diff --git "a/spinoza_project/database_building/doc_parsing_and_vectorization.ipynb" "b/spinoza_project/database_building/doc_parsing_and_vectorization.ipynb"
new file mode 100644--- /dev/null
+++ "b/spinoza_project/database_building/doc_parsing_and_vectorization.ipynb"
@@ -0,0 +1,6678 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unzip data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "0zL_SR2WsqDW"
+   },
+   "outputs": [],
+   "source": [
+    "import zipfile\n",
+    "with zipfile.ZipFile('Data.zip', 'r') as zip_ref:\n",
+    "    zip_ref.extractall()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install all libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "collapsed": true,
+    "id": "3-hDvVTvEQil",
+    "outputId": "17d5e530-b544-4411-b4b1-abc7f1f98869"
+   },
+   "outputs": [],
+   "source": [
+    "%pip install -r /content/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "collapsed": true,
+    "id": "c2OafNrNuMdW",
+    "outputId": "83e8a6ad-5ba5-4474-c937-ac5e54d8f0eb"
+   },
+   "outputs": [],
+   "source": [
+    "%pip install -U langchain-community\n",
+    "%pip install openpyxl\n",
+    "%pip install pypdf\n",
+    "%pip install --upgrade 'urllib3==1.26.7'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "nnW8fUByzzSJ"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import pickle\n",
+    "from tqdm import tqdm\n",
+    "from langchain_community.document_loaders import PyPDFLoader\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "from langchain.vectorstores import Qdrant\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "5yHatyYzyhDr"
+   },
+   "outputs": [],
+   "source": [
+    "path_data = \"/content/Data/\"\n",
+    "path_raw =  \"0 - Raw\"\n",
+    "path_parsed = \"1 - Parsed\"\n",
+    "path_vectorized = \"2 - Vectorized\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parsing documents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 89
+    },
+    "id": "v1Ecbat_yaU0",
+    "outputId": "ee3567c1-1461-477c-810e-c4b99f1e711a"
+   },
+   "outputs": [],
+   "source": [
+    "# fichier Excel de métadonnées des documents chargé\n",
+    "df_metadata = pd.read_excel(os.path.join(path_data, path_raw, \"metadata_raw.xlsx\"), skiprows=1)\n",
+    "\n",
+    "df_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OfmfOHGvvl0C",
+    "outputId": "2a19fee6-2cfc-4d22-b527-741229b80f9b"
+   },
+   "outputs": [],
+   "source": [
+    "docs = []\n",
+    "for file_ in tqdm(os.listdir(os.path.join(path_data, path_raw))):\n",
+    "  if \".pdf\" in file_:\n",
+    "    # ici on utilise un loader pour charger le pdf, on peut aussi utiliser un loader pour charger des json, des fichiers texte, etc.\n",
+    "    loader = PyPDFLoader(\n",
+    "        file_path = os.path.join(path_data, path_raw, file_),\n",
+    "        extract_images = False,\n",
+    "    )\n",
+    "    # preprompt à ajouter au début de chaque document pour indiquer le type de contenu (lié aux embeddings utilisés)\n",
+    "    preprompt = \"passage: \"\n",
+    "    # ici on utilise un text splitter pour découper le texte en morceaux de taille fixe\n",
+    "    text_splitter = RecursiveCharacterTextSplitter(\n",
+    "            chunk_size=256, chunk_overlap=50, length_function=lambda x: len(x.split())\n",
+    "        )\n",
+    "    splitted_docs = loader.load_and_split(text_splitter)\n",
+    "\n",
+    "    df_metadata_file = df_metadata[df_metadata[\"file_name\"] == file_.replace(\".pdf\", \"\")]\n",
+    "\n",
+    "    for doc in splitted_docs:\n",
+    "      # on ajoute les métadonnées du fichier à chaque document, à adapter en fonction des métadonnées disponibles\n",
+    "      dict_metadata = {}\n",
+    "      dict_metadata[\"content_page_number\"] = 0\n",
+    "      dict_metadata[\"content_sub_type\"] = \"Text\"\n",
