LangChain-Llama-2.ipynb

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      "cell_type": "markdown",
      "id": "-tTPGUk-m-Xt",
      "metadata": {
        "id": "-tTPGUk-m-Xt"
      },
      "source": [
        "# RAG Llama-2-7b Chat Model\n",
        "📒Notebook Created by ❤️ [@prasadmahamulkar](https://x.com/prsdm17)."
      ]
    },
    {
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      "id": "ebpxupaJMVlC",
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          "text": [
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install torch"
      ]
    },
    {
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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      ],
      "source": [
        "!pip install -q -U transformers langchain bitsandbytes accelerate sentence_transformers faiss-gpu"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "P3FMbclWKwT2",
      "metadata": {
        "id": "P3FMbclWKwT2"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "uY_xru-MJAat",
      "metadata": {
        "id": "uY_xru-MJAat"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from langchain import HuggingFacePipeline\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline,BitsAndBytesConfig"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "j6JoK3PFpKdt",
      "metadata": {
        "id": "j6JoK3PFpKdt"
      },
      "source": [
        "### Load model and tokenizer"
      ]
    },
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      ],
      "source": [
        "base_model = \"meta-llama/Llama-2-7b-chat-hf\"  # if you dont have huggingface account use this model:\"NousResearch/Llama-2-7b-chat-hf\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "XzezKmFkVyBb",
      "metadata": {
        "id": "XzezKmFkVyBb"
      },
      "outputs": [],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f2i5hZNJXwub",
      "metadata": {
        "id": "f2i5hZNJXwub"
      },
      "outputs": [],
      "source": [
        "generation_config = GenerationConfig.from_pretrained(base_model, max_new_tokens = 1048)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1u_YG9GlUtPe",
      "metadata": {
        "id": "1u_YG9GlUtPe"
      },
      "outputs": [],
      "source": [
        "text_pipeline = pipeline(\n",
        "    task=\"text-generation\",\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    generation_config=generation_config,\n",
        ")\n",
        "\n",
        "llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={\"temperature\": 0})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b610123",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2b610123",
        "outputId": "e45cec42-bdf7-4bd0-dd65-7fbf8e41aa13"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.7 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "result = llm(\"who is mrbeast and why is he so rich?\")\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "765695b5",
      "metadata": {
        "id": "765695b5"
      },
      "source": [
        "### Load CSV File"
      ]
    },
    {
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      "id": "RPx-oJBHaohD",
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        "colab": {
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        },
        "id": "RPx-oJBHaohD",
        "outputId": "135f59d8-9885-410a-f194-6d2028f8cccd"
      },
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-352636eb-a423-47ac-8d77-6a07fb23eb3a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-352636eb-a423-47ac-8d77-6a07fb23eb3a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5d3308a9-62a3-4270-a73b-069f762577d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5d3308a9-62a3-4270-a73b-069f762577d9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5d3308a9-62a3-4270-a73b-069f762577d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                                            Question  \\\n",
              "0  What is the fundamental goal of machine learning?   \n",
              "1  Explain the concept of overfitting in machine ...   \n",
              "2  Can you distinguish between bias and variance ...   \n",
              "3  What is the role of a loss function in machine...   \n",
              "4           Explain the concept of gradient descent.   \n",
              "\n",
              "                                             Answers  \n",
              "0  The fundamental goal of machine learning is to...  \n",
              "1  Overfitting occurs when a machine learning mod...  \n",
              "2  Bias refers to the error introduced by approxi...  \n",
              "3  A loss function measures the difference betwee...  \n",
              "4  Gradient descent is an optimization algorithm ...  "
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"Machine-Learning-QA-dataset-RAG.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c62e35c",
      "metadata": {
        "id": "0c62e35c"
      },
      "outputs": [],
      "source": [
        "from langchain.document_loaders.csv_loader import CSVLoader\n",
        "loader = CSVLoader(file_path='Machine-Learning-QA-dataset-RAG.csv', encoding='utf-8')\n",
