "os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"] = \"0.9\""
      ],
      "metadata": {
        "id": "zuUMcuQ9R5oO"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creación de Folders necesarios\n",
        "\n",
        "Usaremos dos carpetas:\n",
        "- Mis Datos, almacenara los PDFs con la info propia\n",
        "- VectorDB, en esta carpeta se creará la base de datos vectorial"
      ],
      "metadata": {
        "id": "_XtYfNeg641e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/MisDatos\n",
        "!mkdir /content/VectorDB"
      ],
      "metadata": {
        "id": "VTGteWixWdgl"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adiciona los archivos PDF a la carpeta Mis Datos\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PYBdnn-nXLb0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Adicionando PDFs"
      ],
      "metadata": {
        "id": "JvlhP5SEdSGV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora descargaremos algunos PDFs a la carpeta MisDatos"
      ],
      "metadata": {
        "id": "tM-AT26kBgPp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -P MisDatos https://sdgs.un.org/sites/default/files/2020-09/SDG%20Resource%20Document_Targets%20Overview.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBqeIgdVZRiu",
        "outputId": "118528f9-2271-474d-a7bd-8ffd535e3065"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-27 19:02:45--  https://sdgs.un.org/sites/default/files/2020-09/SDG%20Resource%20Document_Targets%20Overview.pdf\n",
            "Resolving sdgs.un.org (sdgs.un.org)... 34.238.11.122\n",
            "Connecting to sdgs.un.org (sdgs.un.org)|34.238.11.122|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 423899 (414K) [application/pdf]\n",
            "Saving to: ‘MisDatos/SDG Resource Document_Targets Overview.pdf’\n",
            "\n",
            "SDG Resource Docume 100%[===================>] 413.96K   374KB/s    in 1.1s    \n",
            "\n",
            "2024-09-27 19:02:47 (374 KB/s) - ‘MisDatos/SDG Resource Document_Targets Overview.pdf’ saved [423899/423899]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget -P MisDatos https://www.lostiempos.com/sites/default/files/edicion_online/las_delicias_de_mi_llajta.pdf"
      ],
      "metadata": {
        "id": "goFVvhMUVPu-"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget -P MisDatos https://www.volkswagen.com.ar/idhub/content/dam/onehub_pkw/importers/ar/tengo-un-volkswagen/manuales/manuales/2020/Manual%20T-Cross%202020.pdf"
      ],
      "metadata": {
        "id": "VHdTPj3CBjPZ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "También puedes adicionar manualmente archivos a la carpeta Mis Datos"
      ],
      "metadata": {
        "id": "i-Ce_cwXVbLP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. INDEXING"
      ],
      "metadata": {
        "id": "s_sn_QWZOf1i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Librerías necesarias"
      ],
      "metadata": {
        "id": "SMmDGkvr8f41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.document_loaders import PyPDFDirectoryLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.embeddings import SentenceTransformerEmbeddings"
      ],
      "metadata": {
        "id": "ogQ3AqKFOe0V"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Configura el origen de los datos\n",
        "source_data_folder = \"/content/MisDatos\" # @param {type:\"string\"}\n"
      ],
      "metadata": {
        "id": "JVamUHaSPhYA"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preparando y formateando el contenido necesario"
      ],
      "metadata": {
        "id": "_51IqCQD8tDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Leyendo los PDFs del directorio configurado\n",
        "loader = PyPDFDirectoryLoader(source_data_folder)\n",
        "data_on_pdf = loader.load()\n",
        "# cantidad de páginas cargadas\n",
        "len(data_on_pdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MAEzY2EO8qn",
        "outputId": "21cda16c-5a8e-4ccb-8cdf-8352ab4d7513"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Particionando los datos. Con un tamaño delimitado (chunks) y 200 caracters de overlapping para preservar el contexto\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    separators=[\"\\n\\n\", \"\\n\", \". \", \" \", \"\"],\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=200\n",
        ")\n",
        "splits = text_splitter.split_documents(data_on_pdf)\n",
        "# Cantidad de chunks obtenidos\n",
        "len(splits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z46WGUrmP56M",
        "outputId": "702db633-3cc0-4b16-b6ed-6ed452a6c917"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2\n",
        "embeddings_model = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
      ],
      "metadata": {
        "id": "Oh5Z8PnWQi7w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597,
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          ]
        },
        "outputId": "2b241d60-833f-4cba-90be-22883b317ac2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-1d3747edcb09>:2: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFaceEmbeddings``.\n",
            "  embeddings_model = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c67e20519feb40f1a65da2c92b596bcf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "298a9fd6bd62407c96ae3349a727f62b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d208b73f39c34636b9571af56ef189fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e840b6fed0924505a982799d73e764c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02269cef7ebf4baba2ede1bf2f35c8e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39614d41b6a14c3a9dfd7133e6fcc70d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5323833ae6e498d9bef6b79883e7f5d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b8112bc619c45489d0d5e06345d6dbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b2caf0caa2d44c3afa74a2927f2aa48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6bdd1cc763e40b081d994ecfdce782c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24d443c020b04b8a92e56ccf9b54be93"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Base de datos vectorial"
      ],
      "metadata": {
        "id": "N7zT2cT6ABGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Configura el path a la base de datos\n",
        "path_db = \"/content/VectorDB\" # @param {type:\"string\"}\n"
      ],
      "metadata": {
        "id": "foSxJAayRKSL"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Almacenamos los chunks en la base de datos\n",
        "vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings_model, persist_directory=path_db)"
      ],
      "metadata": {
        "id": "ZyzgToxGQJgP"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. RETRIEVAL"
      ],
      "metadata": {
