embedding_encoders.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hzELU9jd9Mmy"
      },
      "outputs": [],
      "source": [
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Gated Huggingface models need user access token\n",
        "# Get access token and specify below: https://huggingface.co/docs/hub/en/security-tokens\n",
        "\n",
        "ACCESS_TOKEN = \"\"\n",
        "os.environ['HF_TOKEN'] = ACCESS_TOKEN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pDU_Uxth9Mm0"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torch import Tensor, device\n",
        "\n",
        "def cos_sim(a: Tensor, b: Tensor) -> Tensor:\n",
        "    \"\"\"\n",
        "    Computes the cosine similarity cos_sim(a[i], b[j]) for all i and j.\n",
        "\n",
        "    :return: Matrix with res[i][j]  = cos_sim(a[i], b[j])\n",
        "    \"\"\"\n",
        "    if not isinstance(a, torch.Tensor):\n",
        "        a = torch.tensor(a)\n",
        "\n",
        "    if not isinstance(b, torch.Tensor):\n",
        "        b = torch.tensor(b)\n",
        "\n",
        "    if len(a.shape) == 1:\n",
        "        a = a.unsqueeze(0)\n",
        "\n",
        "    if len(b.shape) == 1:\n",
        "        b = b.unsqueeze(0)\n",
        "\n",
        "    a_norm = torch.nn.functional.normalize(a, p=2, dim=1)\n",
        "    b_norm = torch.nn.functional.normalize(b, p=2, dim=1)\n",
        "    return torch.mm(a_norm, b_norm.transpose(0, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hEX6KeLr9Mm1"
      },
      "outputs": [],
      "source": [
        "# utility for precision @ k evaluation metric \n",
        "def check_label(golden_cui:str , predicted_cuis:list, k:int ):\n",
        "    \"\"\"\n",
        "    Some composite annotation didn't consider orders\n",
        "    So, return 'True' if any cui is matched within composite cui (or single cui)\n",
        "    Otherwise, return 'False'\n",
        "    \"\"\"\n",
        "    result = []\n",
        "    for predicted_cui in predicted_cuis[:k]:\n",
        "        ans = len(set(predicted_cui.split(\"|\")).intersection(set(golden_cui.split(\"|\")))) > 0\n",
        "        result.append(ans)\n",
        "    # print(k)\n",
        "    # print(result)\n",
        "\n",
        "    return any(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OhN-XAH29Mm1"
      },
      "outputs": [],
      "source": [
        "# check_label(\"A|B\" , [\"D\", \"A\", \"A|B|C\"], k=1 )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eQNsHGbP9Mm2"
      },
      "outputs": [],
      "source": [
        "# from sentence_transformers import SentenceTransformer\n",
        "\n",
        "# model = SentenceTransformer(\"nomic-ai/nomic-embed-text-v1\", trust_remote_code=True)\n",
        "# model.max_seq_length = 30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lKXSq1u09Mm2"
      },
      "outputs": [],
      "source": [
        "def main(kb_embeddings, query_embeddings, queries_cui_map , kb_cui_map):\n",
        "\n",
        "        top_k = 5\n",
        "\n",
        "        total_entities = 0\n",
        "        correct_at_1 = 0\n",
        "        correct_at_2 = 0\n",
        "        correct_at_5 = 0\n",
        "\n",
        "\n",
        "        for i, q_emb in tqdm(enumerate(query_embeddings), total = len(query_embeddings)):\n",
        "                label = queries_cui_map[i]\n",
        "\n",
        "\n",
        "\n",
        "                cos_scores = cos_sim(q_emb, kb_embeddings)[0]\n",
        "                top_results = torch.topk(cos_scores, k=top_k)\n",
        "\n",
        "\n",
        "                candidate_ids = []\n",
        "\n",
        "                for idx in top_results[1].tolist():\n",
        "                        # import pdb; pdb.set_trace()\n",
        "                        candidate_ids.append(kb_cui_map[idx])\n",
        "\n",
        "\n",
        "                if check_label(golden_cui = label , predicted_cuis= candidate_ids, k=1 ):\n",
        "                        correct_at_1 += 1\n",
        "                if check_label(golden_cui = label , predicted_cuis= candidate_ids, k=2 ):\n",
        "                        correct_at_2 += 1\n",
        "                if check_label(golden_cui = label , predicted_cuis= candidate_ids, k=5 ):\n",
