Updating colab link in google.ipynb

47a5b83a · Stephen Witkowski · c651bd29 · 47a5b83a
Commit 47a5b83a authored 1 year ago by Stephen Witkowski
--- a/docs/encoders/google.ipynb
+++ b/docs/encoders/google.ipynb
@@ -4,7 +4,7 @@
   "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/huggingface.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/huggingface.ipynb)"
+    "[![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/google.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/google.ipynb)"
   ]
  },
  {
@@ -53,18 +53,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/Stephen.Witkowski/miniforge3/envs/semantic-router/lib/python3.11/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",
@@ -90,7 +81,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -117,7 +108,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -138,17 +129,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\u001b[32m2024-04-01 09:25:34 INFO semantic_router.utils.logger local\u001b[0m\n"
-     ]
-    }
-   ],
   "source": [
    "from semantic_router.layer import RouteLayer\n",
    "\n",
@@ -164,62 +147,18 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "chitchat 0.6414884458605304\n",
-      "politics 0.7184715572295559\n",
-      "politics 0.7439275034824486\n",
-      "politics 0.7782497192765216\n",
-      "politics 0.896539779971592\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RouteChoice(name='politics', function_call=None, similarity_score=None)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
   "source": [
    "rl(\"don't you love politics?\")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "politics 0.6707593528326423\n",
-      "chitchat 0.7714977687548606\n",
-      "chitchat 0.8433716231360381\n",
-      "chitchat 0.8563028552685018\n",
-      "chitchat 1.0000000000000004\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
   "source": [
    "rl(\"how's the weather today?\")"
   ]
@@ -233,31 +172,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "politics 0.5136569490418237\n",
-      "politics 0.5147603969017434\n",
-      "politics 0.5481785565189041\n",
-      "chitchat 0.5495495505326361\n",
-      "chitchat 0.5587930037610617\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "RouteChoice(name=None, function_call=None, similarity_score=None)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
   "source": [
    "rl(\"I'm interested in learning about llama 2\")"
   ]

 %% 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/encoders/huggingface.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/huggingface.ipynb)
+[![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/google.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/google.ipynb)
 %% Cell type:markdown id: tags:
 # Using GoogleEncoder
 %% Cell type:markdown id: tags:
 Google's [Pathways Language Model](https://blog.research.google/2022/04/pathways-language-model-palm-scaling-to.html) (PaLM) is a dense decoder-only model that is trained on a large corpus of text data. The hidden states of the model can be used as embeddings for text data, and Google has released versions of those layers for public use. This notebook demonstrates how to use the GoogleEncoder with the Semantic Router.
 %% Cell type:markdown id: tags:
 ## Getting Started
 %% Cell type:markdown id: tags:
 We start by installing semantic-router. Support for the new `GoogleEncoder` class was added in `semantic-router==0.0.X`.
 %% Cell type:code id: tags:
 ``` python
 !pip install -qU semantic-router==0.0.X
 ```
 %% 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",
        "they're going to destroy this country!",
        "they will save the country!",
    ],
 )
 ```
-%% Output
-    /Users/Stephen.Witkowski/miniforge3/envs/semantic-router/lib/python3.11/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
-      from .autonotebook import tqdm as notebook_tqdm
 %% 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. To do this with GoogleEncoder, you'll need to have an active Google Cloud Platform account and a project with the Embeddings API enabled. You can find more information on how to set up a development project in the [Google Cloud documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-text-embeddings).
 %% Cell type:code id: tags:
 ``` python
 from semantic_router.encoders import GoogleEncoder
 PROJECT_ID = "your-project-id"
 LOCATION = "us-central1"
 encoder = GoogleEncoder(project_id=PROJECT_ID, location=LOCATION)
 ```
 %% 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
 rl = RouteLayer(encoder=encoder, routes=routes)
 ```
-%% Output
-    [32m2024-04-01 09:25:34 INFO semantic_router.utils.logger local[0m
 %% Cell type:markdown id: tags:
 Now we can test it:
 %% Cell type:code id: tags:
 ``` python
 rl("don't you love politics?")
 ```
-%% Output
-    chitchat 0.6414884458605304
-    politics 0.7184715572295559
-    politics 0.7439275034824486
-    politics 0.7782497192765216
-    politics 0.896539779971592
-    RouteChoice(name='politics', function_call=None, similarity_score=None)
 %% Cell type:code id: tags:
 ``` python
 rl("how's the weather today?")
 ```
-%% Output
-    politics 0.6707593528326423
-    chitchat 0.7714977687548606
-    chitchat 0.8433716231360381
-    chitchat 0.8563028552685018
-    chitchat 1.0000000000000004
-    RouteChoice(name='chitchat', function_call=None, similarity_score=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
 rl("I'm interested in learning about llama 2")
 ```
-%% Output
-    politics 0.5136569490418237
-    politics 0.5147603969017434
-    politics 0.5481785565189041
-    chitchat 0.5495495505326361
-    chitchat 0.5587930037610617
-    RouteChoice(name=None, function_call=None, similarity_score=None)
 %% Cell type:markdown id: tags:
 In this case, we return `None` because no matches were identified.