Intro_to_RAG.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nathalyAlarconT/GenAI_Workshops/blob/main/Intro_to_RAG.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": "code",
      "source": [
        "# IP Asignada a Google Colab\n",
        "!curl ipecho.net/plain\n",
        "# https://ipinfo.io/IP"
      ],
      "metadata": {
        "id": "efmJLyPaaZEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instalando las librerías Requeridas"
      ],
      "metadata": {
        "id": "hdd6n79L6OG4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "70m9T35fHOq7"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade -q langchain\n",
        "!pip install google-generativeai langchain-google-genai\n",
        "!pip install chromadb pypdf2 python-dotenv\n",
        "!pip install PyPDF\n",
        "!pip install -U langchain-community\n",
        "!pip install sentence-transformers\n",
        "!pip install langchainhub"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Librerías Generales"
      ],
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Configurando credenciales"
      ],
      "metadata": {
        "id": "4XZPHVVJ6v4k"
      }
    },
    {
      "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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adiciona los archivos PDF a la carpeta Mis Datos\n",
        "\n",
        "El archivo utilizado en la demo es:\n",
        "https://www.lostiempos.com/sites/default/files/edicion_online/las_delicias_de_mi_llajta.pdf\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PYBdnn-nXLb0"
      }
    },
    {
      "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": null,
      "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": null,
      "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 data cargada\n",
        "len(data_on_pdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MAEzY2EO8qn",
        "outputId": "78d5afa2-e9a2-4b75-8e23-459fb2090ad6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "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": "479bf61b-8c29-4b2b-8930-899371dc004f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2\n",
        "embeddings_model = SentenceTransformerEmbeddings(model_name=\"all-MiniLM-L6-v2\")"
      ],
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          "height": 597,
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        },
        "id": "Oh5Z8PnWQi7w",
        "outputId": "60caa1ba-391a-44b5-ea9f-b7868eec9a4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 0.3.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
            "  warn_deprecated(\n",
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: 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": "caa7f7d6f7294908a7c94c908b0ce42a"
            }
          },
          "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": "fc958192f5824e9989a9d017026a9327"
            }
          },
          "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": "69736aa2da6340c19d06d6dd24ff8cd7"
            }
          },
          "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": "6c37a06444aa4f3e8f706a4b68138f8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "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": "6c1030ad9b0a4c9b8d38793a4a61a5be"
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          "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": "d1ca3a841fc147cd96cce93363e39774"
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          "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": "65eaa292ee9947a89f1b7df7ffbe14f3"
            }
          },
          "metadata": {}
        },
        {
          "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,
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          },
          "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": null,
      "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": null,
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Selección / Configuración del LLM"
      ],
      "metadata": {
        "id": "BfVN830KAblv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_google_genai import ChatGoogleGenerativeAI\n",
        "\n",
        "llm = ChatGoogleGenerativeAI(model=\"gemini-1.5-pro\", google_api_key=GOOGLE_API_KEY)"
      ],
      "metadata": {
        "id": "e3RpHSOvKJgS"
      },
      "execution_count": null,
      "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\")"
      ],
      "metadata": {
        "id": "PfAWLnX-RxiI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_docs(docs):\n",
        "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "rag_chain = (\n",
        "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
        "    | prompt\n",
        "    | llm\n",
        "    | StrOutputParser()\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "3H1CF505S7yA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejecución del RAG"
      ],
      "metadata": {
        "id": "PlMQfYdgBEgv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"Cuáles son los ingredientes del pique macho ?\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "vrZao12WTLxt",
        "outputId": "89be27cf-c3fb-4084-fec2-4571f2250fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Los ingredientes del pique macho son carne suave (lomo o pulpa), chorizos, papas imilla, tomates, locoto verde, cebolla colorada, cerveza, salsa de soya y pimienta negra. También lleva sal, pero esa es la única cantidad que no especifican. Este plato típico es fácil de preparar y rinde cuatro porciones. \n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"Qué recetas usan carne?\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "j9FhCZjcRIUg",
        "outputId": "963e37c5-129a-4492-ee87-512b142bbe98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "La receta de Silpancho y la receta de Pique Macho usan carne. El Silpancho usa carne de res, mientras que el Pique Macho usa carne de res y chorizo. Ambas recetas son platos tradicionales bolivianos. \n"
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Preguntas al Documento\n",
        "pregunta = \"Dame los ingredientes del CHICHARRÓN DE SURUBÍ en formato de tabla\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(pregunta )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "pgVowXV_RKGv",
        "outputId": "f1ac6881-4251-4d07-f092-6e933c8b7006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "| Ingrediente | Cantidad |\n|---|---|\n| Surubí | 1 kilo |\n| Zumo de limón | 6 cucharadas |\n| Sal | Al gusto |\n| Pimienta | Al gusto |\n| Harina | 1 taza |\n| Aceite | 2 tazas | \n"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cleanup\n",
        "# vectorstore.delete_collection()"
      ],
      "metadata": {
        "id": "UBtujO2XU9wo"
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    }
  ]
}