L6-Chat.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "20f7f2ce",
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   "source": [
    "# Chat\n",
    "Recall the overall workflow for retrieval augmented generation (RAG):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99686069",
   "metadata": {},
   "source": [
    "We discussed Document Loading and Splitting as well as Storage and Retrieval.\n",
    "\n",
    "We then showed how Retrieval can be used for output generation in Q+A using RetrievalQA chain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35b263a1",
   "metadata": {},
   "outputs": [
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       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
       "          window.PyViz.comms[comm_id] = comm;\n",
       "          if (msg_handler) {\n",
       "            var messages = comm.messages[Symbol.asyncIterator]();\n",
       "            function processIteratorResult(result) {\n",
       "              var message = result.value;\n",
       "              var content = {data: message.data};\n",
       "              var metadata = message.metadata || {comm_id};\n",
       "              var msg = {content, metadata}\n",
       "              msg_handler(msg);\n",
       "              return messages.next().then(processIteratorResult);\n",
       "            }\n",
       "            return messages.next().then(processIteratorResult);\n",
       "          }\n",
       "        }) \n",
       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
       "          return comm_promise.then((comm) => {\n",
       "            comm.send(data, metadata, buffers, disposeOnDone);\n",
       "          });\n",
       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
       "        };\n",
       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
       "    }\n",
       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
       "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
       "var CLASS_NAME = 'output';\n",
       "\n",
       "/**\n",
       " * Render data to the DOM node\n",
       " */\n",
       "function render(props, node) {\n",
       "  var div = document.createElement(\"div\");\n",
       "  var script = document.createElement(\"script\");\n",
       "  node.appendChild(div);\n",
       "  node.appendChild(script);\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when a new output is added\n",
       " */\n",
       "function handle_add_output(event, handle) {\n",
       "  var output_area = handle.output_area;\n",
       "  var output = handle.output;\n",
       "  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "    return\n",
       "  }\n",
       "  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "  if (id !== undefined) {\n",
       "    var nchildren = toinsert.length;\n",
       "    var html_node = toinsert[nchildren-1].children[0];\n",
       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
       "      if (nodelist.hasOwnProperty(i)) {\n",
       "        scripts.push(nodelist[i])\n",
       "      }\n",
       "    }\n",
       "\n",
       "    scripts.forEach( function (oldScript) {\n",
       "      var newScript = document.createElement(\"script\");\n",
       "      var attrs = [];\n",
       "      var nodemap = oldScript.attributes;\n",
       "      for (var j in nodemap) {\n",
       "        if (nodemap.hasOwnProperty(j)) {\n",
       "          attrs.push(nodemap[j])\n",
       "        }\n",
       "      }\n",
       "      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
       "      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
       "      oldScript.parentNode.replaceChild(newScript, oldScript);\n",
       "    });\n",
       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
       "    } else {\n",
       "      window.PyViz.plot_index[id] = null;\n",
       "    }\n",
       "  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "    var bk_div = document.createElement(\"div\");\n",
       "    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var script_attrs = bk_div.children[0].attributes;\n",
       "    for (var i = 0; i < script_attrs.length; i++) {\n",
       "      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "    }\n",
       "    // store reference to server id on output_area\n",
       "    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle when an output is cleared or removed\n",
       " */\n",
       "function handle_clear_output(event, handle) {\n",
       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
       "    comm.send({event_type: 'server_delete', 'id': server_id});\n",
       "    return;\n",
       "  } else if (comm !== null) {\n",
       "    comm.send({event_type: 'delete', 'id': id});\n",
       "  }\n",
       "  delete PyViz.plot_index[id];\n",
       "  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
       "    var doc = window.Bokeh.index[id].model.document\n",
       "    doc.clear();\n",
       "    const i = window.Bokeh.documents.indexOf(doc);\n",
       "    if (i > -1) {\n",
       "      window.Bokeh.documents.splice(i, 1);\n",
       "    }\n",
       "  }\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle kernel restart event\n",
       " */\n",
       "function handle_kernel_cleanup(event, handle) {\n",
       "  delete PyViz.comms[\"hv-extension-comm\"];\n",
       "  window.PyViz.plot_index = {}\n",
       "}\n",
       "\n",
       "/**\n",
