example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import json\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "src_dir = os.path.abspath('./src')\n",
    "sys.path.append(src_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marveso/miniconda3/envs/tailor/lib/python3.11/site-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"
     ]
    }
   ],
   "source": [
    "from utils import init\n",
    "from embedding_stores import Chroma_EmbeddingStore\n",
    "from preprocess.text_operations import ConvertJsonToString\n",
    "from dataset import AIoD_Documents\n",
    "\n",
    "client = init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Collection(name=chunk_embeddings-multilingual_e5_large-relevant-v0),\n",
       " Collection(name=embeddings-gte_large-basic-v0),\n",
       " Collection(name=embeddings-gte_large-relevant-v0),\n",
       " Collection(name=chunk_embeddings-bge_large-relevant-v0),\n",
       " Collection(name=chunk_embeddings-gte_large_hierarchical-relevant-v0),\n",
       " Collection(name=chunk_embeddings-bge_large-basic-v0),\n",
       " Collection(name=embeddings-multilingual_e5_large-basic-v0),\n",
       " Collection(name=datasets),\n",
       " Collection(name=chunk_embeddings-gte_large_hierarchical-basic-v0),\n",
       " Collection(name=chunk_embeddings-multilingual_e5_large-basic-v0),\n",
       " Collection(name=embeddings-multilingual_e5_large-relevant-v0)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.list_collections()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = client.get_collection(\"chunk_embeddings-gte_large_hierarchical-relevant-v0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hnsw:space': 'cosine'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 1, 2: 1, 3: 1}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\n",
    "    x: 1\n",
    "    for x in [1,2,3]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'json_string': '{\"platform\": \"huggingface\", \"platform_resource_identifier\": \"acronym_identification\", \"name\": \"acronym_identification\", \"date_published\": \"2022-03-02T23:29:22\", \"same_as\": \"https://huggingface.co/datasets/acronym_identification\", \"is_accessible_for_free\": true, \"ai_asset_identifier\": 2, \"ai_resource_identifier\": 2, \"aiod_entry\": {\"editor\": [], \"status\": \"published\", \"date_modified\": \"2023-12-19T10:25:29\", \"date_created\": \"2023-12-19T10:25:29\"}, \"alternate_name\": [], \"application_area\": [], \"citation\": [], \"contact\": [], \"creator\": [], \"description\": {\"plain\": \"Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21.\"}, \"distribution\": [{\"content_url\": \"https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet\", \"content_size_kb\": 206100, \"description\": \"acronym_identification. Config: default. Split: test\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet\", \"content_size_kb\": 1657991, \"description\": \"acronym_identification. Config: default. Split: train\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/validation/0000.parquet\", \"content_size_kb\": 206916, \"description\": \"acronym_identification. Config: default. Split: validation\", \"name\": \"0000.parquet\"}], \"funder\": [], \"has_part\": [], \"industrial_sector\": [], \"is_part_of\": [], \"keyword\": [\"source_datasets:original\", \"task_categories:token-classification\", \"multilinguality:monolingual\", \"size_categories:10k<n<100k\", \"language:en\", \"acronym-identification\", \"region:us\", \"language_creators:found\", \"annotations_creators:expert-generated\", \"arxiv:2010.14678\", \"license:mit\"], \"license\": \"mit\", \"media\": [], \"note\": [], \"relevant_link\": [], \"relevant_resource\": [], \"relevant_to\": [], \"research_area\": [], \"scientific_domain\": [], \"identifier\": 1}'},\n",
       " {'json_string': '{\"platform\": \"huggingface\", \"platform_resource_identifier\": \"allegro_reviews\", \"name\": \"allegro_reviews\", \"date_published\": \"2022-03-02T23:29:22\", \"same_as\": \"https://huggingface.co/datasets/allegro_reviews\", \"is_accessible_for_free\": true, \"ai_asset_identifier\": 12, \"ai_resource_identifier\": 13, \"aiod_entry\": {\"editor\": [], \"status\": \"published\", \"date_modified\": \"2023-12-19T10:25:33\", \"date_created\": \"2023-12-19T10:25:33\"}, \"alternate_name\": [], \"application_area\": [], \"citation\": [], \"contact\": [], \"creator\": [], \"description\": {\"plain\": \"Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted\\\\nfrom Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale\\\\nfrom one (negative review) to five (positive review).\\\\n\\\\nWe recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden.