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            "\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "\u001b[?25hDownloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)\n",
            "Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)\n",
            "Installing collected packages: python-dotenv, pypdf, pinecone-plugin-interface, nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, mypy-extensions, marshmallow, httpx-sse, typing-inspect, tiktoken, syrupy, pytest-socket, pytest-asyncio, pinecone-plugin-inference, nvidia-cusparse-cu12, nvidia-cudnn-cu12, aiohttp, pydantic-settings, pinecone, nvidia-cusolver-cu12, dataclasses-json, langchain-core, langchain-tests, langchain_openai, langchain-pinecone, langchain_huggingface, langchain, langchain-community\n",
            "  Attempting uninstall: nvidia-nvjitlink-cu12\n",
            "    Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
            "    Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-curand-cu12\n",
            "    Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
            "    Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
            "      Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
            "  Attempting uninstall: nvidia-cufft-cu12\n",
            "    Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
            "    Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
            "      Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
            "  Attempting uninstall: nvidia-cuda-runtime-cu12\n",
            "    Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
            "    Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cuda-cupti-cu12\n",
            "    Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
            "    Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
            "      Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
            "  Attempting uninstall: nvidia-cublas-cu12\n",
            "    Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
            "    Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
            "      Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
            "  Attempting uninstall: nvidia-cusparse-cu12\n",
            "    Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
            "    Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
            "      Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
            "  Attempting uninstall: nvidia-cudnn-cu12\n",
            "    Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
            "    Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
            "      Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
            "  Attempting uninstall: aiohttp\n",
            "    Found existing installation: aiohttp 3.11.13\n",
            "    Uninstalling aiohttp-3.11.13:\n",
            "      Successfully uninstalled aiohttp-3.11.13\n",
            "  Attempting uninstall: nvidia-cusolver-cu12\n",
            "    Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
            "    Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
            "      Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
            "  Attempting uninstall: langchain-core\n",
            "    Found existing installation: langchain-core 0.3.40\n",
            "    Uninstalling langchain-core-0.3.40:\n",
            "      Successfully uninstalled langchain-core-0.3.40\n",
            "  Attempting uninstall: langchain\n",
            "    Found existing installation: langchain 0.3.19\n",
            "    Uninstalling langchain-0.3.19:\n",
            "      Successfully uninstalled langchain-0.3.19\n",
            "Successfully installed aiohttp-3.10.11 dataclasses-json-0.6.7 httpx-sse-0.4.0 langchain-0.3.20 langchain-community-0.3.19 langchain-core-0.3.41 langchain-pinecone-0.2.3 langchain-tests-0.3.13 langchain_huggingface-0.1.2 langchain_openai-0.3.7 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 pinecone-5.4.2 pinecone-plugin-inference-3.1.0 pinecone-plugin-interface-0.0.7 pydantic-settings-2.8.1 pypdf-5.3.1 pytest-asyncio-0.25.3 pytest-socket-0.7.0 python-dotenv-1.0.1 syrupy-4.8.2 tiktoken-0.9.0 typing-inspect-0.9.0\n"
          ]
        }
      ],
      "source": [
        "!pip install langchain langchain-community langchain_huggingface langchain-pinecone langchain_openai pypdf pinecone"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "# GOOGLE_APPLICATION_CREDENTIALS = userdata.get('GOOGLE_APPLICATION_CREDENTIALS') # need to talk with Rafay about this\n",
        "OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n",
        "PINECONE_API_KEY = userdata.get('PINECONE_API_KEY')"
      ],
      "metadata": {
        "id": "uUTq88KvC3ef"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Embeddings**"
      ],
      "metadata": {
        "id": "8j9p3hmAC9q8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_huggingface import HuggingFaceEmbeddings\n",
        "\n",
        "#Download the Embeddings from Hugging Face\n",
        "def download_hugging_face_embeddings():\n",
        "    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')\n",
        "    return embeddings\n",
        "\n",
        "embeddings = download_hugging_face_embeddings()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493,
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        },
        "id": "HK3OKXe3C999",
        "outputId": "773d80b0-6790-42cf-e91a-3b73e7fc859c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
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          "metadata": {}
        },
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          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "58b3e029310542a6a2ad3d56e0d4210f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0fc19c08d104f9e82f71579adcb03c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b74f00b5205e449a94ba2805853228ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85f6b353c2db4ffe94b7c6966b74cd92"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Schema**"
      ],
      "metadata": {
        "id": "MBFiaPjXDTRO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pydantic import BaseModel, Field\n",
        "from typing import List, Union, Tuple, Any\n",
        "from typing_extensions import Literal\n",
        "\n",
        "class MetadataSchema(BaseModel):\n",
        "    page: float = Field(..., description=\"Page number of the document\")\n",
        "    page_label: str = Field(..., description=\"Page label of the document\")\n",
