docs/00-introduction.ipynb

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      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Am2hmLzTf3py"
      },
      "source": [
        "# Semantic Router Intro"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k1nRRAbYf3py"
      },
      "source": [
        "The Semantic Router library can be used as a super fast route making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make routes. Cutting route making time down from seconds to milliseconds."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## Getting Started"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zP-l_T7f3py"
      },
      "source": [
        "We start by installing the library:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "!pip install -qU semantic-router"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfB8252ff3pz"
      },
      "source": [
        "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "outputId": "c13e3e77-310c-4b86-e291-4b6005d698bd"
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      "outputs": [
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          "name": "stderr",
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          "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 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": {
        "id": "WYLHUDa1f3p0"
      },
      "source": [
        "Let's define another for good measure:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "LAdY1jdxf3p0"
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      "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",
        "routes = [politics, chitchat]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReN59ieGf3p0"
      },
      "source": [
        "Now we initialize our embedding model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MF47W_Sof3p2"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
        "\n",
        "# os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
        "#     \"Enter Cohere API Key: \"\n",
        "# )\n",
        "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
        "    \"Enter OpenAI API Key: \"\n",
        ")\n",
        "\n",
        "# encoder = CohereEncoder()\n",
        "encoder = OpenAIEncoder()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lYuLO0l9f3p3"
      },
      "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`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dh1U8IDOf3p3",
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      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[32m2024-05-07 15:02:46 INFO semantic_router.utils.logger local\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from semantic_router.layer import RouteLayer\n",
        "\n",
        "rl = RouteLayer(encoder=encoder, routes=routes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xj32uEF-f3p3"
      },
      "source": [
        "Now we can test it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "fIXOjRp9f3p3",
        "outputId": "8b9b5746-ae7c-43bb-d84f-5fa7c30e423e"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "RouteChoice(name='politics', function_call=None, similarity_score=None)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rl(\"don't you love politics?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0UN2mKvjf3p4",
        "outputId": "062f9499-7db3-49d2-81ef-e7d5dc9a88f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rl(\"how's the weather today?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHZWZKoTf3p4"
      },
      "source": [
        "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0WnvGJByf3p4",
        "outputId": "4496e9b2-7cd8-4466-fe1a-3e6f5cf30b0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "RouteChoice(name=None, function_call=None, similarity_score=None)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rl(\"I'm interested in learning about llama 2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDZF2eN4f3p4"
      },
      "source": [
        "We can also retrieve multiple routes with its associated score:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n27I7kmpf3p4",
        "outputId": "2138e077-190b-41b7-a3eb-4fd76e2f59c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[RouteChoice(name='politics', function_call=None, similarity_score=0.8595844842560181),\n",
              " RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356704527362284)]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rl.retrieve_multiple_routes(\"Hi! How are you doing in politics??\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zi4XJ7Amf3p4",
        "outputId": "cf05cd65-d4f4-454a-ef05-77f16f37cc8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rl.retrieve_multiple_routes(\"I'm interested in learning about llama 2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QF_wATjYf3p4"
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