01-intro/rag-intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "155f85a4-49cf-4489-afe8-bc595f622b3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-06-06 12:12:51--  https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.110.133, 185.199.108.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3832 (3.7K) [text/plain]\n",
      "Saving to: ‘minsearch.py’\n",
      "\n",
      "minsearch.py        100%[===================>]   3.74K  --.-KB/s    in 0s      \n",
      "\n",
      "2024-06-06 12:12:51 (32.2 MB/s) - ‘minsearch.py’ saved [3832/3832]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/alexeygrigorev/minsearch/main/minsearch.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63b0fd25-41a2-48ad-b9bf-3f1265308bd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import minsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b37cce2-ed84-408b-9106-d61360aa82c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bdaf6ce-2540-494f-989c-5b94b1b6626c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('documents.json', 'rt') as f_in:\n",
    "    docs_raw = json.load(f_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "98c41787",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests \n",
    "\n",
    "docs_url = 'https://github.com/DataTalksClub/llm-zoomcamp/blob/main/01-intro/documents.json?raw=1'\n",
    "docs_response = requests.get(docs_url)\n",
    "documents_raw = docs_response.json()\n",
    "\n",
    "documents = []\n",
    "\n",
    "for course in documents_raw:\n",
    "    course_name = course['course']\n",
    "\n",
    "    for doc in course['documents']:\n",
    "        doc['course'] = course_name\n",
    "        documents.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42b9b1f9-3c90-42b0-beb4-cb419f9cdcea",
   "metadata": {},
   "outputs": [],
   "source": [
    "documents = []\n",
    "\n",
    "for course_dict in docs_raw:\n",
    "    for doc in course_dict['documents']:\n",
    "        doc['course'] = course_dict['course']\n",
    "        documents.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57de60e5-b96c-499c-a7cf-0f30fc33b324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       " 'section': 'General course-related questions',\n",
       " 'question': 'Course - When will the course start?',\n",
       " 'course': 'data-engineering-zoomcamp'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c499838b-73b3-44be-8ba6-f46d3693aa59",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = minsearch.Index(\n",
    "    text_fields=[\"question\", \"text\", \"section\"],\n",
    "    keyword_fields=[\"course\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e201f0f3-b380-43a5-95ee-62b1b79002d1",
   "metadata": {},
   "source": [
    "SELECT * WHERE course = 'data-engineering-zoomcamp';"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8d8ea88-7412-49c1-8a8e-44d0d0862a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "q = 'the course has already started, can I still enroll?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce7d0d18-5c07-4010-9f90-bbd021f110c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<minsearch.Index at 0x7a7310adc6a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index.fit(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "856ed776",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/key_binding/key_bindings.py:447: RuntimeWarning: coroutine 'Application.run_async' was never awaited\n",
      "  return Keys(key)\n",
      "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n",
      "Task exception was never retrieved\n",
      "future: <Task finished name='Task-1' coro=<Application.run_async() done, defined at /home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py:620> exception=KeyboardInterrupt()>\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/site-packages/wit/wit.py\", line 106, in interactive\n",
      "    message = prompt(INTERACTIVE_PROMPT, history=history, mouse_support=True).rstrip()\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py\", line 1417, in prompt\n",
      "    return session.prompt(\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py\", line 1027, in prompt\n",
      "    return self.app.run(\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 1002, in run\n",
      "    return asyncio.run(coro)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 30, in run\n",
      "    return loop.run_until_complete(task)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 92, in run_until_complete\n",
      "    self._run_once()\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 133, in _run_once\n",
      "    handle._run()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/events.py\", line 80, in _run\n",
      "    self._context.run(self._callback, *self._args)\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 315, in __wakeup\n",
      "    self.__step()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n",
      "    result = coro.send(None)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 886, in run_async\n",
      "    return await _run_async(f)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 746, in _run_async\n",
      "    result = await f\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/futures.py\", line 285, in __await__\n",
      "    yield self  # This tells Task to wait for completion.\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n",
      "    future.result()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/futures.py\", line 201, in result\n",
      "    raise self._exception.with_traceback(self._exception_tb)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "                                                                                  \n"
     ]
    }
   ],
   "source": [
    "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import sys\n",
    "\n",
    "from requests import get\n",
    "from wit import Wit\n",
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()\n",
    "\n",
    "if len(sys.argv) != 2:\n",
    "    print(\"usage: python \" + sys.argv[0] + \" M6OUETZX72ZOLHGRDXQVLXLMAISRPKQG\")\n",
    "    exit(1)\n",
    "access_token = sys.argv[1]\n",
    "\n",
    "# Celebrities example\n",
    "# See https://wit.ai/aleka/wit-example-celebrities/\n",
    "\n",
    "def first_entity_resolved_value(entities, entity):\n",
    "    if entity not in entities:\n",
    "        return None\n",
    "    val = entities[entity][0]\n",
    "    if \"resolved\" not in val:\n",
    "        return None\n",
    "    val = val[\"resolved\"][\"values\"][0]\n",
    "    if not val:\n",
    "        return None\n",
    "    return val\n",
    "\n",
    "def first_trait_value(traits, trait):\n",
    "    if trait not in traits:\n",
    "        return None\n",
    "    val = traits[trait][0][\"value\"]\n",
    "    if not val:\n",
    "        return None\n",
    "    return val\n",
    "\n",
    "def handle_message(response):\n",
    "    greetings = first_trait_value(response[\"traits\"], \"wit$greetings\")\n",
    "    celebrity = first_entity_resolved_value(\n",
    "        response[\"entities\"], \"wit$notable_person:notable_person\"\n",
    "    )\n",
    "    if celebrity:\n",
    "        # We can call the wikidata API using the wikidata ID for more info\n",
    "        return wikidata_description(celebrity)\n",
    "    elif greetings:\n",
    "        return 'Hi! You can say something like \"Tell me about Beyonce\"'\n",
    "    else:\n",
    "        return (\n",
    "            \"Um. I don't recognize that name. \"\n",
    "            \"Which celebrity do you want to learn about?\"\n",
    "        )\n",
    "\n",
    "def wikidata_description(celebrity):\n",
    "    try:\n",
    "        wikidata_id = celebrity[\"external\"][\"wikidata\"]\n",
    "    except KeyError:\n",
    "        return \"I recognize %s\" % celebrity[\"name\"]\n",
    "    rsp = get(\n",
    "        \"https://www.wikidata.org/w/api.php\",\n",
    "        {\n",
    "            \"ids\": wikidata_id,\n",
    "            \"action\": \"wbgetentities\",\n",
    "            \"props\": \"descriptions\",\n",
    "            \"format\": \"json\",\n",
    "            \"languages\": \"en\",\n",
    "        },\n",
    "    ).json()\n",
    "    description = rsp[\"entities\"][wikidata_id][\"descriptions\"][\"en\"][\"value\"]\n",
    "    return \"ooo yes I know %s -- %s\" % (celebrity[\"name\"], description)\n",
    "\n",
    "client = Wit(access_token=access_token)\n",
    "client.interactive(handle_message=handle_message)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a1b19c0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\n",
    "\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "import sys\n",
    "\n",
    "from requests import get\n",
    "from wit import Wit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "936a87b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "if len(sys.argv) != 2:\n",
    "    print(\"usage: python \" + sys.argv[0] + \" M6OUETZX72ZOLHGRDXQVLXLMAISRPKQG\")\n",
    "    exit(1)\n",
    "access_token = sys.argv[1]\n",
    "\n",
    "# Celebrities example\n",
