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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for hnswlib (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",
            "google-colab 1.0.0 requires requests==2.27.1, but you have requests 2.31.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip -q install langchain openai tiktoken chromadb pypdf sentence_transformers InstructorEmbedding"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9IjIFDgDXEqI",
        "outputId": "0826ba3b-ce0b-456f-ac4a-7288901669a2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: langchain\n",
            "Version: 0.0.215\n",
            "Summary: Building applications with LLMs through composability\n",
            "Home-page: https://www.github.com/hwchase17/langchain\n",
            "Author: \n",
            "Author-email: \n",
            "License: MIT\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: aiohttp, async-timeout, dataclasses-json, langchainplus-sdk, numexpr, numpy, openapi-schema-pydantic, pydantic, PyYAML, requests, SQLAlchemy, tenacity\n",
            "Required-by: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.vectorstores import Chroma\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "# from langchain.llms import OpenAI\n",
        "from langchain.chains import RetrievalQA\n",
        "# from langchain.document_loaders import TextLoader\n",
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.document_loaders import DirectoryLoader\n",
        "\n",
        "\n",
        "from InstructorEmbedding import INSTRUCTOR\n",
        "from langchain.embeddings import HuggingFaceInstructEmbeddings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtIGGkfSXMLg",
        "outputId": "9d7a806d-04c7-4ec3-f624-68b0a8e99cd7"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/InstructorEmbedding/instructor.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import trange\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate, LLMChain\n",
        "from langchain.llms import GPT4All\n",
        "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
      ],
      "metadata": {
        "id": "K-1pRpAkW9n0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loader = DirectoryLoader('./', glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n",
        "\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "Aloo-8w-W85_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dfa9NuZnXbd_",
        "outputId": "15979d7d-218b-4f45-9511-c8e3f88263c7"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documents[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPBsiTI_Xwfk",
        "outputId": "8c1873ed-faf1-4f98-df14-d3d9d9537501"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content=' \\n1 / 5 \\nThis document  belongs to Wareconn Technology Services (Tianjin) Co., Ltd. It is only intended to be \\nused to introduce Wareconn functions and procedures. Please do not use it for other purposes.  1 wareconn standard operating procedure  \\n \\n \\n \\n \\n \\n \\nwareconn standard operating procedure  \\nRoot cause modification  SOP (WEB and API)  \\n \\nVersion : \\nVersion  Date  Editor  Description  \\nv 1.0  2023/05/15 Eric Sun First draft (English version)  \\n \\n \\nContent  \\nSOP ................................ ................................ ................................ ................................ . 2 \\n1.1 Root cause modification (wareconn web)  ................................ .........  2 \\n1.2 Root cause modification (API)  ................................ ...........................  3 \\n1.3 Statistics report  ................................ ................................ ..................  4 \\n \\n \\n \\n \\n', metadata={'source': '09.wareconn-Modify root cause API.pdf', 'page': 0})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| file name | pages |\n",
        "|---|---|\n",
        "|09.wareconn-Modify root cause API.pdf|5|\n",
        "|MSFT RMA request SOP v1.0.pdf|6|"
      ],
      "metadata": {
        "id": "_M_RV_wVX7dL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Docs into Chunks"
      ],
      "metadata": {
        "id": "-gpVMTEXiUVF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n",
        "texts = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "CGfxcK59ibXX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9A6aN9iibZ9",
        "outputId": "528a38e9-1687-4270-8813-e5057d6e1457"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texts[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SB_AJnhPifM9",
        "outputId": "b6ae5236-6a10-4336-89b5-747c9048278f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='1 / 5 \\nThis document  belongs to Wareconn Technology Services (Tianjin) Co., Ltd. It is only intended to be \\nused to introduce Wareconn functions and procedures. Please do not use it for other purposes.  1 wareconn standard operating procedure  \\n \\n \\n \\n \\n \\n \\nwareconn standard operating procedure  \\nRoot cause modification  SOP (WEB and API)  \\n \\nVersion : \\nVersion  Date  Editor  Description  \\nv 1.0  2023/05/15 Eric Sun First draft (English version)  \\n \\n \\nContent  \\nSOP ................................ ................................ ................................ ................................ . 2 \\n1.1 Root cause modification (wareconn web)  ................................ .........  2 \\n1.2 Root cause modification (API)  ................................ ...........................  3 \\n1.3 Statistics report  ................................ ................................ ..................  4', metadata={'source': '09.wareconn-Modify root cause API.pdf', 'page': 0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Embeddings"
      ],
      "metadata": {
