update versions and notebook fix

3bf33bbc · James Briggs · a40f9db5 · 3bf33bbc · 3bf33bbc
Unverified Commit 3bf33bbc authored 1 year ago by James Briggs
--- a/pyproject.toml
+++ b/pyproject.toml
 [tool.poetry]
 name = "semantic-router"
-version = "0.0.5"
+version = "0.0.6"
 description = "Super fast semantic router for AI decision making"
 authors = [
    "James Briggs <james@aurelio.ai>",

--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -34,7 +34,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "!pip install -qU semantic-router==0.0.1"
+    "!pip install -qU semantic-router==0.0.6"
   ]
  },
  {
@@ -46,9 +46,19 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jamesbriggs/opt/anaconda3/envs/decision-layer/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",
+      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
+     ]
+    }
+   ],
   "source": [
    "from semantic_router.schema import Decision\n",
    "\n",
@@ -74,7 +84,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -101,7 +111,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -109,7 +119,7 @@
    "from getpass import getpass\n",
    "import os\n",
    "\n",
-    "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or \\\n",
+    "os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or \\\n",
    "    getpass(\"Enter Cohere API Key: \")\n",
    "\n",
    "encoder = CohereEncoder()"
@@ -124,11 +134,11 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
-    "from semantic_router import DecisionLayer\n",
+    "from semantic_router.layer import DecisionLayer\n",
    "\n",
    "dl = DecisionLayer(encoder=encoder, decisions=decisions)"
   ]
@@ -142,18 +152,40 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'politics'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "dl(\"don't you love politics?\")"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'chitchat'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
   "source": [
    "dl(\"how's the weather today?\")"
   ]
@@ -167,7 +199,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -198,7 +230,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.11.5"
  }
 },
 "nbformat": 4,

 %% 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
+!pip install -qU semantic-router==0.0.6
 ```
 %% 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!"
    ]
 )
 ```
+%% Output
+    /Users/jamesbriggs/opt/anaconda3/envs/decision-layer/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
+    None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
 %% 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"] = os.environ["COHERE_API_KEY"] or \
+os.environ["COHERE_API_KEY"] = os.getenv("COHERE_API_KEY") or \
    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
+from semantic_router.layer 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?")
 ```
+%% Output
+    'politics'
 %% Cell type:code id: tags:
 ``` python
 dl("how's the weather today?")
 ```
+%% Output
+    'chitchat'
 %% 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.