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  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Adnan525/RAG/blob/main/camel_5b_RAG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install llama-index llama-index-llms-huggingface llama-index-embeddings-huggingface transformers accelerate bitsandbytes llama-index-readers-web"
      ],
      "metadata": {
        "id": "Yh10w_dfzq94"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from llama_index.core import SimpleDirectoryReader\n",
        "from llama_index.core.prompts import PromptTemplate\n",
        "\n",
        "from llama_index.core.llms import ChatMessage, MessageRole\n",
        "from llama_index.core import ChatPromptTemplate\n",
        "from llama_index.core.response.notebook_utils import display_response\n",
        "\n",
        "import torch\n",
        "from llama_index.llms.huggingface import HuggingFaceLLM # llm\n",
        "from llama_index.embeddings.huggingface import HuggingFaceEmbedding # embedding\n",
        "from llama_index.core import Settings # pass llm and embedding\n",
        "\n",
        "from llama_index.core import VectorStoreIndex # GPTVectorStoreIndex has been renamed to VectorStoreIndex, but it is only a cosmetic change. Please see the following links for more details on usage."
      ],
      "metadata": {
        "id": "trYJrn2cu_My"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "hf_token = userdata.get(\"hf_token\")"
      ],
      "metadata": {
        "id": "2VGXDPMAumba"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p \"data/chess/\"\n",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2r7usJgfu6QO",
        "outputId": "a22e77be-5bb5-4962-d122-78b6dcac0306"
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      "execution_count": 12,
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        {
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          "text": [
            "--2024-05-29 01:02:30--  http://dspace.vnbrims.org:13000/jspui/bitstream/123456789/4980/1/Chess_%20The%20Complete%20Guide%20To%20Chess%20-%20Master_%20Chess%20Tactics%2C%20Chess%20Openings%2C%20and%20Chess%20Strategies.pdf\n",
            "Resolving dspace.vnbrims.org (dspace.vnbrims.org)... 124.108.19.235\n",
            "Connecting to dspace.vnbrims.org (dspace.vnbrims.org)|124.108.19.235|:13000... connected.\n",
            "HTTP request sent, awaiting response... 200 \n",
            "Length: 3003524 (2.9M) [application/pdf]\n",
            "Saving to: ‘data/chess/chess.pdf’\n",
            "\n",
            "data/chess/chess.pd 100%[===================>]   2.86M  1.62MB/s    in 1.8s    \n",
            "\n",
            "2024-05-29 01:02:32 (1.62 MB/s) - ‘data/chess/chess.pdf’ saved [3003524/3003524]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget \"http://www.cs.columbia.edu/~vovsha/coms3101/perl/acn.pdf\" -O \"data/chess/columbia.pdf\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjXPsxbE7-ZM",
        "outputId": "42b402f6-adf8-4ab0-f48c-4796f8b43d8b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-29 01:22:25--  http://www.cs.columbia.edu/~vovsha/coms3101/perl/acn.pdf\n",
            "Resolving www.cs.columbia.edu (www.cs.columbia.edu)... 128.59.11.206\n",
            "Connecting to www.cs.columbia.edu (www.cs.columbia.edu)|128.59.11.206|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 34630 (34K) [application/pdf]\n",
            "Saving to: ‘data/chess/columbia.pdf’\n",
            "\n",
            "data/chess/columbia 100%[===================>]  33.82K  --.-KB/s    in 0.06s   \n",
            "\n",
            "2024-05-29 01:22:26 (565 KB/s) - ‘data/chess/columbia.pdf’ saved [34630/34630]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# book 3\n",
        "!wget \"https://www.fide.com/FIDE/handbook/LawsOfChess.pdf\" -O \"data/chess/LawsOfChess.pdf\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EGxj0o0sBduj",
        "outputId": "f28e23ec-c50c-4915-e7d0-8fc74939a357"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-05-29 01:45:55--  https://www.fide.com/FIDE/handbook/LawsOfChess.pdf\n",
            "Resolving www.fide.com (www.fide.com)... 80.241.218.178\n",
            "Connecting to www.fide.com (www.fide.com)|80.241.218.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 300696 (294K) [application/pdf]\n",
            "Saving to: ‘data/chess/LawsOfChess.pdf’\n",
            "\n",
            "data/chess/LawsOfCh 100%[===================>] 293.65K   481KB/s    in 0.6s    \n",
            "\n",
            "2024-05-29 01:45:57 (481 KB/s) - ‘data/chess/LawsOfChess.pdf’ saved [300696/300696]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "rSuHnW09pIW4"
      },
      "outputs": [],
      "source": [
        "documents = SimpleDirectoryReader(\"data/chess\").load_data()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "books = set()\n",
        "for book in documents:\n",
        "  books.add(book.metadata[\"file_name\"])\n",
        "\n",
        "books"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96g9pNfy8Yiy",
        "outputId": "74712d63-e97e-4d1c-f9c1-4e44784f8541"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'LawsOfChess.pdf', 'chess.pdf', 'columbia.pdf'}"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"Writer/camel-5b-hf\""
      ],
      "metadata": {
        "id": "ePMkWICPwETf"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plain prompt  template"
      ],
      "metadata": {
        "id": "4N2YE00MxKcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "query_wrapper_prompt = PromptTemplate(\n",
        "    \"Always answer the question, even if the context isn't helpful.\"\n",
        "    \"Write a response that appropriately completes the request.\\n\\n\"\n",
        "    \"### Instruction:\\n{query_str}\\n\\n### Response:\"\n",
        ")"
      ],
      "metadata": {
        "id": "VhMsPLulwUZH"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using System Prompt"
      ],
      "metadata": {
        "id": "trZRbHesxRUu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "qa_prompt_str = (\n",