        "    total_pages: float = Field(..., description=\"Total pages in the document\")\n",
        "    source: str = Field(..., description=\"Source file path of the document\")\n",
        "    score: float = Field(..., description=\"Similarity score of the retrieved document\")\n",
        "\n",
        "class DocumentSchema(BaseModel):\n",
        "    metadata: MetadataSchema = Field(..., description=\"Filtered metadata of the document\")\n",
        "    page_content: str = Field(..., description=\"Content of the document page\")\n",
        "\n",
        "class RetrievedDocsSchema(BaseModel):\n",
        "    documents: List[DocumentSchema]\n",
        "\n",
        "\n",
        "# Define the individual Question model\n",
        "class Question(BaseModel):\n",
        "    question: str = Field(..., description=\"The question prompt that the user needs to answer.\")\n",
        "    type: Literal['fill_missing', 'MCQ', 'short_answer'] = Field(..., description=\"The type of question: fill_missing, MCQ, or short_answer.\")\n",
        "    options: Union[List[str], None] = Field(None, description=\"The options for the question, used only for MCQ type.\")\n",
        "\n",
        "# Define the Questions model that contains a list of Question objects and the total number of questions\n",
        "class Questions(BaseModel):\n",
        "    no_of_questions: int = Field(..., description=\"The total number of questions generated.\")\n",
        "    questions: List[Question] = Field(..., description=\"A list of Question objects.\")"
      ],
      "metadata": {
        "id": "p8aWFnUUDVIg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Parser**"
      ],
      "metadata": {
        "id": "wkET3UbCDYqW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.output_parsers import PydanticOutputParser\n",
        "\n",
        "parser = PydanticOutputParser(pydantic_object = Questions)\n",
        "print(parser.get_format_instructions())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxlYyEG9DYch",
        "outputId": "d4783b4a-5da6-40db-9dd7-f12015685d41"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The output should be formatted as a JSON instance that conforms to the JSON schema below.\n",
            "\n",
            "As an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\n",
            "the object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\n",
            "\n",
            "Here is the output schema:\n",
            "```\n",
            "{\"$defs\": {\"Question\": {\"properties\": {\"question\": {\"description\": \"The question prompt that the user needs to answer.\", \"title\": \"Question\", \"type\": \"string\"}, \"type\": {\"description\": \"The type of question: fill_missing, MCQ, or short_answer.\", \"enum\": [\"fill_missing\", \"MCQ\", \"short_answer\"], \"title\": \"Type\", \"type\": \"string\"}, \"options\": {\"anyOf\": [{\"items\": {\"type\": \"string\"}, \"type\": \"array\"}, {\"type\": \"null\"}], \"default\": null, \"description\": \"The options for the question, used only for MCQ type.\", \"title\": \"Options\"}}, \"required\": [\"question\", \"type\"], \"title\": \"Question\", \"type\": \"object\"}}, \"properties\": {\"no_of_questions\": {\"description\": \"The total number of questions generated.\", \"title\": \"No Of Questions\", \"type\": \"integer\"}, \"questions\": {\"description\": \"A list of Question objects.\", \"items\": {\"$ref\": \"#/$defs/Question\"}, \"title\": \"Questions\", \"type\": \"array\"}}, \"required\": [\"no_of_questions\", \"questions\"]}\n",
            "```\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Vector DB**"
      ],
      "metadata": {
        "id": "eV5dTkLiDkRZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pinecone import Pinecone, ServerlessSpec\n",
        "\n",
        "pc = Pinecone(api_key=PINECONE_API_KEY)\n",
        "index_name = \"yolotest\"\n",
        "\n",
        "existing_indexes = [index_info[\"name\"] for index_info in pc.list_indexes()]\n",
        "if index_name not in existing_indexes:\n",
        "  pc.create_index(\n",
        "      name=index_name,\n",
        "      dimension=384, # Replace with your model dimensions\n",
        "      metric=\"cosine\", # Replace with your model metric\n",
        "      spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\")\n",
        "      )\n"
      ],
      "metadata": {
        "id": "tOsd3zNkDoiS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index = pc.Index(index_name)\n",
        "index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYpARiRhDof2",
        "outputId": "9982aad7-eb6e-414f-ff00-9235c9510fa6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pinecone.data.index.Index at 0x78dabdb3cfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_pinecone import PineconeVectorStore\n",
        "\n",
        "vector_store = PineconeVectorStore(pinecone_api_key= PINECONE_API_KEY,\n",
        "                                index=index,\n",
        "                                embedding=embeddings)"
      ],
      "metadata": {
        "id": "FI7ptMwyDoc6"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Retrieval Function**"
      ],
      "metadata": {
        "id": "YUVWv_8HD5eh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def retrieve_and_format_results(vector_store: Any, query: str, k: int = 5, filter: dict = {}) -> RetrievedDocsSchema:\n",
        "    \"\"\"\n",
        "    Retrieves documents using similarity search and formats them into the Pydantic schema.\n",
        "\n",
        "    Args:\n",
        "        vector_store (Any): The vector store used for retrieval.\n",
        "        query (str): The search query.\n",
        "        k (int): Number of documents to retrieve.\n",
        "        filter (dict): Optional filter for the search.\n",
        "\n",
        "    Returns:\n",
        "        RetrievedDocsSchema: A structured schema containing documents and metadata.\n",
        "    \"\"\"\n",
        "    # Ensure query is valid\n",
        "    if not query or not isinstance(query, str):\n",
        "        raise ValueError(\"Query must be a non-empty string.\")\n",
        "\n",
        "    # Retrieve documents with similarity scores\n",
        "    try:\n",
        "        retrieved_docs = vector_store.similarity_search_with_score(query, k=k, filter=filter)\n",
        "    except Exception as e:\n",
        "        raise RuntimeError(f\"Error retrieving documents: {e}\")\n",
        "\n",
        "    # Convert retrieved documents into the Pydantic schema\n",
        "    documents_list = [\n",
        "        DocumentSchema(\n",
        "            metadata=MetadataSchema(\n",
