tweaks

d582bc70 · James Briggs · 73e1996f · d582bc70 · d582bc70
Unverified Commit d582bc70 authored 1 year ago by James Briggs
--- a/README.md
+++ b/README.md
@@ -104,4 +104,4 @@ In this case, no decision could be made as we had no matches — so our decision
 |     |     |
 | --- | --- |
-| 🏃 Walkthrough | Quickstart Python notebook |
+| 🏃 [Walkthrough](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/walkthrough.ipynb) | Quickstart Python notebook |
\ No newline at end of file
--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -107,11 +107,11 @@
   "source": [
    "from semantic_router.encoders import CohereEncoder\n",
    "from getpass import getpass\n",
-    "\n",
    "import os\n",
    "\n",
-    "# os.environ[\"COHERE_API_KEY\"] = getpass(\"Enter Cohere API Key: \")\n",
+    "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or \\\n",
-    "os.environ[\"COHERE_API_KEY\"]\n",
+    "    getpass(\"Enter Cohere API Key: \")\n",
+    "\n",
    "encoder = CohereEncoder()"
   ]
  },

 %% Cell type:markdown id: tags:
 # Semantic Router Walkthrough
 %% Cell type:markdown id: tags:
 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 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==0.0.1
 ```
 %% Cell type:markdown id: tags:
 We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions.
 %% Cell type:code id: tags:
 ``` python
 from semantic_router.schema import Decision
 politics = Decision(
    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",
        "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 = Decision(
    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"
    ]
 )
 decisions = [politics, chitchat]
 ```
 %% Cell type:markdown id: tags:
 Now we initialize our embedding model:
 %% Cell type:code id: tags:
 ``` python
 from semantic_router.encoders import CohereEncoder
 from getpass import getpass
 import os
-# os.environ["COHERE_API_KEY"] = getpass("Enter Cohere API Key: ")
+os.environ["COHERE_API_KEY"] = os.environ["COHERE_API_KEY"] or \
-os.environ["COHERE_API_KEY"]
+    getpass("Enter Cohere API Key: ")
 encoder = CohereEncoder()
 ```
 %% Cell type:markdown id: tags:
 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 id: tags:
 ``` python
 from semantic_router import DecisionLayer
 dl = DecisionLayer(encoder=encoder, decisions=decisions)
 ```
 %% Cell type:markdown id: tags:
 Now we can test it:
 %% Cell type:code id: tags:
 ``` python
 dl("don't you love politics?")
 ```
 %% Cell type:code id: tags:
 ``` python
 dl("how's the weather today?")
 ```
 %% Cell type:markdown id: tags:
 Both are classified accurately, what if we send a query that is unrelated to our existing `Decision` objects?
 %% Cell type:code id: tags:
 ``` python
 dl("I'm interested in learning about llama 2")
 ```
 %% Cell type:markdown id: tags:
 In this case, we return `None` because no matches were identified.