        "                        correct_at_5 += 1\n",
        "\n",
        "                total_entities += 1\n",
        "\n",
        "        print(\"Total entities: \", total_entities)\n",
        "        print(\n",
        "                \"Correct at 1: \", correct_at_1, \"Recall at 1: \", correct_at_1 / total_entities\n",
        "        )\n",
        "        print(\n",
        "                \"Correct at 2: \", correct_at_2, \"Recall at 2: \", correct_at_2 / total_entities\n",
        "        )\n",
        "        print(\n",
        "                \"Correct at 5: \",\n",
        "                correct_at_5,\n",
        "                \"Recall at 5: \",\n",
        "                correct_at_5 / total_entities,\n",
        "                )\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Knowledge Base\n",
        "\n",
        "* data/umls_onto_all_lang_cased_wikimed_only_399931.txt: \n",
        "  \n",
        " UMLS subset KB from SAPBERT Repo\n",
        "\n",
        "\n",
        "* \"data/qids_with_cui_kb.txt\":  \n",
        "  \n",
        "WikiData Sparql KB. It is already provided in the repo. However, it can be prepared as follows\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# kb = []\n",
        "\n",
        "# with open( \"data/raw/kbs/qids_with_cui_output.jsonl\", \"r\", encoding=\"utf-8\") as f:\n",
        "#     for line in f:\n",
        "#         entry = json.loads(line)\n",
        "#         aliases = entry[\"aliases\"]\n",
        "#         for cui in set(entry[\"cui\"]):\n",
        "            \n",
        "#             if entry['label']:\n",
        "               \n",
        "#                 kb.append(f\"{cui}||{entry['label']}\\n\")\n",
        "\n",
        "#             for alias in aliases:\n",
        "#                 if alias:\n",
        "#                     kb.append(f\"{cui}||{alias}\\n\")\n",
        "                    \n",
        "# kb = set(kb)\n",
        "\n",
        "# with open('data/qids_with_cui_kb.txt', 'w', encoding=\"utf-8\") as f:\n",
        "#     for entry in kb:\n",
        "#         f.write(entry)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Query Datasets:\n",
        "\n",
        "#### XLBEL:\n",
        "\n",
        "\"data/de_1k_test_query.txt\": provided by sapbert repo\n",
        "\n",
        "\n",
        "#### WikiMed-BEL-DE\n",
        "\n",
        "\"data/de_wikimed_bel_train_query.txt\"\n",
        "\n",
        "\"data/de_wikimed_bel_dev_query.txt\"\n",
        "\n",
        "\"data/de_wikimed_bel_test_query.txt\"\n",
        "\n",
        "\n",
        "The files are already in the Repo. However, they can be prepared in the follwing way from WikiMed-BEL-DE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def prepare_query_data(data):\n",
        "    from collections import defaultdict\n",
        "    name_cui_map = defaultdict(set)\n",
        "    for entry in data:\n",
        "        entry_title = entry[\"title\"]\n",
        "        entry_cui = entry[\"cui\"]\n",
        "\n",
        "        if entry_title and entry_cui !=\"None\":\n",
        "            name_cui_map[entry_title].add(entry_cui)\n",
        "\n",
        "        mentions = entry[\"mentions\"]\n",
        "        for m in mentions:\n",
        "            mention_title = m[\"mention\"]\n",
        "            mention_cui = m[\"cui\"]\n",
        "\n",
        "            if mention_title and mention_cui !=\"None\":\n",
        "                name_cui_map[mention_title].add(mention_cui)\n",
        "                \n",
        "    \n",
        "    test_queries = [f\"{'|'.join(cuis)}||{name}\\n\" for name, cuis in name_cui_map.items()] \n",
        "    # some names have more than one cui\n",
        "    return test_queries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# import json\n",
        "\n",
        "# # train\n",
        "# with open( \"data/raw/BEL-silver-standard/WikiMed-DE-BEL/train_data_bel.json\", \"r\", encoding=\"utf-8\") as f:\n",
        "#     data = json.loads(f.read())\n",
        "\n",
        "# test_queries = set(prepare_query_data(data))\n",
        "\n",
        "# with open('data/de_wikimed_bel_train_query.txt', 'w', encoding=\"utf-8\") as f:\n",
        "#     for entry in test_queries:\n",