        "id": "awF0eFicipth"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
        "\n",
        "instructor_embeddings = HuggingFaceInstructEmbeddings(model_name=\"hkunlp/instructor-xl\",\n",
        "                                                      model_kwargs={\"device\": \"cuda\"})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809,
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        "id": "uzZY-cC5irvh",
        "outputId": "77b436b8-4dad-476e-c46f-bab5379ae4b2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)7f436/.gitattributes: 0.00B [00:00, ?B/s]"
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          "metadata": {}
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          },
          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
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        {
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          },
          "metadata": {}
        },
        {
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              "Downloading (…)0daf57f436/README.md: 0.00B [00:00, ?B/s]"
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          "metadata": {}
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              "model_id": "06dcab15d80443d6a488675cde4c7c55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(instructor_embeddings)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUonmYAsiszk",
        "outputId": "0089f45b-726a-4851-e77f-f39e50a186e0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "client=INSTRUCTOR(\n",
            "  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: T5EncoderModel \n",
            "  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})\n",
            "  (2): Dense({'in_features': 1024, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})\n",
            "  (3): Normalize()\n",
            ") model_name='hkunlp/instructor-xl' cache_folder=None model_kwargs={'device': 'cuda'} encode_kwargs={} embed_instruction='Represent the document for retrieval: ' query_instruction='Represent the question for retrieving supporting documents: '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Local Knowledge DB"
      ],
      "metadata": {
        "id": "pPQb9dcNixsR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Embed and store the texts\n",
        "# Supplying a persist_directory will store the embeddings on disk\n",
        "persist_directory = 'db'\n",
        "\n",
        "## Here is the nmew embeddings being used\n",
        "embedding = instructor_embeddings\n",
        "\n",
        "vectordb = Chroma.from_documents(documents=texts,\n",
        "                                 embedding=embedding,\n",
        "                                 persist_directory=persist_directory)"
      ],
      "metadata": {
        "id": "NSWwerRpis9T"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# persiste the db to disk\n",
        "vectordb.persist()\n",
        "vectordb = None\n",
        ""
      ],
      "metadata": {
        "id": "CmB2o3EyjDLl"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we can load the persisted database from disk, and use it as normal.\n",
        "vectordb = Chroma(persist_directory=persist_directory,\n",
        "                  embedding_function=embedding)"
      ],
      "metadata": {
        "id": "AZQbcjysjG9Y"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Make a retriever"
      ],
      "metadata": {
        "id": "rytv7KnvjJc8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectordb.as_retriever()"
      ],
      "metadata": {
        "id": "kgmcKMi1jHAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# docs = retriever.get_relevant_documents(\"What is Flash attention?\")\n",
        "docs = retriever.get_relevant_documents(\"What is the RMA request SOP?\")"
      ],
      "metadata": {
        "id": "hAS8WkJYjHDy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "id": "mGW1ioipjOT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "docs[0]"
      ],
      "metadata": {
        "id": "QH3ksxiKjOap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 1})"
      ],
      "metadata": {
        "id": "NYlyiDloj8-M"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "retriever.search_type"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "a1uM-8r1j-Uk",
        "outputId": "4bea7f0d-a458-4f09-a801-8508e6ac765d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'similarity'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retriever.search_kwargs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ts4P-0L6kGMi",
        "outputId": "d964822c-066b-4db1-ad83-d62e0851751b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'k': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# docs = retriever.get_relevant_documents(\"What is Flash attention?\")\n",
        "docs = retriever.get_relevant_documents(\"What is the RMA request SOP?\")"
      ],
      "metadata": {
        "id": "If-jSMI9j-X_"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(docs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "46viBSiVj-bc",
        "outputId": "bde7c0fb-1801-4cc6-cb43-bf6ed05d3b48"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV2FYMRMw33I",
        "outputId": "0fe068e0-ce43-44f0-cc12-01d71c108d75"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='2 / 6 \\nThis document  belongs to Wareconn  Technology Services (Tianjin) Co., Ltd. It is only intended to be \\nused to introduce Wareconn functions and procedures. Please do not use it for other purposes.  2 wareconn standard operating procedure  \\nSOP  \\nI. This guide will walk you through the process of requesting  RMA  through the \\nWareconn Customer Portal.  \\nII. Details on how to fill in the Part Number (PN) and Serial Number (SN)  \\n1.1 RMA request  procedure (Parts return)  \\n1. System login  \\n⚫ Module: https://www.wareconn.com/  \\n⚫ Description:  Login  with account and password  \\n \\nTo log in, follow these steps:  \\nGo to https://www.wareconn.com/ and click Log in  \\n \\nFill in your email and password, then click Login\\n \\nClick Enter  in the Customer portal', metadata={'source': 'MSFT RMA request SOP v1.0.pdf', 'page': 1})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LLM: GPT4All"
