diff --git a/models/llama-index-hack/llama2_wikis.ipynb b/models/llama-index-hack/llama2_wikis.ipynb
index 781dffa3..e38e3056 100644
--- a/models/llama-index-hack/llama2_wikis.ipynb
+++ b/models/llama-index-hack/llama2_wikis.ipynb
@@ -16,10 +16,7 @@
     "from llama_index import VectorStoreIndex\n",
     "from llama_index import LLMPredictor, PromptHelper, ServiceContext\n",
     "from llama_index import StorageContext, load_index_from_storage\n",
-    "from llama_index.readers import SimpleDirectoryReader\n",
-    "\n",
-    "import pandas as pd\n",
-    "import re"
+    "from llama_index.readers import SimpleDirectoryReader"
    ]
   },
   {
@@ -399,27 +396,6 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'research-engineering-group.wiki.git'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "url = \"https://github.com/alan-turing-institute/research-engineering-group.wiki.git\"\n",
-    "url.split(\"/\")[-1]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -453,15 +429,6 @@
     "    return documents"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents = load_documents_from_url(url)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
diff --git a/models/llama-index-hack/llama2_wikis_chat.ipynb b/models/llama-index-hack/llama2_wikis_chat.ipynb
index 00982c59..478b68b5 100644
--- a/models/llama-index-hack/llama2_wikis_chat.ipynb
+++ b/models/llama-index-hack/llama2_wikis_chat.ipynb
@@ -6,15 +6,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os \n",
+    "\n",
+    "from git import Repo\n",
+    "\n",
     "from llama_index.llms import LlamaCPP\n",
     "from llama_index.llms.llama_utils import messages_to_prompt, completion_to_prompt\n",
     "from llama_index import Document\n",
     "from llama_index import VectorStoreIndex\n",
     "from llama_index import LLMPredictor, PromptHelper, ServiceContext\n",
     "from llama_index import StorageContext, load_index_from_storage\n",
-    "from llama_index.readers import SimpleDirectoryReader\n",
-    "\n",
-    "import pandas as pd"
+    "from llama_index.readers import SimpleDirectoryReader"
    ]
   },
   {
@@ -400,19 +402,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def load_documents():\n",
-    "    documents = SimpleDirectoryReader(\n",
-    "        input_dir=\"../../data/wikis/\",\n",
+    "def load_documents_from_url(url):\n",
+    "    reader = SimpleDirectoryReader(\n",
+    "        input_dir=url.split(\"/\")[-1],\n",
     "        required_exts=[\".md\"],\n",
     "        recursive=True, \n",
     "        filename_as_id=True,\n",
-    "        exclude=[\"*_course/*\"] # my laptop struggles to create a VectorStoreIndex when I have too many documents\n",
-    "    ).load_data()\n",
+    "    )\n",
     "    \n",
-    "    turingacuk=pd.read_csv(\"../../data/public/turingacuk-no-boilerplate.csv\")\n",
-    "    turingacuk.dropna(subset=\"body\", inplace=True)\n",
-    "    turingacuk_text=[str(i) for i in turingacuk[\"body\"].values]\n",
-    "    documents.extend([Document(text=i) for i in turingacuk_text])\n",
+    "    # get base url and names of files\n",
+    "    url_stripped = url.removesuffix(\".wiki.git\")\n",
+    "    fnames = [str(file) for file in reader.input_files]\n",
+    "    # create fname: file_url_dict\n",
+    "    file_urls = [os.path.join(url_stripped, \"wiki\", fname.split(\"/\")[-1].removesuffix(\".md\")) for fname in fnames]\n",
+    "    file_urls_dict = {fname: file_url for fname, file_url in zip(fnames, file_urls)}\n",
+    "    # define function to get url and add to reader\n",
+    "    get_urls = lambda fname: {\"url\": file_urls_dict.get(fname)}\n",
+    "    reader.file_metadata = get_urls\n",
+    "\n",
+    "    documents = reader.load_data()\n",
+    "\n",
+    "    # turingacuk=pd.read_csv(\"../../data/public/turingacuk-no-boilerplate.csv\")\n",
+    "    # turingacuk.dropna(subset=\"body\", inplace=True)\n",
+    "    # turingacuk_text=[str(i) for i in turingacuk[\"body\"].values]\n",
+    "    # documents.extend([Document(text=i) for i in turingacuk_text])\n",
     "    return documents"
    ]
   },
@@ -465,6 +478,7 @@
     "        mode,\n",
     "        service_context,\n",
     "        persist_dir=None,\n",
+    "        wiki_urls=None,\n",
     "):\n",
     "    if mode == \"reload\":\n",
     "        if persist_dir is None:\n",
@@ -474,7 +488,15 @@
     "        index = load_index_from_storage(storage_context, service_context=service_context)\n",
     "    \n",
     "    elif mode == \"create\":\n",
-    "        documents = load_documents()\n",
+    "        documents = []\n",
+    "        for url in wiki_urls:\n",
+    "            if os.path.exists(url.split(\"/\")[-1]):\n",
+    "                repo = Repo(url.split(\"/\")[-1])\n",
+    "                repo.remotes.origin.pull()\n",
+    "            else:\n",
+    "                _ = Repo.clone_from(url, url.split(\"/\")[-1])\n",
+    "            wiki_docs = load_documents_from_url(url)\n",
+    "            documents.extend(wiki_docs)\n",
