Memory_PoC_MK0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chromadb import PersistentClient, Client\n",
    "from chromadb.api import EmbeddingFunction, Documents, Embeddings\n",
    "from Nexus import globalNexus\n",
    "from chromadb.utils import embedding_functions\n",
    "#default_ef = embedding_functions.SentenceTransformerEmbeddingFunction(modelName=\"all-mpnet-base-v2\",\n",
    "#                                                                      device=\"cuda\", normalize_embeddings=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model Embeddings.Embeddings activated\n",
      "model Embeddings.Embeddings deactivated\n"
     ]
    }
   ],
   "source": [
    "\n",
    "class MyEmbeddingFunction(EmbeddingFunction):\n",
    "    def __call__(self, input: Documents) -> Embeddings:\n",
    "            return globalNexus.ComputeEmbeddings(input)\n",
    "\n",
    "\n",
    "client = Client()\n",
    "episodicMemory = client.get_or_create_collection(name=\"episodicMemory\",\n",
    "                                                 embedding_function=MyEmbeddingFunction()\n",
    ",\n",
    "                                                 metadata={\"hnsw:space\": \"l2\"})\n",
    "\n",
    "\n",
    "\n",
    "episodicMemory.add(\n",
    "    documents=[\"Lemons are a type of citrus fruit with a sweet and sour taste. They are known for their unique flavor profile that can range from tangy to tart, depending on the variety. Lemons are one of the most popular fruits in the world due to their versatility. They are used in many culinary dishes, such as lemonade, lemon meringue pie, and lemon chicken. Additionally, they have numerous health benefits including boosting immunity, lowering blood pressure, and promoting weight loss.\",\n",
    "               \"poetry\", \"microchip\", \"starship\", \"good\"],    \n",
    "    metadatas=[{\"chapter\": 3, \"verse\": \"16\"}, {\"chapter\": 3, \"verse\": \"5\"}, \n",
    "               {\"chapter\": 29, \"verse\": \"11\"}, {\"chapter\": 29, \"verse\": \"12\"}, {\"chapter\": 29, \"verse\": \"15\"}], \n",
    "    ids=[\"id1\", \"id2\", \"id3\", \"id4\", \"id5\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model Embeddings.Embeddings activated\n",
      "model Embeddings.Embeddings deactivated\n",
      "['id5', 'id4', 'id3']\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "where={\"chapter\": {\"$gt\": 4}}\n",
    "ct = episodicMemory.count()\n",
    "\n",
    "query =episodicMemory.query(query_texts=[\"\"], n_results=ct,where=where,include=[])\n",
    "\n",
    "print(query[\"ids\"][0])\n",
    "ids = query[\"ids\"][0]\n",
    "print(len(ids))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Number of requested results 10 is greater than number of elements in index 5, updating n_results = 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model Embeddings.Embeddings activated\n",
      "model Embeddings.Embeddings deactivated\n",
      "['id1', 'id5', 'id2', 'id4', 'id3']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Number of requested results 10 is greater than number of elements in index 5, updating n_results = 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model Embeddings.Embeddings activated\n",
      "model Embeddings.Embeddings deactivated\n",
