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        "<a href=\"https://colab.research.google.com/github/yousenwang/langchain_llm/blob/main/retriever_results_to_llm.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m414.1/414.1 kB\u001b[0m \u001b[31m34.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.1/4.1 MB\u001b[0m \u001b[31m114.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m81.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m13.0 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==0.0.215 chromadb pypdf sentence_transformers InstructorEmbedding"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/nomic-ai/gpt4all/commit/213e033540fa3b68202bb12cf7f0134cfe6638aa?short_path=76b0339#diff-76b0339e6b9639ccaf2de9d1dde8b392e8e7bcd3cd514e625d5420239974af70"
      ],
      "metadata": {
        "id": "HZ9nbGRb83sy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show langchain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-q0dU_m0qNX",
        "outputId": "fc1188d0-8e02-4e8f-c8d9-f4c202094ce9"
      },
      "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, RetrievalQAWithSourcesChain\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": "WkDaGy-X0s5L",
        "outputId": "69f03962-3661-4d84-eccb-5924820b6351"
      },
      "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": [
        "loader = DirectoryLoader('./', glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n",
        "\n",
        "documents = loader.load()"
      ],
      "metadata": {
        "id": "hARbIMJ901vR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(documents)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btPc2OMM1dOO",
        "outputId": "4c29cf03-7ea6-433b-f56c-c0c3cdeecb96"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "documents[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hWTqa_B1eTH",
        "outputId": "006f667d-2adf-40f2-f2b8-e9520fa843bd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content=' \\n1 / 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.  1 wareconn standard operating procedure  \\n \\n \\n \\n \\n \\n \\nwareconn standard operating procedure  \\nRMA request  SOP  \\n \\nVersion : \\nVersion  Date  Editor  Description  \\nv 1.0  2023/03/16 Eric Sun First draft (English version)  \\n \\n \\nContent  \\nSOP ................................ ................................ ................................ ................................  2 \\n1.1 RMA request procedure (Parts return)  ................................ ...............  2 \\n \\n \\n \\n \\n', metadata={'source': 'MSFT RMA request SOP v1.0.pdf', 'page': 0})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Docs into Chunks"
      ],
      "metadata": {
        "id": "S7tmSan_1iyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000//2, chunk_overlap=200//2)\n",
        "texts = text_splitter.split_documents(documents)"
      ],
      "metadata": {
        "id": "vgVksm281feR"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OA7Gneyc1lDU",
        "outputId": "5d4a0a82-f162-4338-e466-3b122af5a17e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texts[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWCSFrHH1mMZ",
        "outputId": "cd374ae0-a064-444b-fd09-6d7dacf85f67"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Document(page_content='1.1 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": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Embeddings"
      ],
      "metadata": {
        "id": "HLkoH6ZS1pdT"
      }
    },
    {
      "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": 501,
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        },
        "id": "Reo9CVdV1nZC",
        "outputId": "ae7dfb13-1080-4373-9aca-1a6a619dac3f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)7f436/.gitattributes:   0%|          | 0.00/1.48k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)_Pooling/config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
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        {
          "output_type": "display_data",
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            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "7c8af953eaf140b8bd4e1ac5f30c7032"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)f57f436/modules.json:   0%|          | 0.00/461 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9028a4ee30c548b5bc1bd77c119e05ab"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        }
      ]
    },
    {
      "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": "C-pq2YG81rGc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Retriever"
      ],
      "metadata": {
        "id": "vKKMuFkz10OR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "retriever = vectordb.as_retriever(search_kwargs={\"k\": 2})"
      ],
      "metadata": {
        "id": "jGC7Ge671uw4"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "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": "TQNCDXkX113d"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gpt4all==0.3.6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "my_Hgr_v3Aso",
        "outputId": "3393efda-c9e7-4c21-8e89-5a50c521bb49"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gpt4all==0.3.6\n",