     "        index = VectorStoreIndex.from_documents(documents, service_context=service_context, show_progress=True)\n",
     "    \n",
     "    else:\n",
@@ -488,12 +510,44 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83e0f380d02c4de093969f4f2a59435a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Parsing documents into nodes:   0%|          | 0/392 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ea921b91bad46938e10a024dfd352c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating embeddings:   0%|          | 0/400 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "wiki_urls = [\"https://github.com/alan-turing-institute/research-engineering-group.wiki.git\", \"https://github.com/alan-turing-institute/Hut23.wiki.git\"]\n",
+    "\n",
     "index = load_index(\n",
-    "    \"reload\",\n",
+    "    \"create\",\n",
     "    service_context=service_context, \n",
     "    persist_dir=\"./storage_context_wikis/\", \n",
+    "    wiki_urls=wiki_urls\n",
     "    )"
    ]
   },
@@ -508,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -536,11 +590,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.50 ms /   256 runs   (    0.71 ms per token,  1410.45 tokens per second)\n",
-      "llama_print_timings: prompt eval time =  1190.10 ms /    23 tokens (   51.74 ms per token,    19.33 tokens per second)\n",
-      "llama_print_timings:        eval time = 13659.78 ms /   255 runs   (   53.57 ms per token,    18.67 tokens per second)\n",
-      "llama_print_timings:       total time = 15381.57 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   180.13 ms /   256 runs   (    0.70 ms per token,  1421.18 tokens per second)\n",
+      "llama_print_timings: prompt eval time =  6149.09 ms /    23 tokens (  267.35 ms per token,     3.74 tokens per second)\n",
+      "llama_print_timings:        eval time = 13436.91 ms /   255 runs   (   52.69 ms per token,    18.98 tokens per second)\n",
+      "llama_print_timings:       total time = 20110.51 ms\n"
      ]
     }
    ],
@@ -551,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -569,25 +623,25 @@
      "text": [
       "Llama.generate: prefix-match hit\n",
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    38.37 ms /    53 runs   (    0.72 ms per token,  1381.36 tokens per second)\n",
-      "llama_print_timings: prompt eval time =  6293.20 ms /   189 tokens (   33.30 ms per token,    30.03 tokens per second)\n",
-      "llama_print_timings:        eval time =  2816.27 ms /    52 runs   (   54.16 ms per token,    18.46 tokens per second)\n",
-      "llama_print_timings:       total time =  9217.38 ms\n",
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    37.37 ms /    53 runs   (    0.71 ms per token,  1418.17 tokens per second)\n",
+      "llama_print_timings: prompt eval time =  6205.72 ms /   189 tokens (   32.83 ms per token,    30.46 tokens per second)\n",
+      "llama_print_timings:        eval time =  2762.72 ms /    52 runs   (   53.13 ms per token,    18.82 tokens per second)\n",
+      "llama_print_timings:       total time =  9074.67 ms\n",
       "Llama.generate: prefix-match hit\n",
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.30 ms /   256 runs   (    0.71 ms per token,  1411.99 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 19661.95 ms /   639 tokens (   30.77 ms per token,    32.50 tokens per second)\n",
-      "llama_print_timings:        eval time = 15877.12 ms /   255 runs   (   62.26 ms per token,    16.06 tokens per second)\n",
-      "llama_print_timings:       total time = 36080.36 ms\n",
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   170.50 ms /   243 runs   (    0.70 ms per token,  1425.19 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 19675.33 ms /   645 tokens (   30.50 ms per token,    32.78 tokens per second)\n",
+      "llama_print_timings:        eval time = 14798.94 ms /   242 runs   (   61.15 ms per token,    16.35 tokens per second)\n",
+      "llama_print_timings:       total time = 34977.84 ms\n",
       "Llama.generate: prefix-match hit\n",
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.32 ms /   256 runs   (    0.71 ms per token,  1411.84 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 27809.01 ms /   956 tokens (   29.09 ms per token,    34.38 tokens per second)\n",
-      "llama_print_timings:        eval time = 16749.84 ms /   255 runs   (   65.69 ms per token,    15.22 tokens per second)\n",
-      "llama_print_timings:       total time = 45098.64 ms\n",
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   181.73 ms /   256 runs   (    0.71 ms per token,  1408.71 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 27043.57 ms /   951 tokens (   28.44 ms per token,    35.17 tokens per second)\n",
+      "llama_print_timings:        eval time = 16633.25 ms /   255 runs   (   65.23 ms per token,    15.33 tokens per second)\n",
+      "llama_print_timings:       total time = 44216.49 ms\n",
       "Llama.generate: prefix-match hit\n"
      ]
     },
