chatbot_01.ipynb

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   "cell_type": "markdown",
   "id": "2b52d915-1ec7-45ba-8d85-a5a51c039d9a",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa8e72b2-c3aa-4fd7-8b19-446f0eb45541",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from openai import OpenAI\n",
    "from langchain.vectorstores import DocArrayInMemorySearch\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.document_loaders import PyPDFLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_openai import OpenAIEmbeddings #may delete this for line below\n",
    "\n",
    "#new stuff\n",
    "from langchain_openai import ChatOpenAI\n",
    "from langchain.chains import (\n",
    "    create_history_aware_retriever,\n",
    "    create_retrieval_chain,\n",
    ")\n",
    "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "from langchain_openai import ChatOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e1e71ef6-d482-49bb-b441-7ddeb734efca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "\n",
    "# Load the environment file explicitly\n",
    "from pathlib import Path\n",
    "dotenv_path = Path('/Users/kyli/Documents/GitHub/lab_gpt/.env')\n",
    "load_dotenv(dotenv_path=dotenv_path)\n",
    "\n",
    "# Access variables\n",
    "openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
    "google_maps_api_key = os.getenv(\"GOOGLE_MAPS_API_KEY\")\n",
    "huggingface_api_key = os.getenv(\"HUGGINGFACE_API_KEY\")\n",
    "pinecone_api_key = os.getenv(\"PINECONE_API_KEY\")\n",
    "langchain_api_key = os.getenv(\"LANGCHAIN_API_KEY\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e2221848-2b00-4dd6-b0d1-320486e846b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if openai_api_key:\n",
    "    print('yes')\n",
    "else:\n",
    "    print('ugh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "768a21fd-d919-4479-be9c-6d2ea8a1b641",
   "metadata": {},
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      "text/plain": [
       "'\\n#Create a temporary database with your files\\ndef load_db(file, chain_type, k):\\n    #Load document - need to load multiple files\\n    loader = PyPDFLoader(file)\\n    #Split the documents into pages\\n    documents = loader.load()\\n    #Split text\\n    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\\n    docs = text_splitter.split_documents(documents)\\n    #Define embedding - turn text chunks into vectors\\n    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\\n    #Create vector database from data\\n    db = DocArrayInMemorySearch.from_documents(docs, embeddings)\\n    #Define retriever\\n    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\\'k\\': k})\\n    #Create chatbot chain. Memory is managed externally\\n    qa = ConversationalRetrievalChain.from_llm(\\n        llm = ChatOpenAI(model_name=llm_name, temperature=0),\\n        chain_type=chain_type,\\n        retriever=retriever,\\n        return_source_documents=True,\\n        return_generated_question=True,\\n    )\\n    return qa\\n    '"
      ]
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     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "#Create a temporary database with your files\n",
    "def load_db(file, chain_type, k):\n",
    "    #Load document - need to load multiple files\n",
    "    loader = PyPDFLoader(file)\n",
    "    #Split the documents into pages\n",
    "    documents = loader.load()\n",
    "    #Split text\n",
    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\n",
    "    docs = text_splitter.split_documents(documents)\n",
    "    #Define embedding - turn text chunks into vectors\n",
    "    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\n",
    "    #Create vector database from data\n",
    "    db = DocArrayInMemorySearch.from_documents(docs, embeddings)\n",
    "    #Define retriever\n",
    "    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={'k': k})\n",
    "    #Create chatbot chain. Memory is managed externally\n",
    "    qa = ConversationalRetrievalChain.from_llm(\n",
    "        llm = ChatOpenAI(model_name=llm_name, temperature=0),\n",
    "        chain_type=chain_type,\n",
    "        retriever=retriever,\n",
    "        return_source_documents=True,\n",
    "        return_generated_question=True,\n",
    "    )\n",
    "    return qa\n",
    "    '''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ee7b571b-fdc6-4aaf-a823-5a0df457f246",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a temporary database with your files\n",
    "def load_db_new(file, chain_type, k):\n",
    "    #Load document - need to load multiple files\n",
    "    loader = PyPDFLoader(file)\n",
    "    #Split the documents into pages\n",
    "    documents = loader.load()\n",
