diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index cf22ea9b75332a14d994c57a58f946dc7b53b807..017c23c02503e45e36b8580c0b5c6664d59f016f 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -15,7 +15,10 @@ jobs:
strategy:
matrix:
python-version:
+ - "3.9"
+ - "3.10"
- "3.11"
+ # - "3.12"
steps:
- uses: actions/checkout@v4
- name: Cache Poetry
diff --git a/.gitignore b/.gitignore
index cb4c0022d27d777ec80531b834da365db0729125..cc461499d2d720e715c86c33e513b9c259cb5737 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,6 +9,7 @@ venv/
**/__pycache__
**/*.py[cod]
+
# local env files
.env*.local
.env
@@ -18,5 +19,11 @@ mac.env
.coverage
.coverage.*
.pytest_cache
+coverage.xml
test.py
output
+node_modules
+package-lock.json
+package.json
+
+```
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 6a80529022ae36491cbbb22511c1a93a0050351f..b24594f35ab4a09c525946915f027ed267ac78c5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,5 +1,5 @@
default_language_version:
- python: python3.11.3
+ python: python3.9
repos:
- repo: meta
hooks:
@@ -17,14 +17,12 @@ repos:
- id: blacken-docs
additional_dependencies: [black==22.10.0]
- - repo: https://github.com/astral-sh/ruff-pre-commit
- rev: v0.0.290
+ - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
+ rev: v9.11.0
hooks:
- - id: ruff
- types_or: [ python, pyi, jupyter ]
- args: [ --fix ]
- - id: ruff-format
- types_or: [ python, pyi, jupyter ]
+ - id: commitlint
+ stages: [commit-msg]
+ additional_dependencies: ['@commitlint/config-conventional']
- repo: https://github.com/codespell-project/codespell
rev: v2.2.4
@@ -36,13 +34,11 @@ repos:
args: [ "--write-changes", "--ignore-words-list", "asend" ]
exclude: "poetry.lock"
-
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
hooks:
- id: check-vcs-permalinks
- id: end-of-file-fixer
- # exclude: "tests/((commands|data)/|test_).+"
- id: trailing-whitespace
args: [ --markdown-linebreak-ext=md ]
- id: debug-statements
@@ -62,8 +58,20 @@ repos:
- post-commit
- push
- - repo: https://github.com/alessandrojcm/commitlint-pre-commit-hook
- rev: v9.11.0
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: v0.0.290
+ hooks:
+ - id: ruff
+ types_or: [python, pyi, jupyter]
+
+ # - repo: https://github.com/pre-commit/mirrors-mypy
+ # rev: v1.8.0
+ # hooks:
+ # - id: mypy
+ # args: [--ignore-missing-imports]
+
+ - repo: https://github.com/PyCQA/bandit
+ rev: 1.7.6
hooks:
- - id: commitlint
- stages: [commit-msg]
+ - id: bandit
+ args: ['-lll']
diff --git a/Makefile b/Makefile
index aeb3d3b19ff9262b933b9022f6fc80c240279040..adf4eb0c4079749a2f86a0a862292f3ed0931265 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,5 @@
format:
- poetry run black .
+ poetry run black --target-version py39 .
poetry run ruff --select I --fix .
PYTHON_FILES=.
@@ -7,7 +7,7 @@ lint: PYTHON_FILES=.
lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$')
lint lint_diff:
- poetry run black $(PYTHON_FILES) --check
+ poetry run black --target-version py39 $(PYTHON_FILES) --check
poetry run ruff .
poetry run mypy $(PYTHON_FILES)
diff --git a/README.md b/README.md
index 5809e85a14c9e8b13e30c16dd86c1dbec7250ea3..fd4c12a2e5f441e8cb792d6705c976c7e67b55d4 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,7 @@ To get started with _semantic-router_ we install it like so:
pip install -qU semantic-router
```
-â—ï¸ _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]`._
+â—ï¸ _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_:
diff --git a/coverage.xml b/coverage.xml
index 6726da20c0072e4a9458817d708f40d897fb84a3..5850def2dfb8bf3ee91ce0e20a28f5312ec8c0b2 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,12 +1,12 @@
<?xml version="1.0" ?>
-<coverage version="7.4.0" timestamp="1704449872184" lines-valid="676" lines-covered="592" line-rate="0.8757" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
+<coverage version="7.4.0" timestamp="1704881517474" lines-valid="893" lines-covered="778" line-rate="0.8712" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.4.0 -->
<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
<sources>
<source>/Users/jakit/customers/aurelio/semantic-router/semantic_router</source>
</sources>
<packages>
- <package name="." line-rate="0.9317" branch-rate="0" complexity="0">
+ <package name="." line-rate="0.9263" branch-rate="0" complexity="0">
<classes>
<class name="__init__.py" filename="__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
@@ -17,301 +17,308 @@
<line number="5" hits="1"/>
</lines>
</class>
- <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="1" branch-rate="0">
+ <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="0.9796" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
<line number="2" hits="1"/>
<line number="4" hits="1"/>
+ <line number="8" hits="1"/>
+ <line number="9" hits="1"/>
<line number="10" hits="1"/>
- <line number="11" hits="1"/>
+ <line number="13" hits="1"/>
<line number="14" hits="1"/>
<line number="15" hits="1"/>
<line number="16" hits="1"/>
<line number="17" hits="1"/>
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- <line number="24" hits="1"/>
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+ <line number="19" hits="1"/>
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<line number="27" hits="1"/>
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+ <line number="30" hits="0"/>
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+ <line number="33" hits="1"/>
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<line number="41" hits="1"/>
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+ <line number="46" hits="1"/>
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+ <line number="57" hits="1"/>
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<line number="141" hits="1"/>
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<line number="144" hits="1"/>
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</lines>
</class>
- <class name="layer.py" filename="layer.py" complexity="0" line-rate="0.8836" branch-rate="0">
+ <class name="layer.py" filename="layer.py" complexity="0" line-rate="0.8827" branch-rate="0">
<methods/>
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<line number="1" hits="1"/>
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+ <line number="330" hits="1"/>
+ <line number="331" hits="1"/>
</lines>
</class>
<class name="linear.py" filename="linear.py" complexity="0" line-rate="1" branch-rate="0">
@@ -332,7 +339,7 @@
<line number="30" hits="1"/>
</lines>
</class>
- <class name="route.py" filename="route.py" complexity="0" line-rate="0.9275" branch-rate="0">