@@ -595,12 +649,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Thank you for providing additional context! Based on the updated information, the expectations and outcomes for an employee who has been performing as expected during their annual appraisal are as follows:\n",
-      "1. The employee will be assessed against objectives set at the start of the year and personal qualities important to the institute.\n",
-      "2. The employee's line manager will make an overall judgment on whether the employee is progressing broadly as expected given their experience level, considering the \"year in review\" appraisal discussion they have with the employee and, where useful, discussions with other team members who have insight into the employee's work over the year.\n",
-      "3. For most people, the outcome will be that they have been progressing broadly as expected for their experience level, and their line manager will recommend a \"small\" progression pay rise.\n",
-      "4. During last year's appraisal process, everyone's pay was reviewed to ensure that Principle 1 held after applying the progression pay rises, therefore, the institute is as confident as they can be that everyone started the appraisal year performing and developing as expected for their position in their pay band.\n",
-      "5. If, during the\n"
+      "  Thank you for providing additional context! Based on the updated information, here is a revised answer that better addresses the original query:\n",
+      "The expectations and outcomes for an employee who has been performing as expected during their annual appraisal are focused on recognizing their achievements and providing a fair and transparent evaluation of their performance. If the employee has consistently met their objectives and demonstrated strong personal qualities throughout the year, they may be eligible for a \"small\" progression pay rise. Additionally, the appraisal process will help identify areas for improvement and development for the employee in the coming year.\n",
+      "In terms of recognition awards, if a team member has made a substantive positive change in their ability to perform their role during the course of the year, they may be eligible for a \"medium\" or \"large\" pay rise. However, if there is no significant increase in performance or development, no progression pay rise will be awarded.\n",
+      "Promoted team members who have not completed probation by 31 December of the previous year will not be eligible for further salary increases but are eligible for a recognition award. Those who completed probation after 31 December will usually not undergo the\n"
      ]
     },
     {
@@ -608,11 +660,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.25 ms /   256 runs   (    0.71 ms per token,  1412.37 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 23064.24 ms /   765 tokens (   30.15 ms per token,    33.17 tokens per second)\n",
-      "llama_print_timings:        eval time = 16241.59 ms /   255 runs   (   63.69 ms per token,    15.70 tokens per second)\n",
-      "llama_print_timings:       total time = 39847.11 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   179.81 ms /   256 runs   (    0.70 ms per token,  1423.75 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 23086.30 ms /   790 tokens (   29.22 ms per token,    34.22 tokens per second)\n",
+      "llama_print_timings:        eval time = 16017.17 ms /   255 runs   (   62.81 ms per token,    15.92 tokens per second)\n",
+      "llama_print_timings:       total time = 39633.45 ms\n"
      ]
     }
    ],
@@ -623,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -632,32 +684,18 @@
      "text": [
       "Llama.generate: prefix-match hit\n",
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    13.47 ms /    19 runs   (    0.71 ms per token,  1410.54 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 10799.07 ms /   398 tokens (   27.13 ms per token,    36.86 tokens per second)\n",
-      "llama_print_timings:        eval time =  1024.28 ms /    18 runs   (   56.90 ms per token,    17.57 tokens per second)\n",
-      "llama_print_timings:       total time = 11863.66 ms\n",
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   131.41 ms /   185 runs   (    0.71 ms per token,  1407.83 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 10811.60 ms /   398 tokens (   27.16 ms per token,    36.81 tokens per second)\n",
+      "llama_print_timings:        eval time = 10669.08 ms /   184 runs   (   57.98 ms per token,    17.25 tokens per second)\n",
+      "llama_print_timings:       total time = 21864.42 ms\n",
       "Llama.generate: prefix-match hit\n",
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    67.58 ms /    96 runs   (    0.70 ms per token,  1420.52 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 18552.20 ms /   606 tokens (   30.61 ms per token,    32.66 tokens per second)\n",
-      "llama_print_timings:        eval time =  5695.43 ms /    95 runs   (   59.95 ms per token,    16.68 tokens per second)\n",
-      "llama_print_timings:       total time = 24444.00 ms\n",
-      "Llama.generate: prefix-match hit\n",