            "  Downloading gpt4all-0.3.6-py3-none-manylinux1_x86_64.whl (2.5 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m82.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m53.1 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==0.3.6) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gpt4all==0.3.6) (4.65.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all==0.3.6) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all==0.3.6) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all==0.3.6) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->gpt4all==0.3.6) (2023.5.7)\n",
            "Installing collected packages: gpt4all\n",
            "Successfully installed gpt4all-0.3.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_name = \"ggml-gpt4all-j-v1.3-groovy.bin\""
      ],
      "metadata": {
        "id": "Mh_mJUJz3KrP"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import gpt4all\n",
        "gptj = gpt4all.GPT4All(llm_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGq_Cbr33MsS",
        "outputId": "5c1e9d7f-c802-45fa-88b5-fb941fe68ec2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3.79G/3.79G [01:30<00:00, 41.7MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model downloaded at:  /root/.cache/gpt4all/ggml-gpt4all-j-v1.3-groovy.bin\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_from_template(context, query):\n",
        "  return  f\"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
        "\n",
        "  {context}\n",
        "\n",
        "  Question: {query}\n",
        "  Answer:\"\"\""
      ],
      "metadata": {
        "id": "VoqAV8KB4cey"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Q1 :How can I navigate to the RMA request page in the Wareconn Customer Portal? -3\n",
        "\n",
        "A1: To navigate to the RMA request page, click on \"Warranty Claims\" in the left menu, then click on the \"+Add\" button.\n"
      ],
      "metadata": {
        "id": "ZrnrSkCY6hwc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q1 = \"How can I navigate to the RMA request page in the Wareconn Customer Portal?\"\n",
        "ans1 = retriever.get_relevant_documents(q1)"
      ],
      "metadata": {
        "id": "Of5w_uyj3iLF"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content_with_ans1 = \"\"\n",
        "for i, a in enumerate(ans1):\n",
        "  content_with_ans1+=a.page_content\n",
        "\n",
        "prompt1 = generate_from_template(content_with_ans1, q1)\n",
        "print(prompt1)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOA7hePG3iUu",
        "outputId": "38f36660-0840-479a-98f7-9d2168ca9cb8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
            "\n",
            "  1.1 RMA request  procedure (Parts return)  \n",
            "1. System login  \n",
            "⚫ Module: https://www.wareconn.com/  \n",
            "⚫ Description:  Login  with account and password  \n",
            " \n",
            "To log in, follow these steps:  \n",
            "Go to https://www.wareconn.com/ and click Log in  \n",
            " \n",
            "Fill in your email and password, then click Login\n",
            " \n",
            "Click Enter  in the Customer portal3 / 6 \n",
            "This document  belongs to Wareconn  Technology Services (Tianjin) Co., Ltd. It is only intended to be \n",
            "used to introduce Wareconn functions and procedures. Please do not use it for other purposes.  3 wareconn standard operating procedure  \n",
            "2. Go to  RMA Request  Page \n",
            "⚫ Module: Customer portal -Warranty Claims -Warranty Claims  \n",
            "⚫ Description: Go to RMA request page  \n",
            " \n",
            "To go to the RMA request page, follow these steps:  \n",
            "Click Warranty Claims  in the left menu, then click +Add\n",
            "\n",
            "  Question: How can I navigate to the RMA request page in the Wareconn Customer Portal?\n",
            "  Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "res = gptj.generate(prompt1)\n",
        "print(res)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZL24nO6p3N1l",
        "outputId": "8e1e7af7-0f33-4865-a5ef-2aa2d67c3de7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " To navigate to the RMA request page in the Wareconn Customer Portal, click on \"Warranty Claims\" in the left menu and then click \"+Add\".\n",
            " To navigate to the RMA request page in the Wareconn Customer Portal, click on \"Warranty Claims\" in the left menu and then click \"+Add\".\n",
            "CPU times: user 8min 23s, sys: 1.02 s, total: 8min 24s\n",
            "Wall time: 5min 15s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "pieje0x64_gH",
        "outputId": "d7fa42ba-51ed-4923-d970-6bbff10358f9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' To navigate to the RMA request page in the Wareconn Customer Portal, click on \"Warranty Claims\" in the left menu and then click \"+Add\".'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q2: What does the Reminder field indicate when submitting an RMA request? -6\n",
        "\n",
        "Ans2: The Reminder field shows whether the RMA request is approvable (acceptable) for the warranty provider or not."