+ <class name="route.py" filename="route.py" complexity="0" line-rate="0.9342" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
@@ -343,114 +350,131 @@
<line number="8" hits="1"/>
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<line number="10" hits="1"/>
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<line number="85" hits="0"/>
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+ <line number="86" hits="0"/>
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+ <line number="90" hits="1"/>
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- <line number="93" hits="0"/>
<line number="94" hits="0"/>
<line number="95" hits="0"/>
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+ <line number="96" hits="0"/>
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- <line number="110" hits="1"/>
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+ <line number="107" hits="0"/>
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+ <line number="112" hits="1"/>
<line number="115" hits="0"/>
- <line number="116" hits="0"/>
+ <line number="117" hits="0"/>
<line number="118" hits="0"/>
<line number="119" hits="0"/>
+ <line number="120" hits="0"/>
+ <line number="122" hits="0"/>
+ <line number="123" hits="0"/>
+ <line number="124" hits="0"/>
</lines>
</class>
- <class name="llm.py" filename="utils/llm.py" complexity="0" line-rate="0.2857" branch-rate="0">
+ <class name="llm.py" filename="utils/llm.py" complexity="0" line-rate="0" branch-rate="0">
<methods/>
<lines>
- <line number="1" hits="1"/>
- <line number="3" hits="1"/>
- <line number="5" hits="1"/>
- <line number="8" hits="1"/>
+ <line number="1" hits="0"/>
+ <line number="3" hits="0"/>
+ <line number="5" hits="0"/>
+ <line number="6" hits="0"/>
<line number="9" hits="0"/>
<line number="10" hits="0"/>
- <line number="15" hits="0"/>
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- <line number="29" hits="0"/>
+ <line number="11" hits="0"/>
+ <line number="16" hits="0"/>
+ <line number="28" hits="0"/>
<line number="30" hits="0"/>
<line number="31" hits="0"/>
<line number="32" hits="0"/>
<line number="33" hits="0"/>
<line number="34" hits="0"/>
+ <line number="35" hits="0"/>
</lines>
</class>
<class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="1" branch-rate="0">
@@ -746,34 +997,34 @@
<line number="1" hits="1"/>
<line number="3" hits="1"/>
<line number="6" hits="1"/>
- <line number="29" hits="1"/>
- <line number="30" hits="1"/>
<line number="31" hits="1"/>
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+ <line number="33" hits="1"/>
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+ <line number="38" hits="1"/>
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- <line number="40" hits="1"/>
<line number="41" hits="1"/>
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<line number="43" hits="1"/>
+ <line number="44" hits="1"/>
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- <line number="46" hits="1"/>
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<line number="48" hits="1"/>
<line number="49" hits="1"/>
+ <line number="50" hits="1"/>
<line number="51" hits="1"/>
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+ <line number="53" hits="1"/>
<line number="54" hits="1"/>
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+ <line number="56" hits="1"/>
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+ <line number="69" hits="1"/>
+ <line number="74" hits="1"/>
+ <line number="75" hits="1"/>
</lines>
</class>
</classes>
diff --git a/docs/00-introduction.ipynb b/docs/00-introduction.ipynb
index 4d672f379a44b236ad60a04eaf439c7a0c29ec94..0665070391a00545a4230aead9f22bd9b22717b3 100644
--- a/docs/00-introduction.ipynb
+++ b/docs/00-introduction.ipynb
@@ -44,13 +44,6 @@
"!pip install -qU semantic-router==0.0.15"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_**âš ï¸ If using Google Colab, install the prerequisites and then restart the notebook before continuing**_"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/docs/01-save-load-from-file.ipynb b/docs/01-save-load-from-file.ipynb
index 43937d2b7d74f1161073d866128df7b43ce87dca..6c5945cb9020a5d2296b86a1c282553abfb15736 100644
--- a/docs/01-save-load-from-file.ipynb
+++ b/docs/01-save-load-from-file.ipynb
@@ -39,13 +39,6 @@
"!pip install -qU semantic-router==0.0.15"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_**âš ï¸ If using Google Colab, install the prerequisites and then restart the notebook before continuing**_"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
diff --git a/docs/02-dynamic-routes.ipynb b/docs/02-dynamic-routes.ipynb
index 70c5eced9ece37a822ac0af0174f03f9ec07768a..88b7794aadac761ec74dc872d2203203208791e7 100644
--- a/docs/02-dynamic-routes.ipynb
+++ b/docs/02-dynamic-routes.ipynb
@@ -1,376 +1,453 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/02-dynamic-routes.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/02-dynamic-routes.ipynb)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Dynamic Routes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In semantic-router there are two types of routes that can be chosen. Both routes belong to the `Route` object, the only difference between them is that _static_ routes return a `Route.name` when chosen, whereas _dynamic_ routes use an LLM call to produce parameter input values.\n",
- "\n",
- "For example, a _static_ route will tell us if a query is talking about mathematics by returning the route name (which could be `\"math\"` for example). A _dynamic_ route can generate additional values, so it may decide a query is talking about maths, but it can also generate Python code that we can later execute to answer the user's query, this output may look like `\"math\", \"import math; output = math.sqrt(64)`."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Installing the Library"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "!pip install -qU semantic-router==0.0.15"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_**âš ï¸ If using Google Colab, install the prerequisites and then restart the notebook before continuing**_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Initializing Routes and RouteLayer"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Dynamic routes are treated in the same way as static routes, let's begin by initializing a `RouteLayer` consisting of static routes."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router import Route\n",
- "\n",
- "politics = Route(\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\" \"don't you just hate the president\",\n",
- " \"they're going to destroy this country!\",\n",
- " \"they will save the country!\",\n",
- " ],\n",
- ")\n",