-      "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.14 ms /   256 runs   (    0.71 ms per token,  1413.26 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 22109.23 ms /   748 tokens (   29.56 ms per token,    33.83 tokens per second)\n",
-      "llama_print_timings:        eval time = 16040.45 ms /   255 runs   (   62.90 ms per token,    15.90 tokens per second)\n",
-      "llama_print_timings:       total time = 38690.03 ms\n",
-      "Llama.generate: prefix-match hit\n",
-      "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   165.72 ms /   233 runs   (    0.71 ms per token,  1405.94 tokens per second)\n",
-      "llama_print_timings: prompt eval time =  8109.42 ms /   296 tokens (   27.40 ms per token,    36.50 tokens per second)\n",
-      "llama_print_timings:        eval time = 13485.19 ms /   232 runs   (   58.13 ms per token,    17.20 tokens per second)\n",
-      "llama_print_timings:       total time = 22094.40 ms\n",
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   179.86 ms /   256 runs   (    0.70 ms per token,  1423.32 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 22896.84 ms /   779 tokens (   29.39 ms per token,    34.02 tokens per second)\n",
+      "llama_print_timings:        eval time = 15872.10 ms /   255 runs   (   62.24 ms per token,    16.07 tokens per second)\n",
+      "llama_print_timings:       total time = 39298.88 ms\n",
       "Llama.generate: prefix-match hit\n"
      ]
     },
@@ -665,10 +703,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Thank you for providing additional context! Based on what you have shared, here is a refined version of the original answer that takes into account the new information:\n",
-      "\"If an employee has not been performing as expected during the appraisal period, they may not be eligible for a progression pay rise. However, this situation should not come as a surprise at appraisal time, as ongoing support and feedback should have been provided throughout the year to help the employee improve their performance. The employee may still be eligible for recognition awards or bonuses based on their individual performance and contributions to the team or organization, even if they are not receiving a pay rise.\n",
-      "In addition, if an employee has consistently added value to the team and wider organization in the past year, they may still be considered for a pay rise or other forms of recognition. It's important to evaluate each employee on their individual performance and contributions, rather than solely focusing on their lack of progress during the appraisal period.\"\n",
-      "This refined answer takes into account the new context provided, specifically the fact that employees who have not completed probation by 31 December of the previous year will not be eligible for a progression pay rise or\n"
+      "  Thank you for providing additional context. Based on the updated mechanism, if an employee has been consistently performing and developing as expected during their annual appraisal, they may be eligible for a \"small\" progression pay rise. The actual percentage increase will be determined by HR each year, but it is expected that if the employee has been progressing steadily through the pay band, they would reach the top of their band in around 6-8 years.\n",
+      "It's important to note that exceptional performance or significant improvements in areas for development may result in further adjustments to ensure that Principle 1 holds. Additionally, promotions within 6 months preceding the appraisal period will already have received their progression pay increase for the year, and no further progression increase will be awarded at appraisal time.\n",
+      "In rare cases where an employee is not performing or developing as expected, no progression increase will be awarded. However, ongoing support throughout the year should help address any issues. Team members who are at the top of their pay band are eligible for a bonus if consistently performing at a high level.\n",
+      "Finally, team members who have not completed probation by 31 December the previous\n"
      ]
     },
     {
@@ -676,11 +714,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   180.91 ms /   256 runs   (    0.71 ms per token,  1415.03 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 22989.20 ms /   777 tokens (   29.59 ms per token,    33.80 tokens per second)\n",
-      "llama_print_timings:        eval time = 16038.50 ms /   255 runs   (   62.90 ms per token,    15.90 tokens per second)\n",
-      "llama_print_timings:       total time = 39566.90 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   179.68 ms /   256 runs   (    0.70 ms per token,  1424.76 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 23892.72 ms /   815 tokens (   29.32 ms per token,    34.11 tokens per second)\n",
+      "llama_print_timings:        eval time = 15957.03 ms /   255 runs   (   62.58 ms per token,    15.98 tokens per second)\n",
+      "llama_print_timings:       total time = 40377.89 ms\n"
      ]
     }
    ],