\\\\nYou can evaluate your model using the online evaluation tool available on klejbenchmark.com.\"}, \"distribution\": [{\"content_url\": \"https://huggingface.co/datasets/allegro_reviews/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet\", \"content_size_kb\": 349137, \"description\": \"allegro_reviews. Config: default. Split: test\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/allegro_reviews/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet\", \"content_size_kb\": 3225749, \"description\": \"allegro_reviews. Config: default. Split: train\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/allegro_reviews/resolve/refs%2Fconvert%2Fparquet/default/validation/0000.parquet\", \"content_size_kb\": 348771, \"description\": \"allegro_reviews. Config: default. Split: validation\", \"name\": \"0000.parquet\"}], \"funder\": [], \"has_part\": [], \"industrial_sector\": [], \"is_part_of\": [], \"keyword\": [\"source_datasets:original\", \"multilinguality:monolingual\", \"size_categories:10k<n<100k\", \"region:us\", \"language_creators:found\", \"task_categories:text-classification\", \"license:cc-by-sa-4.0\", \"annotations_creators:found\", \"task_ids:sentiment-scoring\", \"task_ids:text-scoring\", \"language:pl\"], \"license\": \"cc-by-sa-4.0\", \"media\": [], \"note\": [], \"relevant_link\": [], \"relevant_resource\": [], \"relevant_to\": [], \"research_area\": [], \"scientific_domain\": [], \"identifier\": 10}'},\n",
       " {'json_string': '{\"platform\": \"huggingface\", \"platform_resource_identifier\": \"coached_conv_pref\", \"name\": \"coached_conv_pref\", \"date_published\": \"2022-03-02T23:29:22\", \"same_as\": \"https://huggingface.co/datasets/coached_conv_pref\", \"is_accessible_for_free\": true, \"ai_asset_identifier\": 105, \"ai_resource_identifier\": 115, \"aiod_entry\": {\"editor\": [], \"status\": \"published\", \"date_modified\": \"2023-12-19T10:26:19\", \"date_created\": \"2023-12-19T10:26:19\"}, \"alternate_name\": [], \"application_area\": [], \"citation\": [], \"contact\": [], \"creator\": [], \"description\": {\"plain\": \"A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing\\\\nmovie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers,\\\\nwhere one worker plays the role of an \\'assistant\\', while the other plays the role of a \\'user\\'. The \\'assistant\\' elicits\\\\nthe \\'user\\\\u2019s\\' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The\\\\nassistant asks questions designed to minimize the bias in the terminology the \\'user\\' employs to convey his or her\\\\npreferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with\\\\nentity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of\\\\nentities.\"}, \"distribution\": [], \"funder\": [], \"has_part\": [], \"industrial_sector\": [], \"is_part_of\": [], \"keyword\": [\"source_datasets:original\", \"task_categories:token-classification\", \"multilinguality:monolingual\", \"language:en\", \"region:us\", \"language_creators:found\", \"annotations_creators:expert-generated\", \"size_categories:n<1k\", \"license:cc-by-sa-4.0\", \"task_ids:dialogue-modeling\", \"task_categories:fill-mask\", \"task_categories:text-generation\", \"task_ids:parsing\", \"task_categories:other\", \"conversational recommendation\"], \"license\": \"cc-by-sa-4.0\", \"media\": [], \"note\": [], \"relevant_link\": [], \"relevant_resource\": [], \"relevant_to\": [], \"research_area\": [], \"scientific_domain\": [], \"identifier\": 100}'},\n",
       " {'json_string': '{\"platform\": \"huggingface\", \"platform_resource_identifier\": \"Jack0508/TED2020vi_kor\", \"name\": \"Jack0508/TED2020vi_kor\", \"date_published\": \"2022-03-02T23:29:22\", \"same_as\": \"https://huggingface.co/datasets/Jack0508/TED2020vi_kor\", \"is_accessible_for_free\": true, \"ai_asset_identifier\": 1005, \"ai_resource_identifier\": 1015, \"aiod_entry\": {\"editor\": [], \"status\": \"published\", \"date_modified\": \"2023-12-19T10:33:06\", \"date_created\": \"2023-12-19T10:33:06\"}, \"alternate_name\": [], \"application_area\": [], \"citation\": [], \"contact\": [], \"creator\": [], \"distribution\": [{\"content_url\": \"https://huggingface.co/datasets/Jack0508/TED2020vi_kor/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet\", \"content_size_kb\": 44308885, \"description\": \"Jack0508/TED2020vi_kor. Config: default. Split: train\", \"name\": \"0000.parquet\"}], \"funder\": [], \"has_part\": [], \"industrial_sector\": [], \"is_part_of\": [], \"keyword\": [\"region:us\"], \"media\": [], \"note\": [], \"relevant_link\": [], \"relevant_resource\": [], \"relevant_to\": [], \"research_area\": [], \"scientific_domain\": [], \"identifier\": 1000}'},\n",