- "chitchat = Route(\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",
- "routes = [politics, chitchat]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We initialize our `RouteLayer` with our `encoder` and `routes`. We can use popular encoder APIs like `CohereEncoder` and `OpenAIEncoder`, or local alternatives like `FastEmbedEncoder`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
+ "cells": [
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2024-01-07 18:11:05 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "from getpass import getpass\n",
- "from semantic_router import RouteLayer\n",
- "from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
- "\n",
- "# dashboard.cohere.ai\n",
- "# os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
- "# \"Enter Cohere API Key: \"\n",
- "# )\n",
- "# platform.openai.com\n",
- "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
- " \"Enter OpenAI API Key: \"\n",
- ")\n",
- "\n",
- "# encoder = CohereEncoder()\n",
- "encoder = OpenAIEncoder()\n",
- "\n",
- "rl = RouteLayer(encoder=encoder, routes=routes)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We run the solely static routes layer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UxqB7_Ieur0s"
+ },
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/02-dynamic-routes.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/02-dynamic-routes.ipynb)"
+ ]
+ },
{
- "data": {
- "text/plain": [
- "RouteChoice(name='chitchat', function_call=None)"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EduhQaNAur0u"
+ },
+ "source": [
+ "# Dynamic Routes"
]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rl(\"how's the weather today?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Creating a Dynamic Route"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As with static routes, we must create a dynamic route before adding it to our route layer. To make a route dynamic, we need to provide a `function_schema`. The function schema provides instructions on what a function is, so that an LLM can decide how to use it correctly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "from datetime import datetime\n",
- "from zoneinfo import ZoneInfo\n",
- "\n",
- "\n",
- "def get_time(timezone: str) -> str:\n",
- " \"\"\"Finds the current time in a specific timezone.\n",
- "\n",
- " :param timezone: The timezone to find the current time in, should\n",
- " be a valid timezone from the IANA Time Zone Database like\n",
- " \"America/New_York\" or \"Europe/London\".\n",
- " :type timezone: str\n",
- " :return: The current time in the specified timezone.\"\"\"\n",
- " now = datetime.now(ZoneInfo(timezone))\n",
- " return now.strftime(\"%H:%M\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "'12:11'"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_4JgNeX4ur0v"
+ },
+ "source": [
+ "In semantic-router there are two types of routes that can be chosen. Both routes belong to the `Route` object, the only difference between them is that _static_ routes return a `Route.name` when chosen, whereas _dynamic_ routes use an LLM call to produce parameter input values.\n",
+ "\n",
+ "For example, a _static_ route will tell us if a query is talking about mathematics by returning the route name (which could be `\"math\"` for example). A _dynamic_ route can generate additional values, so it may decide a query is talking about maths, but it can also generate Python code that we can later execute to answer the user's query, this output may look like `\"math\", \"import math; output = math.sqrt(64)`."
]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "get_time(\"America/New_York\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To get the function schema we can use the `get_schema` function from the `function_call` module."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
+ },
{
- "data": {
- "text/plain": [
- "{'name': 'get_time',\n",
- " 'description': 'Finds the current time in a specific timezone.\\n\\n:param timezone: The timezone to find the current time in, should\\n be a valid timezone from the IANA Time Zone Database like\\n \"America/New_York\" or \"Europe/London\".\\n:type timezone: str\\n:return: The current time in the specified timezone.',\n",
- " 'signature': '(timezone: str) -> str',\n",
- " 'output': \"<class 'str'>\"}"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bbmw8CO4ur0v"
+ },
+ "source": [
+ "## Installing the Library"
]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from semantic_router.utils.function_call import get_schema\n",
- "\n",
- "schema = get_schema(get_time)\n",
- "schema"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We use this to define our dynamic route:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "time_route = Route(\n",
- " name=\"get_time\",\n",
- " utterances=[\n",
- " \"what is the time in new york city?\",\n",
- " \"what is the time in london?\",\n",
- " \"I live in Rome, what time is it?\",\n",
- " ],\n",
- " function_schema=schema,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Add the new route to our `layer`:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
+ },
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2024-01-07 18:11:20 INFO semantic_router.utils.logger Adding `get_time` route\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "rl.add(time_route)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we can ask our layer a time related question to trigger our new dynamic route."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dLElfRhgur0v"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -qU semantic-router==0.0.15"
+ ]
+ },
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[33m2024-01-07 18:11:23 WARNING semantic_router.utils.logger No LLM provided for dynamic route, will use OpenAI LLM default. Ensure API key is set in OPENAI_API_KEY environment variable.\u001b[0m\n",
- "\u001b[32m2024-01-07 18:11:23 INFO semantic_router.utils.logger Extracting function input...\u001b[0m\n"
- ]
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BixZd6Eour0w"
+ },
+ "source": [
+ "## Initializing Routes and RouteLayer"
+ ]
},
{
- "data": {
- "text/plain": [
- "RouteChoice(name='get_time', function_call={'timezone': 'new york city'})"
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PxnW9qBvur0x"
+ },
+ "source": [
+ "Dynamic routes are treated in the same way as static routes, let's begin by initializing a `RouteLayer` consisting of static routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "kc9Ty6Lgur0x"
+ },
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "politics = Route(\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\" \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")\n",