        "    \"Context information is below.\\n\"\n",
        "    \"---------------------\\n\"\n",
        "    \"{context_str}\\n\"\n",
        "    \"---------------------\\n\"\n",
        "    \"Given the context information and not prior knowledge, \"\n",
        "    \"answer the question: {query_str}\\n\"\n",
        ")"
      ],
      "metadata": {
        "id": "cAFzmkaXxI1y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chat_text_qa_msgs = [\n",
        "    ChatMessage(\n",
        "        role=MessageRole.SYSTEM,\n",
        "        content=(\n",
        "            \"Always answer the question, even if the context isn't helpful.\"\n",
        "        ),\n",
        "    ),\n",
        "    ChatMessage(role=MessageRole.USER, content=qa_prompt_str),\n",
        "]\n",
        "text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)"
      ],
      "metadata": {
        "id": "f7hVGSwkxUlP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = HuggingFaceLLM(\n",
        "    context_window=2048,\n",
        "    max_new_tokens=256,\n",
        "    generate_kwargs={\"temperature\": 0, \"do_sample\": False},\n",
        "    query_wrapper_prompt=query_wrapper_prompt,\n",
        "    tokenizer_name=model_id,\n",
        "    model_name=model_id,\n",
        "    device_map=\"auto\",\n",
        "    tokenizer_kwargs={\"max_length\": 2048},\n",
        "    model_kwargs={\"torch_dtype\": torch.float16, \"load_in_8bit\" : True}\n",
        ")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503,
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        },
        "id": "cMzO3x8rxk9Q",
        "outputId": "b257eafd-e58d-4aa8-81af-a7781de75a6c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/787 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7196660e82e457d99c349949eab6f74"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin.index.json:   0%|          | 0.00/25.4k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00003.bin:   0%|          | 0.00/10.0G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a74a2023db6b4411ab0e0ec6e1f41297"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00002-of-00003.bin:   0%|          | 0.00/9.99G [00:00<?, ?B/s]"
            ],
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model-00003-of-00003.bin:   0%|          | 0.00/1.09G [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/748 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/798k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/21.0 [00:00<?, ?B/s]"
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      "execution_count": 18,
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        {
          "output_type": "display_data",
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            "text/plain": [
              "modules.json:   0%|          | 0.00/461 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
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            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/122 [00:00<?, ?B/s]"
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              "README.md:   0%|          | 0.00/66.2k [00:00<?, ?B/s]"
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              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
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        },
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            "text/plain": [
              "config.json:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "vector_index = VectorStoreIndex.from_documents(documents)"
      ],
      "metadata": {
        "id": "80vxUmApyjMO"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine()\n",
        "response = query_engine.query(\"What are the name of the authors for all three books?\")\n",
        "display_response(response)\n",
        "# Mary Lou Retton is an american gymnyst"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "lKVirL2n5_Q0",
        "outputId": "46537610-3ddd-4393-cb2a-d91ea2243392"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The authors for all three books are:\n1. The Complete Guide to Chess by Logan Donovan\n2. Chess Tactics, Chess Openings and Chess Strategies by Mark Taiman\n3. Chess by Garry Kasparov"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "query_engine = vector_index.as_query_engine()\n",
        "response = query_engine.query(\"how does the Knight move in chess?\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "id": "ur_6SzH-5vMr",
        "outputId": "6083c16e-913d-4f7c-e52b-dc66cfef9957"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The Knight moves in an \"L\" shape, taking two straight steps and one step to the right or left, and can also move one step forward or backward."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"knowing how Knight moves in chess and algebraic notation, if a knight in on c1, is kf7 a legal move? Explain\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "id": "yjIqjE0vzFc4",
        "outputId": "187eb209-6569-4d28-bbd8-51591c7165fb"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** Yes, the knight on c1 is a legal move. In algebraic notation, the knight moves as kf7."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"I am using algebraic notation for chess. If i have a Knight on b1, what are my legal moves with the Knight? please give me square position number in algebraic notation.\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "id": "f_n215HL98IJ",
        "outputId": "67cca068-9332-4699-a888-2c6800b0d3e6"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** Nf3"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "response = query_engine.query(\"if current position of a pawn is e2, what will be its position if it takes 1 step forward?\")\n",
        "display_response(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "id": "E507FUCp6gkf",
        "outputId": "d3a09e4a-0a26-4c80-98d7-610aa36193e9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**`Final Response:`** The pawn will move to f3."
          },
          "metadata": {}
        }
      ]
    }
  ]
}