    "    #Split text\n",
    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\n",
    "    docs = text_splitter.split_documents(documents)\n",
    "    #Define embedding - turn text chunks into vectors\n",
    "    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)\n",
    "    #Create vector database from data\n",
    "    db = DocArrayInMemorySearch.from_documents(docs, embeddings)\n",
    "    #Define retriever\n",
    "    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={'k': k})\n",
    "    \n",
    "    llm = ChatOpenAI()\n",
    "\n",
    "    #Contextualize question\n",
    "    contextualize_q_system_prompt = (\n",
    "        \"Given a chat history and the latest user question \"\n",
    "        \"which might reference context in the chat history, \"\n",
    "        \"formulate a standalone question which can be understood \"\n",
    "        \"without the chat history. Do NOT answer the question, just \"\n",
    "        \"reformulate it if needed and otherwise return it as is.\"\n",
    "    )\n",
    "\n",
    "    contextualize_q_prompt = ChatPromptTemplate.from_messages(\n",
    "        [\n",
    "        (\"system\", contextualize_q_system_prompt),\n",
    "        MessagesPlaceholder(\"chat_history\"),\n",
    "        (\"human\", \"{input}\"),\n",
    "        ]\n",
    "    )\n",
    "    history_aware_retriever = create_history_aware_retriever(\n",
    "        llm, retriever, contextualize_q_prompt\n",
    "    )\n",
    "    \n",
    "    # Answer question\n",
    "    qa_system_prompt = (\n",
    "        \"You are an assistant for question-answering tasks. Use \"\n",
    "        \"the following pieces of retrieved context to answer the \"\n",
    "        \"question. If you don't know the answer, just say that you \"\n",
    "        \"don't know. Use three sentences maximum and keep the answer \"\n",
    "        \"concise.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bc5d1cfb-abb4-484e-b533-134e68d5c661",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NEXT TIME YOU CLOSE OUT OF JUPYTER NOTEBOOK ADD DOCARRAY TO CONDA ENV\n",
    "#!pip install docarray\n",
    "#!pip install panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "58d0ff5d-5a35-4d53-bed3-dfceeac4b8e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a class using panel and parem that creates an interactive chatbot\n",
    "#that interacts with the database\n",
    "\n",
    "import param\n",
    "#Imports panel library, used to create interactive interfaces (dashboards,\n",
    "#widgets, etc) GUI\n",
    "import panel as pn\n",
    "\n",
    "#the cbfs class inherits from param.Parameterized\n",
    "class cbfs(param.Parameterized):\n",
    "    #List to store conversation history\n",
    "    chat_history = param.List([])\n",
    "    #String for chatbot's latest response\n",
    "    answer = param.String(\"\")\n",
    "    #Latest question sent to document database\n",
    "    db_query  = param.String(\"\")\n",
    "    #List of documents/context retrieved from database\n",
    "    db_response = param.List([])\n",
    "\n",
    "    #Initialize the class\n",
    "    def __init__(self,  **params):\n",
    "        super(cbfs, self).__init__( **params)\n",
    "        #Empty list to store the chat's visual elements (?)\n",
    "        self.panels = []\n",
    "        #Initialize one file for demonstration\n",
    "        self.loaded_file = \"/Users/kyli/Desktop/lab_gpt_example_docs/01_pathophys_tbi.pdf\"\n",
    "        #Calls load_db() method to create a QA omdel\n",
    "        self.qa = load_db_new(self.loaded_file,\"stuff\", 4)\n",
    "\n",
    "    #Loads new file into database\n",
    "    def call_load_db_new(self, count):\n",
    "        if count == 0 or file_input.value is None:  # init or no file specified :\n",
    "            return pn.pane.Markdown(f\"Loaded File: {self.loaded_file}\")\n",
    "        else:\n",
    "            file_input.save(\"temp.pdf\")  # local copy\n",
    "            #Change the style of the button\n",
    "            self.loaded_file = file_input.filename\n",
    "            button_load.button_style=\"outline\"\n",
    "            self.qa = load_db_new(\"temp.pdf\", \"stuff\", 4)\n",
    "            button_load.button_style=\"solid\"\n",
    "        #Clear chat history\n",
    "        self.clr_history()\n",
    "        return pn.pane.Markdown(f\"Loaded File: {self.loaded_file}\")\n",
    "\n",
    "    #Process queries\n",
    "    def convchain(self, query):\n",
    "        if not query:\n",
    "            return pn.WidgetBox(pn.Row('User:', pn.pane.Markdown(\"\", width=600)), scroll=True)\n",
    "        result = self.qa({\"question\": query, \"chat_history\": self.chat_history})\n",
    "        #Store question and answer in chat history\n",