+ "chitchat = Route(\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",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "voWyqmffur0x"
+ },
+ "source": [
+ "We initialize our `RouteLayer` with our `encoder` and `routes`. We can use popular encoder APIs like `CohereEncoder` and `OpenAIEncoder`, or local alternatives like `FastEmbedEncoder`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "BI9AiDspur0y",
+ "outputId": "27329a54-3f16-44a5-ac20-13a6b26afb97",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\u001b[32m2024-01-08 11:12:24 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router import RouteLayer\n",
+ "from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
+ "\n",
+ "# dashboard.cohere.ai\n",
+ "# os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
+ "# \"Enter Cohere API Key: \"\n",
+ "# )\n",
+ "# platform.openai.com\n",
+ "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API Key: \"\n",
+ ")\n",
+ "\n",
+ "# encoder = CohereEncoder()\n",
+ "encoder = OpenAIEncoder()\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GuLCeIS5ur0y"
+ },
+ "source": [
+ "We run the solely static routes layer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "_rNREh7gur0y",
+ "outputId": "f3a1dc0b-d760-4efb-b634-d3547011dcb7",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "McbLKO26ur0y"
+ },
+ "source": [
+ "## Creating a Dynamic Route"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ANAoEjxYur0y"
+ },
+ "source": [
+ "As with static routes, we must create a dynamic route before adding it to our route layer. To make a route dynamic, we need to provide a `function_schema`. The function schema provides instructions on what a function is, so that an LLM can decide how to use it correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "id": "5jaF1Xa5ur0y"
+ },
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "from zoneinfo import ZoneInfo\n",
+ "\n",
+ "\n",
+ "def get_time(timezone: str) -> str:\n",
+ " \"\"\"Finds the current time in a specific timezone.\n",
+ "\n",
+ " :param timezone: The timezone to find the current time in, should\n",
+ " be a valid timezone from the IANA Time Zone Database like\n",
+ " \"America/New_York\" or \"Europe/London\". Do NOT put the place\n",
+ " name itself like \"rome\", or \"new york\", you must provide\n",
+ " the IANA format.\n",
+ " :type timezone: str\n",
+ " :return: The current time in the specified timezone.\"\"\"\n",
+ " now = datetime.now(ZoneInfo(timezone))\n",
+ " return now.strftime(\"%H:%M\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "YyFKV8jMur0z",
+ "outputId": "29cf80f4-552c-47bb-fbf9-019f5dfdf00a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'06:13'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ],
+ "source": [
+ "get_time(\"America/New_York\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4qyaRuNXur0z"
+ },
+ "source": [
+ "To get the function schema we can use the `get_schema` function from the `function_call` module."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "tOjuhp5Xur0z",
+ "outputId": "ca88a3ea-d70a-4950-be9a-63fab699de3b",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{'name': 'get_time',\n",
+ " 'description': 'Finds the current time in a specific timezone.\\n\\n:param timezone: The timezone to find the current time in, should\\n be a valid timezone from the IANA Time Zone Database like\\n \"America/New_York\" or \"Europe/London\". Do NOT put the place\\n name itself like \"rome\", or \"new york\", you must provide\\n the IANA format.\\n:type timezone: str\\n:return: The current time in the specified timezone.',\n",
+ " 'signature': '(timezone: str) -> str',\n",
+ " 'output': \"<class 'str'>\"}"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ],
+ "source": [
+ "from semantic_router.utils.function_call import get_schema\n",
+ "\n",
+ "schema = get_schema(get_time)\n",
+ "schema"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HcF7jGjAur0z"
+ },
+ "source": [
+ "We use this to define our dynamic route:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "iesBG9P3ur0z"
+ },
+ "outputs": [],
+ "source": [
+ "time_route = Route(\n",
+ " name=\"get_time\",\n",
+ " utterances=[\n",
+ " \"what is the time in new york city?\",\n",
+ " \"what is the time in london?\",\n",
+ " \"I live in Rome, what time is it?\",\n",
+ " ],\n",
+ " function_schema=schema,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZiUs3ovpur0z"
+ },
+ "source": [
+ "Add the new route to our `layer`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "-0vY8PRXur0z",
+ "outputId": "db01e14c-eab3-4f93-f4c2-e30f508c8b5d",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\u001b[32m2024-01-08 11:15:26 INFO semantic_router.utils.logger Adding `get_time` route\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "rl.add(time_route)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7yoE0IrNur0z"
+ },
+ "source": [
+ "Now we can ask our layer a time related question to trigger our new dynamic route."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "Wfb68M0-ur0z",
+ "outputId": "79923883-2a4d-4744-f8ce-e818cb5f14c3",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 53
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "\u001b[32m2024-01-08 11:16:24 INFO semantic_router.utils.logger Extracting function input...\u001b[0m\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'06:16'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 11
+ }
+ ],
+ "source": [
+ "out = rl(\"what is the time in new york city?\")\n",
+ "get_time(**out.function_call)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qt0vkq2Xur00"
+ },
+ "source": [
+ "Our dynamic route provides both the route itself _and_ the input parameters required to use the route."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J0oD1dxIur00"
+ },
+ "source": [
+ "---"
]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
}
- ],
- "source": [
- "# https://platform.openai.com/\n",
- "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
- " \"Enter OpenAI API Key: \"\n",
- ")\n",
- "\n",
- "rl(\"what is the time in new york city?\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Our dynamic route provides both the route itself _and_ the input parameters required to use the route."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "decision-layer",
- "language": "python",
- "name": "python3"
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "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"
+ },
+ "colab": {
+ "provenance": []
+ }
},
- "language_info": {