    "# See https://wit.ai/aleka/wit-example-celebrities/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d9a74966",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def first_entity_resolved_value(entities, entity):\n",
    "    if entity not in entities:\n",
    "        return None\n",
    "    val = entities[entity][0]\n",
    "    if \"resolved\" not in val:\n",
    "        return None\n",
    "    val = val[\"resolved\"][\"values\"][0]\n",
    "    if not val:\n",
    "        return None\n",
    "    return val\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b23df26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def first_trait_value(traits, trait):\n",
    "    if trait not in traits:\n",
    "        return None\n",
    "    val = traits[trait][0][\"value\"]\n",
    "    if not val:\n",
    "        return None\n",
    "    return val\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "231136ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def handle_message(response):\n",
    "    greetings = first_trait_value(response[\"traits\"], \"wit$greetings\")\n",
    "    celebrity = first_entity_resolved_value(\n",
    "        response[\"entities\"], \"wit$notable_person:notable_person\"\n",
    "    )\n",
    "    if celebrity:\n",
    "        # We can call the wikidata API using the wikidata ID for more info\n",
    "        return wikidata_description(celebrity)\n",
    "    elif greetings:\n",
    "        return 'Hi! You can say something like \"Tell me about Beyonce\"'\n",
    "    else:\n",
    "        return (\n",
    "            \"Um. I don't recognize that name. \"\n",
    "            \"Which celebrity do you want to learn about?\"\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "87b0123a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def wikidata_description(celebrity):\n",
    "    try:\n",
    "        wikidata_id = celebrity[\"external\"][\"wikidata\"]\n",
    "    except KeyError:\n",
    "        return \"I recognize %s\" % celebrity[\"name\"]\n",
    "    rsp = get(\n",
    "        \"https://www.wikidata.org/w/api.php\",\n",
    "        {\n",
    "            \"ids\": wikidata_id,\n",
    "            \"action\": \"wbgetentities\",\n",
    "            \"props\": \"descriptions\",\n",
    "            \"format\": \"json\",\n",
    "            \"languages\": \"en\",\n",
    "        },\n",
    "    ).json()\n",
    "    description = rsp[\"entities\"][wikidata_id][\"descriptions\"][\"en\"][\"value\"]\n",
    "    return \"ooo yes I know %s -- %s\" % (celebrity[\"name\"], description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b5c6c272",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "client = Wit(access_token=access_token)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76658925",
   "metadata": {},
   "outputs": [],
   "source": [
    "client.message('set an alarm tomorrow at 7am')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d2b04325",
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "asyncio.run() cannot be called from a running event loop",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minteractive\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhandle_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhandle_message\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/wit/wit.py:106\u001b[0m, in \u001b[0;36mWit.interactive\u001b[0;34m(self, handle_message, context)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    105\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 106\u001b[0m         message \u001b[38;5;241m=\u001b[39m \u001b[43mprompt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mINTERACTIVE_PROMPT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmouse_support\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mrstrip()\n\u001b[1;32m    107\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mEOFError\u001b[39;00m):\n\u001b[1;32m    108\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py:1417\u001b[0m, in \u001b[0;36mprompt\u001b[0;34m(message, history, editing_mode, refresh_interval, vi_mode, lexer, completer, complete_in_thread, is_password, key_bindings, bottom_toolbar, style, color_depth, cursor, include_default_pygments_style, style_transformation, swap_light_and_dark_colors, rprompt, multiline, prompt_continuation, wrap_lines, enable_history_search, search_ignore_case, complete_while_typing, validate_while_typing, complete_style, auto_suggest, validator, clipboard, mouse_support, input_processors, placeholder, reserve_space_for_menu, enable_system_prompt, enable_suspend, enable_open_in_editor, tempfile_suffix, tempfile, default, accept_default, pre_run, set_exception_handler, handle_sigint, in_thread, inputhook)\u001b[0m\n\u001b[1;32m   1413\u001b[0m \u001b[38;5;66;03m# The history is the only attribute that has to be passed to the\u001b[39;00m\n\u001b[1;32m   1414\u001b[0m \u001b[38;5;66;03m# `PromptSession`, it can't be passed into the `prompt()` method.\u001b[39;00m\n\u001b[1;32m   1415\u001b[0m session: PromptSession[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m PromptSession(history\u001b[38;5;241m=\u001b[39mhistory)\n\u001b[0;32m-> 1417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprompt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1418\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1419\u001b[0m \u001b[43m    \u001b[49m\u001b[43mediting_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mediting_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1420\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrefresh_interval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrefresh_interval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1421\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvi_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvi_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1422\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlexer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1423\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompleter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompleter\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcomplete_in_thread\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomplete_in_thread\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1425\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_password\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_password\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1426\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkey_bindings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey_bindings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1427\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbottom_toolbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbottom_toolbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1428\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstyle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1429\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcolor_depth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor_depth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1430\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcursor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1431\u001b[0m \u001b[43m    \u001b[49m\u001b[43minclude_default_pygments_style\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude_default_pygments_style\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1432\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstyle_transformation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstyle_transformation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mswap_light_and_dark_colors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mswap_light_and_dark_colors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1434\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrprompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrprompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1435\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultiline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmultiline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1436\u001b[0m \u001b[43m    \u001b[49m\u001b[43mprompt_continuation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprompt_continuation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1437\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwrap_lines\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwrap_lines\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1438\u001b[0m \u001b[43m    \u001b[49m\u001b[43menable_history_search\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menable_history_search\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1439\u001b[0m \u001b[43m    \u001b[49m\u001b[43msearch_ignore_case\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msearch_ignore_case\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1440\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcomplete_while_typing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomplete_while_typing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1441\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidate_while_typing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_while_typing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1442\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcomplete_style\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcomplete_style\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1443\u001b[0m \u001b[43m    \u001b[49m\u001b[43mauto_suggest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauto_suggest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1444\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1445\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclipboard\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclipboard\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1446\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmouse_support\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmouse_support\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1447\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_processors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_processors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1448\u001b[0m \u001b[43m    \u001b[49m\u001b[43mplaceholder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplaceholder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1449\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreserve_space_for_menu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreserve_space_for_menu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1450\u001b[0m \u001b[43m    \u001b[49m\u001b[43menable_system_prompt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menable_system_prompt\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1451\u001b[0m \u001b[43m    \u001b[49m\u001b[43menable_suspend\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menable_suspend\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1452\u001b[0m \u001b[43m    \u001b[49m\u001b[43menable_open_in_editor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43menable_open_in_editor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1453\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtempfile_suffix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtempfile_suffix\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1454\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtempfile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtempfile\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1455\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdefault\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1456\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_default\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_default\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1457\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpre_run\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpre_run\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1458\u001b[0m \u001b[43m    \u001b[49m\u001b[43mset_exception_handler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mset_exception_handler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1459\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhandle_sigint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhandle_sigint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1460\u001b[0m \u001b[43m    \u001b[49m\u001b[43min_thread\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43min_thread\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1461\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputhook\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputhook\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1462\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py:1027\u001b[0m, in \u001b[0;36mPromptSession.prompt\u001b[0;34m(self, message, editing_mode, refresh_interval, vi_mode, lexer, completer, complete_in_thread, is_password, key_bindings, bottom_toolbar, style, color_depth, cursor, include_default_pygments_style, style_transformation, swap_light_and_dark_colors, rprompt, multiline, prompt_continuation, wrap_lines, enable_history_search, search_ignore_case, complete_while_typing, validate_while_typing, complete_style, auto_suggest, validator, clipboard, mouse_support, input_processors, placeholder, reserve_space_for_menu, enable_system_prompt, enable_suspend, enable_open_in_editor, tempfile_suffix, tempfile, default, accept_default, pre_run, set_exception_handler, handle_sigint, in_thread, inputhook)\u001b[0m\n\u001b[1;32m   1024\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dumb_prompt(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmessage) \u001b[38;5;28;01mas\u001b[39;00m dump_app:\n\u001b[1;32m   1025\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m dump_app\u001b[38;5;241m.\u001b[39mrun(in_thread\u001b[38;5;241m=\u001b[39min_thread, handle_sigint\u001b[38;5;241m=\u001b[39mhandle_sigint)\n\u001b[0;32m-> 1027\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1028\u001b[0m \u001b[43m    \u001b[49m\u001b[43mset_exception_handler\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mset_exception_handler\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1029\u001b[0m \u001b[43m    \u001b[49m\u001b[43min_thread\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43min_thread\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1030\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhandle_sigint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhandle_sigint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1031\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputhook\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputhook\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1032\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py:1002\u001b[0m, in \u001b[0;36mApplication.run\u001b[0;34m(self, pre_run, set_exception_handler, handle_sigint, in_thread, inputhook)\u001b[0m\n\u001b[1;32m    998\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m loop\u001b[38;5;241m.\u001b[39mrun_until_complete(coro)\n\u001b[1;32m   1000\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1001\u001b[0m     \u001b[38;5;66;03m# No loop installed. Run like usual.\u001b[39;00m\n\u001b[0;32m-> 1002\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43masyncio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoro\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/asyncio/runners.py:33\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(main, debug)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Execute the coroutine and return the result.\u001b[39;00m\n\u001b[1;32m     10\u001b[0m \n\u001b[1;32m     11\u001b[0m \u001b[38;5;124;03mThis function runs the passed coroutine, taking care of\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;124;03m    asyncio.run(main())\u001b[39;00m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m events\u001b[38;5;241m.\u001b[39m_get_running_loop() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 33\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m     34\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124masyncio.run() cannot be called from a running event loop\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m coroutines\u001b[38;5;241m.\u001b[39miscoroutine(main):\n\u001b[1;32m     37\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma coroutine was expected, got \u001b[39m\u001b[38;5;132;01m{!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(main))\n",
      "\u001b[0;31mRuntimeError\u001b[0m: asyncio.run() cannot be called from a running event loop"
     ]
    }
   ],
   "source": [
    "client.interactive(handle_message=handle_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d722cdf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'entities': {},\n",
       " 'intents': [],\n",
       " 'text': 'set an alarm tomorrow at 7am',\n",
       " 'traits': {}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from wit import Wit\n",
    "\n",
    "client = Wit('M6OUETZX72ZOLHGRDXQVLXLMAISRPKQG')\n",
    "client.message('set an alarm tomorrow at 7am')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e130b269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Yay, got Wit.ai response: {'entities': {}, 'intents': [], 'text': 'what is the weather in London?', 'traits': {}}\n"
     ]
    }
   ],
   "source": [
    "resp = client.message('what is the weather in London?')\n",
    "print('Yay, got Wit.ai response: ' + str(resp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa755a08-b98d-4e92-8994-04e6108499d9",
   "metadata": {},
   "outputs": [
    {
     "ename": "WitError",