      "{'ids': [['id1', 'id4', 'id5', 'id3', 'id2']], 'distances': [[1.4529932737350464, 1.916156530380249, 1.9447579383850098, 1.9607347249984741, 2.00964617729187]], 'metadatas': [[{'chapter': 3, 'liber': 'al', 'verse': '16'}, {'chapter': 29, 'liber': 'al', 'verse': '12'}, {'chapter': 29, 'liber': 'al', 'verse': '15'}, {'chapter': 29, 'liber': 'al', 'verse': '11'}, {'chapter': 3, 'liber': 'al', 'verse': '5'}]], 'embeddings': None, 'documents': [['Lemons are a type of citrus fruit with a sweet and sour taste. They are known for their unique flavor profile that can range from tangy to tart, depending on the variety. Lemons are one of the most popular fruits in the world due to their versatility. They are used in many culinary dishes, such as lemonade, lemon meringue pie, and lemon chicken. Additionally, they have numerous health benefits including boosting immunity, lowering blood pressure, and promoting weight loss.', 'starship', 'good', 'microchip', 'poetry']], 'uris': None, 'data': None}\n"
     ]
    }
   ],
   "source": [
    "query =episodicMemory.query(query_texts=[\"lime\"], n_results=10,\n",
    "                     where={\"chapter\": {\"$gt\": 1}},\n",
    "                     include=[])\n",
    "\n",
    "print(query[\"ids\"][0])\n",
    "ids = query[\"ids\"][0]\n",
    "metadatas = []\n",
    "\n",
    "for id in ids:\n",
    "    metadatas.append({\"liber\":\"al\"})\n",
    "\n",
    "episodicMemory.update(ids, metadatas=metadatas)\n",
    "\n",
    "query = episodicMemory.query(query_texts=[\"pineapple juice is usually made with peppermint, water and pineapples\"], n_results=10,\n",
    "                     where={\"chapter\": {\"$gt\": 1}})\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 'id1', 'distance': 1.4529932737350464}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getItemsByTreshold(query, threshold):\n",
    "  ids = query[\"ids\"][0]\n",
    "  count = len(ids)\n",
    "  list = []\n",
    "  for i in range(count):\n",
    "    if query[\"distances\"][0][i] <= threshold:\n",
    "      list.append({\"id\": ids[i], \"distance\": query[\"distances\"][0][i]})\n",
    "  return list    \n",
    "\n",
    "getItemsByTreshold(query, 1.455)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from Nexus import NexusEmbeddingFunction\n",
    "from chromadb import Client\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'booleanCollection' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[7], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43msentenceToBoolean\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mno way\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n",
      "Cell \u001b[1;32mIn[7], line 2\u001b[0m, in \u001b[0;36msentenceToBoolean\u001b[1;34m(choice)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msentenceToBoolean\u001b[39m(choice):\n\u001b[1;32m----> 2\u001b[0m   query \u001b[38;5;241m=\u001b[39m \u001b[43mbooleanCollection\u001b[49m\u001b[38;5;241m.\u001b[39mquery(query_texts\u001b[38;5;241m=\u001b[39m[choice], n_results\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, where\u001b[38;5;241m=\u001b[39m[], include\u001b[38;5;241m=\u001b[39m[])\n\u001b[0;32m      3\u001b[0m   \u001b[38;5;28mid\u001b[39m \u001b[38;5;241m=\u001b[39m query[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mids\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m      4\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m(\u001b[38;5;28mid\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "\u001b[1;31mNameError\u001b[0m: name 'booleanCollection' is not defined"