- "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
-}
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/03-basic-langchain-agent.ipynb b/docs/03-basic-langchain-agent.ipynb
index c8c48b3dbedf5c6a09243ff2c93a1071f40c916d..dc728495136a804a79340026d14f56807929e443 100644
--- a/docs/03-basic-langchain-agent.ipynb
+++ b/docs/03-basic-langchain-agent.ipynb
@@ -83,15 +83,6 @@
" openai==1.6.1"
]
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "cHtVnoJPA04x"
- },
- "source": [
- "_**âš ï¸ If using Google Colab, install the prerequisites and then restart the notebook before continuing**_"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
diff --git a/docs/04-chat-history.ipynb b/docs/04-chat-history.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4ac533b1ef4126ac63cb6b8cacf2441d73a538ea
--- /dev/null
+++ b/docs/04-chat-history.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](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",
+ "metadata": {},
+ "source": [
+ "# Considering Chat History"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Applying semantic-router to the most recent interaction in a conversation can work for many cases but it misses scenarios where information provided in the latest interaction."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jamesbriggs/opt/anaconda3/envs/decision-layer/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\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n",
+ "100%|██████████| 83.2M/83.2M [00:09<00:00, 8.45MiB/s]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'split 1': ['User: Hello! Can you tell me the latest news headlines?',\n",
+ " 'Bot: Hi! Sure, here are the top news headlines for today...'],\n",
+ " 'split 2': [\"User: That's quite interesting. I'm also looking for some new music to listen to.\",\n",
+ " 'Bot: What genre do you prefer?',\n",
+ " 'User: I like pop music.',\n",
+ " 'Bot: You might enjoy the latest album by Dua Lipa.',\n",
+ " \"User: I'll give it a listen. Also, I'm planning a trip and need some travel tips.\",\n",
+ " 'Bot: Sure, where are you planning to go?'],\n",
+ " 'split 3': [\"User: I'm thinking of visiting Italy.\",\n",
+ " 'Bot: Italy is a beautiful country. Make sure to visit the Colosseum in Rome and the canals in Venice.'],\n",
+ " 'split 4': ['User: Those sound like great suggestions. I also need some help with my diet.',\n",
+ " 'Bot: What kind of diet are you following?',\n",
+ " \"User: I'm trying to eat more protein.\",\n",
+ " 'Bot: Include lean meats, eggs, and legumes in your diet for a protein boost.',\n",
+ " \"User: Thanks for the tips! I'll talk to you later.\",\n",
+ " \"Bot: You're welcome! Don't hesitate to reach out if you need more help.\",\n",
+ " 'User: I appreciate it. Goodbye!',\n",
+ " 'Bot: Goodbye! Take care!']}"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from semantic_router.schema import Conversation, Message\n",
+ "from semantic_router.encoders import FastEmbedEncoder\n",
+ "\n",
+ "\n",
+ "messages = [\n",
+ " \"User: Hello! Can you tell me the latest news headlines?\",\n",
+ " \"Bot: Hi! Sure, here are the top news headlines for today...\",\n",
+ " \"User: That's quite interesting. I'm also looking for some new music to listen to.\",\n",
+ " \"Bot: What genre do you prefer?\",\n",
+ " \"User: I like pop music.\",\n",
+ " \"Bot: You might enjoy the latest album by Dua Lipa.\",\n",
+ " \"User: I'll give it a listen. Also, I'm planning a trip and need some travel tips.\",\n",
+ " \"Bot: Sure, where are you planning to go?\",\n",
+ " \"User: I'm thinking of visiting Italy.\",\n",
+ " \"Bot: Italy is a beautiful country. Make sure to visit the Colosseum in Rome and the canals in Venice.\",\n",
+ " \"User: Those sound like great suggestions. I also need some help with my diet.\",\n",
+ " \"Bot: What kind of diet are you following?\",\n",
+ " \"User: I'm trying to eat more protein.\",\n",
+ " \"Bot: Include lean meats, eggs, and legumes in your diet for a protein boost.\",\n",
+ " \"User: Thanks for the tips! I'll talk to you later.\",\n",
+ " \"Bot: You're welcome! Don't hesitate to reach out if you need more help.\",\n",
+ " \"User: I appreciate it. Goodbye!\",\n",
+ " \"Bot: Goodbye! Take care!\",\n",
+ "]\n",
+ "\n",
+ "encoder = FastEmbedEncoder(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\n",
+ "\n",
+ "convo = Conversation(\n",
+ " messages=[\n",
+ " Message(role=m.split(\": \")[0], content=m.split(\": \")[1]) for m in messages\n",
+ " ]\n",
+ ")\n",
+ "\n",
+ "convo.split_by_topic(\n",
+ " encoder=encoder, threshold=0.72, split_method=\"cumulative_similarity_drop\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "ml",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "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
+}
diff --git a/docs/encoders/fastembed.ipynb b/docs/encoders/fastembed.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..68b1f69cd7228a25ec2ae97e6ffa4f13ca39a116
--- /dev/null
+++ b/docs/encoders/fastembed.ipynb
@@ -0,0 +1,262 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using FastEmbedEncoder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "FastEmbed is a _lightweight and fast_ embedding library built for generating embeddings. It can be run locally and supports many open source encoders.\n",
+ "\n",
+ "Beyond being a local, open source library, there are two key reasons we might want to run this library over other open source alternatives:\n",
+ "\n",
+ "* **Lightweight and Fast**: The library uses an ONNX runtime so there is no heavy PyTorch dependency, supports quantized model weights (smaller memory footprint), is developed for running on CPU, and uses data-parallelism for encoding large datasets.\n",
+ "\n",
+ "* **Open-weight models**: FastEmbed supports many open source and open-weight models, included some that outperform popular encoders like OpenAI's Ada-002."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router with the `[fastembed]` flag to include all necessary dependencies for `FastEmbedEncoder`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[fastembed]==0.0.15\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "politics = Route(\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": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chitchat = Route(\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",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding model, you can find a list of [all available embedding models here](https://qdrant.github.io/fastembed/examples/Supported_Models/):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.encoders import FastEmbedEncoder\n",