        "                page=doc.metadata.get(\"page\", 0),\n",
        "                page_label=doc.metadata.get(\"page_label\", \"\"),\n",
        "                total_pages=doc.metadata.get(\"total_pages\", 0),\n",
        "                source=doc.metadata.get(\"source\", \"\"),\n",
        "                score=float(score) if score is not None else 0.0  # Ensure score is a float\n",
        "            ),\n",
        "            page_content=doc.page_content.strip()  # Trim extra spaces\n",
        "        )\n",
        "        for doc, score in retrieved_docs if doc.page_content.strip()  # Filter out empty docs\n",
        "    ]\n",
        "\n",
        "    return RetrievedDocsSchema(documents=documents_list)\n"
      ],
      "metadata": {
        "id": "xuWjaKe-D5wv"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "query = \"Reversible Architectures\"\n",
        "formatted_results = retrieve_and_format_results(vector_store, query, k=5, filter={})\n",
        "\n",
        "# Print the structured output\n",
        "formatted_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJ5HL8pfEGbj",
        "outputId": "6855b32a-5c6a-4a2c-c327-d696a5fbc00e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RetrievedDocsSchema(documents=[DocumentSchema(metadata=MetadataSchema(page=2.0, page_label='3', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.581618369), page_content='various scenarios, as a base to develop the proposed method.\\nWe use GELAN to improve the architecture and the training\\nprocess with the proposed PGI. The above novel approach\\nmakes the proposed YOLOv9 the top real-time object de-\\ntector of the new generation.\\n2.2. Reversible Architectures\\nThe operation unit of reversible architectures [3, 16, 19]\\nmust maintain the characteristics of reversible conversion,\\nso it can be ensured that the output feature map of each\\nlayer of operation unit can retain complete original informa-\\ntion. Before, RevCol [3] generalizes traditional reversible\\nunit to multiple levels, and in doing so can expand the se-\\nmantic levels expressed by different layer units. Through\\na literature review of various neural network architectures,\\nwe found that there are many high-performing architectures\\nwith varying degree of reversible properties. For exam-\\nple, Res2Net module [11] combines different input parti-\\ntions with the next partition in a hierarchical manner, and'), DocumentSchema(metadata=MetadataSchema(page=3.0, page_label='4', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.561065912), page_content='to solve problems and conduct relative analysis.\\n3.2. Reversible Functions\\nWhen a function r has an inverse transformation func-\\ntion v, we call this function reversible function, as shown in\\nEq. 2.\\nX = vζ(rψ(X)), (2)\\nwhere ψ and ζ are parameters of r and v, respectively. Data\\nX is converted by reversible function without losing infor-\\nmation, as shown in Eq. 3.\\nI(X, X) =I(X, rψ(X)) =I(X, vζ(rψ(X))). (3)\\nWhen the network’s transformation function is composed\\nof reversible functions, more reliable gradients can be ob-\\ntained to update the model. Almost all of today’s popular\\ndeep learning methods are architectures that conform to the\\nreversible property, such as Eq. 4.\\nXl+1 = Xl + fl+1\\nθ (Xl), (4)\\nwhere l indicates the l-th layer of a PreAct ResNet and\\nf is the transformation function of the l-th layer. PreAct\\nResNet [22] repeatedly passes the original data X to sub-\\nsequent layers in an explicit way. Although such a design\\ncan make a deep neural network with more than a thousand'), DocumentSchema(metadata=MetadataSchema(page=2.0, page_label='3', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.508354664), page_content='tions with the next partition in a hierarchical manner, and\\nconcatenates all converted partitions before passing them\\nbackwards. CBNet [34, 39] re-introduces the original in-\\nput data through composite backbone to obtain complete\\noriginal information, and obtains different levels of multi-\\nlevel reversible information through various composition\\nmethods. These network architectures generally have ex-\\ncellent parameter utilization, but the extra composite layers\\ncause slow inference speeds. DynamicDet [36] combines\\nCBNet [34] and the high-efficiency real-time object detec-\\ntor YOLOv7 [63] to achieve a very good trade-off among\\nspeed, number of parameters, and accuracy. This paper in-\\ntroduces the DynamicDet architecture as the basis for de-\\nsigning reversible branches. In addition, reversible infor-\\nmation is further introduced into the proposed PGI. The\\nproposed new architecture does not require additional con-\\nnections during the inference process, so it can fully retain'), DocumentSchema(metadata=MetadataSchema(page=2.0, page_label='3', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.490535676), page_content='ing. We further design reversible network-based methods to\\nsolve the causes of the above problems. In this section we\\nshall elaborate our analysis of information bottleneck prin-\\nciple and reversible functions.\\n3'), DocumentSchema(metadata=MetadataSchema(page=4.0, page_label='5', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.484387606), page_content='ate reliable gradients and update network parameters. By\\nproviding information that maps from data to targets, the\\nloss function can provide guidance and avoid the possibil-\\nity of finding false correlations from incomplete feedfor-\\nward features that are less relevant to the target. We pro-\\npose the maintenance of complete information by introduc-\\ning reversible architecture, but adding main branch to re-\\nversible architecture will consume a lot of inference costs.\\nWe analyzed the architecture of Figure 3 (b) and found that\\nwhen additional connections from deep to shallow layers\\nare added, the inference time will increase by 20%. When\\nwe repeatedly add the input data to the high-resolution com-\\nputing layer of the network (yellow box), the inference time\\neven exceeds twice the time.\\nSince our goal is to use reversible architecture to ob-\\ntain reliable gradients, “reversible” is not the only neces-\\nsary condition in the inference stage. In view of this, we')])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Prompt**"
      ],
      "metadata": {
        "id": "kEMp8rqID00i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def initialize_prompts(parser):\n",
        "    system_prompt = \"\"\"\n",
        "    You are an AI assistant specializing in question generation. Your role is to analyze the provided query and context to create structured questions that match the given instructions.\n",