        "#         f.write(entry)\n",
        "        \n",
        "# # dev\n",
        "# with open( \"data/raw/BEL-silver-standard/WikiMed-DE-BEL/dev_data_bel.json\", \"r\", encoding=\"utf-8\") as f:\n",
        "#     data = json.loads(f.read())\n",
        "\n",
        "# test_queries = set(prepare_query_data(data))\n",
        "\n",
        "# with open('data/de_wikimed_bel_dev_query.txt', 'w', encoding=\"utf-8\") as f:\n",
        "#     for entry in test_queries:\n",
        "#         f.write(entry)\n",
        "\n",
        "\n",
        "# # test\n",
        "# with open( \"data/raw/BEL-silver-standard/WikiMed-DE-BEL/test_data_bel.json\", \"r\", encoding=\"utf-8\") as f:\n",
        "#     data = json.loads(f.read())\n",
        "\n",
        "# test_queries = set(prepare_query_data(data))\n",
        "\n",
        "# with open('data/de_wikimed_bel_test_query.txt', 'w', encoding=\"utf-8\") as f:\n",
        "#     for entry in test_queries:\n",
        "#         f.write(entry)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVrVc6c-9Mm3"
      },
      "source": [
        "# Select Knowledge Base\n",
        "\n",
        "All the Query datasets will be linked to the selected KB\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "--iVnOh49Mm3"
      },
      "outputs": [],
      "source": [
        "# load KB\n",
        "\n",
        "# kb_path = \"data/qids_with_cui_kb.txt\" # WikiData Sparql KB\n",
        "kb_path = \"data/umls_onto_all_lang_cased_wikimed_only_399931.txt\" # UMLS subset KB from SAPBERT Repo\n",
        "\n",
        "\n",
        "kb_tuples = []\n",
        "\n",
        "with open(kb_path, encoding=\"utf-8\") as file:\n",
        "    for line in file:\n",
        "        cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "        kb_tuples.append((cui, desc))\n",
        "\n",
        "kb_entites = [i[1] for i in kb_tuples]\n",
        "kb_cui_map = {i:e[0] for i, e in enumerate(kb_tuples)} # indexing dict is faster than list\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Entity Linking using  Embedding \n",
        "\n",
        "All the embedding models follow the following steps to link a mention to KB\n",
        "\n",
        "* Make embedding of all the entites in the KB.\n",
        "\n",
        "* Make embedding of a each mention in Query dataset.\n",
        "\n",
        "* Find K Nearest Neigbours by comparing a query embedding with KB embeddings using cosine similarity.\n",
        "\n",
        "##### Todo: Build Approximate Nearest Neighbor Indexes for KB to avoid memory and runtime problems"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model 1: jina-embeddings-v2-base-de\n",
        "\n",
        "A German/English bilingual text embedding model supporting 8192 sequence length. \n",
        "\n",
        "For reference, the max length is set to 25 in the SAPBERT original repo by authors. We restrict the length to 40 for all models as german text is usually longer. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from transformers import AutoModel"
      ]
    },
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        "colab": {
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        "id": "f2p3Fh8V9Mm3",
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      "outputs": [],
      "source": [
        "batch_size = 2 # change the size according to GPU\n",
        "\n",
        "model = AutoModel.from_pretrained(\"jinaai/jina-embeddings-v2-base-de\", trust_remote_code=True, )\n",
        "\n",
        "kb_embeddings = model.encode(kb_entites, show_progress_bar =True, batch_size =batch_size , device = \"cuda\", max_length=40,)\n",
        "\n",
        "\n",
        "# Query Datasets\n",
        "for query_path in [\n",
        "                    \"data/de_1k_test_query.txt\",\n",
        "                   \"data/de_wikimed_bel_train_query.txt\",\n",
        "                   \"data/de_wikimed_bel_dev_query.txt\",\n",
        "                   \"data/de_wikimed_bel_test_query.txt\",\n",
        "                   ]:\n",
        "\n",
        "    # load data\n",
        "    query_tuples = []\n",
        "    with open(query_path, encoding=\"utf-8\") as file:\n",
        "        for line in file:\n",
        "            cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "            query_tuples.append((cui, desc))\n",
