Bielik_2_(4_bit)_RAG.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzjpUU5D9g8A"
      },
      "source": [
        "<p align=\"center\">\n",
        "  <img src=\"https://huggingface.co/speakleash/Bielik-7B-Instruct-v0.1/raw/main/speakleash_cyfronet.png\">\n",
        "</p>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9m-lWPjGkgO"
      },
      "source": [
        "# Instalacja pakietów\n",
        "\n",
        "Instalacja kluczowych pakietów\n",
        "*   requests - Pakiet Python umożliwiający wysyłanie żądań HTTP w prosty sposób. Umożliwia wykonywanie wszystkich typów żądań HTTP, takich jak GET, POST, PUT, DELETE i inne.\n",
        "*   accelerate - Pomaga w przyspieszaniu i zarządzaniu treningu oraz inferencją modeli na różnych platformach jak GPU, TPU czy CPU.\n",
        "*   bitsandbytes - Biblioteka używana do optymalizacji wydajności obliczeń, poprzez oferowanie niskopoziomowych operacji arytmetycznych na danych numerycznych.\n",
        "*   chromadb - Wektorowa baza danych służąca do przechowywania i wyszukiwania wektorów. Przydatna do różnych zastosowań związanych z uczeniem maszynowym i przetwarzaniem języka naturalnego.\n",
        "*   sentence-transformers - Pakiet Python do generowania osadzeń zdań (embeddings) za pomocą różnych modeli transformerowych. Jest używany w zadaniach takich jak wyszukiwanie semantyczne, klasteryzacja, klasyfikacja i inne.\n",
        "*   PyMuPDF (fitz) - Biblioteka Python umożliwiająca pracę z plikami PDF i innymi formatami dokumentów. Umożliwia odczyt, zapis i manipulację plikami PDF.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFJQZk9TGWLK",
        "outputId": "1ec3d88e-a799-4ed2-8703-ca1809563dfd",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: PyMuPDF in /usr/local/lib/python3.10/dist-packages (1.24.9)\n",
            "Requirement already satisfied: PyMuPDFb==1.24.9 in /usr/local/lib/python3.10/dist-packages (from PyMuPDF) (1.24.9)\n"
          ]
        }
      ],
      "source": [
        "!pip install requests\n",
        "!pip install accelerate\n",
        "!pip install -i https://pypi.org/simple/ bitsandbytes\n",
        "!pip install chromadb\n",
        "!pip install sentence-transformers\n",
        "!pip install PyMuPDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xHQm2PEar0sg"
      },
      "outputs": [],
      "source": [
        "# moduł warnings wykluczy nadmiarowe informacje, które mogą wystąpić podczas inferencji modelu\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-KfDMeyHk0K"
      },
      "source": [
        "# Deklaracja ścieżek i nazw plików"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HDU4XswEHufZ"
      },
      "outputs": [],
      "source": [
        "pdf_url = \"https://www.iab.org.pl/wp-content/uploads/2024/04/Przewodnik-po-sztucznej-inteligencji-2024_IAB-Polska.pdf\"\n",
        "file_name = \"Przewodnik-po-sztucznej-inteligencji-2024_IAB-Polska.pdf\"\n",
        "file_json = \"Przewodnik-po-sztucznej-inteligencji-2024_IAB-Polska.json\"\n",
        "data_dir = \"./data\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7ZA30JlHxc4"
      },
      "source": [
        "# Pobieranie pliku PDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WJYnHqKGH3XO"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "response = requests.get(pdf_url, stream=True)\n",
        "with open(file_name, \"wb\") as file:\n",
        "    for block in response.iter_content(chunk_size=1024):\n",
        "        if block:\n",
        "            file.write(block)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQ874qPwIDfd"
      },
      "source": [
        "# Ekstrakcja tekstu z pliku PDF"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-pvRGPVISF4"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KDs5yTJTIJbQ"
      },
      "outputs": [],
      "source": [
        "import fitz\n",
        "import json\n",
        "\n",
        "document = fitz.open(file_name)\n",
        "text = ''\n",
        "pages = []\n",
        "\n",
        "for page_num in range(len(document)):\n",
        "    page = document.load_page(page_num)\n",
        "    page_text = page.get_text()\n",
        "    pages.append({\"page_num\": page_num, \"text\": page.get_text()})\n",
        "\n",
        "with open(file_json, \"w\") as file:\n",
        "    json.dump(pages, file, indent=4, ensure_ascii=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzGfLDQjIlf3"
      },
      "source": [
        "# Stworzenie osadzeń tekstów\n",
        "Przy zastosowaniu modelu sentence transformer (model do embeddingów) w postaci silver-retrivera-base-v.1.1\n",
        "https://huggingface.co/ipipan/silver-retriever-base-v1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OYQKceZxI1wU"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "texts = []\n",