        "\n",
        "    Follow the specified question type format and ensure clarity, correctness, and alignment with the context.\n",
        "    \"\"\"\n",
        "\n",
        "    document_prompt = \"\"\"\n",
        "    **Query**: {query}\n",
        "\n",
        "    **Context**: {context}\n",
        "\n",
        "    **Question Type Instructions:**\n",
        "    - If a **specific question type** is provided, generate questions **only in that type**: {question_type}.\n",
        "    - Do **not** generate other question types if a type is specified.\n",
        "    - If 'MCQ', provide at least **3 options** per question.\n",
        "    - If 'fill_missing', leave a **blank space** for the missing word.\n",
        "    - If 'short_answer', ensure the **answer is clear** from the context.\n",
        "    - If no type is specified (or 'general' is selected), generate a **variety** of question types.\n",
        "\n",
        "    Ensure that the generated questions align with the query and retrieved context.\n",
        "    \"\"\"\n",
        "\n",
        "    footer_prompt = f\"\"\"\n",
        "    **Output Format:**\n",
        "    {parser.get_format_instructions()}\n",
        "\n",
        "    **Important:**\n",
        "    - Ensure all questions match the requested type.\n",
        "    - Do not generate options unless the type is 'MCQ'.\n",
        "    - Maintain high accuracy and relevance to the query and context.\n",
        "    \"\"\"\n",
        "\n",
        "    return system_prompt, document_prompt, footer_prompt\n"
      ],
      "metadata": {
        "id": "a-qGduB3D23c"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt, document_prompt, footer_prompt = initialize_prompts(parser)"
      ],
      "metadata": {
        "id": "ROaE1U4vENJF"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "cLTQUiVCENzW",
        "outputId": "09b6d792-0ab3-446a-ad6e-da246e6633f0"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n    You are an AI assistant specializing in question generation. Your role is to analyze the provided query and context to create structured questions that match the given instructions.\\n\\n    Follow the specified question type format and ensure clarity, correctness, and alignment with the context.\\n    '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**LLM**"
      ],
      "metadata": {
        "id": "y4e5nC6nDD9K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai"
      ],
      "metadata": {
        "id": "b4TT8R1eEQ9_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Generate with Schema**"
      ],
      "metadata": {
        "id": "P5UjhRc6DOrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_text_with_schema(prompt: str, response_format, chat_history: list = None, max_output_tokens: int = None, temperature: float = 0):\n",
        "\n",
        "    client = openai.OpenAI(api_key=OPENAI_API_KEY)\n",
        "\n",
        "    # Ensure chat_history is initialized and includes the prompt\n",
        "    if chat_history is None:\n",
        "        chat_history = []\n",
        "\n",
        "    chat_history.append({\"role\": \"user\", \"content\": prompt})  # Add user message\n",
        "\n",
        "    response = client.beta.chat.completions.parse(\n",
        "        model=\"gpt-4o-2024-08-06\",\n",
        "        messages=chat_history,  # Pass the updated history\n",
        "        max_tokens=max_output_tokens,\n",
        "        temperature=temperature,\n",
        "        response_format=response_format\n",
        "    )\n",
        "\n",
        "    return response.choices[0].message.parsed"
      ],
      "metadata": {
        "id": "FTjmQOwDDDgv"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = generate_text_with_schema(prompt=\"mcq question about logical thinking\", response_format=Questions)\n",
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pSFoAeeDNyA",
        "outputId": "745945ec-df94-4c02-9afc-c7c98849dc15"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Questions(no_of_questions=1, questions=[Question(question='Which of the following statements best describes logical thinking?', type='MCQ', options=['A) The ability to solve problems using intuition and gut feelings.', 'B) The process of reasoning consistently and systematically to arrive at a conclusion.', 'C) The skill of making decisions based on emotions and personal biases.', 'D) The practice of accepting information without questioning its validity.'])])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**LLM Utils**"
      ],
      "metadata": {
        "id": "PDNaF5GJEVfT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def construct_prompt(prompt: str, role: str):\n",
        "   return{\n",
        "          \"role\": role,\n",
        "          \"content\": prompt\n",
        "        }"
      ],
      "metadata": {
        "id": "L81jgeNfDBpO"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Generation Function**"
      ],
      "metadata": {
        "id": "OT0awMdNEdaf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Process and Generate Questions\n",
        "def process_and_generate_questions2(vector_store, query: str, parser, response_format, question_type: str = \"general\",\n",
        "                                   k: int = 5, filter: dict = {}, max_output_tokens: int = None, temperature: float = 0):\n",
        "    \"\"\"\n",
        "    Retrieves relevant documents, formats prompts, and generates questions in a structured format.\n",
        "\n",
        "    Args:\n",
        "        vector_store (Any): The vector store used for retrieval.\n",
        "        query (str): The search query.\n",
        "        parser: Parser object to INST the retriever formatting.\n",
        "        response_format: Expected response format for structured output.\n",
        "        question_type (str): Type of questions to generate ('MCQ', 'fill_missing', 'short_answer', 'general').\n",
        "        k (int): Number of documents to retrieve.\n",
        "        filter (dict): Optional filter for retrieval.\n",
        "        max_output_tokens (int): Maximum number of tokens in the response.\n",
        "        temperature (float): Temperature setting for response generation.\n",
        "\n",
        "    Returns:\n",
        "        Parsed structured response containing generated questions.\n",
        "    \"\"\"\n",
        "    retrieved_docs_schema = retrieve_and_format_results(vector_store, query, k, filter)\n",
        "    retrieved_docs = retrieved_docs_schema.documents\n",
        "    if not retrieved_docs:\n",