@@ -691,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -700,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -715,11 +753,10 @@
      "output_type": "stream",
      "text": [
       "  If you have been performing as expected during the year, your annual appraisal may focus more on future goals and development rather than solely on past performance. Here are some possible outcomes:\n",
-      "1. Positive feedback and recognition: Your supervisor or manager may provide positive feedback on your work, acknowledge your achievements, and express gratitude for your contributions to the organization.\n",
+      "1. Positive feedback and recognition: Your supervisor or manager may provide positive feedback on your work, acknowledge your achievements, and express appreciation for your contributions to the organization.\n",
       "2. Setting new goals and objectives: Based on your current performance, you and your supervisor may discuss and set new goals and objectives for the upcoming year. These goals should be challenging but achievable, and they will help you grow professionally and advance in your career.\n",
-      "3. Performance evaluation: Your supervisor will evaluate your overall performance during the year, taking into account your progress towards your goals, your work quality, and your contributions to the organization. This evaluation may result in a rating or score that reflects your performance.\n",
-      "4. Development opportunities: Depending on your areas of strength and growth areas, your supervisor may identify development opportunities for you to improve your skills and abilities. These opportunities could include training programs, mentorship, coaching, or stretch assignments.\n",
-      "5. Rewards and recognition:\n"
+      "3. Performance evaluation: Your supervisor will evaluate your overall performance during the year, taking into account your progress towards achieving your goals and any other relevant factors. This evaluation may result in a rating or grade that reflects your performance, which can be used to determine your compensation, promotions, or other career advancement opportunities.\n",
+      "4. Development planning: If you have been performing as expected, your supervisor may identify areas where you could improve or develop new skills. They may provide recommendations for training, mentoring, or coaching to help you\n"
      ]
     },
     {
@@ -727,11 +764,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   185.30 ms /   256 runs   (    0.72 ms per token,  1381.53 tokens per second)\n",
-      "llama_print_timings: prompt eval time =  1012.62 ms /    22 tokens (   46.03 ms per token,    21.73 tokens per second)\n",
-      "llama_print_timings:        eval time = 13495.35 ms /   255 runs   (   52.92 ms per token,    18.90 tokens per second)\n",
-      "llama_print_timings:       total time = 15067.20 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   185.15 ms /   256 runs   (    0.72 ms per token,  1382.66 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   974.69 ms /    22 tokens (   44.30 ms per token,    22.57 tokens per second)\n",
+      "llama_print_timings:        eval time = 13242.51 ms /   255 runs   (   51.93 ms per token,    19.26 tokens per second)\n",
+      "llama_print_timings:       total time = 14761.33 ms\n"
      ]
     }
    ],
@@ -742,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -757,11 +794,10 @@
      "output_type": "stream",
      "text": [
       "  If you have been performing as expected during the year, your annual appraisal may focus more on future goals and development rather than solely on past performance. Here are some possible outcomes:\n",
-      "1. Positive feedback and recognition: Your supervisor or manager may provide positive feedback on your work, acknowledge your achievements, and express gratitude for your contributions to the organization.\n",
+      "1. Positive feedback and recognition: Your supervisor or manager may provide positive feedback on your work, acknowledge your achievements, and express appreciation for your contributions to the organization.\n",
       "2. Setting new goals and objectives: Based on your current performance, you and your supervisor may discuss and set new goals and objectives for the upcoming year. These goals should be challenging but achievable, and they will help you grow professionally and advance in your career.\n",
-      "3. Performance evaluation: Your supervisor will evaluate your overall performance during the year, taking into account your progress towards your goals, your work quality, and your contributions to the organization. This evaluation may result in a rating or score that reflects your performance.\n",
-      "4. Development opportunities: Depending on your areas of strength and growth areas, your supervisor may identify development opportunities for you to improve your skills and abilities. These opportunities could include training programs, mentorship, coaching, or stretch assignments.\n",
-      "5. Rewards and recognition:\n"