+ "\n",
+ "encoder = FastEmbedEncoder(name=\"BAAI/bge-small-en-v1.5\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "_**âš ï¸ If you see an ImportError, you must install the FastEmbed library. You can do so by installing Semantic Router using `pip install -qU \"semantic-router[fastembed]\"`.**_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-01-06 16:53:16 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can test it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/encoders/huggingface.ipynb b/docs/encoders/huggingface.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4e9c28cdd9d1cc4c65b84a30de2d896b77ef2049
--- /dev/null
+++ b/docs/encoders/huggingface.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using HuggingFaceEncoder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "HuggingFace is a huge ecosystem of open source models. It can be run locally and supports the largest library of encoders."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router with the `[local]` flag to include all necessary dependencies for `HuggingFaceEncoder`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[local]==0.0.16\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "politics = Route(\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": [
+ "_**âš ï¸ If you see an ImportError, you must install local dependencies. You can do so by installing Semantic Router using `pip install -qU \"semantic-router[local]\"`.**_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chitchat = Route(\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",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jamesbriggs/opt/anaconda3/envs/decision-layer/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\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n",
+ "tokenizer_config.json: 100%|██████████| 350/350 [00:00<00:00, 1.06MB/s]\n",
+ "vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 1.05MB/s]\n",
+ "tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 1.43MB/s]\n",
+ "special_tokens_map.json: 100%|██████████| 112/112 [00:00<00:00, 386kB/s]\n",
+ "config.json: 100%|██████████| 612/612 [00:00<00:00, 2.90MB/s]\n",
+ "pytorch_model.bin: 100%|██████████| 90.9M/90.9M [00:01<00:00, 63.2MB/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.encoders import HuggingFaceEncoder\n",
+ "\n",
+ "encoder = HuggingFaceEncoder()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-01-09 00:22:35 INFO semantic_router.utils.logger Initializing RouteLayer\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can test it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "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
+}
diff --git a/docs/examples/function_calling.ipynb b/docs/examples/function_calling.ipynb
index 401ab04a71296066ad1aa7762d0c4e3f1b7f9d52..5392e54e537cadeb74ec3554c37d8f76c9e4a96c 100644
--- a/docs/examples/function_calling.ipynb
+++ b/docs/examples/function_calling.ipynb
@@ -512,7 +512,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.3"
+ "version": "3.9.18"
}
},
"nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index 2ca6055c061bb6141341cea33a9b7039045d86dd..1328ceea35bda84609bb6f2985c06959522912a5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
@@ -250,6 +250,17 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
uvloop = ["uvloop (>=0.15.2)"]
+[[package]]
+name = "cachetools"
+version = "5.3.2"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "cachetools-5.3.2-py3-none-any.whl", hash = "sha256:861f35a13a451f94e301ce2bec7cac63e881232ccce7ed67fab9b5df4d3beaa1"},
+ {file = "cachetools-5.3.2.tar.gz", hash = "sha256:086ee420196f7b2ab9ca2db2520aca326318b68fe5ba8bc4d49cca91add450f2"},
+]
+
[[package]]
name = "certifi"
version = "2023.11.17"
@@ -325,6 +336,17 @@ files = [
[package.dependencies]
pycparser = "*"
+[[package]]
+name = "chardet"
+version = "5.2.0"
+description = "Universal encoding detector for Python 3"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "chardet-5.2.0-py3-none-any.whl", hash = "sha256:e1cf59446890a00105fe7b7912492ea04b6e6f06d4b742b2c788469e34c82970"},
+ {file = "chardet-5.2.0.tar.gz", hash = "sha256:1b3b6ff479a8c414bc3fa2c0852995695c4a026dcd6d0633b2dd092ca39c1cf7"},
+]
+
[[package]]
name = "charset-normalizer"
version = "3.3.2"
@@ -440,13 +462,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cohere"
-version = "4.40"
+version = "4.41"
description = "Python SDK for the Cohere API"
optional = false
python-versions = ">=3.8,<4.0"
files = [
- {file = "cohere-4.40-py3-none-any.whl", hash = "sha256:75dac8369d97fadc05901352d9db64a0ca6cd40c08423f3c4691f57eb7b131e7"},
- {file = "cohere-4.40.tar.gz", hash = "sha256:d9e5c1fa7f80a193c03330a634954b927bf188ead7dcfdb51865480f73aebda8"},
+ {file = "cohere-4.41-py3-none-any.whl", hash = "sha256:39470cc412fa96a1c612f522d48d7d86b34b3163a04030cff83ec48ebbaff32f"},
+ {file = "cohere-4.41.tar.gz", hash = "sha256:8509ca196dc038eca81e474d3cd5896da2ea168a4d3c578b4cb6969994be34ef"},
]
[package.dependencies]
@@ -624,6 +646,17 @@ files = [
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
]
+[[package]]
+name = "distlib"
+version = "0.3.8"
+description = "Distribution utilities"
+optional = false
+python-versions = "*"
+files = [
+ {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
+ {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
+]
+
[[package]]
name = "distro"
version = "1.9.0"
@@ -679,42 +712,42 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
[[package]]
name = "fastavro"
-version = "1.9.2"
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+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic (<2)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.14,<0.19)"]
+torch = ["accelerate (>=0.21.0)", "torch (>=1.10,!=1.12.0)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "triton"
+version = "2.1.0"
+description = "A language and compiler for custom Deep Learning operations"
+optional = true
+python-versions = "*"
+files = [
+ {file = "triton-2.1.0-0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:66439923a30d5d48399b08a9eae10370f6c261a5ec864a64983bae63152d39d7"},
+ {file = "triton-2.1.0-0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:919b06453f0033ea52c13eaf7833de0e57db3178d23d4e04f9fc71c4f2c32bf8"},
+ {file = "triton-2.1.0-0-cp37-cp37m-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae4bb8a91de790e1866405211c4d618379781188f40d5c4c399766914e84cd94"},
+ {file = "triton-2.1.0-0-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:39f6fb6bdccb3e98f3152e3fbea724f1aeae7d749412bbb1fa9c441d474eba26"},
+ {file = "triton-2.1.0-0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:21544e522c02005a626c8ad63d39bdff2f31d41069592919ef281e964ed26446"},