        "        return {\"error\": \"No relevant documents found for the query.\"}\n",
        "\n",
        "    system_prompt, document_prompt_template, footer_prompt = initialize_prompts(parser)\n",
        "    # print(f\"\\n\\n sys :{system_prompt} \")\n",
        "\n",
        "    chat_history = [construct_prompt(prompt=system_prompt, role=\"system\")]\n",
        "    # print(f\"\\n\\n hist before :{chat_history} \")\n",
        "\n",
        "    context = \"\\n\\n\".join([doc.page_content for doc in retrieved_docs])\n",
        "    # print(f\"\\n\\n context :{context} \")\n",
        "\n",
        "    try:\n",
        "        document_prompt = document_prompt_template.format(query=query, context=context, question_type=question_type)\n",
        "    except KeyError as e:\n",
        "        raise ValueError(f\"Missing format key in document prompt template: {e}\")\n",
        "    # print(f\"\\n\\n document_prompt :{document_prompt} \")\n",
        "    # print(question_type)\n",
        "\n",
        "\n",
        "    full_prompt = f\"\\n\\n{document_prompt}\\n\\n{footer_prompt}\"\n",
        "    # print(f\"\\n\\n full_prompt :{full_prompt} \")\n",
        "\n",
        "    response = generate_text_with_schema(\n",
        "        prompt=full_prompt,\n",
        "        chat_history = chat_history,\n",
        "        response_format=response_format,\n",
        "        max_output_tokens=max_output_tokens,\n",
        "        temperature=temperature\n",
        "    )\n",
        "    chat_history.append(construct_prompt(prompt=response, role=\"assistant\"))\n",
        "    print(f\"\\n\\n hist after:{chat_history} \")\n",
        "\n",
        "\n",
        "    return response, full_prompt, retrieved_docs_schema, chat_history"
      ],
      "metadata": {
        "id": "xHchq6r8EfPq"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Enums**"
      ],
      "metadata": {
        "id": "O0oxq0AKFRxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from enum import Enum\n",
        "\n",
        "class LLMEnums(Enum):\n",
        "    OPENAI = \"OPENAI\"\n",
        "    COHERE = \"COHERE\"\n",
        "\n",
        "class OpenAIEnums(Enum):\n",
        "    SYSTEM = \"system\"\n",
        "    USER = \"user\"\n",
        "    ASSISTANT = \"assistant\"\n",
        "\n",
        "class CoHereEnums(Enum):\n",
        "    SYSTEM = \"SYSTEM\"\n",
        "    USER = \"USER\"\n",
        "    ASSISTANT = \"CHATBOT\"\n",
        "\n",
        "    DOCUMENT = \"search_document\"\n",
        "    QUERY = \"search_query\"\n",
        "\n",
        "\n",
        "class DocumentTypeEnum(Enum):\n",
        "    DOCUMENT = \"document\"\n",
        "    QUERY = \"query\""
      ],
      "metadata": {
        "id": "eF_zq8AEFSGG"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Test archiecture**"
      ],
      "metadata": {
        "id": "iQiBfYAEEjUP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "response, full_prompt, retrieved_docs, hist = process_and_generate_questions2(\n",
        "    vector_store=vector_store,  # Replace with actual vector store instance\n",
        "    query=\"Reversible Architectures\",\n",
        "    parser=parser,  # Replace with actual parser instance\n",
        "    response_format=Questions,  # Replace with actual response schema format\n",
        "    question_type=\"MCQ\",\n",
        "    k=3\n",
        ")\n",
        "\n",
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUFFx_fUEi71",
        "outputId": "4f74424e-d25a-4372-e56b-f3828eb8d9cf"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            " hist after:[{'role': 'system', 'content': '\\n    You are an AI assistant specializing in question generation. Your role is to analyze the provided query and context to create structured questions that match the given instructions.\\n\\n    Follow the specified question type format and ensure clarity, correctness, and alignment with the context.\\n    '}, {'role': 'user', 'content': '\\n\\n\\n    **Query**: Reversible Architectures\\n\\n    **Context**: various scenarios, as a base to develop the proposed method.\\nWe use GELAN to improve the architecture and the training\\nprocess with the proposed PGI. The above novel approach\\nmakes the proposed YOLOv9 the top real-time object de-\\ntector of the new generation.\\n2.2. Reversible Architectures\\nThe operation unit of reversible architectures [3, 16, 19]\\nmust maintain the characteristics of reversible conversion,\\nso it can be ensured that the output feature map of each\\nlayer of operation unit can retain complete original informa-\\ntion. Before, RevCol [3] generalizes traditional reversible\\nunit to multiple levels, and in doing so can expand the se-\\nmantic levels expressed by different layer units. Through\\na literature review of various neural network architectures,\\nwe found that there are many high-performing architectures\\nwith varying degree of reversible properties. For exam-\\nple, Res2Net module [11] combines different input parti-\\ntions with the next partition in a hierarchical manner, and\\n\\nto solve problems and conduct relative analysis.\\n3.2. Reversible Functions\\nWhen a function r has an inverse transformation func-\\ntion v, we call this function reversible function, as shown in\\nEq. 2.\\nX = vζ(rψ(X)), (2)\\nwhere ψ and ζ are parameters of r and v, respectively. Data\\nX is converted by reversible function without losing infor-\\nmation, as shown in Eq. 3.\\nI(X, X) =I(X, rψ(X)) =I(X, vζ(rψ(X))). (3)\\nWhen the network’s transformation function is composed\\nof reversible functions, more reliable gradients can be ob-\\ntained to update the model. Almost all of today’s popular\\ndeep learning methods are architectures that conform to the\\nreversible property, such as Eq. 4.\\nXl+1 = Xl + fl+1\\nθ (Xl), (4)\\nwhere l indicates the l-th layer of a PreAct ResNet and\\nf is the transformation function of the l-th layer. PreAct\\nResNet [22] repeatedly passes the original data X to sub-\\nsequent layers in an explicit way. Although such a design\\ncan make a deep neural network with more than a thousand\\n\\ntions with the next partition in a hierarchical manner, and\\nconcatenates all converted partitions before passing them\\nbackwards. CBNet [34, 39] re-introduces the original in-\\nput data through composite backbone to obtain complete\\noriginal information, and obtains different levels of multi-\\nlevel reversible information through various composition\\nmethods. These network architectures generally have ex-\\ncellent parameter utilization, but the extra composite layers\\ncause slow inference speeds. DynamicDet [36] combines\\nCBNet [34] and the high-efficiency real-time object detec-\\ntor YOLOv7 [63] to achieve a very good trade-off among\\nspeed, number of parameters, and accuracy. This paper in-\\ntroduces the DynamicDet architecture as the basis for de-\\nsigning reversible branches. In addition, reversible infor-\\nmation is further introduced into the proposed PGI. The\\nproposed new architecture does not require additional con-\\nnections during the inference process, so it can fully retain\\n\\n    **Question Type Instructions:**\\n    - If a **specific question type** is provided, generate questions **only in that type**: MCQ.