     ]
    }
   ],
   "source": [
    "def sentenceToBoolean(choice):\n",
    "  query = booleanCollection.query(query_texts=[choice], n_results=1, where=[], include=[])\n",
    "  id = query[\"ids\"][0][0]\n",
    "  if(id == \"1\"):\n",
    "    return True\n",
    "  else:\n",
    "    return False\n",
    "  \n",
    "print(sentenceToBoolean(\"no way\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['the', 'and', 'was', 'for', 'with', 'that', 'his', 'from', 'her', 'she', 'you', 'had', 'were', 'but', 'this', 'are', 'not', 'they', 'one', 'which', 'have', 'him', 'first', 'all', 'also', 'their', 'has', 'who', 'out', 'been', 'when', 'after', 'there', 'into', 'new', 'two', 'its', 'time', 'would', 'what', 'about', 'said', 'over', 'then', 'other', 'more', 'can', 'like', 'back', 'them', 'only', 'some', 'could', 'where', 'just', 'during', 'before', 'made', 'school', 'through', 'than', 'now', 'years', 'most', 'world', 'may', 'between', 'down', 'well', 'three', 'year', 'while', 'will', 'later', 'city', 'under', 'around', 'did', 'such', 'being', 'used', 'state', 'people', 'part', 'know', 'against', 'your', 'many', 'second', 'university', 'both', 'national', 'these', 'don', 'known', 'off', 'way', 'until', 'how', 'even', 'get', 'head', 'didn', 'team', 'american', 'because', 'born', 'united', 'film', 'since', 'still', 'long', 'work', 'south', 'became', 'any', 'high', 'again', 'day', 'family', 'see', 'right', 'man', 'eyes', 'house', 'season', 'war', 'states', 'including', 'took', 'life', 'north', 'same', 'each', 'called', 'name', 'much', 'place', 'however', 'four', 'group', 'another', 'found', 'won', 'area', 'here', 'going', 'away', 'series', 'left', 'home', 'music', 'best', 'make', 'hand', 'number', 'company', 'several', 'never', 'last', 'john', '000', 'very', 'album', 'take', 'end', 'good', 'too', 'following', 'released', 'game', 'played', 'little', 'began', 'district', 'old', 'want', 'those', 'side', 'held', 'own', 'early', 'county', 'league', 'use', 'west', 'face', 'think', '2010', 'government', 'march', 'came', 'small', 'general', 'town', 'june', 'line', 'based', 'something', 'september', 'thought', 'looked', 'along', 'international', '2011', 'air', 'july', 'club', 'went', 'january', 'october', 'our', 'august', 'april', 'york', 'few', '2012', '2008', 'east', 'show', 'member', 'college', '2009', 'father', 'public', 'come', 'men', 'five', 'set', 'station', 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'harassed', '326', 'archway', 'dazzling', '1736', 'sumo', 'wat', '1086', 'honneur', '1605', '403', '1716', 'blogger', 'rents', 'hires', 'howie', 'handler', 'retracted', 'shocks', '1632', 'arun', 'duluth', 'kepler', 'trumpeter', 'peeking', 'seasoned', 'trooper', 'laszlo', 'heterosexual', 'indira', 'jogging', 'beit', 'dissatisfaction', 'malice', 'nedra', 'peeling', '47th', 'stadiums', '475', 'vertigo', 'iced', 'restroom', 'illustrating', 'pear', 'inorganic', 'rappers', 'receipts', 'watery', 'lucinda', 'reintroduced', 'gracefully', 'saxons', 'nutritional', 'wastewater', 'rained', 'favourites', 'bedrock', 'fisted', 'hallways', 'likeness', 'upscale', '1580', 'blinds', 'prequel', 'deter', 'humiliating', 'restraining', 'vents', '1659', 'laundering', 'recess', 'rosary', 'tractors', 'coulter', 'federer', 'persistence', 'geschichte', 'pendulum', 'quakers', 'bassett', 'pictorial', 'buffet', 'koln', 'drills', 'reciprocal', 