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      "metadata": {
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          "text": [
            "Collecting fastapi\n",
            "  Downloading fastapi-0.104.1-py3-none-any.whl (92 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.9/92.9 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: anyio<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from fastapi) (3.7.1)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from fastapi) (1.10.13)\n",
            "Collecting starlette<0.28.0,>=0.27.0 (from fastapi)\n",
            "  Downloading starlette-0.27.0-py3-none-any.whl (66 kB)\n",
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            "\u001b[?25hCollecting typing-extensions>=4.8.0 (from fastapi)\n",
            "  Downloading typing_extensions-4.8.0-py3-none-any.whl (31 kB)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<4.0.0,>=3.7.1->fastapi) (3.4)\n",
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<4.0.0,>=3.7.1->fastapi) (1.1.3)\n",
            "Installing collected packages: typing-extensions, starlette, fastapi\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\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",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\n",
            "lida 0.0.10 requires uvicorn, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.8.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed fastapi-0.104.1 starlette-0.27.0 typing-extensions-4.8.0\n",
            "Collecting uvicorn\n",
            "  Downloading uvicorn-0.23.2-py3-none-any.whl (59 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn) (8.1.7)\n",
            "Collecting h11>=0.8 (from uvicorn)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn) (4.8.0)\n",
            "Installing collected packages: h11, uvicorn\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",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "lida 0.0.10 requires python-multipart, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed h11-0.14.0 uvicorn-0.23.2\n",
            "Collecting pickle5\n",
            "  Downloading pickle5-0.0.11.tar.gz (132 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.1/132.1 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: pickle5\n",
            "  Building wheel for pickle5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pickle5: filename=pickle5-0.0.11-cp310-cp310-linux_x86_64.whl size=255312 sha256=1c164d53dc36864af7c5d0f3760a691a8fb008fd1097aab8fda1866545214c3f\n",
            "  Stored in directory: /root/.cache/pip/wheels/7d/14/ef/4aab19d27fa8e58772be5c71c16add0426acf9e1f64353235c\n",
            "Successfully built pickle5\n",
            "Installing collected packages: pickle5\n",
            "Successfully installed pickle5-0.0.11\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests) (3.3.1)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests) (2023.7.22)\n",
            "Collecting pypi-json\n",
            "  Downloading pypi_json-0.4.0-py3-none-any.whl (26 kB)\n",
            "Collecting apeye>=1.1.0 (from pypi-json)\n",
            "  Downloading apeye-1.4.1-py3-none-any.whl (107 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m108.0/108.0 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: packaging>=21.0 in /usr/local/lib/python3.10/dist-packages (from pypi-json) (23.2)\n",
            "Requirement already satisfied: requests>=2.26.0 in /usr/local/lib/python3.10/dist-packages (from pypi-json) (2.31.0)\n",
            "Collecting apeye-core>=1.0.0b2 (from apeye>=1.1.0->pypi-json)\n",
            "  Downloading apeye_core-1.1.4-py3-none-any.whl (99 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.3/99.3 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting domdf-python-tools>=2.6.0 (from apeye>=1.1.0->pypi-json)\n",
            "  Downloading domdf_python_tools-3.7.0-py3-none-any.whl (127 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.1/127.1 kB\u001b[0m \u001b[31m16.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: platformdirs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from apeye>=1.1.0->pypi-json) (3.11.0)\n",