+      "3. Performance evaluation: Your supervisor will evaluate your overall performance during the year, taking into account your progress towards achieving your goals and any other relevant factors. This evaluation may result in a rating or grade that reflects your performance, which can be used to determine your compensation, promotions, or other career advancement opportunities.\n",
+      "4. Development planning: If you have been performing as expected, your supervisor may identify areas where you could improve or develop new skills. They may provide recommendations for training, mentoring, or coaching to help you\n"
      ]
     },
     {
@@ -769,11 +805,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.45 ms /   256 runs   (    0.71 ms per token,  1410.83 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 58974.51 ms /  1775 tokens (   33.23 ms per token,    30.10 tokens per second)\n",
-      "llama_print_timings:        eval time = 18096.58 ms /   255 runs   (   70.97 ms per token,    14.09 tokens per second)\n",
-      "llama_print_timings:       total time = 77625.73 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   179.63 ms /   256 runs   (    0.70 ms per token,  1425.13 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 60208.06 ms /  1816 tokens (   33.15 ms per token,    30.16 tokens per second)\n",
+      "llama_print_timings:        eval time = 17623.63 ms /   255 runs   (   69.11 ms per token,    14.47 tokens per second)\n",
+      "llama_print_timings:       total time = 78370.81 ms\n"
      ]
     }
    ],
@@ -784,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -798,11 +834,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  If you don't have any areas of improvement identified during your annual appraisal, it's possible that your performance has been consistently high and meets the expectations of your supervisor or manager. In this case, here are some possible outcomes:\n",
-      "1. Positive feedback and recognition: Your supervisor may provide positive feedback on your work, acknowledge your achievements, and express gratitude for your contributions to the organization.\n",
-      "2. Continued growth and development: While there may not be any specific areas of improvement identified, your supervisor may still discuss potential opportunities for growth and development with you. This could include new projects or responsibilities that will challenge you and help you continue to grow professionally.\n",
-      "3. Increased trust and autonomy: Consistently meeting expectations can lead to increased trust and autonomy in your work. Your supervisor may give you more freedom to make decisions and take ownership of your tasks, recognizing your ability to handle responsibility.\n",
-      "4. Career advancement opportunities: If you have consistently performed well over time, you may be considered for career advancement opportunities within the organization. This could include promotions, new roles, or special projects that will help\n"
+      "  assistant:   If you have not been meeting expectations during the year, your annual appraisal may focus more on areas of improvement and development rather than solely on past performance. Here are some possible outcomes:\n",
+      "1. Identification of areas for improvement: Your supervisor or manager will identify specific areas where you need to improve, such as poor time management, lack of productivity, or inadequate communication skills. They may provide feedback and recommendations for improvement.\n",
+      "2. Setting new goals and objectives: Based on your current performance, you and your supervisor may discuss and set new goals and objectives for the upcoming year. These goals should be challenging but achievable, and they will help you grow professionally and advance in your career.\n",
+      "3. Performance evaluation: Your supervisor will evaluate your overall performance during the year, taking into account your progress towards achieving your goals and any other relevant factors. This evaluation may result in a rating or grade that reflects your performance, which can be used to determine your compensation, promotions, or other career advancement opportunities.\n",
+      "4. Development planning: If you have not been meeting expectations, your supervisor may identify areas where you could improve or\n"
      ]
     },
     {
@@ -810,11 +846,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =   181.11 ms /   256 runs   (    0.71 ms per token,  1413.50 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 61657.67 ms /  1788 tokens (   34.48 ms per token,    29.00 tokens per second)\n",
-      "llama_print_timings:        eval time = 18004.55 ms /   255 runs   (   70.61 ms per token,    14.16 tokens per second)\n",
-      "llama_print_timings:       total time = 80213.23 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   179.47 ms /   256 runs   (    0.70 ms per token,  1426.40 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 19598.07 ms /   681 tokens (   28.78 ms per token,    34.75 tokens per second)\n",
