Merge branch 'main' into main

docs/encoders/fastembed.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "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/encoders/fastembed.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/encoders/fastembed.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using FastEmbedEncoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FastEmbed is a _lightweight and fast_ embedding library built for generating embeddings. It can be run locally and supports many open source encoders.\n",
+    "\n",
+    "Beyond being a local, open source library, there are two key reasons we might want to run this library over other open source alternatives:\n",
+    "\n",
+    "* **Lightweight and Fast**: The library uses an ONNX runtime so there is no heavy PyTorch dependency, supports quantized model weights (smaller memory footprint), is developed for running on CPU, and uses data-parallelism for encoding large datasets.\n",
+    "\n",
+    "* **Open-weight models**: FastEmbed supports many open source and open-weight models, included some that outperform popular encoders like OpenAI's Ada-002."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Getting Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by installing semantic-router with the `[fastembed]` flag to include all necessary dependencies for `FastEmbedEncoder`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -qU \"semantic-router[fastembed]==0.0.15\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "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": {},
+   "source": [
+    "Let's define another for good measure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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",
+    "routes = [politics, chitchat]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we initialize our embedding model, you can find a list of [all available embedding models here](https://qdrant.github.io/fastembed/examples/Supported_Models/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from semantic_router.encoders import FastEmbedEncoder\n",
+    "\n",
+    "encoder = FastEmbedEncoder()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_**⚠️ If you see an ImportError, you must install the FastEmbed library. You can do so by installing Semantic Router using `pip install -qU \"semantic-router[fastembed]\"`.**_"
+   ]
+  },
+  {
+   "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`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m2024-01-06 16:53:16 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from semantic_router.layer import RouteLayer\n",
+    "\n",
+    "rl = RouteLayer(encoder=encoder, routes=routes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can test it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RouteChoice(name='politics', function_call=None)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rl(\"don't you love politics?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RouteChoice(name='chitchat', function_call=None)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rl(\"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 `Route` objects?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RouteChoice(name=None, function_call=None)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rl(\"I'm interested in learning about llama 2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, we return `None` because no matches were identified."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "decision-layer",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}