+ {file = "triton-2.1.0-0-pp37-pypy37_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:143582ca31dd89cd982bd3bf53666bab1c7527d41e185f9e3d8a3051ce1b663b"},
+ {file = "triton-2.1.0-0-pp38-pypy38_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:82fc5aeeedf6e36be4e4530cbdcba81a09d65c18e02f52dc298696d45721f3bd"},
+ {file = "triton-2.1.0-0-pp39-pypy39_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:81a96d110a738ff63339fc892ded095b31bd0d205e3aace262af8400d40b6fa8"},
+]
+
+[package.dependencies]
+filelock = "*"
+
+[package.extras]
+build = ["cmake (>=3.18)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
+tutorials = ["matplotlib", "pandas", "tabulate"]
+
[[package]]
name = "types-pyyaml"
version = "6.0.12.12"
@@ -2558,6 +3129,26 @@ brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
+[[package]]
+name = "virtualenv"
+version = "20.25.0"
+description = "Virtual Python Environment builder"
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "virtualenv-20.25.0-py3-none-any.whl", hash = "sha256:4238949c5ffe6876362d9c0180fc6c3a824a7b12b80604eeb8085f2ed7460de3"},
+ {file = "virtualenv-20.25.0.tar.gz", hash = "sha256:bf51c0d9c7dd63ea8e44086fa1e4fb1093a31e963b86959257378aef020e1f1b"},
+]
+
+[package.dependencies]
+distlib = ">=0.3.7,<1"
+filelock = ">=3.12.2,<4"
+platformdirs = ">=3.9.1,<5"
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.2)", "sphinx-argparse (>=0.4)", "sphinxcontrib-towncrier (>=0.2.1a0)", "towncrier (>=23.6)"]
+test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=23.1)", "pytest (>=7.4)", "pytest-env (>=0.8.2)", "pytest-freezer (>=0.4.8)", "pytest-mock (>=3.11.1)", "pytest-randomly (>=3.12)", "pytest-timeout (>=2.1)", "setuptools (>=68)", "time-machine (>=2.10)"]
+
[[package]]
name = "wcwidth"
version = "0.2.13"
@@ -2700,8 +3291,9 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[extras]
fastembed = ["fastembed"]
hybrid = ["pinecone-text"]
+local = ["torch", "transformers"]
[metadata]
lock-version = "2.0"
-python-versions = ">=3.10,<3.12"
-content-hash = "c3d1f027aca765b48cccd119ab67670ecb51248433dda40bc8a9cbcc38f68d7c"
+python-versions = "^3.9"
+content-hash = "4ceb0344e006fc7657c66548321ff51d34a297ee6f9ab069fdc53e1256024a12"
diff --git a/pyproject.toml b/pyproject.toml
index 5b0e1f304e1d57beec6c5454ef32e953add68338..45f105fd9ac63138f7b5d4d82b6300d1052f2ccb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,22 +14,25 @@ readme = "README.md"
packages = [{include = "semantic_router"}]
[tool.poetry.dependencies]
-python = ">=3.10,<3.12"
+python = "^3.9"
pydantic = "^1.8.2"
openai = "^1.3.9"
cohere = "^4.32"
numpy = "^1.25.2"
-pinecone-text = {version = "^0.7.0", optional = true}
colorlog = "^6.8.0"
pyyaml = "^6.0.1"
-fastembed = {version = "^0.1.3", optional = true}
+pinecone-text = {version = "^0.7.1", optional = true}
+fastembed = {version = "^0.1.3", optional = true, python = "<3.12"}
+torch = {version = "^2.1.2", optional = true}
+transformers = {version = "^4.36.2", optional = true}
[tool.poetry.extras]
hybrid = ["pinecone-text"]
fastembed = ["fastembed"]
+local = ["torch", "transformers"]
[tool.poetry.group.dev.dependencies]
-ipykernel = "^6.26.0"
+ipykernel = "^6.25.0"
ruff = "^0.1.5"
black = {extras = ["jupyter"], version = "^23.12.0"}
pytest = "^7.4.3"
diff --git a/replace.py b/replace.py
new file mode 100644
index 0000000000000000000000000000000000000000..508c3f33f32c42600dc9aa25c21f62bbaf119dd3
--- /dev/null
+++ b/replace.py
@@ -0,0 +1,28 @@
+import os
+import re
+
+
+def replace_type_hints(file_path):
+ with open(file_path, "rb") as file:
+ file_data = file.read()
+
+ # Decode the file data with error handling
+ file_data = file_data.decode("utf-8", errors="ignore")
+
+ # Regular expression pattern to find 'Optional[dict[int, int]]' and replace with 'Optional[dict[int, int]]'
+ file_data = re.sub(
+ r"dict\[(\w+), (\w+)\]\s*\|\s*None", r"Optional[dict[\1, \2]]", file_data
+ )
+
+ with open(file_path, "w") as file:
+ file.write(file_data)
+
+
+# Directory path
+dir_path = "/Users/jakit/customers/aurelio/semantic-router"
+
+# Traverse the directory
+for root, dirs, files in os.walk(dir_path):
+ for file in files:
+ if file.endswith(".py"):
+ replace_type_hints(os.path.join(root, file))
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 2f71ff4e5984d310a0bac4efab9b24c0e61ac6ad..713f8a995e7e52db60cf38d3023bf182dde56361 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -2,6 +2,7 @@ from semantic_router.encoders.base import BaseEncoder
from semantic_router.encoders.bm25 import BM25Encoder
from semantic_router.encoders.cohere import CohereEncoder
from semantic_router.encoders.fastembed import FastEmbedEncoder
+from semantic_router.encoders.huggingface import HuggingFaceEncoder
from semantic_router.encoders.openai import OpenAIEncoder
from semantic_router.encoders.tfidf import TfidfEncoder
@@ -12,4 +13,5 @@ __all__ = [
"BM25Encoder",
"TfidfEncoder",
"FastEmbedEncoder",
+ "HuggingFaceEncoder",
]
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index 68150cb767faec1a8c3c5417f9d5f6db3a977cda..451273cdcc78899861c7b64baea4eb4e1cc6b33b 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -1,26 +1,37 @@
-from typing import Any
+from typing import Any, Optional
from semantic_router.encoders import BaseEncoder
from semantic_router.utils.logger import logger
class BM25Encoder(BaseEncoder):
- model: Any | None = None
- idx_mapping: dict[int, int] | None = None
+ model: Optional[Any] = None
+ idx_mapping: Optional[dict[int, int]] = None
type: str = "sparse"
- def __init__(self, name: str = "bm25", score_threshold: float = 0.82):
+ def __init__(
+ self,
+ name: str = "bm25",
+ score_threshold: float = 0.82,
+ use_default_params: bool = True,
+ ):
super().__init__(name=name, score_threshold=score_threshold)
try:
from pinecone_text.sparse import BM25Encoder as encoder
except ImportError:
raise ImportError(
"Please install pinecone-text to use BM25Encoder. "
- "You can install it with: `pip install semantic-router[hybrid]`"
+ "You can install it with: `pip install 'semantic-router[hybrid]'`"
)
- logger.info("Downloading and initializing BM25 model parameters.")
- self.model = encoder.default()
+ self.model = encoder()
+
+ if use_default_params:
+ logger.info("Downloading and initializing default sBM25 model parameters.")
+ self.model = encoder.default()
+ self._set_idx_mapping()
+
+ def _set_idx_mapping(self):
params = self.model.get_params()
doc_freq = params["doc_freq"]
if isinstance(doc_freq, dict):
@@ -53,3 +64,4 @@ class BM25Encoder(BaseEncoder):
if self.model is None:
raise ValueError("Model is not initialized.")