       " {'json_string': '{\"platform\": \"huggingface\", \"platform_resource_identifier\": \"sepidmnorozy/Slovak_sentiment\", \"name\": \"sepidmnorozy/Slovak_sentiment\", \"date_published\": \"2022-08-16T09:56:40\", \"same_as\": \"https://huggingface.co/datasets/sepidmnorozy/Slovak_sentiment\", \"is_accessible_for_free\": true, \"ai_asset_identifier\": 10009, \"ai_resource_identifier\": 10019, \"aiod_entry\": {\"editor\": [], \"status\": \"published\", \"date_modified\": \"2023-12-19T11:20:38\", \"date_created\": \"2023-12-19T11:20:38\"}, \"alternate_name\": [], \"application_area\": [], \"citation\": [], \"contact\": [], \"creator\": [], \"distribution\": [{\"content_url\": \"https://huggingface.co/datasets/sepidmnorozy/Slovak_sentiment/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet\", \"content_size_kb\": 74110, \"description\": \"sepidmnorozy/Slovak_sentiment. Config: default. Split: test\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/sepidmnorozy/Slovak_sentiment/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet\", \"content_size_kb\": 237409, \"description\": \"sepidmnorozy/Slovak_sentiment. Config: default. Split: train\", \"name\": \"0000.parquet\"}, {\"content_url\": \"https://huggingface.co/datasets/sepidmnorozy/Slovak_sentiment/resolve/refs%2Fconvert%2Fparquet/default/validation/0000.parquet\", \"content_size_kb\": 34891, \"description\": \"sepidmnorozy/Slovak_sentiment. Config: default. Split: validation\", \"name\": \"0000.parquet\"}], \"funder\": [], \"has_part\": [], \"industrial_sector\": [], \"is_part_of\": [], \"keyword\": [\"region:us\"], \"media\": [], \"note\": [], \"relevant_link\": [], \"relevant_resource\": [], \"relevant_to\": [], \"research_area\": [], \"scientific_domain\": [], \"identifier\": 10000}'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = client.get_collection(\"datasets\")\n",
    "metas = ds.peek(5)[\"metadatas\"]\n",
    "metas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'platform': 'huggingface',\n",
       " 'platform_resource_identifier': 'acronym_identification',\n",
       " 'name': 'acronym_identification',\n",
       " 'date_published': '2022-03-02T23:29:22',\n",
       " 'same_as': 'https://huggingface.co/datasets/acronym_identification',\n",
       " 'is_accessible_for_free': True,\n",
       " 'ai_asset_identifier': 2,\n",
       " 'ai_resource_identifier': 2,\n",
       " 'aiod_entry': {'editor': [],\n",
       "  'status': 'published',\n",
       "  'date_modified': '2023-12-19T10:25:29',\n",
       "  'date_created': '2023-12-19T10:25:29'},\n",
       " 'alternate_name': [],\n",
       " 'application_area': [],\n",
       " 'citation': [],\n",
       " 'contact': [],\n",
       " 'creator': [],\n",
       " 'description': {'plain': 'Acronym identification training and development sets for the acronym identification task at SDU@AAAI-21.'},\n",
       " 'distribution': [{'content_url': 'https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/test/0000.parquet',\n",
       "   'content_size_kb': 206100,\n",
       "   'description': 'acronym_identification. Config: default. Split: test',\n",
       "   'name': '0000.parquet'},\n",
       "  {'content_url': 'https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet',\n",
       "   'content_size_kb': 1657991,\n",
       "   'description': 'acronym_identification. Config: default. Split: train',\n",
       "   'name': '0000.parquet'},\n",
       "  {'content_url': 'https://huggingface.co/datasets/acronym_identification/resolve/refs%2Fconvert%2Fparquet/default/validation/0000.parquet',\n",
       "   'content_size_kb': 206916,\n",
       "   'description': 'acronym_identification. Config: default. Split: validation',\n",
       "   'name': '0000.parquet'}],\n",
       " 'funder': [],\n",
       " 'has_part': [],\n",
       " 'industrial_sector': [],\n",
       " 'is_part_of': [],\n",
       " 'keyword': ['source_datasets:original',\n",
       "  'task_categories:token-classification',\n",
       "  'multilinguality:monolingual',\n",
       "  'size_categories:10k<n<100k',\n",
       "  'language:en',\n",
       "  'acronym-identification',\n",
       "  'region:us',\n",
       "  'language_creators:found',\n",
       "  'annotations_creators:expert-generated',\n",
       "  'arxiv:2010.14678',\n",
       "  'license:mit'],\n",
       " 'license': 'mit',\n",
       " 'media': [],\n",
       " 'note': [],\n",
       " 'relevant_link': [],\n",
       " 'relevant_resource': [],\n",
       " 'relevant_to': [],\n",
       " 'research_area': [],\n",
       " 'scientific_domain': [],\n",
       " 'identifier': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(metas[0][\"json_string\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "EMBEDDING_COLLECTION_NAME = \"embeddings-BAAI-simple\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  '000077c6-2fa4-4c26-abbe-ecb6be196eb2',\n",