     "evalue": "Wit responded with status: 400 (Bad Request)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mWitError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[37], line 11\u001b[0m\n\u001b[1;32m      9\u001b[0m client \u001b[38;5;241m=\u001b[39m Wit(access_token\u001b[38;5;241m=\u001b[39maccess_token)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# client.interactive()\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmessage\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwhat is the weather in London?\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYay, got Wit.ai response: \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(resp))\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/wit/wit.py:70\u001b[0m, in \u001b[0;36mWit.message\u001b[0;34m(self, msg, context, n, verbose)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[1;32m     69\u001b[0m     params[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mverbose\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m verbose\n\u001b[0;32m---> 70\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mreq\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlogger\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccess_token\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/message\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/wit/wit.py:42\u001b[0m, in \u001b[0;36mreq\u001b[0;34m(logger, access_token, meth, path, params, **kwargs)\u001b[0m\n\u001b[1;32m     34\u001b[0m rsp \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mrequest(\n\u001b[1;32m     35\u001b[0m     meth,\n\u001b[1;32m     36\u001b[0m     full_url,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m     40\u001b[0m )\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rsp\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m200\u001b[39m:\n\u001b[0;32m---> 42\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m WitError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWit responded with status: \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(rsp\u001b[38;5;241m.\u001b[39mstatus_code) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m     43\u001b[0m                    \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m rsp\u001b[38;5;241m.\u001b[39mreason \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     44\u001b[0m json \u001b[38;5;241m=\u001b[39m rsp\u001b[38;5;241m.\u001b[39mjson()\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124merror\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m json:\n",
      "\u001b[0;31mWitError\u001b[0m: Wit responded with status: 400 (Bad Request)"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from wit import Wit\n",
    "\n",
    "if len(sys.argv) != 2:\n",
    "    print(\"usage: python \" + sys.argv[0] + \"M6OUETZX72ZOLHGRDXQVLXLMAISRPKQG\")\n",
    "    exit(1)\n",
    "access_token = sys.argv[1]\n",
    "\n",
    "client = Wit(access_token=access_token)\n",
    "# client.interactive()\n",
    "resp = client.message('what is the weather in London?')\n",
    "print('Yay, got Wit.ai response: ' + str(resp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e7da9664-ecb3-4d89-87da-9b2b942444d0",
   "metadata": {},
   "outputs": [
    {
     "ename": "RateLimitError",
     "evalue": "Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRateLimitError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompletions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgpt-3.5-turbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessages\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrole\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43muser\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontent\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[43m}\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_utils/_utils.py:277\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    276\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[0;32m--> 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/resources/chat/completions.py:606\u001b[0m, in \u001b[0;36mCompletions.create\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, parallel_tool_calls, presence_penalty, response_format, seed, stop, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    574\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    575\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    604\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[1;32m    605\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 606\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/chat/completions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    608\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    609\u001b[0m \u001b[43m            \u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m    610\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmessages\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmessages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    611\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    612\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrequency_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrequency_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    613\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunction_call\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunction_call\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    614\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfunctions\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunctions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    615\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogit_bias\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogit_bias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    616\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlogprobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogprobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    617\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_tokens\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_tokens\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    618\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mn\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    619\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallel_tool_calls\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mparallel_tool_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    620\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpresence_penalty\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mpresence_penalty\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    621\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresponse_format\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mresponse_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    622\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseed\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mChatCompletion\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    639\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    640\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mStream\u001b[49m\u001b[43m[\u001b[49m\u001b[43mChatCompletionChunk\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    641\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1240\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1228\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1235\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1236\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1237\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1238\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1239\u001b[0m     )\n\u001b[0;32m-> 1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:921\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    913\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    914\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    919\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    920\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 921\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1005\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1003\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[1;32m   1004\u001b[0m     err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1006\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1007\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1008\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1009\u001b[0m \u001b[43m        \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1010\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1011\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1012\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1014\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1053\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[1;32m   1051\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[0;32m-> 1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1054\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1055\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1056\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1057\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1058\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1059\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1005\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1003\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m retries \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_retry(err\u001b[38;5;241m.