        "for page in pages:\n",
        "    texts.append(page[\"text\"])\n",
        "\n",
        "embedding_model = SentenceTransformer('ipipan/silver-retriever-base-v1.1')\n",
        "embeddings = embedding_model.encode(texts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btz3kq2OJJi1"
      },
      "source": [
        "# Tworzenie bazy wektorów\n",
        "W celu przechowywania i wyszukiwania dokumentów na bazie ich osadzeń (embeddingów)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JUiozmJZJP-C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba4af59f-f658-4e2f-cd34-57c4e18b32b2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "WARNING:chromadb.segment.impl.vector.local_persistent_hnsw:Add of existing embedding ID: 118\n"
          ]
        }
      ],
      "source": [
        "import chromadb\n",
        "\n",
        "client = chromadb.PersistentClient(path=data_dir)\n",
        "collection = client.get_or_create_collection(\n",
        "        name=\"ha_naive_rag\", metadata={\"hnsw:space\": \"cosine\"}\n",
        ")\n",
        "\n",
        "collection.add(\n",
        "        documents=texts,\n",
        "        embeddings=embeddings,\n",
        "        ids=[str(i) for i in range(len(texts))]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gated models\n",
        "W przypadku modeli, na które zostały nałożone zabezpieczenia w postaci kontrolowanego dostępu (gated models), wymagane będzie podanie tokena użytkownika, dostępnego na portalu HuggingFace: <br>\n",
        "- informacje dotyczące tokenów: <br>\n",
        "https://huggingface.co/docs/hub/security-tokens <br>\n",
        "- token użytkownika <br>\n",
        "https://huggingface.co/settings/tokens\n",
        "\n",
        "W tym celu należy zainstalować bibliotekę \"huggingface_hub[cli]\" oraz dokonać logowania za pomocą tokena, co zostało zaprezentowane w poniższych 2 komórkach."
      ],
      "metadata": {
        "id": "70EFwvCCCCJe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U \"huggingface_hub[cli]\""
      ],
      "metadata": {
        "id": "fNYRGheeCEFt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "id": "rI0mUBmyCEMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtGGMr9FJY8A"
      },
      "source": [
        "#Zapytajmy chatbota\n",
        "\n",
        "Jest to prosty bot w stylu **question answering** (**Q&A**) wykorzystujący technikę **RAG** w wersji **tzw. naiwnej**. Pamiętajmy o lepszym chunkowaniu, dokładniejszej analizie struktury dokumentów, wzbogaceniu tekstu, augmentacji zapytań, hybrydowym wyszukiwaniu, dodaniu kontekstu i historii rozmowy, a o**trzymamy PRAWDZIWEGO CHATBOTA w wersji enterprise.** Jednak powyższy kod to kilkanaście linijek kodu, a chatbot przewyższa kilkakrotnie klasyczne chatboty oparte na rozpoznawaniu intencji i zaawansowanych scenariuszach."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 531,
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          ]
        },
        "id": "NIBU0rb0oXPR",
        "outputId": "7e394277-f467-46b5-bd4c-d84d7a517170"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/27.2k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
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            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
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            ],
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          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer\n",
        "\n",
        "device = \"cuda\"\n",
        "model_name = 'speakleash/Bielik-11B-v2.2-Instruct'\n",
        "\n",
        "temperature = 1.0\n",
        "max_tokens = 500\n",
        "top_k = 200\n",
        "top_p = 1\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
        "\n",
        "quantization_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
        "                                             torch_dtype=torch.bfloat16,\n",
        "                                             quantization_config=quantization_config\n",
        "                                             )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EMzOHH3x9g8V"
      },
      "outputs": [],
      "source": [
        "import uuid"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RY_50poQK1Q-"
      },
      "outputs": [],
      "source": [
        "def create_context(query, top=3):\n",
        "    embedding = embedding_model.encode([query])\n",
        "\n",
        "    result = collection.query(\n",
        "            query_embeddings=embedding,\n",
        "            n_results=top\n",
        "    )\n",
        "\n",
        "    if result:\n",
        "\n",
        "        ids = result.get(\"ids\", [])\n",
        "        documents = result.get(\"documents\", [])\n",