+      "llama_print_timings:        eval time = 15461.65 ms /   255 runs   (   60.63 ms per token,    16.49 tokens per second)\n",
+      "llama_print_timings:       total time = 35599.31 ms\n"
      ]
     }
    ],
@@ -825,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -839,7 +875,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Based on the information provided in the context, the standard REG finance code is \"R-RSE-001\".\n"
+      "  assistant:   The standard REG finance code for the Alan Turing Institute's Research Engineering Group (REG) is `R-RSE-001`. This code is used to specify the source of funding for a cost in the organization's financial system.\n",
+      "According to the documentation provided, `R-RSE-001` is the standard REG code, to which everyone is initially charged before being recharged to a project or other activity. This code is used for all REG costs and is the default code for any cost that is not otherwise classified.\n",
+      "It's important to note that there are additional finance codes available in the REG system, including `R-RSE-002` through `R-RSE-004`, which are used for specific purposes such as core activity, dedicated management and operations, and RCP (Research Computing Platform). However, `R-RSE-001` is the most commonly used code in the organization.\n"
      ]
     },
     {
@@ -847,11 +885,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    20.47 ms /    29 runs   (    0.71 ms per token,  1416.78 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 46961.72 ms /  1473 tokens (   31.88 ms per token,    31.37 tokens per second)\n",
-      "llama_print_timings:        eval time =  1875.36 ms /    28 runs   (   66.98 ms per token,    14.93 tokens per second)\n",
-      "llama_print_timings:       total time = 48899.92 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =   152.50 ms /   217 runs   (    0.70 ms per token,  1422.97 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 38685.69 ms /  1267 tokens (   30.53 ms per token,    32.75 tokens per second)\n",
+      "llama_print_timings:        eval time = 14167.78 ms /   216 runs   (   65.59 ms per token,    15.25 tokens per second)\n",
+      "llama_print_timings:       total time = 53307.20 ms\n"
      ]
     }
    ],
@@ -862,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -876,7 +914,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " assistant:   According to the information provided in the context, the Turing cost of living pay increase in April 2020 was 5.0%.\n"
+      " assistant:   According to the information provided in the document, the Turing cost of living pay increase in April 2020 was 3.0%. This means that all staff received a 3.0% unconditional, universal cost of living increase on April 1st, 2020.\n"
      ]
     },
     {
@@ -884,11 +922,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    24.03 ms /    34 runs   (    0.71 ms per token,  1414.72 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 55901.02 ms /  1625 tokens (   34.40 ms per token,    29.07 tokens per second)\n",
-      "llama_print_timings:        eval time =  2283.32 ms /    33 runs   (   69.19 ms per token,    14.45 tokens per second)\n",
-      "llama_print_timings:       total time = 58260.27 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    47.02 ms /    67 runs   (    0.70 ms per token,  1424.93 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 60419.80 ms /  1752 tokens (   34.49 ms per token,    29.00 tokens per second)\n",
+      "llama_print_timings:        eval time =  4529.97 ms /    66 runs   (   68.64 ms per token,    14.57 tokens per second)\n",
+      "llama_print_timings:       total time = 65088.44 ms\n"
      ]
     }
    ],
@@ -899,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -913,8 +951,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " assistant:  In the context provided, the CPIH (Consumer Price Index including owner occupiers' housing costs) for January 2020 was 1.8%, and the previous December's CPIH was 0.9%.\n",
-      "Comparing these figures to the Turing cost of living pay increase of 5.0% in April 2020, we can see that the pay increase exceeded the inflation rate for the month. This means that the real value of the employee's income increased by more than the actual inflation rate during that period.\n"
+      " assistant:  According to the document, in April 2020, the CPIH (Consumer Price Index including owner occupiers' housing costs) was 2.7%. This means that the Turing cost of living pay increase (3.0%) exceeded the CPIH growth rate (2.7%) for that month.\n",
+      "In other words, the cost of living increased by more than the CPIH growth rate in April 2020, which is reflected in the 3.0% pay increase given to all staff. This is the first year that the annual CoL increase has not matched or exceeded CPIH growth rate.\n"
      ]
     },
     {
@@ -922,11 +960,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    88.32 ms /   125 runs   (    0.71 ms per token,  1415.29 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 59252.05 ms /  1717 tokens (   34.51 ms per token,    28.98 tokens per second)\n",