\u001b[39mresponse):\n\u001b[1;32m   1004\u001b[0m     err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m-> 1005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1006\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1007\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1008\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1009\u001b[0m \u001b[43m        \u001b[49m\u001b[43merr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1010\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1011\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1012\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1014\u001b[0m \u001b[38;5;66;03m# If the response is streamed then we need to explicitly read the response\u001b[39;00m\n\u001b[1;32m   1015\u001b[0m \u001b[38;5;66;03m# to completion before attempting to access the response text.\u001b[39;00m\n\u001b[1;32m   1016\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mis_closed:\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1053\u001b[0m, in \u001b[0;36mSyncAPIClient._retry_request\u001b[0;34m(self, options, cast_to, remaining_retries, response_headers, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[38;5;66;03m# In a synchronous context we are blocking the entire thread. Up to the library user to run the client in a\u001b[39;00m\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;66;03m# different thread if necessary.\u001b[39;00m\n\u001b[1;32m   1051\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(timeout)\n\u001b[0;32m-> 1053\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1054\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1055\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1056\u001b[0m \u001b[43m    \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1057\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1058\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1059\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/openai/_base_client.py:1020\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1020\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1027\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[1;32m   1028\u001b[0m )\n",
      "\u001b[0;31mRateLimitError\u001b[0m: Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}"
     ]
    }
   ],
   "source": [
    "response = client.chat.completions.create(\n",
    "    model='gpt-3.5-turbo',\n",
    "    messages=[{\"role\": \"user\", \"content\": q}]\n",
    ")\n",
    "\n",
    "response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b21237c3-80e9-429c-a089-d45428087046",
   "metadata": {},
   "outputs": [],
   "source": [
    "def search(query):\n",
    "    boost = {'question': 3.0, 'section': 0.5}\n",
    "\n",
    "    results = index.search(\n",
    "        query=query,\n",
    "        filter_dict={'course': 'data-engineering-zoomcamp'},\n",
    "        boost_dict=boost,\n",
    "        num_results=5\n",
    "    )\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85748da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'text': \"Yes, even if you don't register, you're still eligible to submit the homeworks.\\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I still join the course after the start date?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I follow the course after it finishes?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - When will the course start?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'You can start by installing and setting up all the dependencies and requirements:\\nGoogle cloud account\\nGoogle Cloud SDK\\nPython 3 (installed with Anaconda)\\nTerraform\\nGit\\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - What can I do before the course starts?',\n",
       "  'course': 'data-engineering-zoomcamp'},\n",
       " {'text': 'Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.',\n",
       "  'section': 'General course-related questions',\n",
       "  'question': 'Course - Can I get support if I take the course in the self-paced mode?',\n",
       "  'course': 'data-engineering-zoomcamp'}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8cc5784e-6515-42e5-be62-8fb915df1088",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_prompt(query, search_results):\n",
    "    prompt_template = \"\"\"\n",
    "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\n",
    "Use only the facts from the CONTEXT when answering the QUESTION.\n",
    "\n",
    "QUESTION: {question}\n",
    "\n",
    "CONTEXT: \n",
    "{context}\n",
    "\"\"\".strip()\n",
    "\n",
    "    context = \"\"\n",
    "    \n",
    "    for doc in search_results:\n",
    "        context = context + f\"section: {doc['section']}\\nquestion: {doc['question']}\\nanswer: {doc['text']}\\n\\n\"\n",
    "    \n",
    "    prompt = prompt_template.format(question=query, context=context).strip()\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "97d35dec-c25f-472d-b961-20d5c30902ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "def llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "        model='gpt-4o',\n",
    "        messages=[{\"role\": \"user\", \"content\": prompt}]\n",
    "    )\n",
    "    \n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d9853e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wit import Wit\n",
    "\n",
    "# Initialize Wit.ai client with your access token\n",
    "client = Wit(access_token='M6OUETZX72ZOLHGRDXQVLXLMAISRPKQG')\n",
    "\n",
    "def wit_ai_llm(prompt):\n",
    "    # Send the user message to Wit.ai\n",
    "    response = client.message(prompt)\n",
    "\n",
    "    # Extract the response content from Wit.ai\n",
    "    if 'text' in response:\n",
    "        return response\n",
    "    else:\n",
    "        return \"Sorry, I couldn't understand that.\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f68c0a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wit.ai Response: {'entities': {}, 'intents': [], 'text': 'What is the weather in London tomorrow?', 'traits': {}}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Example usage:\n",
    "prompt = \"What is the weather in London tomorrow?\"\n",
    "response = wit_ai_llm(prompt)\n",
    "print(\"Wit.ai Response:\", response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8602f40b-ad3b-49c9-b3cc-051a79c888bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'how do I run kafka?'\n",
    "\n",
    "def rag(query):\n",
    "    search_results = search(query)\n",
    "    prompt = build_prompt(query, search_results)\n",
    "    answer = wit_ai_llm(prompt)\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5fd4497b-c5d5-4258-b950-6b35d1af4ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You\\'re a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\\nUse only the facts from the CONTEXT when answering the QUESTION.\\n\\nQUESTION: how do I run kafka?\\n\\nCONTEXT: \\nsection: Module 6: streaming with kafka\\nquestion: Java Kafka: How to run producer/consumer/kstreams/etc in terminal\\nanswer: In the project directory, run:\\njava -cp build/libs/<jar_name>-1.0-SNAPSHOT.jar:out src/main/java/org/example/JsonProducer.java\\n\\nsection: Module 6: streaming with kafka\\nquestion: Module “kafka” not found when trying to run producer.py\\nanswer: Solution from Alexey: create a virtual environment and run requirements.txt and the python files in that environment.\\nTo create a virtual env and install packages (run only once)\\npython -m venv env\\nsource env/bin/activate\\npip install -r ../requirements.txt\\nTo activate it (you\\'ll need to run it every time you need the virtual env):\\nsource env/bin/activate\\nTo deactivate it:\\ndeactivate\\nThis works on MacOS, Linux and Windows - but for Windows the path is slightly different (it\\'s env/Scripts/activate)\\nAlso the virtual environment should be created only to run the python file. Docker images should first all be up and running.\\n\\nsection: Workshop 1 - dlthub\\nquestion: How do I install the necessary dependencies to run the code?\\nanswer: Answer: To run the provided code, ensure that the \\'dlt[duckdb]\\' package is installed. You can do this by executing the provided installation command: !pip install dlt[duckdb]. If you’re doing it locally, be sure to also have duckdb pip installed (even before the duckdb package is loaded).\\n\\nsection: Module 6: streaming with kafka\\nquestion: Python Kafka: ./build.sh: Permission denied Error\\nanswer: Run this command in terminal in the same directory (/docker/spark):\\nchmod +x build.sh\\n\\nsection: Project\\nquestion: How to fix the error \"ModuleNotFoundError: No module named \\'kafka.vendor.six.moves\\'\"?\\nanswer: According to https://github.com/dpkp/kafka-python/\\n“DUE TO ISSUES WITH RELEASES, IT IS SUGGESTED TO USE https://github.com/wbarnha/kafka-python-ng FOR THE TIME BEING”\\nUse pip install kafka-python-ng instead'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "385b012f-4905-422d-8d7c-3d542dfe5a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\\nUse only the facts from the CONTEXT when answering the QUESTION.\\n\\nQUESTION: the course has already started, can I still enroll?\\n\\nCONTEXT: \\nsection: General course-related questions\\nquestion: Course - Can I still join the course after the start date?\\nanswer: Yes, even if you don't register, you're still eligible to submit the homeworks.\\nBe aware, however, that there will be deadlines for turning in the final projects. So don't leave everything for the last minute.