Merge pull request #54 from avatsaev/patch-1

Update 00-introduction.ipynb

Merge pull request #54 from avatsaev/patch-1
835468e4 · James Briggs · GitHub · dccef56e · b8e47ece · 835468e4
Unverified Commit 835468e4 authored 1 year ago by James Briggs Committed by GitHub 1 year ago
--- a/docs/00-introduction.ipynb
+++ b/docs/00-introduction.ipynb
@@ -71,7 +71,8 @@
    "    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\" \"don't you just hate the president\",\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 id: tags:

 [![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 id: tags:

 # Semantic Router Intro

 %% Cell type:markdown id: tags:

 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 id: tags:

 ## Getting Started

 %% Cell type:markdown id: tags:

 We start by installing the library:

 %% Cell type:code id: tags:

 ``` python
 !pip install -qU semantic-router==0.0.14
 ```

 %% Cell type:markdown id: tags:

 _**⚠️ If using Google Colab, install the prerequisites and then restart the notebook before continuing**_

 %% Cell type:markdown id: tags:

 We start by defining a dictionary mapping routes to example phrases that should trigger those routes.

 %% Cell type:code id: tags:

 ``` python
 from semantic_router import Route

 politics = Route(
    name="politics",
    utterances=[
        "isn't politics the best thing ever",
        "why don't you tell me about your political opinions",
-        "don't you just love the president" "don't you just hate the president",
+        "don't you just love the president",
+        "don't you just hate the president",
        "they're going to destroy this country!",
        "they will save the country!",
    ],
 )
 ```

 %% Cell type:markdown id: tags:

 Let's define another for good measure:

 %% Cell type:code id: tags:

 ``` python
 chitchat = Route(
    name="chitchat",
    utterances=[
        "how's the weather today?",
        "how are things going?",
        "lovely weather today",
        "the weather is horrendous",
        "let's go to the chippy",
    ],
 )

 routes = [politics, chitchat]
 ```

 %% Cell type:markdown id: tags:

 Now we initialize our embedding model:

 %% Cell type:code id: tags:

 ``` python
 import os
 from getpass import getpass
 from semantic_router.encoders import CohereEncoder, OpenAIEncoder

 # os.environ["COHERE_API_KEY"] = os.getenv("COHERE_API_KEY") or getpass(
 #     "Enter Cohere API Key: "
 # )
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") or getpass(
    "Enter OpenAI API Key: "
 )

 # encoder = CohereEncoder()
 encoder = OpenAIEncoder()
 ```

 %% Cell type:markdown id: tags:

 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 id: tags:

 ``` python
 from semantic_router.layer import RouteLayer

 dl = RouteLayer(encoder=encoder, routes=routes)
 ```

 %% Output

    [32m2023-12-28 19:14:34 INFO semantic_router.utils.logger Initializing RouteLayer[0m

 %% Cell type:markdown id: tags:

 Now we can test it:

 %% Cell type:code id: tags:

 ``` python
 dl("don't you love politics?")
 ```

 %% Output

    RouteChoice(name='politics', function_call=None)

 %% Cell type:code id: tags:

 ``` python
 dl("how's the weather today?")
 ```

 %% Output

    RouteChoice(name='chitchat', function_call=None)

 %% Cell type:markdown id: tags:

 Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?

 %% Cell type:code id: tags:

 ``` python
 dl("I'm interested in learning about llama 2")
 ```

 %% Output

    RouteChoice(name=None, function_call=None)

 %% Cell type:markdown id: tags:

 In this case, we return `None` because no matches were identified.