       " * Handle update_display_data messages\n",
       " */\n",
       "function handle_update_output(event, handle) {\n",
       "  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
       "  handle_add_output(event, handle)\n",
       "}\n",
       "\n",
       "function register_renderer(events, OutputArea) {\n",
       "  function append_mime(data, metadata, element) {\n",
       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
       "    CLASS_NAME,\n",
       "    EXEC_MIME_TYPE\n",
       "    );\n",
       "    this.keyboard_manager.register_events(toinsert);\n",
       "    // Render to node\n",
       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
       "    element.append(toinsert);\n",
       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
       "}\n",
       "\n",
       "if (window.Jupyter !== undefined) {\n",
       "  try {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  } catch(err) {\n",
       "  }\n",
       "}\n"
      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import openai\n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "\n",
    "import panel as pn  # GUI\n",
    "pn.extension()\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "\n",
    "openai.api_key  = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba070cad",
   "metadata": {},
   "source": [
    "The code below was added to assign the openai LLM version filmed until it is deprecated, currently in Sept 2023. LLM responses can often vary, but the responses may be significantly different when using a different model version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62592649",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gpt-3.5-turbo-0301\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "current_date = datetime.datetime.now().date()\n",
    "if current_date < datetime.date(2023, 9, 2):\n",
    "    llm_name = \"gpt-3.5-turbo-0301\"\n",
    "else:\n",
    "    llm_name = \"gpt-3.5-turbo\"\n",
    "print(llm_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a09e7aff",
   "metadata": {},
   "source": [
    "If you wish to experiment on LangChain plus platform:\n",
    "\n",
    "- Go to [langchain plus platform](https://smith.langchain.com/) and sign up\n",
    "- Create an api key from your account's settings\n",
    "- Use this api key in the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83eed5d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "# os.environ[\"LANGCHAIN_ENDPOINT\"] = \"https://api.langchain.plus\"\n",
    "# os.environ[\"LANGCHAIN_API_KEY\"] = \"...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8444dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "persist_directory = 'docs/chroma/'\n",
    "embedding = OpenAIEmbeddings()\n",
    "vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a44ef2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"What are major topics for this class?\"\n",
    "docs = vectordb.similarity_search(question,k=3)\n",
    "len(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "868e8cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello there! How can I assist you today?'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "llm = ChatOpenAI(model_name=llm_name, temperature=0)\n",
    "llm.predict(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ecc1b0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, probability is assumed to be a prerequisite for this class. The instructor assumes familiarity with basic probability and statistics, and will go over some of the prerequisites in the discussion sections as a refresher course. Thanks for asking!'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build prompt\n",
    "from langchain.prompts import PromptTemplate\n",
    "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. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n",
    "{context}\n",
    "Question: {question}\n",
    "Helpful Answer:\"\"\"\n",
    "QA_CHAIN_PROMPT = PromptTemplate(input_variables=[\"context\", \"question\"],template=template,)\n",
    "\n",
    "# Run chain\n",
    "from langchain.chains import RetrievalQA\n",
    "question = \"Is probability a class topic?\"\n",
    "qa_chain = RetrievalQA.from_chain_type(llm,\n",
    "                                       retriever=vectordb.as_retriever(),\n",
    "                                       return_source_documents=True,\n",
    "                                       chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT})\n",
    "\n",
    "\n",
    "result = qa_chain({\"query\": question})\n",
    "result[\"result\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf08c148",
   "metadata": {},
   "source": [
    "## Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c7b21fcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory\n",
    "memory = ConversationBufferMemory(\n",
    "    memory_key=\"chat_history\",\n",
    "    return_messages=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05f7361c",
   "metadata": {},
   "source": [
    "## ConversationalRetrievalChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "575f7b54",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain\n",
    "retriever=vectordb.as_retriever()\n",
    "qa = ConversationalRetrievalChain.from_llm(\n",
    "    llm,\n",
    "    retriever=retriever,\n",
    "    memory=memory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "12dcaaeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"Is probability a class topic?\"\n",