    "        self.chat_history.extend([(query, result[\"answer\"])])\n",
    "        #Update database query with new question\n",
    "        self.db_query = result[\"generated_question\"]\n",
    "        #Update database with response with new source documents\n",
    "        self.db_response = result[\"source_documents\"]\n",
    "        self.answer = result['answer']\n",
    "        #Add user and chatbot responses to new rows\n",
    "        self.panels.extend([\n",
    "            pn.Row('User:', pn.pane.Markdown(query, width=600)),\n",
    "            pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, background='#F6F6F6'))\n",
    "        ])\n",
    "        inp.value = ''  #clears loading indicator when cleared\n",
    "        return pn.WidgetBox(*self.panels,scroll=True)\n",
    "\n",
    "    #Decorator (?)\n",
    "    @param.depends('db_query ', )\n",
    "    #Shows latest db query, or message if no queries have been made\n",
    "    def get_lquest(self):\n",
    "        if not self.db_query :\n",
    "            return pn.Column(\n",
    "                pn.Row(pn.pane.Markdown(f\"Last question to DB:\", styles={'background-color': '#F6F6F6'})),\n",
    "                pn.Row(pn.pane.Str(\"no DB accesses so far\"))\n",
    "            )\n",
    "        return pn.Column(\n",
    "            pn.Row(pn.pane.Markdown(f\"DB query:\", styles={'background-color': '#F6F6F6'})),\n",
    "            pn.pane.Str(self.db_query )\n",
    "        )\n",
    "\n",
    "    @param.depends('db_response', )\n",
    "    #Shows database lookup results as a list of retrieved docs\n",
    "    def get_sources(self):\n",
    "        if not self.db_response:\n",
    "            return\n",
    "        rlist=[pn.Row(pn.pane.Markdown(f\"Result of DB lookup:\", styles={'background-color': '#F6F6F6'}))]\n",
    "        for doc in self.db_response:\n",
    "            rlist.append(pn.Row(pn.pane.Str(doc)))\n",
    "        return pn.WidgetBox(*rlist, width=600, scroll=True)\n",
    "\n",
    "    @param.depends('convchain', 'clr_history')\n",
    "    #Shows conversation history with each exchange in a new row\n",
    "    def get_chats(self):\n",
    "        if not self.chat_history:\n",
    "            return pn.WidgetBox(pn.Row(pn.pane.Str(\"No History Yet\")), width=600, scroll=True)\n",
    "        rlist=[pn.Row(pn.pane.Markdown(f\"Current Chat History variable\", styles={'background-color': '#F6F6F6'}))]\n",
    "        for exchange in self.chat_history:\n",
    "            rlist.append(pn.Row(pn.pane.Str(exchange)))\n",
    "        return pn.WidgetBox(*rlist, width=600, scroll=True)\n",
    "\n",
    "    #Clears chat history\n",
    "    def clr_history(self,count=0):\n",
    "        self.chat_history = []\n",
    "        return\n"
   ]
  },
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   "id": "e375ae0c-2c90-4d46-bbaf-1ef3b6f2269e",
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       "  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n",
       "  const js_modules = [];\n",
       "  const js_exports = {};\n",
       "  const css_urls = [];\n",
       "  const inline_js = [    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "function(Bokeh) {} // ensure no trailing comma for IE\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (let i = 0; i < inline_js.length; i++) {\n",
       "        try {\n",
       "          inline_js[i].call(root, root.Bokeh);\n",
       "        } catch(e) {\n",
       "          if (!reloading) {\n",
       "            throw e;\n",
       "          }\n",
       "        }\n",
       "      }\n",
       "      // Cache old bokeh versions\n",
       "      if (Bokeh != undefined && !reloading) {\n",
       "        var NewBokeh = root.Bokeh;\n",
       "        if (Bokeh.versions === undefined) {\n",
       "          Bokeh.versions = new Map();\n",
       "        }\n",
       "        if (NewBokeh.version !== Bokeh.version) {\n",
       "          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
       "        }\n",
       "        root.Bokeh = Bokeh;\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    }\n",
       "    root._bokeh_is_initializing = false\n",
       "  }\n",
       "\n",
       "  function load_or_wait() {\n",
       "    // Implement a backoff loop that tries to ensure we do not load multiple\n",
       "    // versions of Bokeh and its dependencies at the same time.\n",
       "    // In recent versions we use the root._bokeh_is_initializing flag\n",
       "    // to determine whether there is an ongoing attempt to initialize\n",
       "    // bokeh, however for backward compatibility we also try to ensure\n",
       "    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