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            "Requirement already satisfied: natsort>=7.0.1 in /usr/local/lib/python3.10/dist-packages (from domdf-python-tools>=2.6.0->apeye>=1.1.0->pypi-json) (8.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.1 in /usr/local/lib/python3.10/dist-packages (from domdf-python-tools>=2.6.0->apeye>=1.1.0->pypi-json) (4.8.0)\n",
            "Installing collected packages: domdf-python-tools, apeye-core, apeye, pypi-json\n",
            "Successfully installed apeye-1.4.1 apeye-core-1.1.4 domdf-python-tools-3.7.0 pypi-json-0.4.0\n",
            "Collecting pyngrok\n",
            "  Downloading pyngrok-7.0.0.tar.gz (718 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m718.7/718.7 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from pyngrok) (6.0.1)\n",
            "Building wheels for collected packages: pyngrok\n",
            "  Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyngrok: filename=pyngrok-7.0.0-py3-none-any.whl size=21129 sha256=11a5cd11d9713825c428a9cd6384d543af4b224f9636ac5164d25d45c100b296\n",
            "  Stored in directory: /root/.cache/pip/wheels/60/29/7b/f64332aa7e5e88fbd56d4002185ae22dcdc83b35b3d1c2cbf5\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-7.0.0\n",
            "Requirement already satisfied: nest-asyncio in /usr/local/lib/python3.10/dist-packages (1.5.8)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m83.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m43.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting replicate\n",
            "  Downloading replicate-0.15.5-py3-none-any.whl (25 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from replicate) (23.2)\n",
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            "Collecting httpx<1,>=0.21.0 (from replicate)\n",
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            "Collecting httpcore<0.19.0,>=0.18.0 (from httpx<1,>=0.21.0->replicate)\n",
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            "Installing collected packages: httpcore, httpx, replicate\n",
            "Successfully installed httpcore-0.18.0 httpx-0.25.0 replicate-0.15.5\n",
            "Collecting wikipedia\n",
            "  Downloading wikipedia-1.4.0.tar.gz (27 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from wikipedia) (4.11.2)\n",
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            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->wikipedia) (2.5)\n",
            "Building wheels for collected packages: wikipedia\n",
            "  Building wheel for wikipedia (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11678 sha256=1c89fcff200d1944e3a0444f56452c9a2ba9bb30de09823001634a570f558bd9\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/b6/c5/93f3dec388ae76edc830cb42901bb0232504dfc0df02fc50de\n",
            "Successfully built wikipedia\n",
            "Installing collected packages: wikipedia\n",
            "Successfully installed wikipedia-1.4.0\n"
          ]
        }
      ],
      "source": [
        "!pip install fastapi\n",
        "!pip install uvicorn\n",
        "!pip install pickle5\n",
        "!pip install requests\n",
        "!pip install pypi-json\n",
        "!pip install pyngrok\n",
        "!pip install nest-asyncio\n",
        "\n",
        "!pip install -qq -U langchain tiktoken faiss-gpu\n",
        "!pip install -qq -U transformers InstructorEmbedding sentence_transformers\n",
        "!pip install -qq -U accelerate bitsandbytes xformers einops\n",
        "!pip install replicate\n",
        "!pip install wikipedia"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import difflib\n",
        "from langchain.document_loaders import GutenbergLoader\n",
        "from langchain.document_loaders import WikipediaLoader\n",
        "import os\n",
        "import uvicorn\n",
        "\n",
        "import langchain\n",
        "from fastapi import FastAPI\n",
        "\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "\n",
        "from langchain import PromptTemplate, ConversationChain, LLMChain\n",
        "from langchain.docstore.document import Document\n",
        "from langchain.vectorstores import Chroma, FAISS\n",
        "\n",
        "from langchain.llms import HuggingFacePipeline\n",
        "from InstructorEmbedding import INSTRUCTOR\n",
        "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
