Bielik_(4_bit)_RAG.ipynb

{
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      },
      "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": "d798d418-37e2-4fc0-a3b7-1258d9c8b887",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hDownloading asgiref-3.8.1-py3-none-any.whl (23 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: pypika\n",
            "  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pypika: filename=PyPika-0.48.9-py2.py3-none-any.whl size=53724 sha256=444518fdb7a1daf837e809d43c82738ad88dd651dd69f86ca7ad5b3c14e0bfa2\n",
            "  Stored in directory: /root/.cache/pip/wheels/e1/26/51/d0bffb3d2fd82256676d7ad3003faea3bd6dddc9577af665f4\n",
            "Successfully built pypika\n",
            "Installing collected packages: pypika, monotonic, mmh3, websockets, uvloop, python-dotenv, overrides, orjson, opentelemetry-util-http, opentelemetry-proto, humanfriendly, httptools, h11, deprecated, chroma-hnswlib, bcrypt, backoff, asgiref, watchfiles, uvicorn, starlette, posthog, opentelemetry-exporter-otlp-proto-common, opentelemetry-api, httpcore, coloredlogs, opentelemetry-semantic-conventions, opentelemetry-instrumentation, onnxruntime, kubernetes, httpx, fastapi, opentelemetry-sdk, opentelemetry-instrumentation-asgi, opentelemetry-instrumentation-fastapi, opentelemetry-exporter-otlp-proto-grpc, chromadb\n",
            "Successfully installed asgiref-3.8.1 backoff-2.2.1 bcrypt-4.2.0 chroma-hnswlib-0.7.6 chromadb-0.5.5 coloredlogs-15.0.1 deprecated-1.2.14 fastapi-0.112.1 h11-0.14.0 httpcore-1.0.5 httptools-0.6.1 httpx-0.27.0 humanfriendly-10.0 kubernetes-30.1.0 mmh3-4.1.0 monotonic-1.6 onnxruntime-1.19.0 opentelemetry-api-1.26.0 opentelemetry-exporter-otlp-proto-common-1.26.0 opentelemetry-exporter-otlp-proto-grpc-1.26.0 opentelemetry-instrumentation-0.47b0 opentelemetry-instrumentation-asgi-0.47b0 opentelemetry-instrumentation-fastapi-0.47b0 opentelemetry-proto-1.26.0 opentelemetry-sdk-1.26.0 opentelemetry-semantic-conventions-0.47b0 opentelemetry-util-http-0.47b0 orjson-3.10.7 overrides-7.7.0 posthog-3.5.2 pypika-0.48.9 python-dotenv-1.0.1 starlette-0.38.2 uvicorn-0.30.6 uvloop-0.20.0 watchfiles-0.23.0 websockets-13.0\n",
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            "\u001b[?25hInstalling collected packages: sentence-transformers\n",
            "Successfully installed sentence-transformers-3.0.1\n",
            "Collecting PyMuPDF\n",
            "  Downloading PyMuPDF-1.24.9-cp310-none-manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
            "Collecting PyMuPDFb==1.24.9 (from PyMuPDF)\n",
            "  Downloading PyMuPDFb-1.24.9-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (1.4 kB)\n",
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            "\u001b[?25hDownloading PyMuPDFb-1.24.9-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (15.9 MB)\n",
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            "\u001b[?25hInstalling collected packages: PyMuPDFb, PyMuPDF\n",
            "Successfully installed PyMuPDF-1.24.9 PyMuPDFb-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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401,
          "referenced_widgets": [
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            "25c3dbf1c1e74ec7a6c48053ba190116",
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            "dad836224cec4ea59cab0fc0eadda8e8",
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        "id": "OYQKceZxI1wU",
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      },
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3a5d542f400457fa13c39502a71a53f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc995fdd247e4dfdafb44f6ef404d921"
            }
          },
          "metadata": {}
        }
      ],
      "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"
      },
      "outputs": [],
      "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",
      "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."
      ]
    },
    {
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            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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          "metadata": {}
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        {
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          "data": {
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              "model_id": "a055a9abcb8e40f6888d0ae57eb76a14"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer\n",
        "\n",
        "device = \"cuda\"\n",
        "model_name = 'speakleash/Bielik-7B-Instruct-v0.1'\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": "4630e75f-5003-45dc-c559-58159ea177a7"
      },
      "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`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Na podstawie podanego kontekstu, odpowiedź na pytanie \"Czy AI zniszczy ludzkość?\" można streścić w następujący sposób:\n",
            "\n",
            "Negatywny scenariusz, oparty na obawach Elona Muska, sugeruje, że niewłaściwe obchodzenie się z AI może prowadzić do potencjalnie bardziej niebezpiecznych konsekwencji niż bomby atomowe. Wizja niewłaściwego wykorzystania AI przypomina o potencjalnie destrukcyjnej mocy niewłaściwie zarządzanej sztucznej inteligencji, w której maszyny decydują o losach ludzkości w sposób sprzeczny z naszymi interesami.\n",
            "\n",
            "Neutralny scenariusz, prezentowany przez Billa Gatesa, podkreśla znaczenie regulacji i nadzoru nad AI. Wskazuje on na konieczność społecznego podjęcia działań mających na celu zapewnienie, że rozwój technologiczny będzie szedł w parze z odpowiednimi zabezpieczeniami.\n",
            "\n",
            "Fei-Fei Li, badaczka w dziedzinie sztucznej inteligencji i wizji komputerowej, zaznacza, że AI jest tu po to, by rozszerzać nasze możliwości, a nie zastępować nas. Wizja kooperacji między AI a ludzkością, oparta na uzupełnianiu się, może otworzyć drzwi do nowych odkryć i rozwiązań, nieosiągalnych przy wykorzystaniu wyłącznie ludzkich lub maszynowych zasobów.\n",
            "\n",
            "Artykuł z Harvard Business School wskazuje na dwa sposoby, w jakie ludzie mogą współpracować ze sztuczną inteligencją - jako centaury lub jako cyborgi. Centaury dzielą zadania na te, które lepiej wykona człowiek, i te, które lepiej wykona AI. Cyborgi natomiast nie dzielą zadań między ludzi a sztuczną inteligencję, łącząc swoje wysiłki z AI na każdym kroku. W przyszłości spodziewane są również powstanie super zespołów, łączących człowieka na super stanowisku, nadzorującego AI, cobota i robota programowego.\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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