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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/67.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.17.1 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 5.29.2 which is incompatible.\n",
            "tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 5.29.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!mkdir -p \"./documents\"\n",
        "!wget https://www.gov.nl.ca/ecc/files/env-protection-pesticides-business-manuals-applic-chapter7.pdf -O \"./documents/env-protection-pesticides-business-manuals-applic-chapter7.pdf\"\n",
        "\n",
        "import PyPDF2\n",
        "from langchain_core.documents import Document\n",
        "import os\n",
        "\n",
        "def extract_text_from_pdf(pdf_path):\n",
        "    \"\"\"Extract text from PDF file\"\"\"\n",
        "    documents = []\n",
        "    with open(pdf_path, 'rb') as file:\n",
        "        pdf_reader = PyPDF2.PdfReader(file)\n",
        "\n",
        "        for page_num in range(len(pdf_reader.pages)):\n",
        "            page = pdf_reader.pages[page_num]\n",
        "            text = page.extract_text()\n",
        "\n",
        "            # تقسيم النص إلى أجزاء أصغر (chunks)\n",
        "            chunks = split_text_into_chunks(text, max_chars=512)\n",
        "\n",
        "            for chunk in chunks:\n",
        "                metadata = {\n",
        "                    \"source\": pdf_path,\n",
        "                    \"page\": page_num + 1\n",
        "                }\n",
        "                documents.append(Document(page_content=chunk, metadata=metadata))\n",
        "\n",
        "    return documents\n",
        "\n",
        "def split_text_into_chunks(text, max_chars=512):\n",
        "    \"\"\"Split text into parts of a specified size\"\"\"\n",
        "    words = text.split()\n",
        "    chunks = []\n",
        "    current_chunk = []\n",
        "    current_length = 0\n",
        "\n",
        "    for word in words:\n",
        "        word_length = len(word) + 1  # +1 للمسافة\n",
        "        if current_length + word_length > max_chars:\n",
        "            chunks.append(' '.join(current_chunk))\n",
        "            current_chunk = [word]\n",
        "            current_length = word_length\n",
        "        else:\n",
        "            current_chunk.append(word)\n",
        "            current_length += word_length\n",
        "\n",
        "    if current_chunk:\n",
        "        chunks.append(' '.join(current_chunk))\n",
        "\n",
        "    return chunks\n",
        "\n",
        "# معالجة جميع ملفات PDF في مجلد documents\n",
        "documents = []\n",
        "docs_dir = \"./documents\"\n",
        "for filename in os.listdir(docs_dir):\n",
        "    if filename.endswith('.pdf'):\n",
        "        filepath = os.path.join(docs_dir, filename)\n",
        "        documents.extend(extract_text_from_pdf(filepath))\n",
        "\n",
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\")\n",
        "vectorstore = Chroma.from_documents(documents, embeddings)\n",
        "retriever = vectorstore.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n",
        "\n",
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()\n",
        "\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "model_name = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=bnb_config)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "terminators = [\n",
        "    tokenizer.eos_token_id,\n",
        "    tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n",
        "]\n",
        "\n",
        "text_generation_pipeline = pipeline(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    task=\"text-generation\",\n",
        "    temperature=0.2,\n",
        "    do_sample=True,\n",
        "    repetition_penalty=1.1,\n",
        "    return_full_text=False,\n",
        "    max_new_tokens=200,\n",
        "    eos_token_id=terminators,\n",
        ")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n",
        "\n",
        "prompt_template = \"\"\"\n",
        "<|start_header_id|>user<|end_header_id|>\n",
        "You are an assistant for answering questions using provided context.\n",
        "You are given the extracted parts of a long document and a question. Provide a conversational answer.\n",
        "If you don't know the answer, just say \"I do not know.\" Don't make up an answer.\n",
        "Question: {question}\n",
        "Context: {context}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"context\", \"question\"],\n",
        "    template=prompt_template,\n",
        ")\n",
        "\n",
        "qa_chain = RetrievalQA.from_chain_type(\n",
        "    llm,\n",
        "    retriever=retriever,\n",
        "    chain_type_kwargs={\"prompt\": prompt}\n",
        ")\n",
        "\n",
        "# اختبار النظام\n",
        "question = \"Are aphids a pest?\"\n",
        "print(qa_chain.invoke(question)['result'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "outputId": "0259063c-a3a4-4a7e-b39b-20e0c5dcf3f6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-01-08 20:14:41--  https://www.gov.nl.ca/ecc/files/env-protection-pesticides-business-manuals-applic-chapter7.pdf\n",
            "Resolving www.gov.nl.ca (www.gov.nl.ca)... 98.143.128.70\n",
            "Connecting to www.gov.nl.ca (www.gov.nl.ca)|98.143.128.70|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1914250 (1.8M) [application/pdf]\n",
            "Saving to: ‘./documents/env-protection-pesticides-business-manuals-applic-chapter7.pdf’\n",
            "\n",
            "./documents/env-pro 100%[===================>]   1.83M   943KB/s    in 2.0s    \n",
            "\n",
            "2025-01-08 20:14:44 (943 KB/s) - ‘./documents/env-protection-pesticides-business-manuals-applic-chapter7.pdf’ saved [1914250/1914250]\n",
            "\n"
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        {
          "output_type": "stream",
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          "text": [
            "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n"
          ]
        },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n",
            "<ipython-input-5-40c094fab7fc>:107: LangChainDeprecationWarning: The class `HuggingFacePipeline` was deprecated in LangChain 0.0.37 and will be removed in 1.0. An updated version of the class exists in the :class:`~langchain-huggingface package and should be used instead. To use it run `pip install -U :class:`~langchain-huggingface` and import as `from :class:`~langchain_huggingface import HuggingFacePipeline``.\n",
            "  llm = HuggingFacePipeline(pipeline=text_generation_pipeline)\n",
            "Setting `pad_token_id` to `eos_token_id`:128009 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Based on the context, it seems that aphids are indeed considered a pest, as lady beetles are mentioned as being present in their colonies, suggesting that they are a problem that needs to be managed. The text also highlights the importance of understanding the biology of pests and beneficial organisms in making informed pest management decisions. So, to answer your question, yes, aphids are considered a pest.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "epS6CFDPjNw4",
        "outputId": "c17f5693-e2d1-42d0-a90e-af4fc5bc7a0a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: read).\n",
            "The token `read` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `read`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YZzHENISjd7I"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}