        "\n",
        "    queries = [i[1] for i in query_tuples]\n",
        "    queries_cui_map = {i:e[0] for i, e in enumerate(query_tuples)}\n",
        "\n",
        "    # embed\n",
        "    query_embeddings = model.encode(queries, show_progress_bar =True, batch_size =batch_size , device = \"cuda\", max_length=40,)\n",
        "\n",
        "\n",
        "    query_embeddings = torch.tensor(query_embeddings).to(\"cuda\")\n",
        "    kb_embeddings = torch.tensor(kb_embeddings).to(\"cuda\")\n",
        "\n",
        "    print(\"======================\")\n",
        "    print(query_path)\n",
        "    main( kb_embeddings, query_embeddings, queries_cui_map , kb_cui_map )\n",
        "    print(\"======================\")\n",
        "    kb_embeddings = kb_embeddings.to(\"cpu\")\n",
        "\n",
        "del model\n",
        "del kb_embeddings\n",
        "del query_embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Model 2: SAPBERT\n",
        "\n",
        "We use \"cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR\" model. There is large version \"cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large\" also which could be tried"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9GUVF2uG9Mm4"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "\n",
        "\n",
        "def gererate_embedding(model, tokenizer , all_names, batch_size):\n",
        "    bs = batch_size # batch size during inference\n",
        "    all_embs = []\n",
        "    for i in tqdm(np.arange(0, len(all_names), bs)):\n",
        "        toks = tokenizer.batch_encode_plus(all_names[i:i+bs],\n",
        "                                        padding=\"max_length\",\n",
        "                                        max_length=40,\n",
        "                                        truncation=True,\n",
        "                                        return_tensors=\"pt\")\n",
        "        toks_cuda = {}\n",
        "        for k,v in toks.items():\n",
        "            toks_cuda[k] = v.cuda()\n",
        "        cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding\n",
        "        all_embs.append(cls_rep.cpu().detach().numpy())\n",
        "\n",
        "    all_embs = np.concatenate(all_embs, axis=0)\n",
        "    return all_embs\n"
      ]
    },
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          ]
        },
        "id": "UfKNN7419Mm4",
        "outputId": "46ef64ea-243c-4bf2-f6ac-c8dfa6cc503f"
      },
      "outputs": [],
      "source": [
        "batch_size = 2 # change the size according to GPU\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR\")\n",
        "model = AutoModel.from_pretrained(\"cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR\").cuda()\n",
        "\n",
        "kb_embeddings = gererate_embedding(model, tokenizer, kb_entites, batch_size=batch_size)\n",
        "\n",
        "\n",
        "\n",
        "for query_path in [\n",
        "                    \"data/de_1k_test_query.txt\",\n",
        "                   \"data/de_wikimed_bel_train_query.txt\",\n",
        "                   \"data/de_wikimed_bel_dev_query.txt\",\n",
        "                   \"data/de_wikimed_bel_test_query.txt\",\n",
        "                   ]:\n",
        "\n",
        "    # load data\n",
        "    query_tuples = []\n",
        "    with open(query_path, encoding=\"utf-8\") as file:\n",
        "        for line in file:\n",
        "            cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "            query_tuples.append((cui, desc))\n",
        "\n",
        "    queries = [i[1] for i in query_tuples]\n",
        "    queries_cui_map = {i:e[0] for i, e in enumerate(query_tuples)}\n",
        "\n",
        "    # embed\n",
        "    query_embeddings  = gererate_embedding(model, tokenizer, queries, batch_size=batch_size)\n",
        "\n",
        "    kb_embeddings = torch.tensor(kb_embeddings).to(\"cuda\")\n",
        "    query_embeddings = torch.tensor(query_embeddings).to(\"cuda\")\n",
        "\n",
        "    print(\"======================\")\n",
        "    print(query_path)\n",
        "    main( kb_embeddings, query_embeddings, queries_cui_map , kb_cui_map )\n",
        "    print(\"======================\")\n",
        "    kb_embeddings = kb_embeddings.to(\"cpu\")\n",
