DocsBot_colab.ipynb

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            "Requirement already satisfied: transformers<5.0.0,>=4.32.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb==0.3.23) (4.38.2)\n",
            "Requirement already satisfied: torch>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb==0.3.23) (2.2.1+cu121)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from sentence-transformers>=2.2.2->chromadb==0.3.23) (1.2.2)\n",
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            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain==0.0.316) (3.0.3)\n",
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            "Requirement already satisfied: h11>=0.8 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb==0.3.23) (0.14.0)\n",
            "Requirement already satisfied: httptools>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb==0.3.23) (0.6.1)\n",
            "Requirement already satisfied: uvloop!=0.15.0,!=0.15.1,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb==0.3.23) (0.19.0)\n",
            "Requirement already satisfied: watchfiles>=0.13 in /usr/local/lib/python3.10/dist-packages (from uvicorn[standard]>=0.18.3->chromadb==0.3.23) (0.21.0)\n",
            "Requirement already satisfied: deprecated~=1.2.0 in /usr/local/lib/python3.10/dist-packages (from argilla->unstructured==0.6.6) (1.2.14)\n",
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            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio==3.50.2) (1.0.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured==0.6.6) (1.3.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->unstructured==0.6.6) (2023.12.25)\n",
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            "Requirement already satisfied: XlsxWriter>=0.5.7 in /usr/local/lib/python3.10/dist-packages (from python-pptx->unstructured==0.6.6) (3.2.0)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.12->cryptography>=36.0.0->pdfminer.six==20221105) (2.21)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50.2) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50.2) (0.34.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio==3.50.2) (0.18.0)\n",
            "Requirement already satisfied: easygui in /usr/local/lib/python3.10/dist-packages (from oletools>=0.56->RTFDE==0.0.2->extract-msg==0.41.1) (0.98.3)\n",
            "Requirement already satisfied: colorclass in /usr/local/lib/python3.10/dist-packages (from oletools>=0.56->RTFDE==0.0.2->extract-msg==0.41.1) (2.2.2)\n",
            "Requirement already satisfied: pcodedmp>=1.2.5 in /usr/local/lib/python3.10/dist-packages (from oletools>=0.56->RTFDE==0.0.2->extract-msg==0.41.1) (1.2.6)\n",
            "Requirement already satisfied: msoffcrypto-tool in /usr/local/lib/python3.10/dist-packages (from oletools>=0.56->RTFDE==0.0.2->extract-msg==0.41.1) (5.3.1)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich!=13.1.0->argilla->unstructured==0.6.6) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich!=13.1.0->argilla->unstructured==0.6.6) (2.16.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (3.2.1)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (12.4.99)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers<5.0.0,>=4.32.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (0.4.2)\n",
            "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain==0.0.316) (1.0.0)\n",
            "Requirement already satisfied: tzdata in /usr/local/lib/python3.10/dist-packages (from pytz-deprecation-shim->tzlocal==4.2->extract-msg==0.41.1) (2024.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->sentence-transformers>=2.2.2->chromadb==0.3.23) (3.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich!=13.1.0->argilla->unstructured==0.6.6) (0.1.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers>=2.2.2->chromadb==0.3.23) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "# install requirements\n",
        "!pip install langchain==0.0.316 \\\n",
        "openai==0.28.1 \\\n",
        "pygpt4all==1.1.0 \\\n",
        "chromadb==0.3.23 \\\n",
        "urllib3==2.0.2 \\\n",
        "pdfminer.six==20221105 \\\n",
        "python-dotenv==1.0.0 \\\n",
        "unstructured==0.6.6 \\\n",
        "extract-msg==0.41.1 \\\n",
        "tabulate==0.9.0 \\\n",
        "pandoc==2.3 \\\n",
        "pypandoc==1.11 \\\n",
        "llama-cpp-python==0.1.50 \\\n",
        "gradio==3.50.2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pydantic==1.10.14"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XFvKr8jTl1Hl",
        "outputId": "81df7526-d0a6-4458-d59b-5f345e89d04e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pydantic==1.10.14 in /usr/local/lib/python3.10/dist-packages (1.10.14)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic==1.10.14) (4.10.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ingest data"
      ],
      "metadata": {
        "id": "EP4Fl-L0k9bI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.embeddings import HuggingFaceEmbeddings"
      ],
      "metadata": {
        "id": "t-sRIs89lJ7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create knowledge base folder\n",
        "if not os.path.exists('/content/db'):\n",
        "  os.makedirs('/content/db')\n",
        "# Create source_documents folder to store file txt\n",
