Mergining with main

f403c257 · theanupllm · ae21e6d5 · f403c257
Commit f403c257 authored 11 months ago by theanupllm
--- a/docs/00-introduction.ipynb
+++ b/docs/00-introduction.ipynb
@@ -51,7 +51,7 @@
    }
   ],
   "source": [
-    "!pip install -qU semantic-router==0.0.29"
+    "!pip install -qU semantic-router==0.0.35"
   ]
  },
  {
@@ -63,18 +63,9 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
   "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_layer\\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",
@@ -100,7 +91,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -127,7 +118,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
@@ -155,14 +146,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
-      "\u001b[32m2024-04-02 22:45:21 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+      "\u001b[32m2024-04-19 18:34:06 INFO semantic_router.utils.logger local\u001b[0m\n"
     ]
    }
   ],
@@ -181,16 +172,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "RouteChoice(name='politics', function_call=None, similarity_score=None, trigger=None)"
+       "RouteChoice(name='politics', function_call=None, similarity_score=None)"
      ]
     },
-     "execution_count": 6,
+     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -201,16 +192,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "RouteChoice(name='chitchat', function_call=None, similarity_score=None, trigger=None)"
+       "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
      ]
     },
-     "execution_count": 7,
+     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -228,16 +219,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "RouteChoice(name=None, function_call=None, similarity_score=None, trigger=None)"
+       "RouteChoice(name=None, function_call=None, similarity_score=None)"
      ]
     },
-     "execution_count": 8,
+     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -255,17 +246,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
-       "[RouteChoice(name='politics', function_call=None, similarity_score=0.8596186767854479, trigger=None),\n",
+       "[RouteChoice(name='politics', function_call=None, similarity_score=0.8596186767854487),\n",
-       " RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356239688161818, trigger=None)]"
+       " RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356239688161808)]"
      ]
     },
-     "execution_count": 9,
+     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -273,6 +264,33 @@
   "source": [
    "rl.retrieve_multiple_routes(\"Hi! How are you doing in politics??\")"
   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 12,
+     "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": {},
+   "outputs": [],
+   "source": []
  }
 ],
 "metadata": {
@@ -291,7 +309,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.2"
  }
 },
 "nbformat": 4,

 %% 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==0.0.29
+!pip install -qU semantic-router==0.0.35
 ```
 %% Output
    [notice] A new release of pip is available: 23.1.2 -> 24.0
    [notice] To update, run: python.exe -m pip install --upgrade pip
 %% 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
-    c:\Users\Siraj\Documents\Personal\Work\Aurelio\Virtual Environments\semantic_layer\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
-      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:
 %% 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
 rl = RouteLayer(encoder=encoder, routes=routes)
 ```
 %% Output
-    [32m2024-04-02 22:45:21 INFO semantic_router.utils.logger Initializing RouteLayer[0m
+    [32m2024-04-19 18:34:06 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
-    RouteChoice(name='politics', function_call=None, similarity_score=None, trigger=None)
+    RouteChoice(name='politics', function_call=None, similarity_score=None)
 %% Cell type:code id: tags:
 ``` python
 rl("how's the weather today?")
 ```
 %% Output
-    RouteChoice(name='chitchat', function_call=None, similarity_score=None, trigger=None)
+    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
-    RouteChoice(name=None, function_call=None, similarity_score=None, trigger=None)
+    RouteChoice(name=None, function_call=None, similarity_score=None)
 %% Cell type:markdown id: tags:
 We can also retrieve multiple routes with its associated score:
 %% Cell type:code id: tags:
 ``` python
 rl.retrieve_multiple_routes("Hi! How are you doing in politics??")
 ```
 %% Output
-    [RouteChoice(name='politics', function_call=None, similarity_score=0.8596186767854479, trigger=None),
+    [RouteChoice(name='politics', function_call=None, similarity_score=0.8596186767854487),
-     RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356239688161818, trigger=None)]
+     RouteChoice(name='chitchat', function_call=None, similarity_score=0.8356239688161808)]
+%% Cell type:code id: tags:
+``` python
+rl.retrieve_multiple_routes("I'm interested in learning about llama 2")
+```
+%% Output
+    []
+%% Cell type:code id: tags:
+``` python
+```