       "  '00008068-af38-4988-ab86-88e26f8b54bc'],\n",
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       "   -0.035690728574991226,\n",
       "   -0.028799977153539658,\n",
       "   -0.02022382616996765,\n",
       "   -0.05844992771744728,\n",
       "   0.01716003566980362,\n",
       "   0.01300287339836359,\n",
       "   0.028641706332564354,\n",
       "   -0.01191256195306778,\n",
       "   -0.016457270830869675,\n",
       "   -0.0582582913339138,\n",
       "   0.06853725761175156,\n",
       "   0.011990770697593689,\n",
       "   -0.03825869411230087,\n",
       "   -0.0027742255479097366,\n",
       "   0.05148835480213165,\n",
       "   0.01769348606467247,\n",
       "   -0.021597307175397873,\n",
       "   0.05162777379155159,\n",
       "   0.03410099446773529,\n",
       "   -0.07320548593997955,\n",
       "   -0.023920441046357155,\n",
       "   -0.0032609645277261734,\n",
       "   -0.010994698852300644,\n",
       "   0.00441429577767849,\n",
       "   0.0073197148740291595,\n",
       "   0.0553504042327404,\n",
       "   -0.09634621441364288,\n",
       "   -0.009971391409635544,\n",
       "   0.03576464205980301,\n",
       "   0.01892581209540367,\n",
       "   -0.059768348932266235,\n",
       "   0.04149695113301277,\n",
       "   -0.025451797991991043,\n",
       "   -0.06340707093477249,\n",
       "   0.0540991872549057,\n",
       "   0.014666646718978882,\n",
       "   0.024843966588377953,\n",
       "   0.009017148986458778,\n",
       "   -0.035305414348840714,\n",
       "   -0.009740694426000118,\n",
       "   0.0022694163490086794,\n",
       "   0.03243861719965935,\n",
       "   -0.01983003504574299,\n",
       "   0.09253719449043274,\n",
       "   0.047970809042453766,\n",
       "   0.01697702705860138,\n",
       "   -0.09755439311265945,\n",
       "   0.035258810967206955,\n",
       "   -0.030434411019086838,\n",
       "   -0.0023287921212613583,\n",
       "   -0.04912496730685234,\n",
       "   -0.020171400159597397,\n",
       "   -0.058306723833084106,\n",
       "   -0.01283248420804739,\n",
       "   -0.027955932542681694,\n",
       "   0.007630739826709032,\n",
       "   -0.04571351408958435,\n",
       "   -0.04817624390125275,\n",
       "   0.03196084871888161,\n",
       "   0.03081377223134041,\n",
       "   -0.0048140110448002815,\n",
       "   0.01600298285484314,\n",
       "   0.0123396385461092,\n",
       "   -0.06890851259231567,\n",
       "   -0.01850973255932331,\n",
       "   -0.03031613677740097,\n",
       "   -0.0211031436920166,\n",
       "   -0.006970803253352642,\n",
       "   0.012134907767176628,\n",
       "   0.0011939815012738109,\n",
       "   0.024029972031712532,\n",
       "   -0.06951716542243958,\n",
       "   0.057964544743299484,\n",
       "   -0.021691979840397835,\n",
       "   0.026837892830371857,\n",
       "   -0.0005413976614363492,\n",
       "   0.010673592798411846,\n",
       "   0.003814003663137555]],\n",
       " 'metadatas': [{'doc_id': '364356'},\n",
       "  {'doc_id': '101504'},\n",
       "  {'doc_id': '256099'},\n",
       "  {'doc_id': '232121'},\n",
       "  {'doc_id': '252835'}],\n",
       " 'documents': [None, None, None, None, None],\n",
       " 'data': None,\n",
       " 'uris': None}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb_col = client.get_collection(EMBEDDING_COLLECTION_NAME)\n",
    "emb_col.peek(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marveso/miniconda3/envs/tailor/lib/python3.11/site-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"
     ]
    }
   ],
   "source": [
    "model = ModelSetup.setup_hierarchical_model(\n",
    "    model_path=\"BAAI/bge-base-en-v1.5\",\n",
    "    max_num_chunks=5,\n",
    "    use_chunk_transformer=False,\n",
    "    token_pooling=\"none\",\n",
    "    chunk_pooling=\"mean\", \n",
    "    parallel_chunk_processing=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "QUERY = \"I need a multilingual textual dataset that has over 10 000 datapoints for the task of summarization. I'd prefer the dataset to primarily contain news articles.\"\n",
    "query_list = [(QUERY, 0)]\n",
    "query_loader = DataLoader(\n",
    "    query_list, collate_fn=AIoD_Documents._collate_fn, \n",
    "    batch_size=4, num_workers=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "QUERY = \"I need a dataset containing movie reviews\"\n",
    "query_list = [(QUERY, 0)]\n",
    "query_loader = DataLoader(\n",
    "    query_list, collate_fn=AIoD_Documents._collate_fn, \n",