+    "      dict_metadata[\"file_name\"] = df_metadata_file[\"file_name\"].values[0]\n",
+    "      dict_metadata[\"file_source_type\"] = df_metadata_file[\"file_source_type\"].values[0]\n",
+    "      dict_metadata[\"file_source_sub_type\"] = \"Article\"\n",
+    "      dict_metadata[\"file_url\"] = \"\"\n",
+    "      dict_metadata[\"file_date_publishing\"] = df_metadata_file[\"file_source_type\"].values[0]\n",
+    "      dict_metadata[\"file_filtering_modality\"] = df_metadata_file[\"file_filtering_modality\"].values[0]\n",
+    "      dict_metadata[\"file_author\"] = \"\"\n",
+    "      dict_metadata[\"file_title\"] = df_metadata_file[\"file_title\"].values[0]\n",
+    "      dict_metadata[\"file_sub_title\"] = \"\"\n",
+    "      dict_metadata[\"file_summary\"] = \"\"\n",
+    "      dict_metadata[\"content_page_number\"] = doc.metadata[\"page\"]\n",
+    "      doc.metadata = dict_metadata\n",
+    "      doc.page_content = preprompt + doc.page_content\n",
+    "\n",
+    "    docs += splitted_docs\n",
+    "\n",
+    "  else:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uopfqlryvnAe",
+    "outputId": "c2cd10d3-66ff-4040-8247-94512088b39a"
+   },
+   "outputs": [],
+   "source": [
+    "print(len(docs))\n",
+    "print(docs[0].metadata)\n",
+    "print(docs[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Embeddings and vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "tYZxsDmq3do9"
+   },
+   "outputs": [],
+   "source": [
+    "def embed_and_dump(docs, name_vector, embeddings, path_vectorized):\n",
+    "    qdrant = Qdrant.from_documents(\n",
+    "        docs,\n",
+    "        embeddings,\n",
+    "        location=\":memory:\"\n",
+    "    )\n",
+    "    db = os.path.join(path_data, path_vectorized, f'database_{name_vector}.pickle')\n",
+    "    with open(db, 'wb') as f:\n",
+    "        pickle.dump(qdrant, f)\n",
+    "\n",
+    "    del qdrant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
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+    "outputId": "0b5c7be1-f716-4797-c3da-00e3f528ac86"
+   },
+   "outputs": [],
+   "source": [
+    "# ici on charge les embeddings via HuggingFace, on peut aussi utiliser d'autres embeddings\n",
+    "embed_model=embeddings = HuggingFaceEmbeddings(\n",
+    "    model_name=\"intfloat/multilingual-e5-base\"\n",
+    ")\n",
+    "\n",
+    "preprompt=\"passage: \""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6GJkkffsfkAn"
+   },
+   "outputs": [],
+   "source": [
+    "# on crée le vectorstore et on le sauvegarde sous format pickle\n",
+    "vector_name = \"Hackathon\"\n",
+    "embed_and_dump(docs, vector_name, embeddings, path_vectorized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quelques notes :\n",
+    "- Vu que le vectorstore est fait à partir de Qdrant \"in-memory\", on stocke les embeddings en local et par conséquent on aura un fichier d'au moins 1 Gb\n",
+    "- Si le volume de documents est important, la création du vecteur store peut prendre pas mal de temps et l'utilisation d'une GPU est fortement recommandé"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wTR71FwJgTT3"
+   },
+   "source": [
+    "# Testing the vectorstore"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "tXkO4v5VgYeU"
+   },
+   "outputs": [],
+   "source": [
+    "with open(os.path.join(path_data, path_vectorized, f'database_{vector_name}.pickle'), 'rb') as picklefile:\n",
+    "    vectorstore = pickle.load(picklefile)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qGL3B_s7hnV-",
+    "outputId": "c5b84a02-e549-4203-ddaf-0d793e85a6bb"
+   },
+   "outputs": [],
+   "source": [
+    "vectorstore.similarity_search_with_score(query=\"changement climatique ?\", k=5)"
+   ]
+  }
+ ],
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+  "colab": {
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