From 28b3f44d089f1563c07eea3d6e2ceb73db103e82 Mon Sep 17 00:00:00 2001
From: James Briggs <james.briggs@hotmail.com>
Date: Thu, 1 Aug 2024 12:50:06 +0400
Subject: [PATCH] fix: formatting
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1 file changed, 5 insertions(+), 104 deletions(-)
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Semantic Router documentation
=============================
-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
-
-To get started with _semantic-router_ we install it like so:
-
-```
-pip install -qU semantic-router
-```
-
-â—ï¸ _If wanting to use a fully local version of semantic router you can use `HuggingFaceEncoder` and `LlamaCppLLM` (`pip install -qU "semantic-router[local]"`, see [here](https://github.com/aurelio-labs/semantic-router/blob/main/docs/05-local-execution.ipynb)). 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
-
-# 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",
- "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]
-```
-
-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
-from semantic_router.encoders import CohereEncoder, OpenAIEncoder
-
-# for Cohere
-os.environ["COHERE_API_KEY"] = "<YOUR_API_KEY>"
-encoder = CohereEncoder()
-
-# or for OpenAI
-os.environ["OPENAI_API_KEY"] = "<YOUR_API_KEY>"
-encoder = OpenAIEncoder()
-```
-
-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
-
-rl = RouteLayer(encoder=encoder, routes=routes)
-```
-
-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
-rl("don't you love politics?").name
-```
-
-```
-[Out]: 'politics'
-```
-
-Correct decision, let's try another:
-
-```python
-rl("how's the weather today?").name
-```
-
-```
-[Out]: 'chitchat'
-```
-
-We get both decisions correct! Now lets try sending an unrelated query:
-
-```python
-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 route layer returned `None`!
+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.
## Integrations
-The _encoders_ of semantic router include easy-to-use integrations with [Cohere](https://github.com/aurelio-labs/semantic-router/blob/main/semantic_router/encoders/cohere.py), [OpenAI](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/openai-embed-3.ipynb), [Hugging Face](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface.ipynb), [FastEmbed](https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.ipynb), and [more](https://github.com/aurelio-labs/semantic-router/tree/main/semantic_router/encoders) — we even support [multi-modality](https://github.com/aurelio-labs/semantic-router/blob/main/docs/07-multi-modal.ipynb)!.
+The *encoders* of semantic router include easy-to-use integrations with `Cohere <https://github.com/aurelio-labs/semantic-router/blob/main/semantic_router/encoders/cohere.py>`_, `OpenAI <https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/openai-embed-3.ipynb>`_, `Hugging Face <https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface.ipynb>`_, `FastEmbed <https://github.com/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.ipynb>`_, and `more <https://github.com/aurelio-labs/semantic-router/tree/main/semantic_router/encoders>`_ — we even support `multi-modality <https://github.com/aurelio-labs/semantic-router/blob/main/docs/07-multi-modal.ipynb>`_!.
-Our utterance vector space also integrates with [Pinecone](https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/pinecone.ipynb) and [Qdrant](https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/qdrant.ipynb)!
+Our utterance vector space also integrates with `Pinecone <https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/pinecone.ipynb>`_ and `Qdrant <https://github.com/aurelio-labs/semantic-router/blob/main/docs/indexes/qdrant.ipynb>`_!
.. toctree::
:maxdepth: 2
:caption: Contents:
+ quickstart
+
--
GitLab