        "data = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "s39QupqCbEbO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s39QupqCbEbO",
        "outputId": "e882c521-19cd-4f2d-9c88-fc95fd6d23d0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Document(page_content='\\ufeffQuestion: What is the fundamental goal of machine learning?\\nAnswers: The fundamental goal of machine learning is to develop algorithms that enable computers to learn from data, recognize patterns, and make intelligent decisions or predictions without explicit programming.', metadata={'source': 'ML-101-QandA.csv', 'row': 0})"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4dd45e51",
      "metadata": {
        "id": "4dd45e51"
      },
      "source": [
        "### Hugging Face Embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "W5fNYdxKfrKB",
      "metadata": {
        "colab": {
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        "id": "W5fNYdxKfrKB",
        "outputId": "dd3aee38-a94a-4341-8ca1-2875b9717dd8"
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        {
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              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "509af5e5fb2d4fc6b9250e817334f7cf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "data_config.json:   0%|          | 0.00/39.3k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a92b823f0f934135af2e739c4f87e494",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a537f90b04864ca9be58c354236570b5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75bde12ea03b4dc6be111506f0caae30",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "adf960afa1d74e26a7472375967b6f4e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1ceffbed6aa48c6848fb86f7831c6a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9be8bdd630f14348971fb3a935e57923",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "train_script.py:   0%|          | 0.00/13.2k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2206515b7f1849b988806941ef56e01c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f032cd5fc924b9da310f4058584a13d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_utils.py:831: UserWarning: TypedStorage is deprecated. It will be removed in the future and UntypedStorage will be the only storage class. This should only matter to you if you are using storages directly.  To access UntypedStorage directly, use tensor.untyped_storage() instead of tensor.storage()\n",
            "  return self.fget.__get__(instance, owner)()\n"
          ]
        }
      ],
      "source": [
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=\"BAAI/bge-small-en-v1.5\",encode_kwargs={\"normalize_embeddings\": True})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "BOpIjGgkeEwh",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOpIjGgkeEwh",
        "outputId": "e154adb1-fad7-4202-ac80-4e7e45c319ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[-0.019954554736614227,\n",
              " 0.009878006763756275,\n",
              " 0.010249636135995388,\n",
              " 0.02955370955169201,\n",
              " 0.02718644216656685,\n",
              " -0.019296562299132347,\n",
              " -0.024129625409841537,\n",
              " -0.03773573413491249,\n",
              " -0.04105421155691147,\n",
              " -0.0014750175178050995]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "check_embeddings = embeddings.embed_query(\"What is Machine Learning?\")\n",
        "check_embeddings[:10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0762eeac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0762eeac",
        "outputId": "9ecd88de-eed3-4315-854b-f426dec3df7c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "384"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(check_embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc80a28a",
      "metadata": {
        "id": "cc80a28a"
      },
      "source": [
        "### Vector store: FAISS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b3c706da",
      "metadata": {
        "id": "b3c706da"
      },
      "outputs": [],
      "source": [
        "from langchain.vectorstores import FAISS\n",
        "vectorstore = FAISS.from_documents(documents=data,embedding=embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "m3DZqVR_hfDf",
      "metadata": {
        "id": "m3DZqVR_hfDf"
      },
      "outputs": [],
      "source": [
        "retriever = vectorstore.as_retriever(score_threshold = 0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cfd58f6f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfd58f6f",
        "outputId": "968f5d83-519a-47b5-d5bb-7d006473db03"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[Document(page_content='\\ufeffQuestion: What is machine learning?\\nAnswers: Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.', metadata={'source': 'ML-101-QandA.csv', 'row': 90}),\n",
              " Document(page_content='\\ufeffQuestion: What is the fundamental goal of machine learning?\\nAnswers: The fundamental goal of machine learning is to develop algorithms that enable computers to learn from data, recognize patterns, and make intelligent decisions or predictions without explicit programming.', metadata={'source': 'ML-101-QandA.csv', 'row': 0}),\n",
