Merge remote-tracking branch 'upstream/main' into add_tfidf

.env.example

@@ -1 +1 @@
-COHERE_API_KEY=
+COHERE_API_KEY=

.github/workflows/test.yml

@@ -35,7 +35,7 @@ jobs:
           cache: poetry
       - name: Install dependencies
         run: |
-          poetry install
+          poetry install --all-extras
       - name: Install nltk
         run: |
           pip install nltk

.gitignore

@@ -5,6 +5,7 @@ __pycache__
 venv/
 /.vscode
 .vscode
+.idea
 **/__pycache__
 **/*.py[cod]
 
@@ -17,4 +18,5 @@ mac.env
 .coverage
 .coverage.*
 .pytest_cache
-test.py
+test.py
+output

LICENSE

@@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+SOFTWARE.

README.md

@@ -13,7 +13,7 @@
 <img alt="Github License" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
 </p>
 
-Semantic Router is a superfast decision layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.
+Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.
 
 ## Quickstart
 
@@ -23,7 +23,9 @@ To get started with _semantic-router_ we install it like so:
 pip install -qU semantic-router
 ```
 
-We begin by defining a set of `Decision` objects. These are the decision paths that the semantic router can decide to use, let's try two simple decisions for now — one for talk on _politics_ and another for _chitchat_:
+❗️ _If wanting to use local embeddings you can use `FastEmbedEncoder` (`pip install -qU semantic-router[fastembed]`). To use the `HybridRouteLayer` you must `pip install -qU semantic-router[hybrid]`._
+
+We begin by defining a set of `Route` objects. These are the decision paths that the semantic router can decide to use, let's try two simple routes for now — one for talk on _politics_ and another for _chitchat_:
 
 ```python
 from semantic_router import Route
@@ -57,7 +59,7 @@ chitchat = Route(
 routes = [politics, chitchat]
 ```
 
-We have our decisions ready, now we initialize an embedding / encoder model. We currently support a `CohereEncoder` and `OpenAIEncoder` — more encoders will be added soon. To initialize them we do:
+We have our routes ready, now we initialize an embedding / encoder model. We currently support a `CohereEncoder` and `OpenAIEncoder` — more encoders will be added soon. To initialize them we do:
 
 ```python
 import os
@@ -72,18 +74,18 @@ os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
 encoder = OpenAIEncoder()
 ```
 
-With our `decisions` and `encoder` defined we now create a `DecisionLayer`. The decision layer handles our semantic decision making.
+With our `routes` and `encoder` defined we now create a `RouteLayer`. The route layer handles our semantic decision making.
 
 ```python
 from semantic_router.layer import RouteLayer
 
-dl = RouteLayer(encoder=encoder, routes=routes)
+rl = RouteLayer(encoder=encoder, routes=routes)
 ```
 
-We can now use our decision layer to make super fast decisions based on user queries. Let's try with two queries that should trigger our decisions:
+We can now use our route layer to make super fast decisions based on user queries. Let's try with two queries that should trigger our route decisions:
 
 ```python
-dl("don't you love politics?").name
+rl("don't you love politics?").name
 ```
 
 ```
@@ -93,7 +95,7 @@ dl("don't you love politics?").name
 Correct decision, let's try another:
 
 ```python
-dl("how's the weather today?").name
+rl("how's the weather today?").name
 ```
 
 ```
@@ -103,20 +105,13 @@ dl("how's the weather today?").name
 We get both decisions correct! Now lets try sending an unrelated query:
 
 ```python
-dl("I'm interested in learning about llama 2").name
+rl("I'm interested in learning about llama 2").name
 ```
 
 ```
 [Out]:
 ```
 
-In this case, no decision could be made as we had no matches — so our decision layer returned `None`!
+In this case, no decision could be made as we had no matches — so our route layer returned `None`!
 
 ## 📚 [Resources](https://github.com/aurelio-labs/semantic-router/tree/main/docs)
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