    "    batch_size=4, num_workers=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "store = Chroma_EmbeddingStore(client)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_set = store.retrieve_topk_documents(model, query_loader, emb_collection_name=EMBEDDING_COLLECTION_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doc IDs: ['468', '126982', '3310', '232388', '141388', '720', '46498', '14', '307718', '145626']\n",
      "Distances: [0.2093261480331421, 0.21153497695922852, 0.22321707010269165, 0.22387754917144775, 0.2266526222229004, 0.23122233152389526, 0.23422420024871826, 0.2385241985321045, 0.23862451314926147, 0.23864179849624634]\n"
     ]
    }
   ],
   "source": [
    "print(\"Doc IDs:\", result_set[\"doc_ids\"][0])\n",
    "print(\"Distances:\", result_set[\"distances\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "document_collection_name = \"datasets\"\n",
    "json_docs = store._retrieve_documents_from_result_set(result_set, document_collection_name)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "DOCUMENT 0\n",
      "Doc ID: 468\n",
      "Description: This repository contains the PG-19 language modeling benchmark.\n",
      "It includes a set of books extracted from the Project Gutenberg books library, that were published before 1919.\n",
      "It also contains metadata of book titles and publication dates.\n",
      "\n",
      "PG-19 is over double the size of the Billion Word benchmark and contains documents that are 20X longer, on average, than the WikiText long-range language modelling benchmark.\n",
      "Books are partitioned into a train, validation, and test set. Book metadata is stored in metadata.csv which contains (book_id, short_book_title, publication_date).\n",
      "\n",
      "Unlike prior benchmarks, we do not constrain the vocabulary size --- i.e. mapping rare words to an UNK token --- but instead release the data as an open-vocabulary benchmark. The only processing of the text that has been applied is the removal of boilerplate license text, and the mapping of offensive discriminatory words as specified by Ofcom to placeholder tokens. Users are free to model the data at the character-level, subword-level, or via any mechanism that can model an arbitrary string of text.\n",
      "To compare models we propose to continue measuring the word-level perplexity, by calculating the total likelihood of the dataset (via any chosen subword vocabulary or character-based scheme) divided by the number of tokens --- specified below in the dataset statistics table.\n",
      "One could use this dataset for benchmarking long-range language models, or use it to pre-train for other natural language processing tasks which require long-range reasoning, such as LAMBADA or NarrativeQA. We would not recommend using this dataset to train a general-purpose language model, e.g. for applications to a production-system dialogue agent, due to the dated linguistic style of old texts and the inherent biases present  [...]\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 1\n",
      "Doc ID: 126982\n",
      "Description: Super-EURLEX dataset containing legal documents from multiple languages.\n",
      "                The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html]\n",
      "                With one split per language and sector, because the available features (metadata) differs for each \n",
      "                sector. Therefore, each sample contains the content of a full legal document in up to 3 different \n",
      "                formats. Those are raw HTML and cleaned HTML (if the HTML format was available on the EURLEX website \n",
      "                during the scrapping process) and cleaned text.\n",
      "                The cleaned text should be available for each sample and was extracted from HTML or PDF.\n",
      "                'Cleaned' HTML stands here for minor cleaning that was done to preserve to a large extent the necessary \n",
      "                HTML information like table structures while removing unnecessary complexity which was introduced to the \n",
      "                original documents due to actions like writing each sentence into a new object. \n",
      "                Additionally, each sample contains metadata which was scrapped on the fly, this implies the following \n",
      "                2 things. First, not every sector contains the same metadata. Second, most metadata might be \n",
      "                irrelevant for most use cases. \n",
      "                In our minds the most interesting metadata is the celex-id which is used to identify the legal \n",
      "                document at hand, but also contains a lot of information about the document \n",
      "                see [https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf] as well as eurovoc-\n",