              " Document(page_content='\\ufeffQuestion: What are the main types of machine learning?\\nAnswers: The main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, models are trained on labeled data, while unsupervised learning involves discovering patterns in unlabeled data. Reinforcement learning focuses on training agents to make decisions by interacting with an environment.', metadata={'source': 'ML-101-QandA.csv', 'row': 91}),\n",
              " Document(page_content=\"\\ufeffQuestion: What is the training and testing process in machine learning?\\nAnswers: The training process involves using a labeled dataset to train a machine learning model by adjusting its parameters. The testing process assesses the model's performance on new, unseen data to evaluate its ability to generalize from the training data.\", metadata={'source': 'ML-101-QandA.csv', 'row': 93})]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "result = retriever.get_relevant_documents(\"what is machine learning?\")\n",
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2d4842c8",
      "metadata": {
        "id": "2d4842c8"
      },
      "outputs": [],
      "source": [
        "from langchain.prompts import PromptTemplate\n",
        "\n",
        "template = \"\"\"\n",
        "<s>[INST] <<SYS>>\n",
        "Act as a Machine Learning expert. Use the context only to answer the question.\n",
        "If the answer is not found in the context then end the conversation by kindly stating \"I am a Machine Learning expert and I am not able to find the answer to your question.\"\n",
        "<</SYS>>\n",
        "\n",
        "{context}\n",
        "\n",
        "{question} [/INST]\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "PROMPT = PromptTemplate(template=template, input_variables=[\"context\", \"question\"])\n",
        "chain_type_kwargs = {\"prompt\": PROMPT}\n",
        "\n",
        "\n",
        "from langchain.chains import RetrievalQA\n",
        "\n",
        "chain = RetrievalQA.from_chain_type(llm=llm,\n",
        "                            chain_type=\"stuff\",\n",
        "                            retriever=retriever,\n",
        "                            input_key=\"query\",\n",
        "                            return_source_documents=True,\n",
        "                            chain_type_kwargs=chain_type_kwargs)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "90166e8d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90166e8d",
        "outputId": "bcf4b7f6-a629-4bc2-eed7-3220e36f29b1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
            "  warn_deprecated(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'query': 'Explain the concept of gradient descent.',\n",
              " 'result': '\\nQuestion: What is the purpose of a learning rate in gradient descent? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between stochastic gradient descent and batch gradient descent, and how do they differ in practice? [/SYS]\\n\\nQuestion: What is the purpose of a learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value? [/SYS]\\n\\nQuestion: What is the difference between st',\n",
              " 'source_documents': [Document(page_content=\"\\ufeffQuestion: Explain the concept of gradient descent.\\nAnswers: Gradient descent is an optimization algorithm used to minimize the loss function during training. It iteratively adjusts the model's parameters in the direction of the steepest decrease in the loss, ultimately reaching a minimum.\", metadata={'source': 'ML-101-QandA.csv', 'row': 4}),\n",
              "  Document(page_content='\\ufeffQuestion: What is the purpose of a learning rate in gradient descent?\\nAnswers: The learning rate in gradient descent controls the size of the steps taken during optimization. A too-small learning rate may result in slow convergence, while a too-large learning rate can cause the algorithm to overshoot the minimum.', metadata={'source': 'ML-101-QandA.csv', 'row': 28}),\n",
              "  Document(page_content='\\ufeffQuestion: What is the purpose of the learning rate in gradient descent, and how do you choose an appropriate value?\\nAnswers: The learning rate in gradient descent controls the size of steps taken during optimization. An appropriate value is crucial; too small a rate can lead to slow convergence, while too large a rate may cause overshooting. Common methods for choosing the learning rate include grid search and adaptive techniques like Adam optimization.', metadata={'source': 'ML-101-QandA.csv', 'row': 50}),\n",
              "  Document(page_content='\\ufeffQuestion: What is the difference between stochastic gradient descent and batch gradient descent?\\nAnswers: In stochastic gradient descent, the model parameters are updated after each individual data point, while in batch gradient descent, the update is based on the average error computed over the entire training dataset. Mini-batch gradient descent is a compromise between the two, using a subset of the data for updates.', metadata={'source': 'ML-101-QandA.csv', 'row': 13})]}"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain('Explain the concept of gradient descent')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "48970302",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48970302",