\\n\\nsection: General course-related questions\\nquestion: Course - Can I follow the course after it finishes?\\nanswer: Yes, we will keep all the materials after the course finishes, so you can follow the course at your own pace after it finishes.\\nYou can also continue looking at the homeworks and continue preparing for the next cohort. I guess you can also start working on your final capstone project.\\n\\nsection: General course-related questions\\nquestion: Course - When will the course start?\\nanswer: The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\\n\\nsection: General course-related questions\\nquestion: Course - What can I do before the course starts?\\nanswer: You can start by installing and setting up all the dependencies and requirements:\\nGoogle cloud account\\nGoogle Cloud SDK\\nPython 3 (installed with Anaconda)\\nTerraform\\nGit\\nLook over the prerequisites and syllabus to see if you are comfortable with these subjects.\\n\\nsection: General course-related questions\\nquestion: Course - Can I get support if I take the course in the self-paced mode?\\nanswer: Yes, the slack channel remains open and you can ask questions there. But always sDocker containers exit code w search the channel first and second, check the FAQ (this document), most likely all your questions are already answered here.\\nYou can also tag the bot @ZoomcampQABot to help you conduct the search, but don’t rely on its answers 100%, it is pretty good though.\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag('the course has already started, can I still enroll?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3e04fb3-b7f7-4e53-8de9-a1c6cde3f038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       " 'section': 'General course-related questions',\n",
       " 'question': 'Course - When will the course start?',\n",
       " 'course': 'data-engineering-zoomcamp'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2c05052f-a85a-4137-8398-0fd0be678599",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elasticsearch import Elasticsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a78df1cc-5a5a-40b4-b673-19c7f0319453",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c9367c18-41ad-495e-9920-1a0c552f0d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'course-questions'})"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_settings = {\n",
    "    \"settings\": {\n",
    "        \"number_of_shards\": 1,\n",
    "        \"number_of_replicas\": 0\n",
    "    },\n",
    "    \"mappings\": {\n",
    "        \"properties\": {\n",
    "            \"text\": {\"type\": \"text\"},\n",
    "            \"section\": {\"type\": \"text\"},\n",
    "            \"question\": {\"type\": \"text\"},\n",
    "            \"course\": {\"type\": \"keyword\"} \n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "index_name = \"course-questions\"\n",
    "\n",
    "es_client.indices.create(index=index_name, body=index_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9f778c93-a5b6-4634-b42e-0c25083a2512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': \"The purpose of this document is to capture frequently asked technical questions\\nThe exact day and hour of the course will be 15th Jan 2024 at 17h00. The course will start with the first  “Office Hours'' live.1\\nSubscribe to course public Google Calendar (it works from Desktop only).\\nRegister before the course starts using this link.\\nJoin the course Telegram channel with announcements.\\nDon’t forget to register in DataTalks.Club's Slack and join the channel.\",\n",
       " 'section': 'General course-related questions',\n",
       " 'question': 'Course - When will the course start?',\n",
       " 'course': 'data-engineering-zoomcamp'}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "documents[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c230059-e219-4a13-a7f8-ede4cf1b028f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/codespace/.python/current/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70fe3c97-916d-42c0-bd7b-4f42d9056409",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/948 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 948/948 [00:22<00:00, 42.35it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1bc1244-b8dc-4228-8171-c0507004db93",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'I just disovered the course. Can I still join it?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "18e39f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = 'How do I execute a command in a running docker container?'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "7c72e000-910b-4fb5-aa88-2561e7bc39f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search(query):\n",
    "    search_query = {\n",
    "        \"size\": 5,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^3\", \"text\", \"section\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    response = es_client.search(index=index_name, body=search_query)\n",
    "    \n",
    "    result_docs = []\n",
    "    \n",
    "    for hit in response['hits']['hits']:\n",
    "        result_docs.append(hit['_source'])\n",
    "    \n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9f8e2bcb",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[86], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m results \u001b[38;5;241m=\u001b[39m elastic_search(query)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Get the score of the top-ranking result\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m top_score \u001b[38;5;241m=\u001b[39m \u001b[43mresults\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_score\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      6\u001b[0m top_score\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# Execute the search query\n",
    "results = elastic_search(query)\n",
    "\n",
    "# Get the score of the top-ranking result\n",
    "top_score = results[0]['_score']\n",
    "top_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fa10e061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute the search query\n",
    "results = elastic_search(query)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "d9cf963c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "de84d59b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'How do I copy files from a different folder into docker container’s working directory?'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third_question = results[2]['_source']['question']\n",
    "third_question\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f21962a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = build_prompt(query, results)\n",
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "81abecbc-eb6b-428f-ab7d-7e21f58b64de",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rag(query):\n",
    "    search_results = elastic_search(query)\n",
    "    prompt = build_prompt(query, search_results)\n",
    "    # answer = llm(prompt)\n",
    "    return prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0ea9315a-a619-4066-9e90-8c260f2c8450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'You\\'re a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\\nUse only the facts from the CONTEXT when answering the QUESTION.\\n\\nQUESTION: How do I execute a command in a running docker container?\\n\\nCONTEXT: \\nsection: 5. Deploying Machine Learning Models\\nquestion: How do I debug a docker container?\\nanswer: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\\ndocker run -it --entrypoint bash <image>\\nIf the container is already running, execute a command in the specific container:\\ndocker ps (find the container-id)\\ndocker exec -it <container-id> bash\\n(Marcos MJD)\\n\\nsection: 5. Deploying Machine Learning Models\\nquestion: How do I debug a docker container?\\nanswer: Launch the container image in interactive mode and overriding the entrypoint, so that it starts a bash command.\\ndocker run -it --entrypoint bash <image>\\nIf the container is already running, execute a command in the specific container:\\ndocker ps (find the container-id)\\ndocker exec -it <container-id> bash\\n(Marcos MJD)\\n\\nsection: 5. Deploying Machine Learning Models\\nquestion: How do I copy files from my local machine to docker container?\\nanswer: You can copy files from your local machine into a Docker container using the docker cp command. Here\\'s how to do it:\\nTo copy a file or directory from your local machine into a running Docker container, you can use the `docker cp command`. The basic syntax is as follows:\\ndocker cp /path/to/local/file_or_directory container_id:/path/in/container\\nHrithik Kumar Advani\\n\\nsection: 5. Deploying Machine Learning Models\\nquestion: How do I copy files from my local machine to docker container?\\nanswer: You can copy files from your local machine into a Docker container using the docker cp command. Here\\'s how to do it:\\nTo copy a file or directory from your local machine into a running Docker container, you can use the `docker cp command`. The basic syntax is as follows:\\ndocker cp /path/to/local/file_or_directory container_id:/path/in/container\\nHrithik Kumar Advani\\n\\nsection: 5. Deploying Machine Learning Models\\nquestion: How do I copy files from a different folder into docker container’s working directory?\\nanswer: You can copy files from your local machine into a Docker container using the docker cp command. Here\\'s how to do it:\\nIn the Dockerfile, you can provide the folder containing the files that you want to copy over. The basic syntax is as follows:\\nCOPY [\"src/predict.py\", \"models/xgb_model.bin\", \"./\"]\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tGopakumar Gopinathan'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d8095274-c9cd-4fd5-80d2-069fc951834f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tiktoken\n",