        "    del query_embeddings\n",
        "\n",
        "del model\n",
        "del tokenizer\n",
        "del kb_embeddings\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXZ3MpiG9Mm6"
      },
      "source": [
        "## Model 3: BGE M3\n",
        "\n",
        "https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3\n",
        "\n",
        "A Multilingual model which can generate dense, sparse, and [colbert style](https://til.simonwillison.net/llms/colbert-ragatouille) embeddings. We only use dense embeddings.\n",
        "\n",
        "\n",
        "It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval (colbert style), and sparse retrieval. This could be tried if GPU permits.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "batch_size = 2 # change the size according to GPU\n",
        "from FlagEmbedding import BGEM3FlagModel\n",
        "\n",
        "model = BGEM3FlagModel('BAAI/bge-m3',  use_fp16=True, device=\"cuda\"  )\n",
        "\n",
        "\n",
        "kb_embeddings = model.encode(kb_entites, return_dense=True, return_sparse=False, \n",
        "                             return_colbert_vecs=False, \n",
        "                             batch_size=batch_size, max_length=40 )\n",
        "\n",
        "\n",
        "kb_embeddings = torch.tensor(kb_embeddings['dense_vecs']).to(torch.float32)\n",
        "\n",
        "for query_path in [\n",
        "                    \"data/de_1k_test_query.txt\",\n",
        "                   \"data/de_wikimed_bel_train_query.txt\",\n",
        "                   \"data/de_wikimed_bel_dev_query.txt\",\n",
        "                   \"data/de_wikimed_bel_test_query.txt\",\n",
        "                   ]:\n",
        "\n",
        "    # load data\n",
        "    query_tuples = []\n",
        "    with open(query_path, encoding=\"utf-8\") as file:\n",
        "        for line in file:\n",
        "            cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "            query_tuples.append((cui, desc))\n",
        "\n",
        "    queries = [i[1] for i in query_tuples]\n",
        "    queries_cui_map = {i:e[0] for i, e in enumerate(query_tuples)}\n",
        "\n",
        "    # embed\n",
        "    query_embeddings = model.encode(queries, return_dense=True, return_sparse=False,\n",
        "                                    return_colbert_vecs=False, batch_size=batch_size, \n",
        "                                    max_length=40 )\n",
        "    \n",
        "    query_embeddings = torch.tensor(query_embeddings['dense_vecs']).to(torch.float32)\n",
        "\n",
        "    kb_embeddings = kb_embeddings.to(\"cuda\")\n",
        "    query_embeddings = query_embeddings.to(\"cuda\")\n",
        "    print(\"======================\")\n",
        "    print(query_path)\n",
        "    main( kb_embeddings, query_embeddings, queries_cui_map , kb_cui_map )\n",
        "    print(\"======================\")\n",
        "    kb_embeddings = kb_embeddings.to(\"cpu\")\n",
        "    del query_embeddings\n",
        "del model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Cohere"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cqj7dD9I9Mm5"
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        "# # This snippet shows and example how to use the Cohere Embed V3 models for semantic search.\n",
        "# # Make sure to have the Cohere SDK in at least v4.30 install: pip install -U cohere\n",
        "# # Get your API                                                 key from: www.cohere.com\n",
        "# import cohere\n",
        "# import numpy as np\n",
        "\n",
        "# cohere_key =   #Get your API key from www.cohere.com\n",
        "# co = cohere.Client(cohere_key)\n",
        "\n",
        "# docs = [\"The capital of France is Paris\",\n",
        "#         \"PyTorch is a machine learning framework based on the Torch library.\",\n",
        "#         \"The average cat lifespan is between 13-17 years\"]\n",
        "\n",
        "\n",
        "# #Encode your documents with input type 'search_document'\n",
        "# doc_emb = co.embed(docs, input_type=\"search_document\", model=\"embed-multilingual-light-v3.0\").embeddings\n",
        "# doc_emb = np.asarray(doc_emb)\n",
        "\n",
        "\n",
