{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "04815d1b-44ee-4bd3-878e-fa0c3bf9fa7f",
      "metadata": {
        "tags": [],
        "id": "04815d1b-44ee-4bd3-878e-fa0c3bf9fa7f"
      },
      "source": [
        "# LangChain QA Panel App\n",
        "\n",
        "This notebook shows how to make this app:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a181568b-9cde-4a55-a853-4d2a41dbfdad",
      "metadata": {
        "tags": [],
        "id": "a181568b-9cde-4a55-a853-4d2a41dbfdad"
      },
      "outputs": [],
      "source": [
        "#!pip install langchain openai chromadb tiktoken pypdf panel\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9a464409-d064-4766-a9cb-5119f6c4b8f5",
      "metadata": {
        "tags": [],
        "id": "9a464409-d064-4766-a9cb-5119f6c4b8f5"
      },
      "outputs": [],
      "source": [
        "import os \n",
        "from langchain.chains import RetrievalQA\n",
        "from langchain.llms import OpenAI\n",
        "from langchain.document_loaders import TextLoader\n",
        "from langchain.document_loaders import PyPDFLoader\n",
        "from langchain.indexes import VectorstoreIndexCreator\n",
        "from langchain.text_splitter import CharacterTextSplitter\n",
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.vectorstores import Chroma\n",
        "import panel as pn\n",
        "import tempfile\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b2d07ea5-9ff2-4c96-a8dc-92895d870b73",
      "metadata": {
        "tags": [],
        "id": "b2d07ea5-9ff2-4c96-a8dc-92895d870b73"
      },
      "outputs": [],
      "source": [
        "pn.extension('texteditor', template=\"bootstrap\", sizing_mode='stretch_width')\n",
        "pn.state.template.param.update(\n",
        "    main_max_width=\"690px\",\n",
        "    header_background=\"#F08080\",\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "763db4d0-3436-41d3-8b0f-e66ce16468cd",
      "metadata": {
        "tags": [],
        "id": "763db4d0-3436-41d3-8b0f-e66ce16468cd"
      },
      "outputs": [],
      "source": [
        "file_input = pn.widgets.FileInput(width=300)\n",
        "\n",
        "openaikey = pn.widgets.PasswordInput(\n",
        "    value=\"\", placeholder=\"Enter your OpenAI API Key here...\", width=300\n",
        ")\n",
        "prompt = pn.widgets.TextEditor(\n",
        "    value=\"\", placeholder=\"Enter your questions here...\", height=160, toolbar=False\n",
        ")\n",
        "run_button = pn.widgets.Button(name=\"Run!\")\n",
        "\n",
        "select_k = pn.widgets.IntSlider(\n",
        "    name=\"Number of relevant chunks\", start=1, end=5, step=1, value=2\n",
        ")\n",
        "select_chain_type = pn.widgets.RadioButtonGroup(\n",
        "    name='Chain type', \n",
        "    options=['stuff', 'map_reduce', \"refine\", \"map_rerank\"]\n",
        ")\n",
        "\n",
        "widgets = pn.Row(\n",
        "    pn.Column(prompt, run_button, margin=5),\n",
        "    pn.Card(\n",
        "        \"Chain type:\",\n",
        "        pn.Column(select_chain_type, select_k),\n",
        "        title=\"Advanced settings\", margin=10\n",
        "    ), width=600\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9b83cc06-3401-498f-8f84-8a98370f3121",
      "metadata": {
        "tags": [],
        "id": "9b83cc06-3401-498f-8f84-8a98370f3121"
      },
      "outputs": [],
      "source": [
        "def qa(file, query, chain_type, k):\n",
        "    # load document\n",
        "    loader = PyPDFLoader(file)\n",
        "    documents = loader.load()\n",
        "    # split the documents into chunks\n",
        "    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
        "    texts = text_splitter.split_documents(documents)\n",
        "    # select which embeddings we want to use\n",
        "    embeddings = OpenAIEmbeddings()\n",
        "    # create the vectorestore to use as the index\n",
        "    db = Chroma.from_documents(texts, embeddings)\n",
        "    # expose this index in a retriever interface\n",
        "    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": k})\n",
        "    # create a chain to answer questions \n",
        "    qa = RetrievalQA.from_chain_type(\n",
        "        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)\n",
        "    result = qa({\"query\": query})\n",
        "    print(result['result'])\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2722f43b-daf6-4d17-a842-41203ae9b140",
      "metadata": {
        "tags": [],
        "id": "2722f43b-daf6-4d17-a842-41203ae9b140"
      },
      "outputs": [],
      "source": [
        "# result = qa(\"example.pdf\", \"what is the total number of AI publications?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "60e1b3d3-c0d2-4260-ae0c-26b03f1b8824",
      "metadata": {
        "id": "60e1b3d3-c0d2-4260-ae0c-26b03f1b8824"
      },
      "outputs": [],
      "source": [
        "convos = []  # store all panel objects in a list\n",
        "\n",
        "def qa_result(_):\n",
        "    os.environ[\"OPENAI_API_KEY\"] = openaikey.value\n",
        "    \n",
        "    # save pdf file to a temp file \n",
        "    if file_input.value is not None:\n",
        "        file_input.save(\"/.cache/temp.pdf\")\n",
        "    \n",
        "        prompt_text = prompt.value\n",
        "        if prompt_text:\n",
        "            result = qa(file=\"/.cache/temp.pdf\", query=prompt_text, chain_type=select_chain_type.value, k=select_k.value)\n",
        "            convos.extend([\n",
        "                pn.Row(\n",
        "                    pn.panel(\"\\U0001F60A\", width=10),\n",
        "                    prompt_text,\n",
        "                    width=600\n",
        "                ),\n",
        "                pn.Row(\n",
        "                    pn.panel(\"\\U0001F916\", width=10),\n",
        "                    pn.Column(\n",
        "                        result[\"result\"],\n",
        "                        \"Relevant source text:\",\n",
        "                        pn.pane.Markdown('\\n--------------------------------------------------------------------\\n'.join(doc.page_content for doc in result[\"source_documents\"]))\n",
        "                    )\n",
        "                )\n",
        "            ])\n",
        "            #return convos\n",
        "    return pn.Column(*convos, margin=15, width=575, min_height=400)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c3a70857-0b98-4f62-a9c0-b62ca42b474c",
      "metadata": {
        "tags": [],
        "id": "c3a70857-0b98-4f62-a9c0-b62ca42b474c"
      },
      "outputs": [],
      "source": [
        "qa_interactive = pn.panel(\n",
        "    pn.bind(qa_result, run_button),\n",
        "    loading_indicator=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "228e2b42-b1ed-43af-b923-031a70241ab0",
      "metadata": {
        "tags": [],
        "id": "228e2b42-b1ed-43af-b923-031a70241ab0"
      },
      "outputs": [],
      "source": [
        "output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1b0ec253-2bcd-4f91-96d8-d8456e900a58",
      "metadata": {
        "tags": [],
        "id": "1b0ec253-2bcd-4f91-96d8-d8456e900a58"
      },
      "outputs": [],
      "source": [
        "# layout\n",
        "pn.Column(\n",
        "    pn.pane.Markdown(\"\"\"\n",
        "    ## \\U0001F60A! Question Answering with your PDF file\n",
        "    \n",
        "    1) Upload a PDF. 2) Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account). 3) Type a question and click \"Run\".\n",
        "    \n",
        "    \"\"\"),\n",
        "    pn.Row(file_input,openaikey),\n",
        "    output,\n",
        "    widgets\n",
        "\n",
        ").servable()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.10"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}