{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semantic Router Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Semantic Router library can be used as a super fast decision 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 decisions. Cutting decision making time down from seconds to milliseconds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by installing the library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -qU semantic-router==0.0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_router.schema import Decision\n",
    "\n",
    "politics = Decision(\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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chitchat = Decision(\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",
    "decisions = [politics, chitchat]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize our embedding model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_router.encoders import CohereEncoder\n",
    "from getpass import getpass\n",
    "import os\n",
    "\n",
    "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or \\\n",
    "    getpass(\"Enter Cohere API Key: \")\n",
    "\n",
    "encoder = CohereEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_router import DecisionLayer\n",
    "\n",
    "dl = DecisionLayer(encoder=encoder, decisions=decisions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can test it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl(\"don't you love politics?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl(\"how's the weather today?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both are classified accurately, what if we send a query that is unrelated to our existing `Decision` objects?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dl(\"I'm interested in learning about llama 2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we return `None` because no matches were identified."
   ]
  }
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