\\n    - Do **not** generate other question types if a type is specified.\\n    - If \\'MCQ\\', provide at least **3 options** per question.\\n    - If \\'fill_missing\\', leave a **blank space** for the missing word.\\n    - If \\'short_answer\\', ensure the **answer is clear** from the context.\\n    - If no type is specified (or \\'general\\' is selected), generate a **variety** of question types.\\n\\n    Ensure that the generated questions align with the query and retrieved context.\\n    \\n\\n\\n    **Output Format:**\\n    The output should be formatted as a JSON instance that conforms to the JSON schema below.\\n\\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\\n\\nHere is the output schema:\\n```\\n{\"$defs\": {\"Question\": {\"properties\": {\"question\": {\"description\": \"The question prompt that the user needs to answer.\", \"title\": \"Question\", \"type\": \"string\"}, \"type\": {\"description\": \"The type of question: fill_missing, MCQ, or short_answer.\", \"enum\": [\"fill_missing\", \"MCQ\", \"short_answer\"], \"title\": \"Type\", \"type\": \"string\"}, \"options\": {\"anyOf\": [{\"items\": {\"type\": \"string\"}, \"type\": \"array\"}, {\"type\": \"null\"}], \"default\": null, \"description\": \"The options for the question, used only for MCQ type.\", \"title\": \"Options\"}}, \"required\": [\"question\", \"type\"], \"title\": \"Question\", \"type\": \"object\"}}, \"properties\": {\"no_of_questions\": {\"description\": \"The total number of questions generated.\", \"title\": \"No Of Questions\", \"type\": \"integer\"}, \"questions\": {\"description\": \"A list of Question objects.\", \"items\": {\"$ref\": \"#/$defs/Question\"}, \"title\": \"Questions\", \"type\": \"array\"}}, \"required\": [\"no_of_questions\", \"questions\"]}\\n```\\n\\n    **Important:**\\n    - Ensure all questions match the requested type.\\n    - Do not generate options unless the type is \\'MCQ\\'.\\n    - Maintain high accuracy and relevance to the query and context.\\n    '}, {'role': 'assistant', 'content': Questions(no_of_questions=3, questions=[Question(question='What is the main characteristic that reversible architectures must maintain?', type='MCQ', options=['Reversible conversion', 'High speed', 'Low power consumption', 'Minimal memory usage']), Question(question='Which module combines different input partitions with the next partition in a hierarchical manner?', type='MCQ', options=['Res2Net', 'RevCol', 'CBNet', 'DynamicDet']), Question(question=\"What is the benefit of using reversible functions in a network's transformation function?\", type='MCQ', options=['More reliable gradients', 'Faster computation', 'Reduced memory usage', 'Simplified architecture'])])}] \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Questions(no_of_questions=3, questions=[Question(question='What is the main characteristic that reversible architectures must maintain?', type='MCQ', options=['Reversible conversion', 'High speed', 'Low power consumption', 'Minimal memory usage']), Question(question='Which module combines different input partitions with the next partition in a hierarchical manner?', type='MCQ', options=['Res2Net', 'RevCol', 'CBNet', 'DynamicDet']), Question(question=\"What is the benefit of using reversible functions in a network's transformation function?\", type='MCQ', options=['More reliable gradients', 'Faster computation', 'Reduced memory usage', 'Simplified architecture'])])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " full_prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "SHadqGYPE31Z",
        "outputId": "5c1e731f-f074-44ad-b722-a40e5d5ce23c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n\\n\\n    **Query**: Reversible Architectures\\n\\n    **Context**: various scenarios, as a base to develop the proposed method.\\nWe use GELAN to improve the architecture and the training\\nprocess with the proposed PGI. The above novel approach\\nmakes the proposed YOLOv9 the top real-time object de-\\ntector of the new generation.\\n2.2. Reversible Architectures\\nThe operation unit of reversible architectures [3, 16, 19]\\nmust maintain the characteristics of reversible conversion,\\nso it can be ensured that the output feature map of each\\nlayer of operation unit can retain complete original informa-\\ntion. Before, RevCol [3] generalizes traditional reversible\\nunit to multiple levels, and in doing so can expand the se-\\nmantic levels expressed by different layer units. Through\\na literature review of various neural network architectures,\\nwe found that there are many high-performing architectures\\nwith varying degree of reversible properties. For exam-\\nple, Res2Net module [11] combines different input parti-\\ntions with the next partition in a hierarchical manner, and\\n\\nto solve problems and conduct relative analysis.\\n3.2. Reversible Functions\\nWhen a function r has an inverse transformation func-\\ntion v, we call this function reversible function, as shown in\\nEq. 2.\\nX = vζ(rψ(X)), (2)\\nwhere ψ and ζ are parameters of r and v, respectively. Data\\nX is converted by reversible function without losing infor-\\nmation, as shown in Eq. 3.\\nI(X, X) =I(X, rψ(X)) =I(X, vζ(rψ(X))). (3)\\nWhen the network’s transformation function is composed\\nof reversible functions, more reliable gradients can be ob-\\ntained to update the model. Almost all of today’s popular\\ndeep learning methods are architectures that conform to the\\nreversible property, such as Eq. 4.