        "if not os.path.exists('/content/source_documents'):\n",
        "  os.makedirs('/content/source_documents')"
      ],
      "metadata": {
        "id": "xQ-qJIV4ougc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the cell multiple times if you want to upload multiple files\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "# Move files to source_documents folder\n",
        "for filename in uploaded.keys():\n",
        "    os.rename(filename, os.path.join('/content/source_documents', filename))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "id": "Zk4TXG5LmoJI",
        "outputId": "c90428af-9731-4eda-aa1c-9280e99e2e3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e07a029d-6c6a-44db-b474-247e6297600e\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e07a029d-6c6a-44db-b474-247e6297600e\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PERSIST_DIRECTORY='/content/db'\n",
        "EMBEDDINGS_MODEL_NAME='all-MiniLM-L6-v2'\n",
        "source_directory='/content/source_documents'"
      ],
      "metadata": {
        "id": "ALp-96ugkd6c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from chromadb.config import Settings\n",
        "# Define the Chroma settings\n",
        "CHROMA_SETTINGS = Settings(\n",
        "        chroma_db_impl='duckdb+parquet',\n",
        "        persist_directory=PERSIST_DIRECTORY,\n",
        "        anonymized_telemetry=False\n",
        ")"
      ],
      "metadata": {
        "id": "kFrqv6lnk85g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Map file extensions to document loaders and their arguments\n",
        "LOADER_MAPPING = {\n",
        "    \".txt\": (TextLoader, {\"encoding\": \"utf8\"})\n",
        "    # Add more mappings for other file extensions and loaders as needed\n",
        "}\n",
        "def load_single_document(file_path: str):\n",
        "    ext = \".\" + file_path.rsplit(\".\", 1)[-1]\n",
        "    if ext in LOADER_MAPPING:\n",
        "        loader_class, loader_args = LOADER_MAPPING[ext]\n",
        "        loader = loader_class(file_path, **loader_args)\n",
        "        return loader.load()[0]\n",
        "\n",
        "    raise ValueError(f\"Unsupported file extension '{ext}'\")\n",
        "\n",
        "def load_documents(source_dir: str):\n",
        "    # Loads all documents from source documents directory\n",
        "    all_files = []\n",
        "    for ext in LOADER_MAPPING:\n",
        "        all_files.extend(\n",
        "            glob.glob(os.path.join(source_dir, f\"**/*{ext}\"), recursive=True)\n",
        "        )\n",
        "    return [load_single_document(file_path) for file_path in all_files]\n",
        "\n",
        "chunk_size = 500\n",
        "chunk_overlap = 50\n",
        "documents = load_documents(source_directory)\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)\n",
        "texts = text_splitter.split_documents(documents)\n",
        "print(f\"Loaded {len(documents)} documents from {source_directory}\")\n",
        "print(f\"Split into {len(texts)} chunks of text (max. {chunk_size} characters each)\")\n",
        "\n",
        "# Create embeddings\n",
        "embeddings = HuggingFaceEmbeddings(model_name=EMBEDDINGS_MODEL_NAME)\n",
        "\n",
        "# Create and store locally vectorstore\n",
        "db = Chroma.from_documents(texts,\n",
        "                           embeddings,\n",
        "                           persist_directory=PERSIST_DIRECTORY,\n",
        "                           client_settings=CHROMA_SETTINGS)\n",
        "db.persist()\n",
        "db = None"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545,
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        },
        "id": "-Iu_7vB9lYTn",
        "outputId": "b9aef21e-ff65-47a6-df74-59c5459c7d90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loaded 2 documents from /content/source_documents\n",
            "Split into 25 chunks of text (max. 500 characters each)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
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          "text": [
            "WARNING:chromadb:Using embedded DuckDB with persistence: data will be stored in: /content/db\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Ask questions to your documents using Gradio UI"
      ],
      "metadata": {
        "id": "kVYtgZlkrqgR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "from langchain.vectorstores import Chroma\n",
        "from langchain.chat_models import ChatOpenAI\n",
        "from langchain.chains import (StuffDocumentsChain,\n",
        "                              LLMChain)\n",
        "from langchain.schema import HumanMessage, AIMessage\n",
        "from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate, MessagesPlaceholder\n",
        "from langchain.callbacks.manager import trace_as_chain_group\n",
        "\n",
        "import gradio as gr"
      ],
      "metadata": {
        "id": "PLuWN0YAlebi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "import LANGCHAIN_API_KEY in case you encounter the error:\n",
        "langsmith.utils.LangSmithUserError: API key must be provided when using hosted LangSmith API\n",
        "Create here: https://smith.langchain.com/\n",
        "\"\"\"\n",
        "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