      "                concepts, which are labels that define the content of the documents. \n",
      "                Eurovoc-Concepts are, for example, only available  [...]\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 2\n",
      "Doc ID: 3310\n",
      "Description: CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB\n",
      "\n",
      "We show that margin-based bitext mining in LASER's multilingual sentence space can be applied to\n",
      "monolingual corpora of billions of sentences to produce high quality aligned translation data.\n",
      "We use thirty-two snapshots of a curated common crawl corpus [1] totaling 69 billion unique sentences.\n",
      "Using one unified approach for 80 languages, we were able to mine 10.8 billion parallel sentences,\n",
      "out of which only 2.9 billion are aligned with English.\n",
      "\n",
      "IMPORTANT: Please cite reference [2][3] if you use this data.\n",
      "\n",
      "[1] Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli\n",
      "    and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data\n",
      "\n",
      "[2] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin,\n",
      "    CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB\n",
      "\n",
      "[3] Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines,\n",
      "    Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky,\n",
      "    Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin.\n",
      "    Beyond English-Centric Multilingual Machine Translation\n",
      "    \n",
      "90 languages, 1,197 bitexts\n",
      "total number of files: 90\n",
      "total number of tokens: 112.14G\n",
      "total number of sentence fragments: 7.37G\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 3\n",
      "Doc ID: 232388\n",
      "Description: CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB\n",
      "\n",
      "We show that margin-based bitext mining in LASER's multilingual sentence space can be applied to\n",
      "monolingual corpora of billions of sentences to produce high quality aligned translation data.\n",
      "We use thirty-two snapshots of a curated common crawl corpus [1] totaling 69 billion unique sentences.\n",
      "Using one unified approach for 80 languages, we were able to mine 10.8 billion parallel sentences,\n",
      "out of which only 2.9 billion are aligned with English.\n",
      "\n",
      "IMPORTANT: Please cite reference [2][3] if you use this data.\n",
      "\n",
      "[1] Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli\n",
      "    and Edouard Grave, CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data\n",
      "\n",
      "[2] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin,\n",
      "    CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB\n",
      "\n",
      "[3] Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines,\n",
      "    Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky,\n",
      "    Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin.\n",
      "    Beyond English-Centric Multilingual Machine Translation\n",
      "    \n",
      "90 languages, 1,197 bitexts\n",
      "total number of files: 90\n",
      "total number of tokens: 112.14G\n",
      "total number of sentence fragments: 7.37G\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 4\n",
      "Doc ID: 141388\n",
      "Description: Abstract:\n",
      "\n",
      "\n",
      "We introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. The second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single-task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalizing on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding. \n",
      "\n",
      "\n",
      "Introduction:\n",
      "Multiple Languages and Modalities comprises data points on 236k human settlements for evaluating and optimizing multitask learning systems. MLM presents a dataset with a high level of diversity in terms of modality and language. For each entity, we have extracted text summaries, images, coordinates, and their respective triple classes. Text summaries are available in three languages (English, French, and German) with each entity having between one and three language entries. \n",
      "\n",
      "\n",
      "Human settlements from all continents are provided in the overall dataset (MLM [...]\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 5\n",
      "Doc ID: 720\n",
      "Description: XGLUE is a new benchmark dataset to evaluate the performance of cross-lingual pre-trained\n",
      "models with respect to cross-lingual natural language understanding and generation.\n",