        "\n",
        "from langchain.chains import RetrievalQA, ConversationalRetrievalChain\n",
        "\n",
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n",
        "\n",
        "\n",
        "from langchain.llms import Replicate\n",
        "from langchain import PromptTemplate, LLMChain"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0WMOx211_zQa",
        "outputId": "1e478c01-ab8a-4af8-f7dd-cb0c56400e1d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/InstructorEmbedding/instructor.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import trange\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastapi import FastAPI\n",
        "from pydantic import BaseModel\n",
        "import pickle\n",
        "import json\n",
        "import uvicorn\n",
        "from pyngrok import ngrok\n",
        "from fastapi.middleware.cors import CORSMiddleware\n",
        "import nest_asyncio\n",
        "import textwrap"
      ],
      "metadata": {
        "id": "t0llmI1YCeaA"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = FastAPI()"
      ],
      "metadata": {
        "id": "i2dyjYlrCFo-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "origins = [\"*\"]\n",
        "\n",
        "app.add_middleware(\n",
        "    CORSMiddleware,\n",
        "    allow_origins=origins,\n",
        "    allow_credentials=True,\n",
        "    allow_methods=[\"*\"],\n",
        "    allow_headers=[\"*\"],\n",
        ")"
      ],
      "metadata": {
        "id": "2weSU9lPCDTV"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book_embeddings = None\n",
        "book_content = None"
      ],
      "metadata": {
        "id": "DlJzcPRfBVcx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Configuration:\n",
        "    model_name = \"llama2-13b\"\n",
        "    temperature = 0.5\n",
        "    top_p = 0.95\n",
        "    repetition_penalty = 1.15\n",
        "\n",
        "    split_chunk_size = 1000\n",
        "    split_overlap = 100\n",
        "\n",
        "    embeddings_model_repo = \"hkunlp/instructor-large\"\n",
        "\n",
        "    k = 3\n",
        "\n",
        "    Persist_directory = \"./embeddings/\""
      ],
      "metadata": {
        "id": "tA8K0YcZBdCh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_project_gutenberg_sections(text):\n",
        "    start_marker = \"*** START OF THE PROJECT GUTENBERG EBOOK\"\n",
        "    end_marker = \"*** END OF THE PROJECT GUTENBERG EBOOK\"\n",
        "    start_index = text.find(start_marker)\n",
        "    start_end_index = text.find(\"***\", start_index + len(start_marker))\n",
        "    end_index = text.find(end_marker)\n",
        "    text = text[start_end_index + 3 : end_index]\n",
        "    return text"
      ],
      "metadata": {
        "id": "hDcLNvOiBeKC"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def does_book_exist(book_id):\n",
        "    global book_embeddings\n",
        "    print(\"Checking for existing instructor embeddings...\")\n",
        "    try:\n",
        "        embedding_name = f\"BabblerEmbedding-{book_id}\"\n",
        "        book_embeddings = FAISS.load_local(\n",
        "            embedding_name,\n",
        "            instructor_embeddings\n",
        "        )\n",
        "        return True\n",
        "    except:\n",
        "        return False"
      ],
      "metadata": {
        "id": "Twj6wg38Bfoq"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def select_book(book_id, book_name):\n",
        "    formatted_url = f\"https://www.gutenberg.org/cache/epub/{book_id}/pg{book_id}.txt\"\n",
        "    print(formatted_url)\n",
        "    loader = GutenbergLoader(formatted_url)\n",
        "    docs = WikipediaLoader(query=book_name, load_max_docs=1).load()[0].page_content\n",
        "    book_content = loader.load()[0].page_content\n",
        "    filtered_book_content = remove_project_gutenberg_sections(book_content)\n",
        "    filtered_book_content = docs + filtered_book_content\n",
        "    print(\"Book content loaded.\")\n",
        "    return [\n",
        "        Document(page_content=filtered_book_content, metadata={\"source\": formatted_url})\n",
        "    ]"
      ],
      "metadata": {