'shooters', 'converge', 'pip', 'dmitri', 'donnelly', 'yamamoto', 'aqua', 'azores', 'demographics', 'hypnotic', 'spitfire', 'suspend', 'wryly', 'roderick', 'sebastien', 'mavericks', 'himalayan', 'prodigy', 'transvaal', 'demonstrators', 'handcuffs', 'dodged', 'mcnamara', 'sublime', '1726', 'crazed', 'ivo', 'pondered', 'reconciled', 'shrill', 'sava', 'bal', 'cad', 'heresy', 'jaipur', 'goran', '341', 'lux', 'shelly', 'whitehall', 'israelis', 'peacekeeping', '1703', 'demetrius', 'ousted', 'beale', 'anwar', 'backstroke', 'raged', 'shrinking', 'cremated', 'benign', 'towing', 'wadi', 'darmstadt', 'landfill', 'parana', 'soothe', 'colleen', 'sidewalks', 'mayfair', 'tumble', 'hepatitis', 'ferrer', 'superstructure', 'anthropological', 'translators', 'closeness', 'hooves', 'mondays', 'landscaping', 'purification', 'sock', 'thorns', 'thwarted', 'jalan', 'tiberius', 'saline', 'confidently', 'khyber', 'sculptors', 'brahms', 'hammersmith', 'inspectors', 'battista', 'fivb', 'fragmentation', 'hackney', 'arresting', 'exercising', 'antoinette', 'bedfordshire', 'dyed', '1656', 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'disciplined', 'inspirational', 'banco', 'confuse', 'grafton', 'tod', 'championed', 'simi', 'anomaly', 'biplane', 'electrode', '1697', 'cleavage', 'crossbow', 'swirl', 'informant', 'afi', 'bonfire', 'spec', 'lakeside', 'slump', '1016', 'facades', 'borg', 'inwardly', 'cervical', 'pointedly', '050', 'stabilization', 'chests', '1699', 'hacked', 'ctv', 'orthogonal', 'suzy', 'gaulle', 'jacobite', 'rearview', 'ashby', 'affectionately', 'canine', 'disperse', 'latham', 'spielberg', 'ezekiel', 'cid', 'durga', 'middletown', 'customized', 'frontiers', 'harden', '1604', 'bolsheviks', 'coloration', 'yoko', 'briefs', 'slabs', 'debra', 'liquidation', 'plumage', 'blossoms', 'dementia', 'subsidy', '1611', 'proctor', 'relational', 'jerseys', 'parochial', 'ter', 'esa', 'peshawar', 'cavalier', 'loren', 'cpi', 'idiots', 'shamrock', '1646', 'dutton', 'malabar', 'mustache', 'referencing', 'terminates', 'marche', 'yarmouth', 'acton', 'mated', 'seton', 'subtly', 'baptised', 'beige', 'extremes', 'jolted', 'kristina', 'telecast', 'safeguard', 'waldo', 'endeavors', 'sloppy', 'subterranean', 'delicately', 'pigment', '1626', 'collisions', 'coveted', 'herds', 'chopra', 'abnormalities', 'defective', 'galician', 'lucie', 'alligator', 'likened', 'burundi', 'clears', 'complexion', 'derelict', 'deafening', 'diablo', 'fingered', 'champaign', 'dogg', 'enlist', 'isotope', 'labeling', 'mrna', 'brilliance', 'marvelous', '1652', 'crawley', 'ether', 'footed', 'dwellers', 'deserts', 'hamish', 'rubs', 'warlock', 'skimmed', '870', 'buick', 'embark', 'heraldic', 'irregularities', 'kiara', 'antigen', 'kowalski', 'oakley', 'visitation', '1570', 'murderers', 'chimneys', 'condemn', 'custer', 'exchequer', 'havre', 'fluctuations', 'dfb', 'hendricks', 'vaccines', 'nietzsche', 'biking', 'juicy', 'brooding', 'scrolling', 'selangor', '352', 'annum', 'boomed', 'seminole', 'sugarcane', 'departmental', 'dismissing', 'innsbruck', 'arteries', 'ashok', 'batavia', 'daze', 'kun', 'overtook', 'beheaded', 'gaddafi', 'holm', 'electronically', 'faulty', 'galilee', 'fractures', 'kobayashi', 'gunmen', 'magma', 'aramaic', 'mala', 'eastenders', 'inference', 'messengers', '407', 'bathrooms', '1658', 'flashbacks', 'ideally', 'misunderstood', 'mendez', '505', 'uncanny', '1709', 'friendships', 'sacrament', 'accommodated', 