      ],
      "metadata": {
        "id": "SPwEGyVR6w9u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q2 = \"What does the Reminder field indicate when submitting an RMA request?\"\n",
        "ans2 = retriever.get_relevant_documents(q2)"
      ],
      "metadata": {
        "id": "NcHyjmxi6P_T"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content_with_ans2 = \"\"\n",
        "for i, a in enumerate(ans2):\n",
        "  content_with_ans2+=a.page_content\n",
        "\n",
        "prompt2 = generate_from_template(content_with_ans2, q2)\n",
        "print(prompt2)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjlctJRj61Iq",
        "outputId": "44218894-b3eb-4103-da25-7314c39ed633"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
            "\n",
            "  The Reminder field will show you whether this RMA request is approvable  \n",
            "(acceptable)  for warranty provider  or not.  \n",
            "Please remember to fill in WAL# in Customer No. field  \n",
            "Lastly, click Submit  in the top right corner to finish the  RMA requesting process6 / 6 \n",
            "This document  belongs to Wareconn  Technology Services (Tianjin) Co., Ltd. It is only intended to be \n",
            "used to introduce Wareconn functions and procedures. Please do not use it for other purposes.  6 wareconn standard operating procedure  \n",
            "5. Submit the RMA  request  \n",
            " \n",
            "⚫ Module: Customer portal -Warranty Claims -Warranty Claims -Submit  \n",
            "⚫ Description: Submit the RMA request  \n",
            " \n",
            "The Reminder field will show you whether this RMA request is approvable\n",
            "\n",
            "  Question: What does the Reminder field indicate when submitting an RMA request?\n",
            "  Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "res2 = gptj.generate(prompt2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-a7aTYUE7Clv",
        "outputId": "0ca334d3-8d05-4235-efe1-6319de7585c4"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The Reminder field indicates whether the RMA request is approvable for a warranty provider or not.\n",
            "CPU times: user 7min 10s, sys: 1.08 s, total: 7min 11s\n",
            "Wall time: 4min 26s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(res2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWe_d1rl9LAc",
        "outputId": "799cfbbf-2a18-47f2-8e85-4a8b8d82fed2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The Reminder field indicates whether the RMA request is approvable for a warranty provider or not.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Q3: What information should be filled in the Customer No. field when submitting an RMA request? -6\n",
        "\n",
        " Ans3: When submitting an RMA request, you should fill in the WAL# (Warranty Authorization Number) in the Customer No. field."
      ],
      "metadata": {
        "id": "ah5JaOv_87wK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q3 = \"What information should be filled in the Customer No. field when submitting an RMA request?\"\n",
        "ans3 = retriever.get_relevant_documents(q3)"
      ],
      "metadata": {
        "id": "6-9dG_8K7LZN"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content_with_ans3 = \"\"\n",
        "for i, a in enumerate(ans3):\n",
        "  content_with_ans3+=a.page_content\n",
        "\n",
        "prompt3 = generate_from_template(content_with_ans3, q3)\n",
        "print(prompt3)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfFkePkA89n4",
        "outputId": "29c9de3e-966c-49a6-fe3e-5b8627a250e1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
            "\n",
            "  The Reminder field will show you whether this RMA request is approvable  \n",
            "(acceptable)  for warranty provider  or not.  \n",
            "Please remember to fill in WAL# in Customer No. field  \n",
            "Lastly, click Submit  in the top right corner to finish the  RMA requesting process1.1 RMA request  procedure (Parts return)  \n",
            "1. System login  \n",
            "⚫ Module: https://www.wareconn.com/  \n",
            "⚫ Description:  Login  with account and password  \n",
            " \n",
            "To log in, follow these steps:  \n",
            "Go to https://www.wareconn.com/ and click Log in  \n",
            " \n",
            "Fill in your email and password, then click Login\n",
            " \n",
            "Click Enter  in the Customer portal\n",
            "\n",
            "  Question: What information should be filled in the Customer No. field when submitting an RMA request?\n",
            "  Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "res3 = gptj.generate(prompt3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQFgdV819Egi",
        "outputId": "2153f249-9e86-4160-e385-f46f25cb2608"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The Customer No. field should be filled in with the customer's Wareconn account number.\n",
            "CPU times: user 7min, sys: 1.13 s, total: 7min 1s\n",
            "Wall time: 4min 21s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q4: What steps should be followed when filling in the request information for an RMA? -5\n",
        "\n",
        "Ans4: To fill in the request information for an RMA, follow these steps:\n",
        "- Fill in the Product SN (Serial Number), Product PN (Part Number), and defective reason.\n",
        "- Select the Rack PN (Part Number) by clicking on \"Please select Parts\" and choose the desired quantity.\n",
        "- Click on \"Confirm\" to proceed.\n",
        "- Optionally, fill in the Parts SN (Serial Number) for the failure part (not required).\n",
        "- Click on \"Save\" to save the information."