        "id": "zN2c4JitBiYL"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_book_embeddings(book_content, book_id: str):\n",
        "    global book_embeddings\n",
        "    print(\"Creating instructor embeddings...\")\n",
        "    text_splitter = RecursiveCharacterTextSplitter(\n",
        "        chunk_size=Configuration.split_chunk_size,\n",
        "        chunk_overlap=Configuration.split_overlap,\n",
        "    )\n",
        "    print(\"Book content split into chunks.\")\n",
        "    texts = text_splitter.split_documents(book_content)\n",
        "    print(\"Creating book embeddings...\")\n",
        "    book_embeddings = FAISS.from_documents(\n",
        "        documents=texts,\n",
        "        embedding=instructor_embeddings,\n",
        "    )\n",
        "    print(\"Book embeddings created.\")\n",
        "    embedding_name = f\"BabblerEmbedding-{book_id}\"\n",
        "    book_embeddings.save_local(\n",
        "       embedding_name\n",
        "    )\n",
        "    print(\"Book embeddings saved.\")"
      ],
      "metadata": {
        "id": "hOuNZSaXBkLS"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wrap_text_preserve_newlines(text, width=200):  # 110\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split(\"\\n\")\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = \"\\n\".join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text"
      ],
      "metadata": {
        "id": "MJlONTZQBmwr"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def wrap_text_preserve_newlines(text, width=200):  # 110\n",
        "    # Split the input text into lines based on newline characters\n",
        "    lines = text.split(\"\\n\")\n",
        "\n",
        "    # Wrap each line individually\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "\n",
        "    # Join the wrapped lines back together using newline characters\n",
        "    wrapped_text = \"\\n\".join(wrapped_lines)\n",
        "\n",
        "    return wrapped_text"
      ],
      "metadata": {
        "id": "gMGguZvKBpJq"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def process_llm_response(llm_response):\n",
        "    print(llm_response)\n",
        "    ans = wrap_text_preserve_newlines(llm_response[\"result\"])\n",
        "    # sources_used = \" \\n\".join(\n",
        "    #     [str(source.metadata[\"source\"]) for source in llm_response[\"source_documents\"]]\n",
        "    # )\n",
        "    ans = ans #+ \"\\n\\nSources: \\n\" + sources_used\n",
        "    return ans"
      ],
      "metadata": {
        "id": "mPSxpF4_Br17"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt_template = \"\"\"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: {question}\n",
        "Answer:\"\"\"\n",
        "PROMPT = PromptTemplate(\n",
        "    template=prompt_template, input_variables=[\"context\", \"question\"]\n",
        ")\n"
      ],
      "metadata": {
        "id": "6qe4M7rIBugj"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_answer_from_embeddings(query, book_id: str):\n",
        "    \"\"\"\n",
        "    Retrieve documents from the vector database and then pass them to the language model to generate an answer.\n",
        "\n",
        "    Args:\n",
        "        query: The user's question.\n",
        "        book_id: The id of the book to search.\n",
        "\n",
        "    Returns:\n",
        "        The answer to the question.\n",
        "    \"\"\"\n",
        "    # select books embeddings from database.\n",
        "    # book_embeddings = Chroma(\n",
        "    #     persist_directory=Configuration.Persist_directory,\n",
        "    #     collection_name=f\"BabblerEmbedding-{book_id}\",\n",
        "    #     embedding_function=instructor_embeddings,\n",
        "    # )\n",
        "    # print(f\"Loaded book embeddings from {book_embeddings._collection.name}\")\n",
        "    retriever = book_embeddings.as_retriever(\n",
        "        search_kwargs={\"k\": Configuration.k, \"search_type\": \"similarity\"}\n",
        "    )\n",
        "    # docs = book_embeddings.similarity_search(query)\n",
        "    # a = [doc.page_content for doc in docs]\n",
        "    # for i in a:\n",
        "    #     print(i)\n",
        "    # return \"lorem ipsum\"\n",
        "    qa_chain = RetrievalQA.from_chain_type(\n",