\\nXl+1 = Xl + fl+1\\nθ (Xl), (4)\\nwhere l indicates the l-th layer of a PreAct ResNet and\\nf is the transformation function of the l-th layer. PreAct\\nResNet [22] repeatedly passes the original data X to sub-\\nsequent layers in an explicit way. Although such a design\\ncan make a deep neural network with more than a thousand\\n\\ntions with the next partition in a hierarchical manner, and\\nconcatenates all converted partitions before passing them\\nbackwards. CBNet [34, 39] re-introduces the original in-\\nput data through composite backbone to obtain complete\\noriginal information, and obtains different levels of multi-\\nlevel reversible information through various composition\\nmethods. These network architectures generally have ex-\\ncellent parameter utilization, but the extra composite layers\\ncause slow inference speeds. DynamicDet [36] combines\\nCBNet [34] and the high-efficiency real-time object detec-\\ntor YOLOv7 [63] to achieve a very good trade-off among\\nspeed, number of parameters, and accuracy. This paper in-\\ntroduces the DynamicDet architecture as the basis for de-\\nsigning reversible branches. In addition, reversible infor-\\nmation is further introduced into the proposed PGI. The\\nproposed new architecture does not require additional con-\\nnections during the inference process, so it can fully retain\\n\\n    **Question Type Instructions:**\\n    - If a **specific question type** is provided, generate questions **only in that type**: MCQ.\\n    - Do **not** generate other question types if a type is specified.\\n    - If \\'MCQ\\', provide at least **3 options** per question.\\n    - If \\'fill_missing\\', leave a **blank space** for the missing word.\\n    - If \\'short_answer\\', ensure the **answer is clear** from the context.\\n    - If no type is specified (or \\'general\\' is selected), generate a **variety** of question types.\\n\\n    Ensure that the generated questions align with the query and retrieved context.\\n    \\n\\n\\n    **Output Format:**\\n    The output should be formatted as a JSON instance that conforms to the JSON schema below.\\n\\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\\n\\nHere is the output schema:\\n```\\n{\"$defs\": {\"Question\": {\"properties\": {\"question\": {\"description\": \"The question prompt that the user needs to answer.\", \"title\": \"Question\", \"type\": \"string\"}, \"type\": {\"description\": \"The type of question: fill_missing, MCQ, or short_answer.\", \"enum\": [\"fill_missing\", \"MCQ\", \"short_answer\"], \"title\": \"Type\", \"type\": \"string\"}, \"options\": {\"anyOf\": [{\"items\": {\"type\": \"string\"}, \"type\": \"array\"}, {\"type\": \"null\"}], \"default\": null, \"description\": \"The options for the question, used only for MCQ type.\", \"title\": \"Options\"}}, \"required\": [\"question\", \"type\"], \"title\": \"Question\", \"type\": \"object\"}}, \"properties\": {\"no_of_questions\": {\"description\": \"The total number of questions generated.\", \"title\": \"No Of Questions\", \"type\": \"integer\"}, \"questions\": {\"description\": \"A list of Question objects.\", \"items\": {\"$ref\": \"#/$defs/Question\"}, \"title\": \"Questions\", \"type\": \"array\"}}, \"required\": [\"no_of_questions\", \"questions\"]}\\n```\\n\\n    **Important:**\\n    - Ensure all questions match the requested type.\\n    - Do not generate options unless the type is \\'MCQ\\'.\\n    - Maintain high accuracy and relevance to the query and context.\\n    '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7usS75WE8Sf",
        "outputId": "880c772e-7568-44ad-f7d7-de81b10c5853"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'role': 'system',\n",
              "  'content': '\\n    You are an AI assistant specializing in question generation. Your role is to analyze the provided query and context to create structured questions that match the given instructions.\\n\\n    Follow the specified question type format and ensure clarity, correctness, and alignment with the context.\\n    '},\n",
              " {'role': 'user',\n",
              "  'content': '\\n\\n\\n    **Query**: Reversible Architectures\\n\\n    **Context**: various scenarios, as a base to develop the proposed method.\\nWe use GELAN to improve the architecture and the training\\nprocess with the proposed PGI. The above novel approach\\nmakes the proposed YOLOv9 the top real-time object de-\\ntector of the new generation.\\n2.2. Reversible Architectures\\nThe operation unit of reversible architectures [3, 16, 19]\\nmust maintain the characteristics of reversible conversion,\\nso it can be ensured that the output feature map of each\\nlayer of operation unit can retain complete original informa-\\ntion. Before, RevCol [3] generalizes traditional reversible\\nunit to multiple levels, and in doing so can expand the se-\\nmantic levels expressed by different layer units. Through\\na literature review of various neural network architectures,\\nwe found that there are many high-performing architectures\\nwith varying degree of reversible properties. For exam-\\nple, Res2Net module [11] combines different input parti-\\ntions with the next partition in a hierarchical manner, and\\n\\nto solve problems and conduct relative analysis.\\n3.2. Reversible Functions\\nWhen a function r has an inverse transformation func-\\ntion v, we call this function reversible function, as shown in\\nEq. 2.\\nX = vζ(rψ(X)), (2)\\nwhere ψ and ζ are parameters of r and v, respectively. Data\\nX is converted by reversible function without losing infor-\\nmation, as shown in Eq. 3.\\nI(X, X) =I(X, rψ(X)) =I(X, vζ(rψ(X))). (3)\\nWhen the network’s transformation function is composed\\nof reversible functions, more reliable gradients can be ob-\\ntained to update the model. Almost all of today’s popular\\ndeep learning methods are architectures that conform to the\\nreversible property, such as Eq. 4.\\nXl+1 = Xl + fl+1\\nθ (Xl), (4)\\nwhere l indicates the l-th layer of a PreAct ResNet and\\nf is the transformation function of the l-th layer. PreAct\\nResNet [22] repeatedly passes the original data X to sub-\\nsequent layers in an explicit way. Although such a design\\ncan make a deep neural network with more than a thousand\\n\\ntions with the next partition in a hierarchical manner, and\\nconcatenates all converted partitions before passing them\\nbackwards. CBNet [34, 39] re-introduces the original in-\\nput data through composite backbone to obtain complete\\noriginal information, and obtains different levels of multi-\\nlevel reversible information through various composition\\nmethods. These network architectures generally have ex-\\ncellent parameter utilization, but the extra composite layers\\ncause slow inference speeds. DynamicDet [36] combines\\nCBNet [34] and the high-efficiency real-time object detec-\\ntor YOLOv7 [63] to achieve a very good trade-off among\\nspeed, number of parameters, and accuracy. This paper in-\\ntroduces the DynamicDet architecture as the basis for de-\\nsigning reversible branches. In addition, reversible infor-\\nmation is further introduced into the proposed PGI. The\\nproposed new architecture does not require additional con-\\nnections during the inference process, so it can fully retain\\n\\n    **Question Type Instructions:**\\n    - If a **specific question type** is provided, generate questions **only in that type**: MCQ.