      ],
      "metadata": {
        "id": "9veerF0MY6jq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb\n",
        "import wandb\n",
        "wandb.init()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "MSYs88FxLvvq",
        "outputId": "e629c511-e06e-49fb-ff89-8614b5fd8554"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting wandb\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Downloading setproctitle-1.3.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n",
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            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
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            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Building wheels for collected packages: pathtools\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8791 sha256=93073a6393f9f3688bfb83efbed34d37b5c8140171ee54c0eaf2d007fcf33af3\n",
            "  Stored in directory: /root/.cache/pip/wheels/e7/f3/22/152153d6eb222ee7a56ff8617d80ee5207207a8c00a7aab794\n",
            "Successfully built pathtools\n",
            "Installing collected packages: pathtools, smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, GitPython, wandb\n",
            "Successfully installed GitPython-3.1.31 docker-pycreds-0.4.0 gitdb-4.0.10 pathtools-0.1.2 sentry-sdk-1.26.0 setproctitle-1.3.2 smmap-5.0.0 wandb-0.15.4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.15.4"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230626_062246-p4bubiuo</code>"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/yousenwang/uncategorized/runs/p4bubiuo' target=\"_blank\">fearless-river-2</a></strong> to <a href='https://wandb.ai/yousenwang/uncategorized' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/yousenwang/uncategorized' target=\"_blank\">https://wandb.ai/yousenwang/uncategorized</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/yousenwang/uncategorized/runs/p4bubiuo' target=\"_blank\">https://wandb.ai/yousenwang/uncategorized/runs/p4bubiuo</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/yousenwang/uncategorized/runs/p4bubiuo?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7fe3d597dea0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate, LLMChain\n",
        "from langchain.llms import GPT4All\n",
        "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
      ],
      "metadata": {
        "id": "-p6p3tQRmv4M"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gpt4all"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLKPPxlcZAgx",
        "outputId": "01385bb2-d2d1-4918-bb64-9732cb4f9c11"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gpt4all\n",
            "  Downloading gpt4all-0.3.5-py3-none-manylinux1_x86_64.whl (2.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m48.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from gpt4all) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gpt4all) (4.65.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all) (2023.5.7)\n",
            "Installing collected packages: gpt4all\n",
            "Successfully installed gpt4all-0.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_name = \"ggml-gpt4all-j-v1.3-groovy.bin\""
      ],
      "metadata": {
        "id": "Lgm_AKrphIzD"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gpt4all\n",
        "gptj = gpt4all.GPT4All(llm_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i9uRdUovXyZO",
        "outputId": "f054e934-1024-47e3-e34b-e8037c55166e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3.79G/3.79G [02:36<00:00, 24.2MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model downloaded at:  /root/.cache/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-bindings/python/README.md"
      ],
      "metadata": {
        "id": "4GYOFXDrZlhQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "messages = [{\"role\": \"user\", \"content\": \"Name 3 colors\"}]\n",
        "gptj.chat_completion(messages)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2mAFmhOYUeH",
        "outputId": "49f207a4-1c2f-4fb1-f829-51acdbb1bd3b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "### Instruction: \n",
            "            The prompt below is a question to answer, a task to complete, or a conversation \n",
            "            to respond to; decide which and write an appropriate response.\n",
            "            \n",
            "### Prompt: \n",
            "Name 3 colors\n",
            "### Response:\n",
            " \n",
            "Blue, Green and Red\n",
            "CPU times: user 1min 48s, sys: 195 ms, total: 1min 48s\n",
            "Wall time: 1min 12s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'model': 'ggml-gpt4all-j-v1.3-groovy',\n",
              " 'usage': {'prompt_tokens': 239, 'completion_tokens': 21, 'total_tokens': 260},\n",
              " 'choices': [{'message': {'role': 'assistant',\n",
              "    'content': ' \\nBlue, Green and Red'}}]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "gptj.generate('Name three colors')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "kq_1XeTvawqY",
        "outputId": "28a483b3-ad46-43b3-c059-be227d15c336"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/gpt4all.html"
      ],
      "metadata": {
        "id": "vxX9pxImZP9z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "local_path = (\n",
        "    f\"../root/.cache/gpt4all/{llm_name}\"  # replace with your desired local file path\n",