        "        llm=llm,\n",
        "        chain_type=\"stuff\",\n",
        "        retriever=retriever,\n",
        "        chain_type_kwargs={\"prompt\": PROMPT},\n",
        "        return_source_documents=False,\n",
        "        verbose=False,\n",
        "    )\n",
        "    llm_response = qa_chain(query)\n",
        "    ans = process_llm_response(llm_response)\n",
        "\n",
        "    return ans"
      ],
      "metadata": {
        "id": "9yPiUMtkBzRU"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "REPLICATE_API_TOKEN = \"r8_KWM7ZPHF27SufFBDWyTQdAHvU07aUHm2aUjQh\"\n",
        "os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n",
        "NGROK_API_TOKEN = \"2XUqYKO0EBFXTtX1j4wweNw0AXX_7LZayy8Lh149n7DFFiQTe\""
      ],
      "metadata": {
        "id": "KgO0mZm4B5Sk"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ngrok authtoken 2XUqYKO0EBFXTtX1j4wweNw0AXX_7LZayy8Lh149n7DFFiQTe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VetG6iIcGBbq",
        "outputId": "b771fdee-6d54-431f-d266-686947aec821"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm = Replicate(\n",
        "    model=\"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\",\n",
        "    input={\"temperature\": 0.75, \"max_length\": 500, \"top_p\": 0.95, \"repetition_penalty\": 1.15},\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2qHGiaKB2q0",
        "outputId": "913afd41-7b6c-4f7b-e1bd-df97360ad49d"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:langchain.llms.replicate:Init param `input` is deprecated, please use `model_kwargs` instead.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "instructor_embeddings = HuggingFaceInstructEmbeddings(\n",
        "    model_name=Configuration.embeddings_model_repo, model_kwargs={\"device\": \"cuda\"}\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500,
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        "outputId": "aee70046-47f4-4958-8ecf-e73eee45f92e"
      },
      "execution_count": 19,
      "outputs": [
        {
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "load INSTRUCTOR_Transformer\n",
            "max_seq_length  512\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "@app.get(\"/book\")\n",
        "async def get_book(book_id: str, book_name: str):\n",
        "    torch.cuda.empty_cache()\n",
        "    has_book = does_book_exist(book_id)\n",
        "    if has_book:\n",
        "        return {\"status\": \"success\"}\n",
        "\n",
        "    print(\"Getting book...\")\n",
        "    book_content = select_book(book_id, book_name)\n",
        "    create_book_embeddings(book_content, book_id)\n",
        "    return {\"status\": \"success\"}"
      ],
      "metadata": {
        "id": "JP0dmBdRCP5-"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@app.get(\"/answer\")\n",
        "async def get_answer(query: str, book_id: str):\n",
        "    has_book = does_book_exist(book_id)\n",
        "    if not has_book:\n",
        "        return {\"status\": \"error\", \"message\": \"Book not found.\"}\n",
        "    return generate_answer_from_embeddings(query, book_id)"
      ],
      "metadata": {
        "id": "kK_uFnFbCSyf"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ngrok_tunnel = ngrok.connect(8000)\n",
        "print('Public URL:', ngrok_tunnel.public_url)\n",
        "nest_asyncio.apply()\n",
        "uvicorn.run(app, port=8000)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7yUhtY7NCW3Z",
        "outputId": "ffd8f43d-4d5b-43e8-bec1-705fc612077a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pyngrok.process.ngrok:t=2023-10-30T20:03:28+0000 lvl=warn msg=\"ngrok config file found at legacy location, move to XDG location\" xdg_path=/root/.config/ngrok/ngrok.yml legacy_path=/root/.ngrok2/ngrok.yml\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Public URL: https://8c33-35-185-138-88.ngrok-free.app\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:     Started server process [1079]\n",
            "INFO:     Waiting for application startup.\n",
            "INFO:     Application startup complete.\n",
            "INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:     2402:4000:2081:27dd:e547:1ae2:e5a4:2b0c:0 - \"GET / HTTP/1.1\" 404 Not Found\n",