'reiterated', 'logistical', 'pebbles', 'thumped', 'administering', 'decrees', 'drafts', 'futuristic', 'picket', 'intimidation', 'winthrop', 'interfered', '339', 'afar', 'francoise', 'morally', 'uta', 'cochin', 'croft', 'dwarfs', 'biker', 'nano', 'brightened', 'meek', 'parcels', 'securely', 'gunners', 'agile', 'hysteria', 'bukit', 'champs', 'chevy', 'cuckoo', 'leith', 'sadler', 'theologians', 'welded', '1663', 'plurality', 'xander', 'shredded', 'temps', 'intimately', 'pau', 'tormented', '1618', 'charred', 'ems', 'essen', 'alarms', 'spraying', 'ascot', 'blooms', 'twinkle', 'internment', 'obsidian', 'snoop', 'malibu', 'quiver', 'hays', 'mcintosh', 'travers', 'walsall', '1623', 'beverley', 'schwarz', 'plunging', 'structurally', 'rosenthal', 'vikram', '770', 'ghz', 'chalmers', 'groningen', 'pew', 'reckon', 'unicef', '55th', '1651', 'sulawesi', 'avila', 'cai', 'metaphysical', 'screwing', 'turbulence', 'augusto', 'samba', '56th', 'baffled', 'momentary', 'toxin', 'aachen', 'condoms', 'dali', 'steppe', 'adolescence', 'dauphin', 'electrically', 'inaccessible', 'microscopy', 'nikita', 'atv', 'accountants', 'punishments', 'wrongly', 'bribes', 'adventurous', 'clinch', 'flinders', 'southland', 'gough', 'lads', 'soared', 'undergoes', 'deformation', 'outlawed', 'rubbish', 'arcs', '1695', 'wheelbase', 'wheeling', 'bombardier', 'campground', 'zebra', 'lullaby', 'donetsk', 'wylie', 'grenada', 'squinting', 'eireann', 'opposes', 'maximal', 'runes', 'additive', 'britney', 'adultery', 'triggering', 'detrimental', 'aarhus', 'containment', 'swapped', 'vichy', 'madly', 'brant', '1560', '1612', 'broughton', 'rustling', 'asbestos', 'mentoring', 'finley', 'leaps', 'apical', 'pry', 'slits', 'substitutes', 'intuitive', 'fantasia', 'insistent', 'unreasonable', 'domed', 'hannover', 'margot', 'ponder', 'impromptu', 'jian', 'rampage', 'stemming', 'andrey', 'gerais', 'whichever', 'amnesia', 'appropriated', 'anzac', 'clicks', 'modifying', 'ultimatum', 'cambrian', 'maids', 'verve', 'yellowstone', 'conservatoire', 'adherence', 'dinners', 'spectra', 'imperfect', 'mysteriously', 'sidekick', 'tatar', 'tuba', 'distrust', 'ronin', 'zac', 'celaena', 'instrumentalist', 'scents', 'skopje', 'comical', 'compensated', 'vidal', 'condor', 'intersect', 'jingle', 'wavelengths', 'mcqueen', 'carp', 'weasel', '422', 'kanye', 'militias', 'postdoctoral', 'eugen', 'gunslinger', 'faux', 'hospice', 'appalled', 'derivation', 'dwarves', 'dilapidated', 'astoria', 'philology', 'inducing', 'philanthropy', 'geek', 'markedly', 'sql', 'bessie', 'indices', '495', 'frowns', 'resolving', 'weightlifting', 'tugs', 'cleric', 'contentious', '1653', 'mania', 'rms', 'bien', 'eels', 'marek', 'discreet', 'unofficially', 'leaks', '1705', '332', 'dung', 'compressor', 'hillsborough', 'pandit', 'shillings', 'distal', '381', 'nosed', 'mangrove', 'undeveloped', 'textures', 'irritating', 'nay', 'amazingly', 'bancroft', 'apologetic', 'compassionate', 'kata', 'symphonies', 'airspace', '930', 'gifford', 'precautions', 'fulfillment', 'sevilla', 'vulgar', 'martinique', 'looting', 'piccolo', 'tidy', 'quadrant', 'armchair', 'incomes', 'mathematicians', 'stampede', 'nilsson', 'foo', 'quarterfinal', 'shang', 'shouldered', 'squirrels', '344', 'vinegar', 'delaying', 'ars', 'dwyer', 'rhapsody', 'sponsoring', 'bipolar', 'cinder', 'starters', '421', 'signage', 'aground', 'figurative', 'mons', 'acquaintances', 'duets', 'erroneously', 'soyuz', 'elliptic', 'recreated', 