       "    // before older versions are fully initialized.\n",
       "    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
       "      // If the timeout and bokeh was not successfully loaded we reset\n",
       "      // everything and try loading again\n",
       "      root._bokeh_timeout = Date.now() + 5000;\n",
       "      root._bokeh_is_initializing = false;\n",
       "      root._bokeh_onload_callbacks = undefined;\n",
       "      root._bokeh_is_loading = 0\n",
       "      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
       "      load_or_wait();\n",
       "    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
       "      setTimeout(load_or_wait, 100);\n",
       "    } else {\n",
       "      root._bokeh_is_initializing = true\n",
       "      root._bokeh_onload_callbacks = []\n",
       "      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n",
       "      if (!reloading && !bokeh_loaded) {\n",
       "        if (root.Bokeh) {\n",
       "          root.Bokeh = undefined;\n",
       "        }\n",
       "        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      }\n",
       "      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
       "        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }\n",
       "  // Give older versions of the autoload script a head-start to ensure\n",
       "  // they initialize before we start loading newer version.\n",
       "  setTimeout(load_or_wait, 100)\n",
       "}(window));"
      ],
      "application/vnd.holoviews_load.v0+json": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  const force = true;\n  const py_version = '3.6.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n  const reloading = false;\n  const Bokeh = root.Bokeh;\n\n  // Set a timeout for this load but only if we are not already initializing\n  if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      // Don't load bokeh if it is still initializing\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      // There is nothing to load\n      run_callbacks();\n      return null;\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error(e) {\n      const src_el = e.srcElement\n      console.error(\"failed to load \" + (src_el.href || src_el.src));\n    }\n\n    const skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n      root._bokeh_is_loading = css_urls.length + 0;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    const existing_stylesheets = []\n    const links = document.getElementsByTagName('link')\n    for (let i = 0; i < links.length; i++) {\n      const link = links[i]\n      if (link.href != null) {\n        existing_stylesheets.push(link.href)\n      }\n    }\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const escaped = encodeURI(url)\n      if (existing_stylesheets.indexOf(escaped) !== -1) {\n        on_load()\n        continue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    var existing_scripts = []\n    const scripts = document.getElementsByTagName('script')\n    for (let i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n        existing_scripts.push(script.src)\n      }\n    }\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (let i = 0; i < js_modules.length; i++) {\n      const url = js_modules[i];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      const url = js_exports[name];\n      const escaped = encodeURI(url)\n      if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n        if (!window.requirejs) {\n          on_load();\n        }\n        continue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.holoviz.org/panel/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/dist/panel.min.js\"];\n  const js_modules = [];\n  const js_exports = {};\n  const css_urls = [];\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (let i = 0; i < inline_js.length; i++) {\n        try {\n          inline_js[i].call(root, root.Bokeh);\n        } catch(e) {\n          if (!reloading) {\n            throw e;\n          }\n        }\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n        var NewBokeh = root.Bokeh;\n        if (Bokeh.versions === undefined) {\n          Bokeh.versions = new Map();\n        }\n        if (NewBokeh.version !== Bokeh.version) {\n          Bokeh.versions.set(NewBokeh.version, NewBokeh)\n        }\n        root.Bokeh = Bokeh;\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      // If the timeout and bokeh was not successfully loaded we reset\n      // everything and try loading again\n      root._bokeh_timeout = Date.now() + 5000;\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      root._bokeh_is_loading = 0\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n      if (!reloading && !bokeh_loaded) {\n        if (root.Bokeh) {\n          root.Bokeh = undefined;\n        }\n        console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n        console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n        run_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));"