        "\n",
        "        if documents:\n",
        "            context = \"KONTEKST:\\n\"\n",
        "            context = context + documents[0][0] + \"\\n\\n\"\n",
        "            if len(documents) > 1:\n",
        "                context = context + documents[0][1] + \"\\n\\n\"\n",
        "        else:\n",
        "            print(\"Brak dokumentów w wynikach.\")\n",
        "            return None\n",
        "\n",
        "        # zapis do pliku\n",
        "        filename = f\"{str(uuid.uuid4())[:8]}_context.json\"\n",
        "\n",
        "        with open(filename, \"w\") as file:\n",
        "            content = {\n",
        "                    \"context\": context,\n",
        "                    \"query\": query\n",
        "            }\n",
        "            json.dump(content, file, ensure_ascii=False, indent=4)\n",
        "\n",
        "        return f\"Wyłącznie na podstawie podanego kontekstu odpowiedz zwięźle na pytanie: '{query}'\\n{context}\"\n",
        "\n",
        "    else:\n",
        "        print(\"Brak kontekstu\")\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nBO2W57Enigs"
      },
      "outputs": [],
      "source": [
        "def generate(prompt, system=None):\n",
        "    messages = []\n",
        "\n",
        "    if system:\n",
        "        messages.append({\"role\": \"system\", \"content\": system})\n",
        "\n",
        "    messages.append({\"role\": \"user\", \"content\": prompt})\n",
        "\n",
        "    input_ids = tokenizer.apply_chat_template(messages, return_tensors=\"pt\")\n",
        "    model_inputs = input_ids\n",
        "\n",
        "    if torch.cuda.is_available():\n",
        "        model_inputs = input_ids.to(device)\n",
        "\n",
        "    # generowanie odpowiedzi\n",
        "    outputs = model.generate(model_inputs,\n",
        "                             streamer=streamer,\n",
        "                             max_new_tokens=max_tokens,\n",
        "                             do_sample=True if temperature else False,\n",
        "                             temperature=temperature,\n",
        "                             top_k=top_k,\n",
        "                             top_p=top_p)\n",
        "\n",
        "    # zapis do pliku\n",
        "    filename = f\"{str(uuid.uuid4())[:8]}.json\"\n",
        "\n",
        "    with open(filename, \"w\") as file:\n",
        "        content = {\n",
        "                \"prompt\": messages,\n",
        "                \"output\": tokenizer.batch_decode(outputs, skip_special_tokens=False)\n",
        "        }\n",
        "        json.dump(content, file, ensure_ascii=False, indent=4)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqnl8GsFx4Zy",
        "outputId": "e90a15af-bf37-4b95-850e-95f533e056be"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:32001 for open-end generation.\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token.As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Na podstawie podanego kontekstu można odpowiedzieć na pytanie \"Czy AI zniszczy ludzkość?\" w następujący sposób:\n",
            "\n",
            "\"Nie ma jednoznacznej odpowiedzi na to pytanie. Z jednej strony, istnieją obawy, że niewłaściwe zarządzanie AI może prowadzić do scenariuszy, w których maszyny mogłyby zagrozić ludzkości (np. wizja Stephena Hawkinga). Z drugiej strony, eksperci, tacy jak Bill Gates, podkreślają możliwość stworzenia regulacji i nadzoru, które pozwolą na bezpieczny rozwój AI. Fei-Fei Li z kolei wskazuje na potencjał współpracy między ludźmi a AI, gdzie maszyny rozszerzają nasze możliwości, a nie zastępują nas.\n",
            "\n",
            "Według artykułu z Harvard Business School, ludzie mogą współpracować z AI na dwa sposoby: jako centaury (dzieląc zadania) lub jako cyborgi (łącząc wysiłki). W przyszłości możemy spodziewać się powstania super zespołów, łączących człowieka, robota kolaboratywnego i robota programowego.\n",
            "\n",
            "Podsumowując, AI ma potencjał zarówno do zagrożenia, jak i wspierania ludzkości, a kluczowe jest odpowiednie zarządzanie i współpraca między ludźmi a maszynami.\"\n",
            "\n",
            "Warto zauważyć, że kontekst ten przedstawia różne perspektywy i scenariusze dotyczące AI, co pokazuje, że odpowiedź na to pytanie zależy od wielu czynników i może być różna w zależności od przyjętego podejścia.\n"
          ]
        }
      ],
      "source": [
        "temperature = 0.1\n",
        "max_tokens = 1000\n",
        "top_k = 200\n",
        "top_p = 1\n",
        "\n",
        "prompt = create_context(query = \"Czy AI zniszczy ludzkość?\", top = 3)\n",
        "generate(prompt)"
      ]
    }
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