'moderator', 'scares', 'juniper', 'sighting', 'britten', 'calabria', 'bop', 'cramer', 'forsyth', 'stillness', 'airmen', 'gathers', 'unfit', 'taunting', 'seeker', 'streamlined', 'holster', 'schumann', 'tread', 'vox', 'strive', 'dil', 'reforming', 'covent', 'newbury', 'predicting', 'decorate', 'tre', 'andover', 'asahi', 'dept', 'dunkirk', 'gills', 'buren', 'huskies', 'abstracts', 'bets', 'loosen', '1682', 'yearning', 'berman', 'effortlessly', 'enamel', 'napoli', 'persist', 'attache', 'elisa', 'invitations', 'accelerating', 'reindeer', 'boardwalk', 'clutches', 'nelly', 'polka', 'starbucks', 'adamant', 'huey', 'lough', 'unbroken', 'adventurer', 'embroidery', 'inspecting', 'stanza', 'naia', 'taluka', 'chases', 'deprivation', 'florian', 'earthly', 'colossal', 'foreigner', 'vet', 'freaks', 'patrice', 'rosewood', 'triassic', 'upstate', 'dominates', 'ata', 'chants', '555', 'agra', 'infiltrate', 'binoculars', 'enthusiast', 'fujian', 'squeak', 'abolitionist', 'almeida', 'boredom', 'hampstead', 'marsden', 'rations', 'inflated', '334', 'bonuses', 'rosalie', 'patna', '329', 'detachments', 'penitentiary', '54th', 'flourishing', 'woolf', 'papyrus', 'bobbed', 'dismounted', 'endelle', 'inhuman', 'motorola', 'tbs', 'wince', 'wreath', 'hideout', 'inspections', 'sanjay', 'disgrace', 'infused', 'pudding', 'stalks', 'arsenic', 'leases', 'collarbone', 'dowry', 'genealogical', 'nitrate', 'salamanca', 'scandals', 'thyroid', 'necessitated']\n"
     ]
    }
   ],
   "source": [
    "from Nexus import NexusEmbeddingFunction\n",
    "\n",
    "sentence = \"the world is comming to an end\"\n",
    "\n",
    "# Get all word embeddings from the model\n",
    "with open(\"B:/PsychoStasis/models/sentence-transformers_all-mpnet-base-v2/vocab.txt\", \"r\") as f:\n",
    "  words = f.read().splitlines()\n",
    "\n",
    "invalidChars = [\"#\", \"@\", \"!\", \"$\", \"%\", \"^\", \"&\", \"*\", \"(\", \")\", \"-\", \"_\", \"=\", \"+\", \"{\", \"}\", \"[\", \"]\", \"|\", \"\\\\\", \":\", \";\", \"'\", \"\\\"\", \"<\", \">\", \",\", \".\", \"?\", \"/\"]\n",
    "\n",
    "words = list(filter(lambda s: not any(s.startswith(c) for c in invalidChars) and len(s)>2, words))\n",
    "print(words)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chromadb import PersistentClient, Client\n",
    "from chromadb.api import EmbeddingFunction, Documents, Embeddings\n",
    "from Nexus import globalNexus\n",
    "from chromadb.utils import embedding_functions\n",
    "\n",
    "client = Client()\n",
    "\n",
    "\n",
    "ids = [\"id{}\".format(i) for i in range(1, len(words) + 1)]\n",
    "wordList =client.get_or_create_collection(name=\"booleanCollection\",\n",
    "                                                 embedding_function=NexusEmbeddingFunction(),\n",
    "                                                 metadata={\"hnsw:space\": \"l2\"})\n",
    "wordList.add(documents=words, ids=ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordList.query(query_texts=[sentence], n_results=5, where=[], include=[\"documents\", \"distances\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n",
      "22209\n",
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n",
      "22209\n"
     ]
    }
   ],
   "source": [
    "from chromadb import PersistentClient, Client\n",
    "from Memory import longTermMemory, Memory\n",
    "from Nexus import NexusEmbeddingFunction\n",