      ],
      "metadata": {
        "id": "7TZ6Nk22-lXK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q4 = \"What steps should be followed when filling in the request information for an RMA?\"\n",
        "ans4 = retriever.get_relevant_documents(q4)"
      ],
      "metadata": {
        "id": "iPhiZq-d9OnE"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content_with_ans4 = \"\"\n",
        "for i, a in enumerate(ans4):\n",
        "  content_with_ans4+=a.page_content\n",
        "\n",
        "prompt4 = generate_from_template(content_with_ans4, q4)\n",
        "print(prompt4)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pX23kDVt-x8i",
        "outputId": "86ed1a76-d39e-44d0-997f-2136c73ec508"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
            "\n",
            "  The Reminder field will show you whether this RMA request is approvable  \n",
            "(acceptable)  for warranty provider  or not.  \n",
            "Please remember to fill in WAL# in Customer No. field  \n",
            "Lastly, click Submit  in the top right corner to finish the  RMA requesting process1.1 RMA request  procedure (Parts return)  \n",
            "1. System login  \n",
            "⚫ Module: https://www.wareconn.com/  \n",
            "⚫ Description:  Login  with account and password  \n",
            " \n",
            "To log in, follow these steps:  \n",
            "Go to https://www.wareconn.com/ and click Log in  \n",
            " \n",
            "Fill in your email and password, then click Login\n",
            " \n",
            "Click Enter  in the Customer portal\n",
            "\n",
            "  Question: What steps should be followed when filling in the request information for an RMA?\n",
            "  Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "res4 = gptj.generate(prompt4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bzRGPArp-5SQ",
        "outputId": "f39c5894-e2e2-4428-eed0-b139087cc264"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " When filling in the request information for an RMA, follow these steps:  \n",
            "1. Enter the customer's Wareconn account number in the Customer No. field \n",
            "2. Enter a valid email address in the Email field \n",
            "3. Enter a valid password in the Password field \n",
            "4. Enter a valid phone number in the Phone field \n",
            "5. Enter a valid address in the Address field \n",
            "6. Enter a valid date in the Date field \n",
            "7. Enter a valid time in the Time field \n",
            "8. Enter a valid duration in the Duration field \n",
            "9. Enter a\n",
            "CPU times: user 17min 14s, sys: 2.87 s, total: 17min 17s\n",
            "Wall time: 10min 45s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Q5: Can this document be used for purposes other than introducing Wareconn functions and procedures? -3\n",
        "\n",
        "Ans5: No, this document explicitly states that it should not be used for other purposes. Its sole intention is to introduce Wareconn functions and procedures."
      ],
      "metadata": {
        "id": "DSlVXB8k_Qj6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "q5 = \"Can this document be used for purposes other than introducing Wareconn functions and procedures?\"\n",
        "ans5 = retriever.get_relevant_documents(q5)"
      ],
      "metadata": {
        "id": "8abUWZbz--Aj"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "content_with_ans5 = \"\"\n",
        "for i, a in enumerate(ans5):\n",
        "  content_with_ans5+=a.page_content\n",
        "\n",
        "prompt5 = generate_from_template(content_with_ans5, q5)\n",
        "print(prompt5)\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qojRJDJI_UFB",
        "outputId": "42a3a030-d3f5-4232-c006-c0ad67703c27"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n",
            "\n",
            "  1 / 6 \n",
            "This document  belongs to Wareconn  Technology Services (Tianjin) Co., Ltd. It is only intended to be \n",
            "used 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",
            " \n",
            "wareconn standard operating procedure  \n",
            "RMA request  SOP  \n",
            " \n",
            "Version : \n",
            "Version  Date  Editor  Description  \n",
            "v 1.0  2023/03/16 Eric Sun First draft (English version)  \n",
            " \n",
            " \n",
            "Content5 / 6 \n",