        "# #Encode your query with input type 'search_query'\n",
        "# query = \"What is Pytorch\"\n",
        "# query_emb = co.embed([query], input_type=\"search_query\", model=\"embed-multilingual-light-v3.0\").embeddings\n",
        "# query_emb = np.asarray(query_emb)\n",
        "# query_emb.shape\n",
        "\n",
        "# #Compute the dot product between query embedding and document embedding\n",
        "# scores = np.dot(query_emb, doc_emb.T)[0]\n",
        "\n",
        "# #Find the highest scores\n",
        "# max_idx = np.argsort(-scores)\n",
        "\n",
        "# print(f\"Query: {query}\")\n",
        "# for idx in max_idx:\n",
        "#   print(f\"Score: {scores[idx]:.2f}\")\n",
        "#   print(docs[idx])\n",
        "#   print(\"--------\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_4W_mGh9Mm6"
      },
      "source": [
        "# Ragatouille Colbert"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c_1RBj5V9Mm7"
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      "source": [
        "# from ragatouille import RAGPretrainedModel\n",
        "# from ragatouille.utils import get_wikipedia_page\n",
        "\n",
        "# from collections import defaultdict\n",
        "# cui_aliases = defaultdict(list)\n",
        "\n",
        "# # taken from sapbert repo\n",
        "# desc_cui_map ={}\n",
        "\n",
        "# with open(\"data/umls_onto_all_lang_cased_wikimed_only_399931.txt\", encoding=\"utf-8\") as file:\n",
        "#     for line in file:\n",
        "#         cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "#         desc_cui_map[desc] = cui\n",
        "\n",
        "# # XLBEL data from SAPBERT repo\n",
        "# test_queries = []\n",
        "# with open(\"data/de_1k_test_query.txt\", encoding=\"utf-8\") as file:\n",
        "#     for line in file:\n",
        "#         cui, desc = line.strip(\"\\n\").split(\"||\")\n",
        "#         test_queries.append((cui, desc))\n",
        "\n",
        "# corpus = list(desc_cui_map.keys())\n",
        "# queries = [i[1] for i in test_queries]\n",
        "\n",
        "# # RAG = RAGPretrainedModel.from_pretrained(\"colbert-ir/colbertv2.0\")\n",
        "# # my_documents = corpus\n",
        "# # index_path = RAG.index(index_name=\"my_index\", collection=my_documents)\n",
        "\n",
        "# query = \"ColBERT my dear ColBERT, who is the fairest document of them all?\"\n",
        "# RAG = RAGPretrainedModel.from_index(\"/home/femustafa/projects/col/.ragatouille/colbert/indexes/my_index\")\n",
        "# # results = RAG.search(queries[0])\n",
        "# # print(results)\n",
        "# # import pdb; pdb.set_trace()\n",
        "\n",
        "# # Find the closest 5 sentences of the corpus for each query sentence based on cosine similarity\n",
        "# from tqdm import tqdm\n",
        "# top_k = 5\n",
        "\n",
        "\n",
        "# total_entities = 0\n",
        "# correct_at_1 = 0\n",
        "# correct_at_2 = 0\n",
        "# correct_at_5 = 0\n",
        "\n",
        "\n",
        "# for label, query in tqdm(test_queries):\n",
        "\n",
        "\n",
        "#     # We use cosine-similarity and torch.topk to find the highest 5 scores\n",
        "#     results = RAG.search(query)\n",
        "#     candidate_ids = [desc_cui_map[i[\"content\"]] for i in results]\n",
        "\n",
        "\n",
        "#     if label in candidate_ids[:1]:\n",
        "#             correct_at_1 += 1\n",
        "#     if label in candidate_ids[:2]:\n",
        "#             correct_at_2 += 1\n",
        "#     if label in candidate_ids[:5]:\n",
        "#             correct_at_5 += 1\n",
        "\n",
        "#     total_entities += 1\n",
        "\n",
        "# print(\"Total entities: \", total_entities)\n",
        "# print(\n",
        "#         \"Correct at 1: \", correct_at_1, \"Recall at 1: \", correct_at_1 / total_entities\n",
        "# )\n",
        "# print(\n",
        "#         \"Correct at 2: \", correct_at_2, \"Recall at 2: \", correct_at_2 / total_entities\n",
        "# )\n",
        "# print(\n",
        "#         \"Correct at 5: \",\n",
        "#         correct_at_5,\n",
        "#         \"Recall at 5: \",\n",
        "#         correct_at_5 / total_entities,\n",
        "#         )\n",
        "\n"
      ]
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