Added simplistic RAG to lab 3

Author: codingbandit

Labs/lab_3_mongodb_vector_search.ipynb

@@ -51,6 +51,7 @@
     "load_dotenv()\n",
     "CONNECTION_STRING = os.environ.get(\"DB_CONNECTION_STRING\")\n",
     "EMBEDDINGS_DEPLOYMENT_NAME = \"embeddings\"\n",
+    "COMPLETIONS_DEPLOYMENT_NAME = \"completions\"\n",
     "AOAI_ENDPOINT = os.environ.get(\"AOAI_ENDPOINT\")\n",
     "AOAI_KEY = os.environ.get(\"AOAI_KEY\")\n",
     "AOAI_API_VERSION = \"2023-05-15\""
@@ -337,6 +338,84 @@
     "for result in results:\n",
     "    print_product_search_result(result)   "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Use vector search results in a RAG pattern with Chat GPT-3.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# A system prompt describes the responsibilities, instructions, and persona of the AI.\n",
+    "system_prompt = \"\"\"\n",
+    "You are a helpful, fun and friendly sales assistant for Cosmic Works, a bicycle and bicycle accessories store. \n",
+    "Your name is Cosmo.\n",
+    "You are designed to answer questions about the products that Cosmic Works sells.\n",
+    "\n",
+    "Only answer questions related to the information provided in the list of products below that are represented\n",
+    "in JSON format.\n",
+    "\n",
+    "If you are asked a question that is not in the list, respond with \"I don't know.\"\n",
+    "\n",
+    "List of products:\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rag_with_vector_search(question: str, num_results: int = 3):\n",
+    "    \"\"\"\n",
+    "    Use the RAG model to generate a prompt using vector search results based on the\n",
+    "    incoming question.  \n",
+    "    \"\"\"\n",
+    "    # perform the vector search and build product list\n",
+    "    results = vector_search(\"products\", question, num_results=num_results)\n",
+    "    product_list = \"\"\n",
+    "    for result in results:\n",
+    "        if \"contentVector\" in result[\"document\"]:\n",
+    "            del result[\"document\"][\"contentVector\"]\n",
+    "        product_list += json.dumps(result[\"document\"], indent=4, default=str) + \"\\n\\n\"\n",
+    "\n",
+    "    # generate prompt for the LLM with vector results\n",
+    "    formatted_prompt = system_prompt + product_list\n",
+    "\n",
+    "    # prepare the LLM request\n",
+    "    messages = [\n",
+    "        {\"role\": \"system\", \"content\": formatted_prompt},\n",
+    "        {\"role\": \"user\", \"content\": question}\n",
+    "    ]\n",
+    "\n",
+    "    completion = ai_client.chat.completions.create(messages=messages, model=COMPLETIONS_DEPLOYMENT_NAME)\n",
+    "    return completion.choices[0].message.content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(rag_with_vector_search(\"What bikes do you have?\", 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(rag_with_vector_search(\"What are the names and skus of yellow products?\", 5))"
+   ]
   }
  ],
  "metadata": {