\\n    - Do **not** generate other question types if a type is specified.\\n    - If \\'MCQ\\', provide at least **3 options** per question.\\n    - If \\'fill_missing\\', leave a **blank space** for the missing word.\\n    - If \\'short_answer\\', ensure the **answer is clear** from the context.\\n    - If no type is specified (or \\'general\\' is selected), generate a **variety** of question types.\\n\\n    Ensure that the generated questions align with the query and retrieved context.\\n    \\n\\n\\n    **Output Format:**\\n    The output should be formatted as a JSON instance that conforms to the JSON schema below.\\n\\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\\n\\nHere is the output schema:\\n```\\n{\"$defs\": {\"Question\": {\"properties\": {\"question\": {\"description\": \"The question prompt that the user needs to answer.\", \"title\": \"Question\", \"type\": \"string\"}, \"type\": {\"description\": \"The type of question: fill_missing, MCQ, or short_answer.\", \"enum\": [\"fill_missing\", \"MCQ\", \"short_answer\"], \"title\": \"Type\", \"type\": \"string\"}, \"options\": {\"anyOf\": [{\"items\": {\"type\": \"string\"}, \"type\": \"array\"}, {\"type\": \"null\"}], \"default\": null, \"description\": \"The options for the question, used only for MCQ type.\", \"title\": \"Options\"}}, \"required\": [\"question\", \"type\"], \"title\": \"Question\", \"type\": \"object\"}}, \"properties\": {\"no_of_questions\": {\"description\": \"The total number of questions generated.\", \"title\": \"No Of Questions\", \"type\": \"integer\"}, \"questions\": {\"description\": \"A list of Question objects.\", \"items\": {\"$ref\": \"#/$defs/Question\"}, \"title\": \"Questions\", \"type\": \"array\"}}, \"required\": [\"no_of_questions\", \"questions\"]}\\n```\\n\\n    **Important:**\\n    - Ensure all questions match the requested type.\\n    - Do not generate options unless the type is \\'MCQ\\'.\\n    - Maintain high accuracy and relevance to the query and context.\\n    '},\n",
              " {'role': 'assistant',\n",
              "  'content': Questions(no_of_questions=3, questions=[Question(question='What is the main characteristic that reversible architectures must maintain?', type='MCQ', options=['Reversible conversion', 'High speed', 'Low power consumption', 'Minimal memory usage']), Question(question='Which module combines different input partitions with the next partition in a hierarchical manner?', type='MCQ', options=['Res2Net', 'RevCol', 'CBNet', 'DynamicDet']), Question(question=\"What is the benefit of using reversible functions in a network's transformation function?\", type='MCQ', options=['More reliable gradients', 'Faster computation', 'Reduced memory usage', 'Simplified architecture'])])}]"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "retrieved_docs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zqbCccHmE9sj",
        "outputId": "b0ac54a6-46fd-40e5-e4c9-30f407740ae1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RetrievedDocsSchema(documents=[DocumentSchema(metadata=MetadataSchema(page=2.0, page_label='3', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.581618369), page_content='various scenarios, as a base to develop the proposed method.\\nWe use GELAN to improve the architecture and the training\\nprocess with the proposed PGI. The above novel approach\\nmakes the proposed YOLOv9 the top real-time object de-\\ntector of the new generation.\\n2.2. Reversible Architectures\\nThe operation unit of reversible architectures [3, 16, 19]\\nmust maintain the characteristics of reversible conversion,\\nso it can be ensured that the output feature map of each\\nlayer of operation unit can retain complete original informa-\\ntion. Before, RevCol [3] generalizes traditional reversible\\nunit to multiple levels, and in doing so can expand the se-\\nmantic levels expressed by different layer units. Through\\na literature review of various neural network architectures,\\nwe found that there are many high-performing architectures\\nwith varying degree of reversible properties. For exam-\\nple, Res2Net module [11] combines different input parti-\\ntions with the next partition in a hierarchical manner, and'), DocumentSchema(metadata=MetadataSchema(page=3.0, page_label='4', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.561065912), page_content='to solve problems and conduct relative analysis.\\n3.2. Reversible Functions\\nWhen a function r has an inverse transformation func-\\ntion v, we call this function reversible function, as shown in\\nEq. 2.\\nX = vζ(rψ(X)), (2)\\nwhere ψ and ζ are parameters of r and v, respectively. Data\\nX is converted by reversible function without losing infor-\\nmation, as shown in Eq. 3.\\nI(X, X) =I(X, rψ(X)) =I(X, vζ(rψ(X))). (3)\\nWhen the network’s transformation function is composed\\nof reversible functions, more reliable gradients can be ob-\\ntained to update the model. Almost all of today’s popular\\ndeep learning methods are architectures that conform to the\\nreversible property, such as Eq. 4.\\nXl+1 = Xl + fl+1\\nθ (Xl), (4)\\nwhere l indicates the l-th layer of a PreAct ResNet and\\nf is the transformation function of the l-th layer. PreAct\\nResNet [22] repeatedly passes the original data X to sub-\\nsequent layers in an explicit way. Although such a design\\ncan make a deep neural network with more than a thousand'), DocumentSchema(metadata=MetadataSchema(page=2.0, page_label='3', total_pages=18.0, source='/content/Data/yolov9_paper.pdf', score=0.508354664), page_content='tions with the next partition in a hierarchical manner, and\\nconcatenates all converted partitions before passing them\\nbackwards. CBNet [34, 39] re-introduces the original in-\\nput data through composite backbone to obtain complete\\noriginal information, and obtains different levels of multi-\\nlevel reversible information through various composition\\nmethods. These network architectures generally have ex-\\ncellent parameter utilization, but the extra composite layers\\ncause slow inference speeds. DynamicDet [36] combines\\nCBNet [34] and the high-efficiency real-time object detec-\\ntor YOLOv7 [63] to achieve a very good trade-off among\\nspeed, number of parameters, and accuracy. This paper in-\\ntroduces the DynamicDet architecture as the basis for de-\\nsigning reversible branches. In addition, reversible infor-\\nmation is further introduced into the proposed PGI. The\\nproposed new architecture does not require additional con-\\nnections during the inference process, so it can fully retain')])"
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}