        "outputId": "28d8ad45-3bc7-4b73-fe86-4b40d29d5dd0",
        "scrolled": false
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'query': 'what is capital of United Kingdom?',\n",
              " 'result': 'I am a Machine Learning expert and I am not able to find the answer to your question. The capital of the United Kingdom is not a term that is commonly associated with machine learning. Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed. It does not provide information about geographical locations or capital cities. Is there anything else I can help you with?',\n",
              " 'source_documents': [Document(page_content=\"\\ufeffQuestion: What is the purpose of a ROC curve, and how does it relate to the area under the curve (AUC)?\\nAnswers: The Receiver Operating Characteristic (ROC) curve visualizes a classification model's performance across different threshold settings. The area under the curve (AUC) represents the model's ability to distinguish between classes, with a higher AUC indicating better performance.\", metadata={'source': 'Q&A.csv', 'row': 59}),\n",
              "  Document(page_content='\\ufeffQuestion: What is machine learning?\\nAnswers: Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.', metadata={'source': 'Q&A.csv', 'row': 90}),\n",
              "  Document(page_content='\\ufeffQuestion: What is the fundamental goal of machine learning?\\nAnswers: The fundamental goal of machine learning is to develop algorithms that enable computers to learn from data, recognize patterns, and make intelligent decisions or predictions without explicit programming.', metadata={'source': 'Q&A.csv', 'row': 0}),\n",
              "  Document(page_content='\\ufeffQuestion: Can you explain the difference between a feature and a label in machine learning?\\nAnswers: In machine learning, a feature is an input variable or attribute used to make predictions, while a label is the output variable or the target variable that the model aims to predict based on the features.', metadata={'source': 'Q&A.csv', 'row': 92})]}"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain(\"what is capital of United Kingdom?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c17dc6c3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c17dc6c3",
        "outputId": "a7c490c9-e6cd-4b44-dd48-ffdb844a5e8a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:397: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'query': 'how to make a coffee?',\n",
              " 'result': \"I am a Machine Learning expert, and I am not able to find the answer to your question. I'm just an AI and do not have the ability to make coffee or provide instructions on how to make it. My training data does not include information on coffee-making, and I cannot provide a response that is not based on my training data. I apologize for any confusion. Is there anything else I can help you with?\",\n",
              " 'source_documents': [Document(page_content='\\ufeffQuestion: Explain the concept of bagging in ensemble learning\\nAnswers: Bagging (Bootstrap Aggregating) involves training multiple instances of the same model on different subsets of the training data, typically using bootstrap sampling. The final prediction is often a combination (average or voting) of the individual models.', metadata={'source': 'Q&A.csv', 'row': 20}),\n",
              "  Document(page_content='\\ufeffQuestion: What is ensemble learning, and how does it improve model performance?\\nAnswers: Ensemble learning involves combining the predictions of multiple models to create a stronger and more accurate model. Techniques like bagging (e.g., Random Forest) and boosting (e.g., AdaBoost) are common approaches to ensemble learning.', metadata={'source': 'Q&A.csv', 'row': 9}),\n",
              "  Document(page_content='\\ufeffQuestion: How does the Rocchio algorithm work in text classification?\\nAnswers: The Rocchio algorithm is a simple and effective algorithm for text classification. It assigns a document to the category that is closest to the centroid of the documents in that category, using vector representations of documents.', metadata={'source': 'Q&A.csv', 'row': 48}),\n",
              "  Document(page_content='\\ufeffQuestion: Can you explain the concept of kernel trick in support vector machines (SVM)?\\nAnswers: The kernel trick in SVM allows the algorithm to implicitly map input data into higher-dimensional spaces without explicitly computing the transformation. This makes it computationally efficient and allows SVMs to work well in non-linearly separable datasets.', metadata={'source': 'Q&A.csv', 'row': 39})]}"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain(\"how to make a coffee?\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "0013750bf33a4dad929e30e1255d7d82": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
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