succ_rag_pdf_cpu.ipynb

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        },
        "e1679b204b9d493f8fd7ac47107342c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#######################################################"
      ],
      "metadata": {
        "id": "-uoeLm1ez935"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "شغال ع المعالج"
      ],
      "metadata": {
        "id": "qfTe8dqpz6so"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q torch transformers accelerate bitsandbytes sentence-transformers PyPDF2 langchain chromadb langchain_community\n"
      ],
      "metadata": {
        "id": "iRwcI2qH0IV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import PyPDF2\n",
        "from langchain_core.documents import Document\n",
        "import os\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from langchain_community.vectorstores import Chroma\n",
        "from langchain.embeddings import HuggingFaceEmbeddings"
      ],
      "metadata": {
        "id": "veNYJjt6tKG_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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",
        "            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\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",
        "# Process PDF files\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",
        "# Initialize embeddings and vector store\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})"
      ],
      "metadata": {
        "id": "5-dzDRkYtKDT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
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        },
        "id": "SVMjPk9otCSr",
        "outputId": "8c99da1a-1e9f-4e79-91d4-6655a68207af"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bdcd2e7649da4c60b0c6984c629ea001"
            }
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
        "\n",
        "# Device map configuration\n",
        "device_map = \"auto\"\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    device_map=device_map,\n",
        "    torch_dtype=torch.bfloat16\n",
        ")\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Configure pipeline\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",
        "    torch_dtype=torch.bfloat16,\n",
        "    device_map=device_map\n",
        ")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_generation_pipeline)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 409,
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      "execution_count": 13,
      "outputs": [
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            "text/plain": [
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        {
          "output_type": "display_data",
          "data": {
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              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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          "output_type": "display_data",
          "data": {
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          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b943e18e1eaa4ddaaf95dd5561053ccf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/296 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d6f951d3ffee4b29a5455f6030dceab9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cpu\n",
            "<ipython-input-13-d0e467664e1b>:28: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_template = \"\"\"\n",
        "<s>[INST] 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",
        "\n",
        "Question: {question}\n",
        "Context: {context} [/INST]\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"context\", \"question\"],\n",
        "    template=prompt_template,\n",
        ")\n",
        "\n",
        "# Create QA chain\n",
        "qa_chain = RetrievalQA.from_chain_type(\n",
        "    llm,\n",
        "    retriever=retriever,\n",
        "    chain_type_kwargs={\"prompt\": prompt}\n",
        ")\n",
        "\n",
        "# Test the system\n",
        "question = \"Are aphids a pest?\"\n",
        "print(qa_chain.invoke(question)['result'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehSCe7LdtCPL",
        "outputId": "777b98a2-9db5-43b4-bf55-729cad3a29eb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: Aphids are considered pests because they can cause significant damage to plants by sucking sap from leaves and stems, which can weaken the plant and reduce its ability to grow and produce fruit. However, as mentioned in the context, having beneficial insects like lady beetles that prey on aphids can help manage their populations without relying on pesticides.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "################################################################"
      ],
      "metadata": {
        "id": "YnPXI_4H0ALm"
      }
    }
  ]
}