Task1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15daa0fb-b225-4daf-b7ae-290fc6488c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "import minsearch\n",
    "import json\n",
    "\n",
    "from elasticsearch import Elasticsearch\n",
    "from elasticsearch import NotFoundError\n",
    "\n",
    "import requests "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9f12594-0227-4734-b325-8db692cc35eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = OpenAI(\n",
    "    base_url='http://localhost:11434/v1/',\n",
    "    api_key='ollama',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6e3b74d-498d-44e8-b4c3-3f5bc7986f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "id": "47e53e4b-e173-4b3d-ab21-453a025cd681",
   "metadata": {},
   "source": [
    "Elastic Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0f41be8-bc98-4781-a675-1049d1b25e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "es_client = Elasticsearch('http://localhost:9200') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa1aff0d-db69-49ed-ad63-02da75be0db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "course-questions already exists\n"
     ]
    }
   ],
   "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",
    "try:\n",
    "    es_client.indices.get(index=index_name)\n",
    "    print(f\"{index_name} already exists\")\n",
    "except NotFoundError:\n",
    "    es_client.indices.create(index=index_name, body=index_settings)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8106f143-ede1-445f-bdab-289f9475eb9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def elastic_search(query):\n",
    "    search_query = {\n",
    "        \"size\": 3,\n",
    "        \"query\": {\n",
    "            \"bool\": {\n",
    "                \"must\": {\n",
    "                    \"multi_match\": {\n",
    "                        \"query\": query,\n",
    "                        \"fields\": [\"question^4\",\"text\"],\n",
    "                        #\"fields\": [],\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",
    "        #print(response['hits'])\n",
    "        result_docs.append(hit['_source'])\n",
    "    \n",
    "    return result_docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6c27196-ef87-4932-9c44-0dcb35c6844a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/python/3.10.13/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": 8,
   "id": "92d172e8-7bf5-4487-9be5-08e9dd7b30ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████| 948/948 [00:21<00:00, 43.10it/s]\n"
     ]
    }
   ],
   "source": [
    "for doc in tqdm(documents):\n",
    "    es_client.index(index=index_name, document=doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b5c7c7c-d212-4464-9cf9-d2b343094d73",
   "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",
    "    \n",
    "    context_template = \"\"\n",
    "\n",
    "    for doc in search_results:\n",
    "         context_template +=  f\"Q: {doc['question']}\\nA: {doc['text']}\\n\\n\" \n",
    "\n",
    "    \n",
    "    final_prompt = prompt_template.format(question=query, context=context_template).strip()\n",
    "    return final_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a98448c9-d88d-4a56-9878-adc62b2e3eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def llm(prompt):\n",
    "    response = client.chat.completions.create(\n",
    "        model = 'phi3',\n",
    "        messages = [{\"role\":\"user\", \"content\":prompt}]\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "427593b3-8995-4360-863b-1ff5a1491459",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How do I execute a command in a running docker container?\" \n",
    "\n",
    "def rag(query):\n",
    "    search_results = elastic_search(query)\n",
    "    prompt = build_prompt(query, search_results)\n",
    "    answer = llm(prompt)\n",
    "    return prompt, search_results, answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "090793ee-a994-4979-8e36-952a0b8ed040",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt, search_results, answer = rag(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74a2135c-78a2-4818-bf90-36a5dd6ce8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a502275-684f-4f59-b8a6-e56e9bf8b950",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78bca142-df3a-4ea0-857b-07b7c2662845",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd59b5b-f1cd-4941-b960-b22497e37de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "832b17a6-5ef5-460c-ba1e-42d20f9fde25",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"How do I execute a command in a running docker container?\"\n",
    "\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_template = \"\"\"\n",
    "Q: {question}\n",
    "A: {text}\n",
    "\"\"\".strip()\n",
    "\n",
    "\n",
    "context_entries = \"\\n\\n\".join([context_template.format(question=doc['question'], text=doc['text']) for doc in search_results])\n",
    "\n",
    "prompt = prompt_template.format(question=query, context=context_entries).strip()\n",
    "print(prompt)\n",
    "print(len(prompt))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5012886e-c859-42ab-8820-cf2380ad4f7d",
   "metadata": {},
   "source": [
    "Tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d461f7d-d3ae-4c5f-8ac3-e1a87810f625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cfaed2c-9456-47a5-80f1-8463e33b4d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-4o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97549342-b1e8-4862-8fc6-0b1d1b2e8aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#encoding.encode(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abb8acda-7f6c-4053-859f-4cd66fc442a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(encoding.encode(prompt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e2fafe8-5226-4d39-a9f9-ac485afaf3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "encoding.decode_single_token_bytes(290)"
   ]
  }
 ],
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