      "  Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.6 kB)\n",
      "Collecting regex>=2022.1.18 (from tiktoken)\n",
      "  Downloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (40 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m424.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: requests>=2.26.0 in /home/codespace/.local/lib/python3.10/site-packages (from tiktoken) (2.32.3)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/python/3.10.13/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/codespace/.local/lib/python3.10/site-packages (from requests>=2.26.0->tiktoken) (2024.2.2)\n",
      "Downloading tiktoken-0.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading regex-2024.5.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (775 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m775.1/775.1 kB\u001b[0m \u001b[31m13.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: regex, tiktoken\n",
      "Successfully installed regex-2024.5.15 tiktoken-0.7.0\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6516349b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task exception was never retrieved\n",
      "future: <Task finished name='Task-6' coro=<Application.run_async() done, defined at /home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py:620> exception=KeyboardInterrupt()>\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/site-packages/wit/wit.py\", line 106, in interactive\n",
      "    message = prompt(INTERACTIVE_PROMPT, history=history, mouse_support=True).rstrip()\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py\", line 1417, in prompt\n",
      "    return session.prompt(\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/shortcuts/prompt.py\", line 1027, in prompt\n",
      "    return self.app.run(\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 1002, in run\n",
      "    return asyncio.run(coro)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 30, in run\n",
      "    return loop.run_until_complete(task)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 92, in run_until_complete\n",
      "    self._run_once()\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/nest_asyncio.py\", line 133, in _run_once\n",
      "    handle._run()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/events.py\", line 80, in _run\n",
      "    self._context.run(self._callback, *self._args)\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 315, in __wakeup\n",
      "    self.__step()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 232, in __step\n",
      "    result = coro.send(None)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 886, in run_async\n",
      "    return await _run_async(f)\n",
      "  File \"/home/codespace/.local/lib/python3.10/site-packages/prompt_toolkit/application/application.py\", line 746, in _run_async\n",
      "    result = await f\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/futures.py\", line 285, in __await__\n",
      "    yield self  # This tells Task to wait for completion.\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/tasks.py\", line 304, in __wakeup\n",
      "    future.result()\n",
      "  File \"/home/codespace/.python/current/lib/python3.10/asyncio/futures.py\", line 201, in result\n",
      "    raise self._exception.with_traceback(self._exception_tb)\n",
      "KeyboardInterrupt\n"
     ]
    },
    {
     "ename": "ConnectionError",
     "evalue": "Connection error caused by: ConnectionError(Connection error caused by: NewConnectionError(<urllib3.connection.HTTPConnection object at 0x7a72de281360>: Failed to establish a new connection: [Errno 111] Connection refused))",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[68], line 36\u001b[0m\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# Execute the search query\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43melastic_search\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;66;03m# Check if there are enough results\u001b[39;00m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhits\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m results \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(results[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhits\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhits\u001b[39m\u001b[38;5;124m'\u001b[39m]) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m:\n",
      "Cell \u001b[0;32mIn[68], line 31\u001b[0m, in \u001b[0;36melastic_search\u001b[0;34m(query)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21melastic_search\u001b[39m(query):\n\u001b[1;32m     11\u001b[0m     search_query \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     12\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m3\u001b[39m,  \u001b[38;5;66;03m# Return 3 results\u001b[39;00m\n\u001b[1;32m     13\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquery\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     28\u001b[0m         }\n\u001b[1;32m     29\u001b[0m     }\n\u001b[0;32m---> 31\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mes_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msearch_query\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elasticsearch/_sync/client/utils.py:446\u001b[0m, in \u001b[0;36m_rewrite_parameters.<locals>.wrapper.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    443\u001b[0m         \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m    444\u001b[0m             \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapi\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elasticsearch/_sync/client/__init__.py:4119\u001b[0m, in \u001b[0;36mElasticsearch.search\u001b[0;34m(self, index, aggregations, aggs, allow_no_indices, allow_partial_search_results, analyze_wildcard, analyzer, batched_reduce_size, ccs_minimize_roundtrips, collapse, default_operator, df, docvalue_fields, error_trace, expand_wildcards, explain, ext, fields, filter_path, force_synthetic_source, from_, highlight, human, ignore_throttled, ignore_unavailable, indices_boost, knn, lenient, max_concurrent_shard_requests, min_compatible_shard_node, min_score, pit, post_filter, pre_filter_shard_size, preference, pretty, profile, q, query, rank, request_cache, rescore, rest_total_hits_as_int, retriever, routing, runtime_mappings, script_fields, scroll, search_after, search_type, seq_no_primary_term, size, slice, sort, source, source_excludes, source_includes, stats, stored_fields, suggest, suggest_field, suggest_mode, suggest_size, suggest_text, terminate_after, timeout, track_scores, track_total_hits, typed_keys, version, body)\u001b[0m\n\u001b[1;32m   4117\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m __body \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   4118\u001b[0m     __headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent-type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 4119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[return-value]\u001b[39;49;00m\n\u001b[1;32m   4120\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4121\u001b[0m \u001b[43m    \u001b[49m\u001b[43m__path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4122\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4123\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4124\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4125\u001b[0m \u001b[43m    \u001b[49m\u001b[43mendpoint_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msearch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4126\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_parts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m__path_parts\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4127\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:271\u001b[0m, in \u001b[0;36mBaseClient.perform_request\u001b[0;34m(self, method, path, params, headers, body, endpoint_id, path_parts)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mperform_request\u001b[39m(\n\u001b[1;32m    256\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    257\u001b[0m     method: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    264\u001b[0m     path_parts: Optional[Mapping[\u001b[38;5;28mstr\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    265\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ApiResponse[Any]:\n\u001b[1;32m    266\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_otel\u001b[38;5;241m.