            "INFO:     2402:4000:2081:27dd:e547:1ae2:e5a4:2b0c:0 - \"GET /favicon.ico HTTP/1.1\" 404 Not Found\n",
            "INFO:     2402:4000:2081:27dd:e547:1ae2:e5a4:2b0c:0 - \"GET /docs HTTP/1.1\" 200 OK\n",
            "INFO:     2402:4000:2081:27dd:e547:1ae2:e5a4:2b0c:0 - \"GET /openapi.json HTTP/1.1\" 200 OK\n",
            "Checking for existing instructor embeddings...\n",
            "INFO:     2402:4000:2081:27dd:e547:1ae2:e5a4:2b0c:0 - \"GET /book?book_id=1342&book_name=Pride%20and%20Prejudice HTTP/1.1\" 200 OK\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# def create_book_embeddings(book_content, book_id: str):\n",
        "#     global book_embeddings\n",
        "#     print(\"Creating instructor embeddings...\")\n",
        "#     text_splitter = RecursiveCharacterTextSplitter(\n",
        "#         chunk_size=Configuration.split_chunk_size,\n",
        "#         chunk_overlap=Configuration.split_overlap,\n",
        "#     )\n",
        "#     print(\"Book content split into chunks.\")\n",
        "#     texts = text_splitter.split_documents(book_content)\n",
        "\n",
        "#     persistent_client = chromadb.PersistentClient(Configuration.Persist_directory)\n",
        "#     print(\"Creating book embeddings...\")\n",
        "#     book_embeddings = Chroma(\n",
        "#         collection_name=f\"BabblerEmbedding-{book_id}\",\n",
        "#         embedding_function=instructor_embeddings,\n",
        "#         client=persistent_client,\n",
        "#         persist_directory=Configuration.Persist_directory,\n",
        "#     )\n",
        "\n",
        "#     # try:\n",
        "#     #     book_embeddings = Chroma.load(persist_directory = '.',\n",
        "#     #                            collection_name = 'book')\n",
        "#     # except:\n",
        "#     # book_embeddings = Chroma.from_documents(\n",
        "#     #     documents=texts,\n",
        "#     #     embedding=instructor_embeddings,\n",
        "#     #     persist_directory=\"./embeddings/\",\n",
        "#     #     collection_name=\"book\",\n",
        "#     # )\n",
        "\n",
        "#     book_embeddings.add_documents(documents=texts)\n",
        "#     book_embeddings.persist()\n",
        "#     print(\"Book embeddings created.\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# def generate_answer_from_embeddings(query, book_id: str):\n",
        "#     \"\"\"\n",
        "#     Retrieve documents from the vector database and then pass them to the language model to generate an answer.\n",
        "\n",
        "#     Args:\n",
        "#         query: The user's question.\n",
        "#         book_id: The id of the book to search.\n",
        "\n",
        "#     Returns:\n",
        "#         The answer to the question.\n",
        "#     \"\"\"\n",
        "#     # select books embeddings from database.\n",
        "#     book_embeddings = Chroma(\n",
        "#         persist_directory=Configuration.Persist_directory,\n",
        "#         collection_name=f\"BabblerEmbedding-{book_id}\",\n",
        "#         embedding_function=instructor_embeddings,\n",
        "#     )\n",
        "#     print(f\"Loaded book embeddings from {book_embeddings._collection.name}\")\n",
        "#     retriever = book_embeddings.as_retriever(\n",
        "#         search_kwargs={\"k\": Configuration.k, \"search_type\": \"similarity\"}\n",
        "#     )\n",
        "#     docs = book_embeddings.similarity_search(query)\n",
        "#     # a = [doc.page_content for doc in docs]\n",
        "#     # for i in a:\n",
        "#     #     print(i)\n",
        "#     # return \"lorem ipsum\"\n",
        "#     qa_chain = RetrievalQA.from_chain_type(\n",
        "#         llm=llm,\n",
        "#         chain_type=\"stuff\",\n",
        "#         retriever=retriever,\n",
        "#         chain_type_kwargs={\"prompt\": PROMPT},\n",
        "#         return_source_documents=True,\n",
        "#         verbose=False,\n",
        "#     )\n",
        "#     llm_response = qa_chain(query)\n",
        "#     ans = process_llm_response(llm_response)\n",
        "\n",
        "#     return ans\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FbTJI7o6A4Bc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}