        ")"
      ],
      "metadata": {
        "id": "X6KO26yYZE52"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Callbacks support token-wise streaming\n",
        "callbacks = [StreamingStdOutCallbackHandler()]\n",
        "# Verbose is required to pass to the callback manager\n",
        "llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)\n",
        "# If you want to use a custom model add the backend parameter\n",
        "# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\n",
        "# llm = GPT4All(model=local_path, backend=\"gptj\", callbacks=callbacks, verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "hMDibt9Jat0r",
        "outputId": "dd565f56-7fea-436d-b057-89a5382129d0"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found model file at  ../root/.cache/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin\n",
            "Found model file at  ../root/.cache/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-8e46f5413aa6>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# If you want to use a custom model add the backend parameter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mllm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGPT4All\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"gptj\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/load/serializable.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lc_kwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so\u001b[0m in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so\u001b[0m in \u001b[0;36mpydantic.main.validate_model\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/langchain/llms/gpt4all.py\u001b[0m in \u001b[0;36mvalidate_environment\u001b[0;34m(cls, values)\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0mmodel_path\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdelimiter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m         values[\"client\"] = GPT4AllModel(\n\u001b[0m\u001b[1;32m    148\u001b[0m             \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_path\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gpt4all/gpt4all.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_name, model_path, model_type, allow_download)\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;31m# Retrieve model and download if allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mmodel_dest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_download\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_download\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/gpt4all/pyllmodel.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(self, model_path)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m             \u001b[0mllmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mllmodel_loadModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_path_enc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unable to instantiate model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# template = \"\"\"Question: {question}\n",
        "\n",
        "# Answer: Let's think step by step.\"\"\"\n",
        "\n",
        "template = \"\"\"Question: {question}\n",
        "\n",
        "Answer: \"\"\"\n",
        "\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
      ],
      "metadata": {
        "id": "T73sNoVta1qb"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
        "\n",
        "# question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
        "question = \"Name 3 colors\""
      ],
      "metadata": {
        "id": "H2fnw5-lav9B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! watch nvidia-smi"
      ],
      "metadata": {
        "id": "Y6YeVBSdNmu7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "llm_chain.run(question)"
      ],
      "metadata": {
        "id": "m-hlwEm2a3cW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "llm_chain.run(question)"
      ],
      "metadata": {
        "id": "sGXPTg9_tfXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CuaqwY4_vhUK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# create the chain to answer questions\n",
        "qa_chain = RetrievalQA.from_chain_type(llm=llm,\n",
        "                                  chain_type=\"stuff\",\n",
        "                                  retriever=retriever,\n",
        "                                  return_source_documents=True)"
      ],
      "metadata": {
        "id": "a2GC5DAckbCK"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "## Cite sources\n",
        "\n",
        "import textwrap\n",
        "# @get_openai_callback()\n",
        "def wrap_text_preserve_newlines(text, width=110):\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split('\\n')\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text\n",
        "# @get_openai_callback()\n",
        "def process_llm_response(llm_response):\n",
        "    print(wrap_text_preserve_newlines(llm_response['result']))\n",
        "    print('\\n\\nSources:')\n",
        "    for source in llm_response[\"source_documents\"]:\n",
        "        print(source.metadata['source'])"
      ],
      "metadata": {
        "id": "1OtaEg4UkbkL"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# full example\n",
        "query = \"What is the RMA request SOP?\"\n",
        "llm_response = qa_chain(query)\n"
      ],
      "metadata": {
        "id": "7PwOGXPbkz_s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(llm_response)"
      ],
      "metadata": {
        "id": "_9-AOqkug-SP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "process_llm_response(llm_response)"
      ],
      "metadata": {
        "id": "UzNDxng_k7bh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Jg83jLF9hA_Y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}