\u001b[39mspan(\n\u001b[1;32m    267\u001b[0m         method,\n\u001b[1;32m    268\u001b[0m         endpoint_id\u001b[38;5;241m=\u001b[39mendpoint_id,\n\u001b[1;32m    269\u001b[0m         path_parts\u001b[38;5;241m=\u001b[39mpath_parts \u001b[38;5;129;01mor\u001b[39;00m {},\n\u001b[1;32m    270\u001b[0m     ) \u001b[38;5;28;01mas\u001b[39;00m otel_span:\n\u001b[0;32m--> 271\u001b[0m         response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_perform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m            \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m            \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    276\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    277\u001b[0m \u001b[43m            \u001b[49m\u001b[43motel_span\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43motel_span\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    279\u001b[0m         otel_span\u001b[38;5;241m.\u001b[39mset_elastic_cloud_metadata(response\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mheaders)\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:316\u001b[0m, in \u001b[0;36mBaseClient._perform_request\u001b[0;34m(self, method, path, params, headers, body, otel_span)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    314\u001b[0m     target \u001b[38;5;241m=\u001b[39m path\n\u001b[0;32m--> 316\u001b[0m meta, resp_body \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    317\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    319\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    320\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    321\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrequest_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    322\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    323\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry_on_status\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_on_status\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    324\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretry_on_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_on_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    325\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclient_meta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_meta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    326\u001b[0m \u001b[43m    \u001b[49m\u001b[43motel_span\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43motel_span\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    327\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;66;03m# HEAD with a 404 is returned as a normal response\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;66;03m# since this is used as an 'exists' functionality.\u001b[39;00m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHEAD\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m meta\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m404\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m meta\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m299\u001b[39m\n\u001b[1;32m    333\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m (\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    337\u001b[0m     )\n\u001b[1;32m    338\u001b[0m ):\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elastic_transport/_transport.py:342\u001b[0m, in \u001b[0;36mTransport.perform_request\u001b[0;34m(self, method, target, body, headers, max_retries, retry_on_status, retry_on_timeout, request_timeout, client_meta, otel_span)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    341\u001b[0m     otel_span\u001b[38;5;241m.\u001b[39mset_node_metadata(node\u001b[38;5;241m.\u001b[39mhost, node\u001b[38;5;241m.\u001b[39mport, node\u001b[38;5;241m.\u001b[39mbase_url, target)\n\u001b[0;32m--> 342\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mnode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mperform_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    343\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    347\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrequest_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_timeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    349\u001b[0m     _logger\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m    350\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m [status:\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m duration:\u001b[39m\u001b[38;5;132;01m%.3f\u001b[39;00m\u001b[38;5;124ms]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    351\u001b[0m         \u001b[38;5;241m%\u001b[39m (\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    357\u001b[0m         )\n\u001b[1;32m    358\u001b[0m     )\n\u001b[1;32m    360\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHEAD\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.python/current/lib/python3.10/site-packages/elastic_transport/_node/_http_urllib3.py:202\u001b[0m, in \u001b[0;36mUrllib3HttpNode.perform_request\u001b[0;34m(self, method, target, body, headers, request_timeout)\u001b[0m\n\u001b[1;32m    194\u001b[0m         err \u001b[38;5;241m=\u001b[39m \u001b[38;5;167;01mConnectionError\u001b[39;00m(\u001b[38;5;28mstr\u001b[39m(e), errors\u001b[38;5;241m=\u001b[39m(e,))\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_request(\n\u001b[1;32m    196\u001b[0m         method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m    197\u001b[0m         target\u001b[38;5;241m=\u001b[39mtarget,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    200\u001b[0m         exception\u001b[38;5;241m=\u001b[39merr,\n\u001b[1;32m    201\u001b[0m     )\n\u001b[0;32m--> 202\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    204\u001b[0m meta \u001b[38;5;241m=\u001b[39m ApiResponseMeta(\n\u001b[1;32m    205\u001b[0m     node\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig,\n\u001b[1;32m    206\u001b[0m     duration\u001b[38;5;241m=\u001b[39mduration,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    209\u001b[0m     headers\u001b[38;5;241m=\u001b[39mresponse_headers,\n\u001b[1;32m    210\u001b[0m )\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_request(\n\u001b[1;32m    212\u001b[0m     method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[1;32m    213\u001b[0m     target\u001b[38;5;241m=\u001b[39mtarget,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    217\u001b[0m     response\u001b[38;5;241m=\u001b[39mdata,\n\u001b[1;32m    218\u001b[0m )\n",
      "\u001b[0;31mConnectionError\u001b[0m: Connection error caused by: ConnectionError(Connection error caused by: NewConnectionError(<urllib3.connection.HTTPConnection object at 0x7a72de281360>: Failed to establish a new connection: [Errno 111] Connection refused))"
     ]
    }
   ],
   "source": [
    "\n",
    "import tiktoken\n",
    "\n",
    "# Initialize the encoding for the gpt-4 model\n",
    "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n",
    "\n",
    "# Generate the final prompt (assuming this was done as in Q5)\n",
    "query = \"How do I execute a command in a running docker container?\"\n",
    "\n",
    "# Function to search Elasticsearch and return results\n",
    "def elastic_search(query):\n",
    "    search_query = {\n",
    "        \"size\": 3,  # Return 3 results\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^4\", \"text\",\"Section\"],\n",
    "                        \"type\": \"best_fields\"\n",
    "                    }\n",
    "                },\n",
    "                \"filter\": {\n",
    "                    \"term\": {\n",
    "                        \"course\": \"machine-learning-zoomcamp\"\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    response = es_client.search(index=index_name, body=search_query)\n",
    "    \n",
    "    return response\n",
    "\n",
    "# Execute the search query\n",
    "results = elastic_search(query)\n",
    "\n",
    "# Check if there are enough results\n",
    "if 'hits' in results and len(results['hits']['hits']) >= 3:\n",
    "    search_results = results['hits']['hits']\n",
    "else:\n",
    "    print(\"Less than 3 results found.\")\n",
    "    search_results = results['hits']['hits']  # Use available results for demonstration\n",
    "\n",
    "# Build the context\n",
    "context_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()\n",
    "\n",
    "context = \"\\n\\n\".join([context_template.format(question=hit['_source']['question'], text=hit['_source']['text']) for hit in search_results])\n",
    "\n",
    "# Construct the final prompt\n",
    "prompt_template = \"\"\"\n",
    "You're a course teaching assistant. Answer the QUESTION based on the CONTEXT from the FAQ database.\n",
    "Use only the facts from the CONTEXT when answering the QUESTION.\n",
    "\n",
    "QUESTION: {question}\n",
    "\n",
    "CONTEXT:\n",
    "{context}\n",
    "\"\"\".strip()\n",
    "\n",
    "final_prompt = prompt_template.format(question=query, context=context)\n",
    "\n",
    "# Tokenize the prompt and count the number of tokens\n",
    "tokens = encoding.encode(final_prompt)\n",
    "num_tokens = len(tokens)\n",
    "print(f\"The number of tokens in the prompt is: {num_tokens}\")\n"
   ]
  }
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