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   "source": [
    "# Semantic Router: Hybrid Layer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Hybrid Layer in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to making based on the keywords contained in our utterances and user queries.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by installing the library:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install -qU semantic-router==0.0.11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by defining a dictionary mapping s to example phrases that should trigger those s.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_router_3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from semantic_router.route import Route\n",
    "\n",
    "politics = Route(\n",
    "    name=\"politics\",\n",
    "    utterances=[\n",
    "        \"isn't politics the best thing ever\",\n",
    "        \"why don't you tell me about your political opinions\",\n",
    "        \"don't you just love the president\",\n",
    "        \"don't you just hate the president\",\n",
    "        \"they're going to destroy this country!\",\n",
    "        \"they will save the country!\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define another for good measure:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "chitchat = Route(\n",
    "    name=\"chitchat\",\n",
    "    utterances=[\n",
    "        \"how's the weather today?\",\n",
    "        \"how are things going?\",\n",
    "        \"lovely weather today\",\n",
    "        \"the weather is horrendous\",\n",
    "        \"let's go to the chippy\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "chitchat = Route(\n",
    "    name=\"chitchat\",\n",
    "    utterances=[\n",
    "        \"how's the weather today?\",\n",
    "        \"how are things going?\",\n",
    "        \"lovely weather today\",\n",
    "        \"the weather is horrendous\",\n",
    "        \"let's go to the chippy\",\n",
    "    ],\n",
    ")\n",
    "\n",
    "routes = [politics, chitchat]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize our embedding model:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from semantic_router.encoders import CohereEncoder, BM25Encoder, TfidfEncoder\n",
    "from getpass import getpass\n",
    "\n",
    "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n",
    "    \"Enter Cohere API Key: \"\n",
    ")\n",
    "\n",
    "dense_encoder = CohereEncoder()\n",
    "# sparse_encoder = BM25Encoder()\n",
    "sparse_encoder = TfidfEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the `RouteLayer`. When called, the route layer will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `RouteLayer` we need our `encoder` model and a list of `routes`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2024-05-07 21:15:31 INFO semantic_router.utils.logger Creating embeddings for all routes...\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from semantic_router.hybrid_layer import HybridRouteLayer\n",
    "\n",
    "dl = HybridRouteLayer(\n",
    "    encoder=dense_encoder, sparse_encoder=sparse_encoder, routes=routes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'politics'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl(\"don't you love politics?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'chitchat'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl(\"how's the weather today?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n"
   ]
  }
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