-      "llama_print_timings:        eval time =  8489.79 ms /   124 runs   (   68.47 ms per token,    14.61 tokens per second)\n",
-      "llama_print_timings:       total time = 68004.60 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    98.24 ms /   140 runs   (    0.70 ms per token,  1425.14 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 68233.54 ms /  1912 tokens (   35.69 ms per token,    28.02 tokens per second)\n",
+      "llama_print_timings:        eval time =  9700.95 ms /   139 runs   (   69.79 ms per token,    14.33 tokens per second)\n",
+      "llama_print_timings:       total time = 78223.88 ms\n"
      ]
     }
    ],
@@ -937,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -951,7 +989,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  I apologize, but I made a mistake in my previous response. The CPIH for January 2020 was actually 8.8%, not 8.44%. Thank you for bringing this to my attention.\n"
+      "  I apologize, but I made a mistake. According to the document, the CPIH in January 2020 was 8.8%, not 8.44%. Thank you for bringing this to my attention.\n"
      ]
     },
     {
@@ -959,11 +997,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    35.38 ms /    50 runs   (    0.71 ms per token,  1413.03 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 64689.14 ms /  1600 tokens (   40.43 ms per token,    24.73 tokens per second)\n",
-      "llama_print_timings:        eval time =  3459.62 ms /    49 runs   (   70.60 ms per token,    14.16 tokens per second)\n",
-      "llama_print_timings:       total time = 68256.30 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    35.11 ms /    50 runs   (    0.70 ms per token,  1424.10 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 83872.07 ms /  1986 tokens (   42.23 ms per token,    23.68 tokens per second)\n",
+      "llama_print_timings:        eval time =  3572.51 ms /    49 runs   (   72.91 ms per token,    13.72 tokens per second)\n",
+      "llama_print_timings:       total time = 87551.48 ms\n"
      ]
     }
    ],
@@ -974,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -988,7 +1026,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " assistant:  I apologize, but that is incorrect. According to the information provided in the context, the CPIH for January 2020 was 8.8%.\n"
+      " assistant:  I apologize, you are correct. According to the document, the CPIH in January 2020 was 1.8%, not 8.8%. My apologies for the mistake. Thank you for correcting me.\n"
      ]
     },
     {
@@ -996,11 +1034,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    27.56 ms /    39 runs   (    0.71 ms per token,  1415.04 tokens per second)\n",
-      "llama_print_timings: prompt eval time =  8273.55 ms /   114 tokens (   72.58 ms per token,    13.78 tokens per second)\n",
-      "llama_print_timings:        eval time =  2715.75 ms /    38 runs   (   71.47 ms per token,    13.99 tokens per second)\n",
-      "llama_print_timings:       total time = 11072.25 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    37.97 ms /    54 runs   (    0.70 ms per token,  1422.18 tokens per second)\n",
+      "llama_print_timings: prompt eval time =  9039.39 ms /   114 tokens (   79.29 ms per token,    12.61 tokens per second)\n",
+      "llama_print_timings:        eval time =  3924.69 ms /    53 runs   (   74.05 ms per token,    13.50 tokens per second)\n",
+      "llama_print_timings:       total time = 13075.87 ms\n"
      ]
     }
    ],
@@ -1011,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1025,8 +1063,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  The first question you asked was:\n",
-      "What happens at annual appraisal if you have been performing as expected?\n"
+      "  The first question you asked is: What happens at annual appraisal if you have been performing as expected?\n"
      ]
     },
     {
@@ -1034,11 +1071,11 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "llama_print_timings:        load time =  1190.16 ms\n",
-      "llama_print_timings:      sample time =    17.73 ms /    25 runs   (    0.71 ms per token,  1409.96 tokens per second)\n",
-      "llama_print_timings: prompt eval time = 44188.90 ms /  1401 tokens (   31.54 ms per token,    31.70 tokens per second)\n",
-      "llama_print_timings:        eval time =  1593.06 ms /    24 runs   (   66.38 ms per token,    15.07 tokens per second)\n",
-      "llama_print_timings:       total time = 45837.70 ms\n"
+      "llama_print_timings:        load time =  6149.16 ms\n",
+      "llama_print_timings:      sample time =    17.21 ms /    24 runs   (    0.72 ms per token,  1394.54 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 62158.98 ms /  1781 tokens (   34.90 ms per token,    28.65 tokens per second)\n",
+      "llama_print_timings:        eval time =  1649.91 ms /    23 runs   (   71.74 ms per token,    13.94 tokens per second)\n",
+      "llama_print_timings:       total time = 63863.71 ms\n"
      ]
     }
    ],