"rl(\"I'm interested in learning about llama 2\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also retrieve multiple routes with its associated score:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"rl.retrieve_multiple_routes(\"Hi! How are you doing in politics??\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
...
...
%% Cell type:markdown id: tags:
[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb) [](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
!pipinstall-qUsemantic-router==0.0.20
```
%% 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
fromsemantic_routerimportRoute
politics=Route(
name="politics",
utterances=[
"isn't politics the best thing ever",
"why don't you tell me about your political opinions",
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
fromsemantic_router.layerimportRouteLayer
rl=RouteLayer(encoder=encoder,routes=routes)
```
%% Output
[32m2024-01-07 18:08:29 INFO semantic_router.utils.logger Initializing RouteLayer[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)
%% Cell type:code id: tags:
``` python
rl("how's the weather today?")
```
%% Output
RouteChoice(name='chitchat', function_call=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
%% Cell type:markdown id: tags:
RouteChoice(name=None, function_call=None)
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??")
```
%% Cell type:markdown id: tags:
In this case, we return `None` because no matches were identified.