    "result = qa({\"question\": question})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1fb8bbc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, probability is a topic that will be assumed to be familiar to students in this class. The instructor mentions that basic probability and statistics are prerequisites for the class and assumes that most undergraduate statistics classes, like Stat 116 taught at Stanford, will be more than enough.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['answer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb494c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"why are those prerequesites needed?\"\n",
    "result = qa({\"question\": question})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "084c7d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The reason for requiring basic probability and statistics as prerequisites for the class is that the class assumes familiarity with these topics. The instructor assumes that most undergraduate statistics classes, like Stat 116 taught at Stanford, will be more than enough. The class will cover machine learning, which is a field that heavily relies on probability and statistics.'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['answer']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42e0596",
   "metadata": {},
   "source": [
    "## Create a chatbot that works on your documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "469a0c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
    "from langchain.vectorstores import DocArrayInMemorySearch\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.chains import RetrievalQA,  ConversationalRetrievalChain\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.document_loaders import TextLoader\n",
    "from langchain.document_loaders import PyPDFLoader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea7bfcfe",
   "metadata": {},
   "source": [
    "The chatbot code has been updated a bit since filming. The GUI appearance also varies depending on the platform it is running on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8045c3c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This will initialize your database and retriever chain \n",
    "def load_db(file, chain_type, k):\n",
    "    # load documents\n",
    "    loader = PyPDFLoader(file)\n",
    "    documents = loader.load()\n",
    "    # split documents\n",
    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\n",
    "    docs = text_splitter.split_documents(documents)\n",
    "    # define embedding\n",
    "    embeddings = OpenAIEmbeddings()\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 a 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": 20,
   "id": "9b8061de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import panel as pn\n",
    "import param\n",
    "\n",
    "class cbfs(param.Parameterized):\n",
    "    chat_history = param.List([])\n",
    "    answer = param.String(\"\")\n",
    "    db_query  = param.String(\"\")\n",
    "    db_response = param.List([])\n",
    "    \n",
    "    def __init__(self,  **params):\n",
    "        super(cbfs, self).__init__( **params)\n",
    "        self.panels = []\n",
    "        self.loaded_file = \"docs/cs229_lectures/MachineLearning-Lecture01.pdf\"\n",
    "        self.qa = load_db(self.loaded_file,\"stuff\", 4)\n",
    "    \n",
    "    def call_load_db(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",
    "            self.loaded_file = file_input.filename\n",
    "            button_load.button_style=\"outline\"\n",
    "            self.qa = load_db(\"temp.pdf\", \"stuff\", 4)\n",
    "            button_load.button_style=\"solid\"\n",
    "        self.clr_history()\n",
    "        return pn.pane.Markdown(f\"Loaded File: {self.loaded_file}\")\n",
    "\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",
    "        self.chat_history.extend([(query, result[\"answer\"])])\n",
    "        self.db_query = result[\"generated_question\"]\n",
    "        self.db_response = result[\"source_documents\"]\n",
    "        self.answer = result['answer'] \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, style={'background-color': '#F6F6F6'}))\n",
    "        ])\n",
    "        inp.value = ''  #clears loading indicator when cleared\n",
    "        return pn.WidgetBox(*self.panels,scroll=True)\n",
    "\n",
    "    @param.depends('db_query ', )\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",
    "    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",
    "    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",
    "    def clr_history(self,count=0):\n",
    "        self.chat_history = []\n",
    "        return \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd5125e6",
   "metadata": {},
   "source": [
    "## Create a chatbot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fada1b76",
   "metadata": {},
   "outputs": [],
   "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, 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",
    "dashboard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67e6b09",
   "metadata": {},
   "source": [
    "## Acknowledgments\n",
    "Panel based chatbot inspired by Sophia Yang, [github](https://github.com/sophiamyang/tutorials-LangChain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28d01e89",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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