        "os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.smith.langchain.com\"\n",
        "os.environ[\"LANGCHAIN_API_KEY\"] = \"YOUR_LANGCHAIN_API_KEY\""
      ],
      "metadata": {
        "id": "i6bmyzpisAbp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set up our retriever\n",
        "db = Chroma(embedding_function=embeddings,\n",
        "            persist_directory=PERSIST_DIRECTORY,\n",
        "            client_settings=CHROMA_SETTINGS)\n",
        "retriever = db.as_retriever()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "scLZPTWZr12V",
        "outputId": "4cdb3585-ab38-46ab-de16-3bf0ec99ac25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:chromadb:Using embedded DuckDB with persistence: data will be stored in: /content/db\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define llm\n",
        "llm = ChatOpenAI(temperature=0, openai_api_key='YOUR_OPENAI_API_KEY')"
      ],
      "metadata": {
        "id": "lU7nmlYVsGy1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Set up our chain that can answer questions based on documents:\n",
        "This controls how each document will be formatted. Specifically,\n",
        "it will be passed to `format_document` - see that function for more details\n",
        "\"\"\"\n",
        "document_prompt = PromptTemplate(\n",
        "    input_variables=[\"page_content\"],\n",
        "    template=\"{page_content}\"\n",
        ")\n",
        "document_variable_name = \"context\"\n",
        "# The prompt here should take as an input variable the `document_variable_name`\n",
        "prompt_template = \"\"\"Use the following pieces of context to answer user questions.\n",
        "If you don't know the answer, just say that can not found in knowledge base,\n",
        "don't try to make up an answer.\n",
        "\n",
        "--------------\n",
        "\n",
        "{context}\"\"\"\n",
        "system_prompt = SystemMessagePromptTemplate.from_template(prompt_template)\n",
        "prompt = ChatPromptTemplate(\n",
        "    messages=[\n",
        "        system_prompt,\n",
        "        MessagesPlaceholder(variable_name=\"chat_history\"),\n",
        "        HumanMessagePromptTemplate.from_template(\"{question}\")\n",
        "\t]\n",
        ")\n",
        "llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
        "combine_docs_chain = StuffDocumentsChain(\n",
        "    llm_chain=llm_chain,\n",
        "    document_prompt=document_prompt,\n",
        "    document_variable_name=document_variable_name,\n",
        "    document_separator=\"---------\"\n",
        ")\n",
        "\n",
        "\"\"\"\n",
        "Set up a chain that controls how the search query for the vectorstore is generated:\n",
        "This controls how the search query is generated.\n",
        "Should take `chat_history` and `question` as input variables.\n",
        "\"\"\"\n",
        "template = \"\"\"Combine the chat history and follow up question into a a search query.\n",
        "\n",
        "Chat History:\n",
        "\n",
        "{chat_history}\n",
        "\n",
        "Follow up question: {question}\n",
        "\"\"\"\n",
        "prompt = PromptTemplate.from_template(template)\n",
        "question_generator_chain = LLMChain(llm=llm, prompt=prompt)"
      ],
      "metadata": {
        "id": "mE9SSLWksIoO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to use\n",
        "def qa_response(message, history):\n",
        "\t# Convert message history into format for the `question_generator_chain`.\n",
        "\tconvo_string = \"\\n\\n\".join([f\"Human: {h}\\nAssistant: {a}\" for h, a in history])\n",
        "\n",
        "\t# Convert message history into LangChain format for the final response chain.\n",
        "\tmessages = []\n",
        "\tfor human, ai in history:\n",
        "\t\tmessages.append(HumanMessage(content=human))\n",
        "\t\tmessages.append(AIMessage(content=ai))\n",
        "\n",
        "\t# Wrap all actual calls to chains in a trace group.\n",
        "\twith trace_as_chain_group(\"qa_response\") as group_manager:\n",
        "\t\t# Generate search query.\n",
        "\t\tsearch_query = question_generator_chain.run(\n",
        "\t\t\tquestion=message,\n",
        "\t\t\tchat_history=convo_string,\n",
        "\t\t\tcallbacks=group_manager\n",
        "\t\t)\n",
        "\n",
        "\t\t# Retrieve relevant docs.\n",
        "\t\tdocs = retriever.get_relevant_documents(search_query, callbacks=group_manager)\n",
        "\n",
        "\t\t# Answer question.\n",
        "\t\treturn combine_docs_chain.run(\n",
        "\t\t\tinput_documents=docs,\n",
        "\t\t\tchat_history=messages,\n",
        "\t\t\tquestion=message,\n",
        "\t\t\tcallbacks=group_manager\n",
        "\t\t)\n",
        "\n",
        "# start the app\n",
        "gr.ChatInterface(qa_response).launch(share=True, debug=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "IB6eIwv7sQH1",
        "outputId": "9f887926-5cb8-4464-9c58-a93e17cbfceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
            "Running on public URL: https://226675851e29cf15b6.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
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            "Killing tunnel 127.0.0.1:7860 <> https://226675851e29cf15b6.gradio.live\n"
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}