Fixed a Windows problem - thank you John S for the fix!

week5/day1.ipynb

@@ -13,7 +13,11 @@
     "\n",
     "This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy.\n",
     "\n",
-    "This first implementation will use a simple, brute-force type of RAG.."
+    "This first implementation will use a simple, brute-force type of RAG..\n",
+    "\n",
+    "### Sidenote: Business applications of this week's projects\n",
+    "\n",
+    "RAG is perhaps the most immediately applicable technique of anything that we cover in the course! In fact, there are commercial products that do precisely what we build this week: nuanced querying across large databases of information, such as company contracts or product specs. RAG gives you a quick-to-market, low cost mechanism for adapting an LLM to your business area."
    ]
   },
   {
@@ -65,6 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# With massive thanks to student Dr John S. for fixing a bug in the below for Windows users!\n",
+    "\n",
     "context = {}\n",
     "\n",
     "employees = glob.glob(\"knowledge-base/employees/*\")\n",
@@ -72,7 +78,7 @@
     "for employee in employees:\n",
     "    name = employee.split(' ')[-1][:-3]\n",
     "    doc = \"\"\n",
-    "    with open(employee, \"r\") as f:\n",
+    "    with open(employee, \"r\", encoding=\"utf-8\") as f:\n",
     "        doc = f.read()\n",
     "    context[name]=doc"
    ]
@@ -99,7 +105,7 @@
     "for product in products:\n",
     "    name = product.split(os.sep)[-1][:-3]\n",
     "    doc = \"\"\n",
-    "    with open(product, \"r\") as f:\n",
+    "    with open(product, \"r\", encoding=\"utf-8\") as f:\n",
     "        doc = f.read()\n",
     "    context[name]=doc"
    ]
@@ -139,6 +145,16 @@
     "    return relevant_context          "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d126cfcb-e85c-4dd9-837e-9d2b8436d4b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_relevant_context(\"Who is Lancaster?\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,