From 7e6fb68789392da0c3430b1afdf50c789cf27ea0 Mon Sep 17 00:00:00 2001
From: James Briggs <james.briggs@hotmail.com>
Date: Mon, 8 Jul 2024 16:41:00 +0800
Subject: [PATCH] fix: bad doc format
---
docs/00-introduction.ipynb | 658 ++++++++++++++++++++-----------------
1 file changed, 357 insertions(+), 301 deletions(-)
diff --git a/docs/00-introduction.ipynb b/docs/00-introduction.ipynb
index b4403ad6..260f1e99 100644
--- a/docs/00-introduction.ipynb
+++ b/docs/00-introduction.ipynb
@@ -1,316 +1,372 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Semantic Router Intro"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "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": {},
- "source": [
- "## Getting Started"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We start by installing the library:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install -qU semantic-router"
- ]
- },
- {
- "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": 2,
- "metadata": {},
- "outputs": [
+ "cells": [
{
- "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 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": 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",
- "routes = [politics, chitchat]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we initialize our embedding model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "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": {},
- "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": 5,
- "metadata": {},
- "outputs": [
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K7NsuSPNf3px"
+ },
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb)"
+ ]
+ },
{
- "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": {},
- "source": [
- "Now we can test it:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Am2hmLzTf3py"
+ },
+ "source": [
+ "# Semantic Router Intro"
+ ]
+ },
{
- "data": {
- "text/plain": [
- "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ "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."
]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rl(\"don't you love politics?\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NggrMQP2f3py"
+ },
+ "source": [
+ "## Getting Started"
]
- },
- "execution_count": 7,
- "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": 8,
- "metadata": {},
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9zP-l_T7f3py"
+ },
+ "source": [
+ "We start by installing the library:"
]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rl(\"I'm interested in learning about llama 2\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can also retrieve multiple routes with its associated score:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "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)]"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4YI81tu0f3pz"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -qU semantic-router"
]
- },
- "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": 10,
- "metadata": {},
- "outputs": [
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HfB8252ff3pz"
+ },
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
{
- "data": {
- "text/plain": [
- "[]"
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lslfqYOEf3pz",
+ "outputId": "c13e3e77-310c-4b86-e291-4b6005d698bd"
+ },
+ "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 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",
+ ")"
]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "WYLHUDa1f3p0"
+ },
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "LAdY1jdxf3p0"
+ },
+ "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",
+ "outputId": "872810da-956a-47af-a91f-217ce351a88b"
+ },
+ "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,
+ "metadata": {
+ "id": "fIXOjRp9f3p3",
+ "outputId": "8b9b5746-ae7c-43bb-d84f-5fa7c30e423e"
+ },
+ "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"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.11.4"
+ },
+ "colab": {
+ "provenance": []
}
- ],
- "source": [
- "rl.retrieve_multiple_routes(\"I'm interested in learning about llama 2\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.11.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
--
GitLab