      "The benchmark is composed of the following 11 tasks:\n",
      "- NER\n",
      "- POS Tagging (POS)\n",
      "- News Classification (NC)\n",
      "- MLQA\n",
      "- XNLI\n",
      "- PAWS-X\n",
      "- Query-Ad Matching (QADSM)\n",
      "- Web Page Ranking (WPR)\n",
      "- QA Matching (QAM)\n",
      "- Question Generation (QG)\n",
      "- News Title Generation (NTG)\n",
      "\n",
      "For more information, please take a look at https://microsoft.github.io/XGLUE/.\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 6\n",
      "Doc ID: 46498\n",
      "Description: MULTITuDE is a dataset for multilingual machine-generated text detection benchmark, described in the EMNLP 2023 conference paper. It consists of 7992 human-written news texts in 11 languages subsampled from MassiveSumm, accompanied by 66089 texts generated by 8 large language models (by using headlines of news articles). The creation process and scripts for replication/extension are located in a GitHub repository.\n",
      "If you use this dataset in any publication, project, tool or in any other form, please, cite the paper.\n",
      "Fields\n",
      "The dataset has the following fields:\n",
      "\n",
      "'text' - a text sample,\n",
      "'label' - 0 for human-written text, 1 for machine-generated text,\n",
      "'multi_label' - a string representing a large language model that generated the text or the string \"human\" representing a human-written text,\n",
      "'split' - a string identifying train or test split of the dataset for the purpose of training and evaluation respectively,\n",
      "'language' - the ISO 639-1 language code identifying the language of the given text,\n",
      "'length' - word count of the given text,\n",
      "'source' - a string identifying the source dataset / news medium of the given text.\n",
      "Statistics (the number of samples)\n",
      "Splits:\n",
      "\n",
      "train - 44786\n",
      "test - 29295\n",
      "Binary labels:\n",
      "\n",
      "0 - 7992\n",
      "1 - 66089\n",
      "Multiclass labels:\n",
      "\n",
      "gpt-3.5-turbo -       8300\n",
      "gpt-4 -                    8300\n",
      "text-davinci-003 -   8297\n",
      "alpaca-lora-30b -   8290\n",
      "vicuna-13b -          8287\n",
      "opt-66b -                8229\n",
      "llama-65b -            8229\n",
      "opt-iml-max-1.3b - 8157\n",
      "human -                 7992\n",
      "Languages:\n",
      "\n",
      "English (en) - 29460 (train + test)\n",
      "Spanish (es) - 11586 (train + test)\n",
      "Russian (ru) - 11578 (train + test)\n",
      "Dutch (nl) - 2695 (test)\n",
      "Catalan (ca) - 2691 (test)\n",
      "Czech (cs) - 2689 (test)\n",
      "German (de) - 2685 (test)\n",
      "Chinese (zh) - 2683 (test)\n",
      "Portuguese (pt) - 2673 (test)\n",
      "Arabic  [...]\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 7\n",
      "Doc ID: 14\n",
      "Description: We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.\n",
      "\n",
      "For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.\n",
      "\n",
      "Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 8\n",
      "Doc ID: 307718\n",
      "Description: This is a republication of the Event Registry dataset originaly published by:\n",
      "\n",
      "\n",
      "Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». Journal of Artificial Intelligence Research 55 (janvier): 283‑316. https://doi.org/10.1613/jair.4780.\n",
      "\n",
      "\n",
      "And reorganised for document tracking by:\n",
      "\n",
      "\n",
      "Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In 2018 Conference on Empirical Methods in Natural Language Processing, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/.\n",
      "\n",
      "\n",
      "In this dataset, we provide multiple features extracted from the text itself. Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.\n",
      "\n",
      "\n",
      "Features are extracted using:\n",
      "\n",
      "\n",
      "- A corpus of reference articles in multiple languages languages for TF-IDF weighting. (features_news) [1]\n",
      "\n",
      "\n",
      "- A corpus of tweets reporting news for TF-IDF weighting. (features_tweets) [1]\n",
      "\n",
      "\n",
      "- A S-BERT model [2] that uses distiluse-base-multilingual-cased-v1 (called features_use) [3]\n",
      "\n",
      "\n",
      "- A S-BERT model [2] that uses paraphrase-multilingual-mpnet-base-v2 (called features_mpnet) [4]\n",
      "\n",
      "\n",
      "References:\n",
      "\n",
      "\n",
      "[1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406\n",
      "\n",
      "\n",