        "id": "3AIv7ga7O5vr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Librerías necesarias"
      ],
      "metadata": {
        "id": "Xk06yy5EAXVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import hub\n",
        "from langchain_core.output_parsers import StrOutputParser\n",
        "from langchain_core.runnables import RunnablePassthrough"
      ],
      "metadata": {
        "id": "iw9FfxuNSmvy"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Selección / Configuración del LLM"
      ],
      "metadata": {
        "id": "BfVN830KAblv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Gemma"
      ],
      "metadata": {
        "id": "3Mit2___PWLY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cargamos Gemma utilizando Langchain\n",
        "\n",
        "keras_backend: str = \"jax\"\n",
        "model_name = \"gemma2_instruct_2b_en\"\n",
        "llm = GemmaChatLocalKaggle(\n",
        "    model_name=model_name,\n",
        "    model=model_name,\n",
        "    keras_backend=keras_backend,\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "4F74zrPL-H3g"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Helpers\n",
        "from langchain_core.output_parsers import BaseTransformOutputParser\n",
        "class GemmaOutputParser(BaseTransformOutputParser[str]):\n",
        "    \"\"\"OutputParser that parses LLM response and extract\n",
        "    the generated part.\"\"\"\n",
        "\n",
        "    @classmethod\n",
        "    def is_lc_serializable(cls) -> bool:\n",
        "        \"\"\"Return whether this class is serializable.\"\"\"\n",
        "        return True\n",
        "\n",
        "    @property\n",
        "    def _type(self) -> str:\n",
        "        \"\"\"Return the output parser type for serialization.\"\"\"\n",
        "        return \"gemma_2_parser\"\n",
        "\n",
        "    def parse(self, text: str) -> str:\n",
        "        \"\"\"Return the input text with no changes.\"\"\"\n",
        "        model_start_token = \"model\\n\"\n",
        "        idx = text.rfind(model_start_token)\n",
        "        return text[idx + len(model_start_token) :] if idx > -1 else \"\"\n",
        "\n",
        "\n",
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)"
      ],
      "metadata": {
        "id": "UpmqqR-j9_VC"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Configuración del Prompt y Retriever\n",
        "\n"
      ],
      "metadata": {
        "id": "jLDhrOuOAny0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectorstore.as_retriever()\n",
        "\n",
        "# https://smith.langchain.com/hub/rlm/rag-prompt?organizationId=6467f92b-9dac-5816-964f-8abcfa4e4456\n",
        "prompt = hub.pull(\"rlm/rag-prompt\")\n",
        "print(f\"Prompt:\\n\\n{prompt.messages[0].prompt.template}\")"
      ],
      "metadata": {
        "id": "PfAWLnX-RxiI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba27f3db-8441-4bca-ad31-4a9b1ba4337f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prompt:\n",
            "\n",
            "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\n",
            "Question: {question} \n",
            "Context: {context} \n",
            "Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rag_chain = (\n",
        "    # Recuperar documentos relevantes a la pregunta\n",
        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "    # El resultado llenará la variable `{question}`\n",
        "    # y `{context}`\n",
        "    | prompt\n",
        "    # El prompt completo se enviará al LLM\n",
        "    | llm\n",
        "    # La respuesta del modelo será parseada por GemmaOutputParser\n",
        "    | GemmaOutputParser()\n",
        ")"
      ],
      "metadata": {
        "id": "7hr9Uop5EaQl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejecución del RAG"
      ],
      "metadata": {
        "id": "PlMQfYdgBEgv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"what is the document about?\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)\n",
        "\n"
      ],
      "metadata": {
        "id": "vrZao12WTLxt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "outputId": "a4201f78-88c2-4e81-96dd-034ebd9f1f4f"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "This document outlines the Sustainable Development Goals (SDGs) and their targets, focusing on issues related to poverty reduction, gender equality, and sustainable development. It highlights goals for economic growth, access to resources, and the empowerment of women and girls. \n<end_of_turn>"
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"What the document says about macroeconomic stability ? \" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)\n",
        "\n"
      ],
      "metadata": {
        "id": "j9FhCZjcRIUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "dc249147-3b07-427d-9374-73ecff696ede"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The document states that Goal 13 aims to enhance global macroeconomic stability, including through policy coordination and policy coherence.  This goal recognizes the importance of stable economic conditions for achieving other development goals.  The document emphasizes the need for coordinated efforts to achieve macroeconomic stability. \n<end_of_turn>"
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"What goals are related to education?\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "dogHm8x4GViT",
        "outputId": "f87c197c-4680-4dbd-ab9b-1f00bb090f9c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The goals related to education are to ensure inclusive and equitable quality education for all, including access to early childhood development, technical and vocational education, and lifelong learning opportunities.  These goals aim to provide quality education for all, regardless of gender, disability, or socioeconomic status.  They also focus on increasing access to education for youth and adults, promoting literacy and numeracy, and ensuring equal opportunities for all. \n<end_of_turn>"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cleanup\n",
        "# vectorstore.delete_collection()"
      ],
      "metadata": {
        "id": "UBtujO2XU9wo"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fuentes:**"
      ],
      "metadata": {
        "id": "yF9tJOT4TYV7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# https://dev.to/timesurgelabs/how-to-use-googles-gemini-pro-with-langchain-1eje\n",
        "\n",
        "# https://python.langchain.com/v0.2/docs/tutorials/rag/\n",
        "\n",
        "# https://smith.langchain.com/o/6467f92b-9dac-5816-964f-8abcfa4e4456/projects/p/d35fb5ce-7bac-4627-858c-621aa689239f?timeModel=%7B%22duration%22%3A%227d%22%7D"
      ],
      "metadata": {
        "id": "EJR2bsISNVkZ"
      },
      "execution_count": 27,
      "outputs": []
    }
  ]
}