Linting.

af111b4f · Siraj R Aizlewood · 694cf2b7 · af111b4f · af111b4f
Unverified Commit af111b4f authored 11 months ago by Siraj R Aizlewood
--- a/docs/09-route-filter.ipynb
+++ b/docs/09-route-filter.ipynb
@@ -41,7 +41,7 @@
   "metadata": {},
   "outputs": [],
   "source": [
-    "!pip install -qU semantic-router==0.0.29\n"
+    "!pip install -qU semantic-router==0.0.29"
   ]
  },
  {
@@ -124,6 +124,7 @@
    "import os\n",
    "from getpass import getpass\n",
    "from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
+    "\n",
    "os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
    "    \"Enter Cohere API Key: \"\n",
    ")\n",
@@ -259,7 +260,7 @@
    }
   ],
   "source": [
-    "rl(\"don't you love politics?\", route_filter=[\"chitchat\"])\n"
+    "rl(\"don't you love politics?\", route_filter=[\"chitchat\"])"
   ]
  },
  {
@@ -288,7 +289,7 @@
    }
   ],
   "source": [
-    "rl(\"how's the weather today?\", route_filter=[\"politics\"])\n"
+    "rl(\"how's the weather today?\", route_filter=[\"politics\"])"
   ]
  },
  {

 %% 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 Filter

 %% 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
 ```

 %% 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/zahidsyed/anaconda3/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:

 %% 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

    [32m2024-03-28 14:24:37 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)

 %% Cell type:code id: tags:

 ``` python
 rl("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 `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)

 %% Cell type:markdown id: tags:

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

 %% Cell type:markdown id: tags:

 # Demonstrating the Filter Feature

 Now, let's demonstrate the filter feature. We can specify a subset of routes to consider when making a classification. This can be useful if we want to restrict the scope of possible routes based on some context.

 For example, let's say we only want to consider the "chitchat" route for a particular query:

 %% Cell type:code id: tags:

 ``` python
 rl("don't you love politics?", route_filter=["chitchat"])
 ```

 %% Output

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

 %% Cell type:markdown id: tags:

 Even though the query might be more related to the "politics" route, it will be classified as "chitchat" because we've restricted the routes to consider.

 Similarly, we can restrict it to the "politics" route:

 %% Cell type:code id: tags:

 ``` python
 rl("how's the weather today?", route_filter=["politics"])
 ```

 %% Output

    RouteChoice(name=None, function_call=None, similarity_score=None)

 %% Cell type:markdown id: tags:

 In this case, it will return None because the query doesn't match the "politics" route well enough to pass the threshold.


--- a/docs/indexes/qdrant.ipynb
+++ b/docs/indexes/qdrant.ipynb
@@ -55,6 +55,7 @@
    "import os\n",
    "from getpass import getpass\n",
    "from semantic_router.encoders import CohereEncoder\n",
+    "\n",
    "os.environ[\"COHERE_API_KEY\"] = os.environ.get(\"COHERE_API_KEY\") or getpass(\n",
    "    \"Enter COHERE API key: \"\n",
    ")\n",

 %% Cell type:code id: tags:

 ``` python
 !pip install -qU "semantic-router[qdrant]"
 ```

 %% Cell type:code id: tags:

 ``` python
 from semantic_router import Route

 # we could use this as a guide for our chatbot to avoid political conversations
 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!",
    ],
 )

 # this could be used as an indicator to our chatbot to switch to a more
 # conversational prompt
 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",
    ],
 )

 # we place both of our decisions together into single list
 routes = [politics, chitchat]
 ```

 %% Cell type:code id: tags:

 ``` python
 import os
 from getpass import getpass
 from semantic_router.encoders import CohereEncoder
+
 os.environ["COHERE_API_KEY"] = os.environ.get("COHERE_API_KEY") or getpass(
    "Enter COHERE API key: "
 )
 encoder = CohereEncoder()
 ```

 %% Cell type:code id: tags:

 ``` python
 from semantic_router.index.qdrant import QdrantIndex


 qd_index = QdrantIndex(location=":memory:")
 ```

 %% Cell type:code id: tags:

 ``` python
 from semantic_router.layer import RouteLayer

 rl = RouteLayer(encoder=encoder, routes=routes, index=qd_index)
 ```

 %% Output

    [32m2024-03-27 18:22:42 INFO semantic_router.utils.logger local[0m

 %% Cell type:markdown id: tags:

 We can check our route layer and index information.

 %% Cell type:code id: tags:

 ``` python
 rl.list_route_names()
 ```

 %% Output

    ['politics', 'chitchat']

 %% Cell type:code id: tags:

 ``` python
 len(rl.index)
 ```

 %% Output

    10

 %% Cell type:markdown id: tags:

 And query:

 %% Cell type:code id: tags:

 ``` python
 rl("don't you love politics?").name
 ```

 %% Output

    'politics'

 %% Cell type:code id: tags:

 ``` python
 rl("how's the weather today?").name
 ```

 %% Output

    'chitchat'

 %% Cell type:code id: tags:

 ``` python
 rl("I'm interested in learning about llama 2").name
 ```

 %% Cell type:markdown id: tags:

 We can delete or update routes.

 %% Cell type:code id: tags:

 ``` python
 len(rl.index)
 ```

 %% Output

    10

 %% Cell type:code id: tags:

 ``` python
 import time

 rl.delete(route_name="chitchat")
 time.sleep(1)
 len(rl.index)
 ```

 %% Output

    5

 %% Cell type:code id: tags:

 ``` python
 rl("how's the weather today?").name
 ```

 %% Cell type:code id: tags:

 ``` python
 rl.index.get_routes()
 ```

 %% Output

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

 %% Cell type:code id: tags:

 ``` python
 rl.index.describe()
 ```

 %% Output

    {'type': 'qdrant', 'dimensions': 1024, 'vectors': 5}