      "[2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In Proceedings of the 2019 Conference on Empirical Methods in Natural Languag [...]\n",
      "----------------------------\n",
      "\n",
      "\n",
      "----------------------------\n",
      "DOCUMENT 9\n",
      "Doc ID: 145626\n",
      "Description: SynQA is a Reading Comprehension dataset created in the work \"Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation\" (https://aclanthology.org/2021.emnlp-main.696/).\n",
      "It consists of 314,811 synthetically generated questions on the passages in the SQuAD v1.1 (https://arxiv.org/abs/1606.05250) training set.\n",
      "\n",
      "In this work, we use a synthetic adversarial data generation to make QA models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA (https://adversarialqa.github.io/) dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation to show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8.8% of the time on average, compared to 17.6% for a model trained without synthetic data.\n",
      "\n",
      "For full details on how the dataset was created, kindly refer to the paper.\n",
      "----------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for it, (doc, id, dist) in enumerate(zip(json_docs, result_set[\"doc_ids\"][0], result_set[\"distances\"][0])):\n",
    "    descr = doc.get(\"description\", {})\n",
    "    descr = descr.get(\"plain\", None)\n",
    "    \n",
    "    print(\"----------------------------\")\n",
    "    print(\"DOCUMENT\", it)\n",
    "    print(\"Doc ID:\", id)\n",
    "    print(\"Description:\", descr)\n",
    "    print(\"----------------------------\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON -> TEXT conversion\n",
    "- mostly concatenate seemingly relevant fields in JSON\n",
    "- omit empty fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'platform': 'zenodo',\n",
       " 'platform_resource_identifier': 'zenodo.org:7928582',\n",
       " 'name': 'Large Movie Review Dataset',\n",
       " 'date_published': '2011-06-01T00:00:00',\n",
       " 'same_as': 'https://zenodo.org/api/records/7928582',\n",
       " 'ai_asset_identifier': 371516,\n",
       " 'ai_resource_identifier': 371546,\n",
       " 'aiod_entry': {'editor': [],\n",
       "  'status': 'published',\n",
       "  'date_modified': '2024-04-22T16:39:07',\n",
       "  'date_created': '2024-04-22T16:39:07'},\n",
       " 'alternate_name': [],\n",
       " 'application_area': [],\n",
       " 'citation': [],\n",
       " 'contact': [],\n",
       " 'creator': [],\n",
       " 'description': {'plain': 'IMDB dataset having 50K movie reviews for natural language processing or Text analytics.\\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.\\nFor more dataset information, please go through the following link,\\nhttp://ai.stanford.edu/~amaas/data/sentiment/.'},\n",
       " 'distribution': [{'checksum': '308443a50e5c993e7b8a1cdb95750026',\n",
       "   'checksum_algorithm': 'md5',\n",
       "   'content_url': 'https://zenodo.org/api/records/7928582/files/IMDB%20Dataset.csv/content',\n",
       "   'encoding_format': 'text/csv',\n",
       "   'name': 'IMDB Dataset.csv'}],\n",
       " 'funder': [],\n",
       " 'has_part': [],\n",
       " 'industrial_sector': [],\n",
       " 'is_part_of': [],\n",
       " 'keyword': ['sentiment classification'],\n",
       " 'license': 'creative commons attribution 4.0 international',\n",
       " 'media': [],\n",
       " 'note': [],\n",
       " 'relevant_link': [],\n",
       " 'relevant_resource': [],\n",
       " 'relevant_to': [],\n",
       " 'research_area': [],\n",
       " 'scientific_domain': [],\n",
       " 'identifier': 355360}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_doc = json_docs[0]\n",
    "json_doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "platform: zenodo\n",
      "name: Large Movie Review Dataset\n",
      "date_published: 2011-06-01T00:00:00\n",
      "year_published: 2011\n",
      "month_published: 6\n",
      "day_published: 1\n",
      "description: IMDB dataset having 50K movie reviews for natural language processing or Text analytics.\n",
      "This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.\n",
      "For more dataset information, please go through the following link,\n",
      "http://ai.stanford.edu/~amaas/data/sentiment/.\n",
      "keyword: sentiment classification\n",
      "DISTRIBUTION:\n",
      "\tname:IMDB Dataset.csv, encoding_format:text/csv\n",
      "\n"
     ]
    }
   ],
   "source": [
    "text_doc = ConvertJsonToString.extract_relevant_info(json_doc)\n",
    "print(text_doc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tailor",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}