diff --git "a/Build_VecStore.ipynb" "b/Build_VecStore.ipynb" new file mode 100644--- /dev/null +++ "b/Build_VecStore.ipynb" @@ -0,0 +1,6661 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "QS0v2bceN4Or" + }, + "source": [ + "Builds a database of vector embeddings from list of abstracts" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "l5RwcIG8OAjX" + }, + "source": [ + "## Some Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sfwT5YW2JCnu", + "outputId": "bf3a4179-f81c-44ca-b2d6-447dda960c80" + }, + "outputs": [], + "source": [ + "!pip install transformers==4.28.0\n", + "!pip install -U sentence-transformers\n", + "!pip install datasets\n", + "!pip install langchain\n", + "!pip install torch\n", + "!pip install faiss-cpu" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "psoTvOp4VkBE" + }, + "outputs": [], + "source": [ + "import os\n", + "import shutil\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from tqdm.auto import tqdm\n", + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "arZiN8QRHS_a" + }, + "outputs": [], + "source": [ + "import locale\n", + "locale.getpreferredencoding = lambda: \"UTF-8\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 209, + "referenced_widgets": [ + "4756793562464107802ca63258cdd741", + "e64b509fcaa140dca388a942581fa88b", + "9a78d50cb9f64f858fb2f78fd1235967", + "e2f1551939d64a96a727ec6a83011b14", + "01631fd683614bba8fc2e1cbb852a903", + "0c6c75568b8f4375ba3cc33f0bbdf18a", + "32e19b6cf34d46dbb502a90396834c5e", + "467335b5d972451c858e81a6b25edd11", + "8ab268be33ba435f983cddb2d837a777", + "1b42f23411214c7e97e57a2ea3b7141b", + 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"3ecf941bc53a4a4bb8b897590705486f", + "97630ae2964449e48868175ac1de6aac", + "8e19141b16f44aec831b7f6c35ab0cea", + "2a3efb6398594f989bfdebf495f60ffb" + ] + }, + "id": "JwWs0-Uu6ohg", + "outputId": "254e0e81-5b3e-492d-bd5d-5f6cabb6df81" + }, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, BertForSequenceClassification\n", + "\n", + "m_tokenizer = AutoTokenizer.from_pretrained(\"biodatlab/MIReAD-Neuro\")\n", + "m_model = BertForSequenceClassification.from_pretrained(\"biodatlab/MIReAD-Neuro\")\n", + "miread_bundle = (m_tokenizer,m_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BR-adEUUz9su" + }, + "outputs": [], + "source": [ + "def create_lbert_embed(sents,bundle):\n", + " tokenizer = bundle[0]\n", + " model = bundle[1]\n", + " model.cuda()\n", + " tokens = tokenizer(sents,padding=True,truncation=True,return_tensors='pt')\n", + " device = torch.device('cuda')\n", + " tokens = tokens.to(device)\n", + " with torch.no_grad():\n", + " embeds = model(**tokens, output_hidden_states=True,return_dict=True).pooler_output\n", + " return embeds.cpu()\n", + "\n", + "def create_miread_embed(sents,bundle):\n", + " tokenizer = bundle[0]\n", + " model = bundle[1]\n", + " model.cuda()\n", + " tokens = tokenizer(sents,\n", + " max_length=512,\n", + " padding=True,\n", + " truncation=True,\n", + " return_tensors=\"pt\"\n", + " )\n", + " device = torch.device('cuda')\n", + " tokens = tokens.to(device)\n", + " with torch.no_grad():\n", + " out = model.bert(**tokens)\n", + " feature = out.last_hidden_state[:, 0, :]\n", + " return feature.cpu()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 426, + "referenced_widgets": [ + "fdbeedf5a9e24131bc5978e38e4a993a", + "fb5d46ba519348e19b709896f29fd080", + "972708040f054ebdbf33cceaadc94c55", + "7120320c541c430fbd755710fb99e88c", + "35e54871f85c49eabc07695b56dc07af", + 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batch\n", + " i_end = min(i+batch_size, len(data))\n", + " # create IDs batch\n", + " ids = [name + '-' + str(x) for x in range(i, i_end)]\n", + " # create metadata batch\n", + " metadatas = [{\n", + " 'journal':row.get('journal','None'),\n", + " 'title':row['title'],\n", + " 'abstract': row['abstract'],\n", + " 'authors':row.get('authors','None'),\n", + " 'link':row.get('link','None'),\n", + " 'date':row.get('date','None'),\n", + " 'submitter':row.get('submitter','None'),\n", + " } for row in data[i:i_end]]\n", + " # create embeddings\n", + " em = [create_embed(row['abstract'],bundle).tolist()[0] for row in data[i:i_end]]\n", + " texts = [row['abstract'] for row in data[i:i_end]]\n", + " records = list(zip(texts, em))\n", + " if vecdb:\n", + " vecdb_batch = FAISS.from_embeddings(records,faiss_embedder,metadatas=metadatas,ids=ids)\n", + " vecdb.merge_from(vecdb_batch)\n", + " else:\n", + " vecdb = FAISS.from_embeddings(records,faiss_embedder,metadatas=metadatas,ids=ids)\n", + " return vecdb" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PfsK3DE4MMou" + }, + "outputs": [], + "source": [ + "nbdt_data = pd.read_json('data_final.json')\n", + "aliases = pd.read_csv('id_list.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "JrGJh5XgNPvU", + "outputId": "9755fd45-c17e-4c07-de8a-6896eeb6c875" + }, + "outputs": [], + "source": [ + "aliases.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "CShYwGwWMZh5", + "outputId": "e6525260-50af-40f6-90b9-3618fa12d88d" + }, + "outputs": [], + "source": [ + "nbdt_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SziJtbggMuyn" + }, + "outputs": [], + "source": [ + "records = []\n", + "def load_nbdt(data,aliases):\n", + " nbdt_records = []\n", + " no_abst_count = 0\n", + " no_journal_count = 0\n", + " for row in aliases.itertuples():\n", + " name = row[1]\n", + " auth_ids = eval(row[2])\n", + " auth_ids = [int(x) for x in auth_ids]\n", + " papers = nbdt_data.loc[nbdt_data['authorId'].isin(auth_ids)]['papers']\n", + " for paper_set in papers:\n", + " for paper in paper_set:\n", + " url = paper['url']\n", + " title = paper['title']\n", + " abst = paper['abstract']\n", + " year = paper['year']\n", + " journal = paper.get('journal')\n", + " if journal:\n", + " journal = journal.get('name')\n", + " else:\n", + " journal = 'None'\n", + " no_journal_count += 1\n", + " authors = [name]\n", + " if not(abst):\n", + " abst = ''\n", + " no_abst_count += 1\n", + " record = {'journal':journal,'title':title,'abstract':abst,'link':url,'date':year,'authors':authors,'submitter':'None'}\n", + " nbdt_records.append(record)\n", + " return nbdt_records, (no_abst_count,no_journal_count)\n", + "nbdt_recs, no_counts = load_nbdt(nbdt_data,aliases)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "e360e5e9c664412ab02f0d4738010d1a", + "c4f1f5bf33da4ffb91928e18f499c5cd", + "4f9b213a844048a39f5666c2b67903c6", + "9fd71519918646b4ae7438a37b6cc738", + "aeffc2c00fd34ebc936caf831241ea0f", + "62d9dff282fd4eca90d261c36bf9d4b5", + "36032cad6a27480abaadc56b10b845fd", + "3d3f0e54ccef44e0b7e1586dc0870800", + "130d77d72d02433398695867db2ae56c", + "110be3028fbb401a9fe69df2c86761f7", + "b34343c171034370979ab27bff9bef2d" + ] + }, + "id": "IovTlDINc2Ds", + "outputId": "753f537f-1cae-443f-c120-1f42f1529946" + }, + "outputs": [], + "source": [ + "nbdt_db = add_to_db(nbdt_recs,create_miread_embed,miread_bundle,'nbdt')\n", + "nbdt_db.save_local(\"nbdt_index\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + 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