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Merge pull request #8 from aurelio-labs/james/matching-pipelines
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WIP restructure and testing different decision layer structures
jamescalam authored Dec 11, 2023

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205 changes: 205 additions & 0 deletions docs/examples/hybrid-layer.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Semantic Router: Hybrid Layer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Hybrid Layer in the Semantic Router library can improve decision making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to decision making based on the keywords contained in our utterances and user queries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting Started"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by installing the library:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -qU semantic-router==0.0.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"COHERE_API_KEY\"] = \"BQBiUqqjDRsYl1QKKux4JsqKdDkjyInS5T3Z3eJP\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from semantic_router.schema import Decision\n",
"\n",
"politics = Decision(\n",
" name=\"politics\",\n",
" utterances=[\n",
" \"isn't politics the best thing ever\",\n",
" \"why don't you tell me about your political opinions\",\n",
" \"don't you just love the president\",\n",
" \"don't you just hate the president\",\n",
" \"they're going to destroy this country!\",\n",
" \"they will save the country!\",\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define another for good measure:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chitchat = Decision(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
" \"how are things going?\",\n",
" \"lovely weather today\",\n",
" \"the weather is horrendous\",\n",
" \"let's go to the chippy\",\n",
" ],\n",
")\n",
"\n",
"chitchat = Decision(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
" \"how are things going?\",\n",
" \"lovely weather today\",\n",
" \"the weather is horrendous\",\n",
" \"let's go to the chippy\",\n",
" ],\n",
")\n",
"\n",
"decisions = [politics, chitchat]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we initialize our embedding model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from semantic_router.encoders import CohereEncoder\n",
"from getpass import getpass\n",
"\n",
"os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n",
" \"Enter Cohere API Key: \"\n",
")\n",
"\n",
"encoder = CohereEncoder()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from semantic_router.layer import HybridDecisionLayer\n",
"\n",
"dl = HybridDecisionLayer(encoder=encoder, decisions=decisions)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dl(\"don't you love politics?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dl(\"how's the weather today?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "decision-layer",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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