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     "output_type": "display_data"
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       "  var render_items = [{\"docid\":\"33a1627d-69f2-4365-99cc-fb34171ad5bf\",\"roots\":{\"1d4253b9-f4d1-4f7a-afeb-df874b2f8580\":\"d5f9f59c-8381-4879-ad23-fea18e56915c\"},\"root_ids\":[\"1d4253b9-f4d1-4f7a-afeb-df874b2f8580\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  async function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    await Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && id_el.children[0].hasAttribute('data-root-id')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t  for (const child of root_el.children) {\n",
       "            // Ensure JupyterLab does not capture keyboard shortcuts\n",
       "            // see: https://jupyterlab.readthedocs.io/en/4.1.x/extension/notebook.html#keyboard-interaction-model\n",
       "\t    child.setAttribute('data-lm-suppress-shortcuts', 'true')\n",
       "\t  }\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] Row\n",
       "        [0] Markdown(str)\n",
       "    [1] Tabs\n",
       "        [0] Column\n",
       "            [0] Row\n",
       "                [0] TextInput(placeholder='Enter text here…')\n",
       "            [1] Divider()\n",
       "            [2] ParamFunction(function, _pane=WidgetBox, defer_load=False, height=300, loading_indicator=True)\n",
       "            [3] Divider()\n",
       "        [1] Column\n",
       "            [0] ParamMethod(method, _pane=Column, defer_load=False)\n",
       "            [1] Divider()\n",
       "            [2] ParamMethod(method, _pane=Str, defer_load=False)\n",
       "        [2] Column\n",
       "            [0] ParamMethod(method, _pane=WidgetBox, defer_load=False)\n",
       "            [1] Divider()\n",
       "        [3] Column\n",
       "            [0] Row\n",
       "                [0] FileInput(accept='.pdf')\n",
       "                [1] Button(button_type='primary', name='Load DB')\n",
       "                [2] ParamFunction(function, _pane=Markdown, defer_load=False)\n",
       "            [1] Row\n",
       "                [0] Button(button_type='warning', name='Clear History')\n",
       "                [1] Markdown(str)\n",
       "            [2] Divider()\n",
       "            [3] Row\n",
       "                [0] Image(str, width=400)"
      ]
     },
     "execution_count": 59,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1d4253b9-f4d1-4f7a-afeb-df874b2f8580"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cb = cbfs()\n",
    "\n",
    "file_input = pn.widgets.FileInput(accept='.pdf')\n",
    "button_load = pn.widgets.Button(name=\"Load DB\", button_type='primary')\n",
    "button_clearhistory = pn.widgets.Button(name=\"Clear History\", button_type='warning')\n",
    "button_clearhistory.on_click(cb.clr_history)\n",
    "inp = pn.widgets.TextInput( placeholder='Enter text here…')\n",
    "\n",
    "bound_button_load = pn.bind(cb.call_load_db_new, button_load.param.clicks)\n",
    "conversation = pn.bind(cb.convchain, inp)\n",
    "\n",
    "jpg_pane = pn.pane.Image( './img/convchain.jpg')\n",
    "\n",
    "tab1 = pn.Column(\n",
    "    pn.Row(inp),\n",
    "    pn.layout.Divider(),\n",
    "    pn.panel(conversation,  loading_indicator=True, height=300),\n",
    "    pn.layout.Divider(),\n",
    ")\n",
    "tab2= pn.Column(\n",
    "    pn.panel(cb.get_lquest),\n",
    "    pn.layout.Divider(),\n",
    "    pn.panel(cb.get_sources ),\n",
    ")\n",
    "tab3= pn.Column(\n",
    "    pn.panel(cb.get_chats),\n",
    "    pn.layout.Divider(),\n",
    ")\n",
    "tab4=pn.Column(\n",
    "    pn.Row( file_input, button_load, bound_button_load),\n",
    "    pn.Row( button_clearhistory, pn.pane.Markdown(\"Clears chat history. Can use to start a new topic\" )),\n",
    "    pn.layout.Divider(),\n",
    "    pn.Row(jpg_pane.clone(width=400))\n",
    ")\n",
    "dashboard = pn.Column(\n",
    "    pn.Row(pn.pane.Markdown('# ChatWithYourData_Bot')),\n",
    "    pn.Tabs(('Conversation', tab1), ('Database', tab2), ('Chat History', tab3),('Configure', tab4))\n",
    ")\n",
    "pn.extension()\n",
    "dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13fbee93-78cf-47f3-936a-a158bdc63f8e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python (lab_gpt_env)",
   "language": "python",
   "name": "lab_gpt_env"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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