Intro_to_RAG_en.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_en.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup Notebook"
      ],
      "metadata": {
        "id": "eBpCroEr6JVq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# IP from Google Colab\n",
        "!curl ipecho.net/plain\n",
        "# https://ipinfo.io/IP"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efmJLyPaaZEK",
        "outputId": "2468ea61-f349-434b-cd30-07e65e2fee5b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34.145.60.126"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Libraries and Utilities installation"
      ],
      "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": [
        "### General Libraries"
      ],
      "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": [
        "### API Keys Configuration"
      ],
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Initial Folder Setup\n",
        "\n",
        "We will create two folders:\n",
        "\n",
        "1. MyData: This will store the additional files that we will use to expand the model's knowledge base.\n",
        "\n",
        "2. VectorDB: This folder will store the Vector database."
      ],
      "metadata": {
        "id": "_XtYfNeg641e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir /content/MyData\n",
        "!mkdir /content/VectorDB"
      ],
      "metadata": {
        "id": "VTGteWixWdgl"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the PDFs you want to use to customize the generated responses into the MyData folder.\n",
        "\n",
        "The file used on this demo is:\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": [
        "### Required Libraries"
      ],
      "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": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Data Source Folder\n",
        "source_data_folder = \"/content/MyData\" # @param {type:\"string\"}\n"
      ],
      "metadata": {
        "id": "JVamUHaSPhYA"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparation"
      ],
      "metadata": {
        "id": "_51IqCQD8tDi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Read PDFs from the configured folder\n",
        "loader = PyPDFDirectoryLoader(source_data_folder)\n",
        "data_on_pdf = loader.load()\n",
        "# Size of the data / documents loaded\n",
        "len(data_on_pdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MAEzY2EO8qn",
        "outputId": "99c28ed2-f643-4aed-bc28-3125dcd06870"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Partitioning the data. With a limited size (chunks) and 200 characters of overlapping to preserve the context\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",
        "# Number of Chunks generated\n",
        "len(splits)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z46WGUrmP56M",
        "outputId": "28245eb1-b906-4f9d-9f77-2a349ea65815"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "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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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603,
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        },
        "id": "Oh5Z8PnWQi7w",
        "outputId": "7871b85d-0452-41f1-93ed-2cc5e1529caf"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:139: 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": "615d31d18a2043a7884f58d8b2db485b"
            }
          },
          "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": "01fe4a86eda84dc1891eb5ded42f4843"
            }
          },
          "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": "dee7cbfeae6a46ee93522530b0d872aa"
            }
          },
          "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": "9d99e22cae604bba977e1041affdf3b6"
            }
          },
          "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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              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d9edcaec88b41679e3244619e8bb962"
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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,
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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": {
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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": "4ac1c70b7bed44b7ac1094e11d4748b9"
            }
          },
          "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,
              "version_minor": 0,
              "model_id": "e5ebb3a00c9c4d3185e0fefa21bcba7c"
            }
          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Base"
      ],
      "metadata": {
        "id": "N7zT2cT6ABGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Database folder path\n",
        "path_db = \"/content/VectorDB\" # @param {type:\"string\"}\n"
      ],
      "metadata": {
        "id": "foSxJAayRKSL"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Store the chunks in the Data Base\n",
        "vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings_model, persist_directory=path_db)"
      ],
      "metadata": {
        "id": "ZyzgToxGQJgP"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. RETRIEVAL"
      ],
      "metadata": {
        "id": "3AIv7ga7O5vr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Required Libraries"
      ],
      "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": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LLM configuration\n",
        "\n",
        "We will use Gemini"
      ],
      "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": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prompt and Retriever\n",
        "\n"
      ],
      "metadata": {
        "id": "jLDhrOuOAny0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectorstore.as_retriever()\n",
        "\n",
        "# https://smith.langchain.com/hub/rlm/rag-prompt\n",
        "prompt = hub.pull(\"rlm/rag-prompt\")"
      ],
      "metadata": {
        "id": "PfAWLnX-RxiI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def format_docs(docs):\n",
        "    \"\"\"Format the documents for the prompt.\"\"\"\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": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RAG Execution"
      ],
      "metadata": {
        "id": "PlMQfYdgBEgv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Questions to the document\n",
        "question = \"What recipes exist on the document?\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(question )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "vrZao12WTLxt",
        "outputId": "7fda954d-cbf3-46d0-a12a-b11533ad70ae"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "The document contains recipes for Silpancho, Pique Macho, and Chicharrón de Surubí. Each recipe includes a list of ingredients, preparation instructions, and information about the chef who provided the recipe. The document seems to be about Bolivian cuisine. \n"
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Questions to the document\n",
        "question = \"How to prepare a Silpancho? \" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(question )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 64
        },
        "id": "j9FhCZjcRIUg",
        "outputId": "4f091e4f-90ad-4e82-bd3b-f39355da97d0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "To prepare Silpancho, season a steak with salt, pepper, and lemon zest, then flatten it. After that, coat the steak with breadcrumbs and pan-fry it in oil. Finally, serve the steak with rice, potatoes, and a salad made of tomato, onion, and locoto. \n"
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Questions to the document\n",
        "question = \"Give me the ingredients of the Chicharrón de Surubí on table format\" # @param {type:\"string\"}\n",
        "response = rag_chain.invoke(question )\n",
        "Markdown(response)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "pgVowXV_RKGv",
        "outputId": "a5908c6f-5544-4fdb-c820-86a7ca3bee67",
        "cellView": "form"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "| Ingredient | Quantity |\n|---|---|\n| Surubí (fish) | 1 kilo |\n| Lemon juice | 6 tablespoons |\n| Salt | To taste |\n| Pepper | To taste |\n| Flour (for dredging) | 1 cup |\n| Oil (for frying) | 2 cups | \n"
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cleanup\n",
        "# vectorstore.delete_collection()"
      ],
      "metadata": {
        "id": "UBtujO2XU9wo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Sources:**"
      ],
      "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": []
    }
  ]
}