            "This document  belongs to Wareconn  Technology Services (Tianjin) Co., Ltd. It is only intended to be \n",
            "used to introduce Wareconn functions and procedures. Please do not use it for other purposes.  5 wareconn standard operating procedure  \n",
            "4. Fill in request info  \n",
            "⚫ Module: Customer portal -Warranty Claims -Warranty Claims -Fill in info  \n",
            "⚫ Description:  \n",
            "Fill in Product SN, Product PN and defective reason  (See the instruction s below )\n",
            "\n",
            "  Question: Can this document be used for purposes other than introducing Wareconn functions and procedures?\n",
            "  Answer:\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# You can run raw generate as well on your input. But performance will degrade.\n",
        "res5 = gptj.generate(prompt5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8y2yU2yS_aFh",
        "outputId": "08de9c63-c539-4597-aead-7be84028b8b5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " No, this document can only be used to introduce Wareconn functions and procedures. It is not intended for any other purposes and should be used solely to introduce Wareconn functions and procedures.\n",
            "CPU times: user 14min 36s, sys: 2.6 s, total: 14min 39s\n",
            "Wall time: 9min 6s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://docs.gpt4all.io/gpt4all_python.html\n",
        "\n",
        "Fetch model list from https://gpt4all.io/models/models.json"
      ],
      "metadata": {
        "id": "nU9ILLR3Dc-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gptj.list_models()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VNeaaaQ2_css",
        "outputId": "46c2d7a2-9db8-4b5a-b9d0-bed1343bd1dd"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'order': 'a',\n",
              "  'md5sum': '4acc146dd43eb02845c233c29289c7c5',\n",
              "  'name': 'Hermes',\n",
              "  'filename': 'nous-hermes-13b.ggmlv3.q4_0.bin',\n",
              "  'filesize': '8136777088',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Best overall model</strong><br><ul><li>Instruction based<li>Gives long responses<li>Curated with 300,000 uncensored instructions<li>Trained by Nous Research<li>Cannot be used commercially</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/Nous-Hermes-13B-GGML/resolve/main/nous-hermes-13b.ggmlv3.q4_0.bin',\n",
              "  'promptTemplate': '### Instruction:\\n%1\\n### Response:\\n'},\n",
              " {'order': 'b',\n",
              "  'md5sum': '725f148218a65ce8ebcc724e52f31b49',\n",
              "  'name': 'GPT4All Falcon',\n",
              "  'filename': 'ggml-model-gpt4all-falcon-q4_0.bin',\n",
              "  'filesize': '4061641216',\n",
              "  'requires': '2.4.9',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'Falcon',\n",
              "  'description': '<strong>Best overall smaller model</strong><br><ul><li>Fast responses</li><li>Instruction based</li><li>Trained by TII<li>Finetuned by Nomic AI<li>Licensed for commercial use</ul>',\n",
              "  'url': 'https://huggingface.co/nomic-ai/gpt4all-falcon-ggml/resolve/main/ggml-model-gpt4all-falcon-q4_0.bin',\n",
              "  'promptTemplate': '### Instruction:\\n%1\\n### Response:\\n'},\n",
              " {'order': 'c',\n",
              "  'md5sum': '81a09a0ddf89690372fc296ff7f625af',\n",
              "  'name': 'Groovy',\n",
              "  'filename': 'ggml-gpt4all-j-v1.3-groovy.bin',\n",
              "  'filesize': '3785248281',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'GPT-J',\n",
              "  'description': '<strong>Creative model can be used for commercial purposes</strong><br><ul><li>Fast responses<li>Creative responses</li><li>Instruction based</li><li>Trained by Nomic AI<li>Licensed for commercial use</ul>'},\n",
              " {'order': 'e',\n",
              "  'md5sum': '11d9f060ca24575a2c303bdc39952486',\n",
              "  'name': 'Snoozy',\n",
              "  'filename': 'GPT4All-13B-snoozy.ggmlv3.q4_0.bin',\n",
              "  'filesize': '8136770688',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Very good overall model</strong><br><ul><li>Instruction based<li>Based on the same dataset as Groovy<li>Slower than Groovy, with higher quality responses<li>Trained by Nomic AI<li>Cannot be used commercially</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/GPT4All-13B-snoozy-GGML/resolve/main/GPT4All-13B-snoozy.ggmlv3.q4_0.bin'},\n",