    "#longTermMemory.client.delete_collection(\"closestWordMemory\")\n",
    "#longTermMemory.client.delete_collection(\"booleanDiscriminationMemory\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['volume', 'louder', 'distortion']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longTermMemory.getClosestWord(\"I'm gonna have the whole hood confused when I pull up playing this at full volume\", 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n",
      "2\n",
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n",
      "{'ids': [['1']], 'distances': None, 'metadatas': None, 'embeddings': None, 'documents': None, 'uris': None, 'data': None}\n"
     ]
    }
   ],
   "source": [
    "positive = [\"yes\", \"sure\", \"ok\", \"okay\", \"yeah\", \"yup\", \"yep\", \"yea\", \"yah\", \"yas\", \"ya\", \"yap\"]\n",
    "negative = [\"no\", \"nope\", \"nah\", \"nay\", \"nope\", \"nah\", \"nay\"]\n",
    "\n",
    "positiveStr = \" \".join(positive)\n",
    "negativeStr = \" \".join(negative)\n",
    "#client = Client()\n",
    "client = PersistentClient()\n",
    "\n",
    "\n",
    "booleanCollection =client.get_or_create_collection(name=\"booleanDiscriminationMemory\",\n",
    "                                                 embedding_function=NexusEmbeddingFunction(),\n",
    "                                                 metadata={\"hnsw:space\": \"l2\"})\n",
    "booleanCollection.add(documents=[negativeStr, positiveStr], ids=[\"0\", \"1\"])\n",
    "\n",
    "print(booleanCollection.count())\n",
    "query = booleanCollection.query(query_texts=[\"Excellent, continue!\"], n_results=1, where=[], include=[])\n",
    "id = query[\"ids\"][0][0]\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(booleanCollection.count())\n",
    "query = booleanCollection.query(query_texts=[\"Excellent, continue!\"], n_results=1, where=[], include=[])\n",
    "id = query[\"ids\"][0][0]\n",
    "print(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22209\n",
      "22209\n",
      "10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "from Memory import longTermMemory, Memory\n",
    "from Nexus import NexusEmbeddingFunction\n",
    "\n",
    "invalidChars = [\"#\", \"@\", \"!\", \"$\", \"%\", \"^\", \"&\", \"*\", \"(\", \")\", \"-\", \"_\", \"=\", \"+\", \"{\", \"}\", \"[\", \"]\", \"|\", \"\\\\\", \":\", \";\", \"'\", \"\\\"\", \"<\", \">\", \",\", \".\", \"?\", \"/\"]\n",
    "\n",
    "with open(\"./models/vocab.txt\", \"r\") as f:\n",
    "  words = f.read().splitlines()\n",
    "words = list(filter(lambda s: not any(s.startswith(c) for c in invalidChars) and len(s)>2, words))\n",
    "print(len(words))\n",
    "\n",
    "ids = [f\"id_{i}\" for i in range(0, len(words))]\n",
    "print(len(ids))\n",
    "\n",
    "words = words[:10]\n",
    "ids = ids[:10]\n",
    "print(len(words))\n",
    "print(len(ids))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings.Embeddings activated :: device = cuda\n",
      "Embeddings.Embeddings deactivated :: device = cpu\n"
     ]
    }
   ],
   "source": [
    "longTermMemory.closestWordMemory = longTermMemory.client.get_or_create_collection(\"closestWordMemory\",\n",
    "                                                                  embedding_function=NexusEmbeddingFunction(), metadata={\"hnsw:space\": \"l2\"})\n",
    "longTermMemory.closestWordMemory.add(documents=words, ids=ids)"
   ]
  }
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