              " {'order': 'f',\n",
              "  'md5sum': '756249d3d6abe23bde3b1ae272628640',\n",
              "  'name': 'MPT Chat',\n",
              "  'filename': 'ggml-mpt-7b-chat.bin',\n",
              "  'filesize': '4854401050',\n",
              "  'requires': '2.4.1',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'MPT',\n",
              "  'description': '<strong>Best overall smaller model</strong><br><ul><li>Fast responses<li>Chat based<li>Trained by Mosaic ML<li>Cannot be used commercially</ul>',\n",
              "  'promptTemplate': '<|im_start|>user\\n%1<|im_end|><|im_start|>assistant\\n',\n",
              "  'systemPrompt': '<|im_start|>system\\n- You are a helpful assistant chatbot trained by MosaicML.\\n- You answer questions.\\n- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.\\n- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>'},\n",
              " {'order': 'g',\n",
              "  'md5sum': 'e64e74375ce9d36a3d0af3db1523fd0a',\n",
              "  'name': 'Orca',\n",
              "  'filename': 'orca-mini-7b.ggmlv3.q4_0.bin',\n",
              "  'filesize': '3791749248',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'OpenLLaMa',\n",
              "  'description': '<strong>New model with novel dataset</strong><br><ul><li>Instruction based<li>Explain tuned datasets<li>Orca Research Paper dataset construction approaches<li>Licensed for commercial use</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/orca_mini_7B-GGML/resolve/main/orca-mini-7b.ggmlv3.q4_0.bin',\n",
              "  'promptTemplate': '### User:\\n%1\\n### Response:\\n',\n",
              "  'systemPrompt': '### System:\\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\\n\\n'},\n",
              " {'order': 'h',\n",
              "  'md5sum': '6a087f7f4598fad0bb70e6cb4023645e',\n",
              "  'name': 'Orca (Small)',\n",
              "  'filename': 'orca-mini-3b.ggmlv3.q4_0.bin',\n",
              "  'filesize': '1928446208',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '4',\n",
              "  'parameters': '3 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'OpenLLaMa',\n",
              "  'description': '<strong>Small version of new model with novel dataset</strong><br><ul><li>Instruction based<li>Explain tuned datasets<li>Orca Research Paper dataset construction approaches<li>Licensed for commercial use</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/orca_mini_3B-GGML/resolve/main/orca-mini-3b.ggmlv3.q4_0.bin',\n",
              "  'promptTemplate': '### User:\\n%1\\n### Response:\\n',\n",
              "  'systemPrompt': '### System:\\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\\n\\n'},\n",
              " {'order': 'i',\n",
              "  'md5sum': '959b7f65b2d12fd1e3ff99e7493c7a3a',\n",
              "  'name': 'Orca (Large)',\n",
              "  'filename': 'orca-mini-13b.ggmlv3.q4_0.bin',\n",
              "  'filesize': '7323329152',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'OpenLLaMa',\n",
              "  'description': '<strong>Largest version of new model with novel dataset</strong><br><ul><li>Instruction based<li>Explain tuned datasets<li>Orca Research Paper dataset construction approaches<li>Licensed for commercial use</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/orca_mini_13B-GGML/resolve/main/orca-mini-13b.ggmlv3.q4_0.bin',\n",
              "  'promptTemplate': '### User:\\n%1\\n### Response:\\n',\n",
              "  'systemPrompt': '### System:\\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\\n\\n'},\n",
              " {'order': 'j',\n",
              "  'md5sum': '29119f8fa11712704c6b22ac5ab792ea',\n",
              "  'name': 'Vicuna',\n",
              "  'filename': 'ggml-vicuna-7b-1.1-q4_2.bin',\n",
              "  'filesize': '4212859520',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_2',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Good small model - trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego</strong><br><ul><li>Instruction based<li>Cannot be used commercially</ul>'},\n",
              " {'order': 'k',\n",
              "  'md5sum': '95999b7b0699e2070af63bf5d34101a8',\n",
              "  'name': 'Vicuna (large)',\n",
              "  'filename': 'ggml-vicuna-13b-1.1-q4_2.bin',\n",
              "  'filesize': '8136770688',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_2',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Good larger model - trained by teams from UC Berkeley, CMU, Stanford, MBZUAI, and UC San Diego</strong><br><ul><li>Instruction based<li>Cannot be used commercially</ul>'},\n",
              " {'order': 'l',\n",
              "  'md5sum': '99e6d129745a3f1fb1121abed747b05a',\n",
              "  'name': 'Wizard',\n",
              "  'filename': 'ggml-wizardLM-7B.q4_2.bin',\n",
              "  'filesize': '4212864640',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_2',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Good small model - trained by by Microsoft and Peking University</strong><br><ul><li>Instruction based<li>Cannot be used commercially</ul>'},\n",
              " {'order': 'm',\n",
              "  'md5sum': '6cb4ee297537c9133bddab9692879de0',\n",
              "  'name': 'Stable Vicuna',\n",
              "  'filename': 'ggml-stable-vicuna-13B.q4_2.bin',\n",
              "  'filesize': '8136777088',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_2',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Trained with RLHF by Stability AI</strong><br><ul><li>Instruction based<li>Cannot be used commercially</ul>',\n",
              "  'systemPrompt': '## Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!\\n\\n'},\n",
              " {'order': 'n',\n",
              "  'md5sum': '1cfa4958f489f0a0d1ffdf6b37322809',\n",
              "  'name': 'MPT Instruct',\n",
              "  'filename': 'ggml-mpt-7b-instruct.bin',\n",
              "  'filesize': '4854401028',\n",
              "  'requires': '2.4.1',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'MPT',\n",
              "  'description': \"<strong>Mosaic's instruction model</strong><br><ul><li>Instruction based<li>Trained by Mosaic ML<li>Licensed for commercial use</ul>\"},\n",
              " {'order': 'o',\n",
              "  'md5sum': '120c32a51d020066288df045ef5d52b9',\n",
              "  'name': 'MPT Base',\n",
              "  'filename': 'ggml-mpt-7b-base.bin',\n",
              "  'filesize': '4854401028',\n",
              "  'requires': '2.4.1',\n",
              "  'ramrequired': '8',\n",
              "  'parameters': '7 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'MPT',\n",
              "  'description': '<strong>Trained for text completion with no assistant finetuning</strong><br><ul><li>Completion based<li>Trained by Mosaic ML<li>Licensed for commercial use</ul>'},\n",
              " {'order': 'p',\n",
              "  'md5sum': 'd5eafd5b0bd0d615cfd5fd763f642dfe',\n",
              "  'name': 'Nous Vicuna',\n",
              "  'filename': 'ggml-nous-gpt4-vicuna-13b.bin',\n",
              "  'filesize': '8136777088',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Trained on ~180,000 instructions</strong><br><ul><li>Instruction based<li>Trained by Nous Research<li>Cannot be used commercially</ul>'},\n",
              " {'order': 'q',\n",
              "  'md5sum': '489d21fd48840dcb31e5f92f453f3a20',\n",
              "  'name': 'Wizard Uncensored',\n",
              "  'filename': 'wizardLM-13B-Uncensored.ggmlv3.q4_0.bin',\n",
              "  'filesize': '8136777088',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '16',\n",
              "  'parameters': '13 billion',\n",
              "  'quant': 'q4_0',\n",
              "  'type': 'LLaMA',\n",
              "  'description': '<strong>Trained on uncensored assistant data and instruction data</strong><br><ul><li>Instruction based<li>Cannot be used commercially</ul>',\n",
              "  'url': 'https://huggingface.co/TheBloke/WizardLM-13B-Uncensored-GGML/resolve/main/wizardLM-13B-Uncensored.ggmlv3.q4_0.bin'},\n",
              " {'order': 'r',\n",
              "  'md5sum': '615890cb571fcaa0f70b2f8d15ef809e',\n",
              "  'disableGUI': 'true',\n",
              "  'name': 'Replit',\n",
              "  'filename': 'ggml-replit-code-v1-3b.bin',\n",
              "  'filesize': '5202046853',\n",
              "  'requires': '2.4.7',\n",
              "  'ramrequired': '4',\n",
              "  'parameters': '3 billion',\n",
              "  'quant': 'f16',\n",
              "  'type': 'Replit',\n",
              "  'description': '<strong>Trained on subset of the Stack</strong><br><ul><li>Code completion based<li>Licensed for commercial use</ul>',\n",
              "  'url': 'https://huggingface.co/nomic-ai/ggml-replit-code-v1-3b/resolve/main/ggml-replit-code-v1-3b.bin'}]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3CToHbbEDI72"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}