self.model.fit(docs)
+ self._set_idx_mapping()
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
index 2f80aaafdc5b5554f86851e6dfc3b12fb3ca08b0..ec8ee0f8fcebd39444f3689d7bdffc2b7e98c812 100644
--- a/semantic_router/encoders/cohere.py
+++ b/semantic_router/encoders/cohere.py
@@ -1,4 +1,5 @@
import os
+from typing import Optional
import cohere
@@ -6,13 +7,13 @@ from semantic_router.encoders import BaseEncoder
class CohereEncoder(BaseEncoder):
- client: cohere.Client | None = None
+ client: Optional[cohere.Client] = None
type: str = "cohere"
def __init__(
self,
- name: str | None = None,
- cohere_api_key: str | None = None,
+ name: Optional[str] = None,
+ cohere_api_key: Optional[str] = None,
score_threshold: float = 0.3,
):
if name is None:
diff --git a/semantic_router/encoders/fastembed.py b/semantic_router/encoders/fastembed.py
index 413e3a6ada8e956fcbb8cbdfdd640a29883647df..98cfc6cc529ff4c06e0a3e7f0d0af779df7f71dd 100644
--- a/semantic_router/encoders/fastembed.py
+++ b/semantic_router/encoders/fastembed.py
@@ -27,7 +27,7 @@ class FastEmbedEncoder(BaseEncoder):
raise ImportError(
"Please install fastembed to use FastEmbedEncoder. "
"You can install it with: "
- "`pip install semantic-router[fastembed]`"
+ "`pip install 'semantic-router[fastembed]'`"
)
embedding_args = {
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
new file mode 100644
index 0000000000000000000000000000000000000000..ace189213b76aed940dd8b4280ce1505339f656f
--- /dev/null
+++ b/semantic_router/encoders/huggingface.py
@@ -0,0 +1,114 @@
+from typing import Any, Optional
+
+from pydantic import PrivateAttr
+
+from semantic_router.encoders import BaseEncoder
+
+
+class HuggingFaceEncoder(BaseEncoder):
+ name: str = "sentence-transformers/all-MiniLM-L6-v2"
+ type: str = "huggingface"
+ score_threshold: float = 0.5
+ tokenizer_kwargs: dict = {}
+ model_kwargs: dict = {}
+ device: Optional[str] = None
+ _tokenizer: Any = PrivateAttr()
+ _model: Any = PrivateAttr()
+ _torch: Any = PrivateAttr()
+
+ def __init__(self, **data):
+ super().__init__(**data)
+ self._tokenizer, self._model = self._initialize_hf_model()
+
+ def _initialize_hf_model(self):
+ try:
+ from transformers import AutoModel, AutoTokenizer
+ except ImportError:
+ raise ImportError(
+ "Please install transformers to use HuggingFaceEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[local]`"
+ )
+
+ try:
+ import torch
+ except ImportError:
+ raise ImportError(
+ "Please install Pytorch to use HuggingFaceEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[local]`"
+ )
+
+ self._torch = torch
+
+ tokenizer = AutoTokenizer.from_pretrained(
+ self.name,
+ **self.tokenizer_kwargs,
+ )
+
+ model = AutoModel.from_pretrained(self.name, **self.model_kwargs)
+
+ if self.device:
+ model.to(self.device)
+
+ else:
+ device = "cuda" if self._torch.cuda.is_available() else "cpu"
+ model.to(device)
+ self.device = device
+
+ return tokenizer, model
+
+ def __call__(
+ self,
+ docs: list[str],
+ batch_size: int = 32,
+ normalize_embeddings: bool = True,
+ pooling_strategy: str = "mean",
+ ) -> list[list[float]]:
+ all_embeddings = []
+ for i in range(0, len(docs), batch_size):
+ batch_docs = docs[i : i + batch_size]
+
+ encoded_input = self._tokenizer(
+ batch_docs, padding=True, truncation=True, return_tensors="pt"
+ ).to(self.device)
+
+ with self._torch.no_grad():
+ model_output = self._model(**encoded_input)
+
+ if pooling_strategy == "mean":
+ embeddings = self._mean_pooling(
+ model_output, encoded_input["attention_mask"]
+ )
+ elif pooling_strategy == "max":
+ embeddings = self._max_pooling(
+ model_output, encoded_input["attention_mask"]
+ )
+ else:
+ raise ValueError(
+ "Invalid pooling_strategy. Please use 'mean' or 'max'."
+ )
+
+ if normalize_embeddings:
+ embeddings = self._torch.nn.functional.normalize(embeddings, p=2, dim=1)
+
+ embeddings = embeddings.tolist()
+ all_embeddings.extend(embeddings)
+ return all_embeddings
+
+ def _mean_pooling(self, model_output, attention_mask):
+ token_embeddings = model_output[0]
+ input_mask_expanded = (
+ attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+ )
+ return self._torch.sum(
+ token_embeddings * input_mask_expanded, 1
+ ) / self._torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+ def _max_pooling(self, model_output, attention_mask):
+ token_embeddings = model_output[0]
+ input_mask_expanded = (
+ attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+ )
+ token_embeddings[input_mask_expanded == 0] = -1e9
+ return self._torch.max(token_embeddings, 1)[0]
diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py
index 4ec87638cf9fc2c9d33c3b7c6a77d9d38ecf9b5b..169761afa8f726a72439a534a69bac3ebf73de29 100644
--- a/semantic_router/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -1,5 +1,6 @@
import os
from time import sleep
+from typing import Optional
import openai
from openai import OpenAIError
@@ -10,13 +11,13 @@ from semantic_router.utils.logger import logger
class OpenAIEncoder(BaseEncoder):
- client: openai.Client | None
+ client: Optional[openai.Client]
type: str = "openai"
def __init__(
self,
- name: str | None = None,
- openai_api_key: str | None = None,
+ name: Optional[str] = None,
+ openai_api_key: Optional[str] = None,
score_threshold: float = 0.82,
):
if name is None:
diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py
index 5791231d44fdfb4cd9ac8a8a3e7ee962529bed93..0979d420ee3e4ad88ce72cb3174604c61cf198d9 100644
--- a/semantic_router/hybrid_layer.py
+++ b/semantic_router/hybrid_layer.py
@@ -1,3 +1,5 @@
+from typing import Optional
+
import numpy as np
from numpy.linalg import norm
@@ -17,14 +19,20 @@ class HybridRouteLayer:
def __init__(
self,
- dense_encoder: BaseEncoder,
- sparse_encoder: BaseEncoder,
+ encoder: BaseEncoder,
+ sparse_encoder: Optional[BM25Encoder] = None,
routes: list[Route] = [],
alpha: float = 0.3,
):
self.encoder = dense_encoder
self.score_threshold = self.encoder.score_threshold
- self.sparse_encoder = sparse_encoder
+
+ if sparse_encoder is None:
+ logger.warning("No sparse_encoder provided. Using default BM25Encoder.")
+ self.sparse_encoder = BM25Encoder()
+ else:
+ self.sparse_encoder = sparse_encoder
+
self.alpha = alpha
self.routes = routes
if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr(
@@ -38,7 +46,7 @@ class HybridRouteLayer:
# self._add_route(route=route)
self._add_routes(routes)
- def __call__(self, text: str) -> str | None:
+ def __call__(self, text: str) -> Optional[str]:
results = self._query(text)
top_class, top_class_scores = self._semantic_classify(results)
passed = self._pass_threshold(top_class_scores, self.score_threshold)
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index 082617566edb395c282491c04c9d38da8ae2ed16..cf546bfc1d791c6843bd62ce5ad4f178b9f0254b 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -1,5 +1,6 @@
import json
import os
+from typing import Optional
import numpy as np
import yaml
@@ -52,7 +53,7 @@ class LayerConfig:
self,
routes: list[Route] = [],
encoder_type: str = "openai",
- encoder_name: str | None = None,
+ encoder_name: Optional[str] = None,
):
self.encoder_type = encoder_type
if encoder_name is None:
@@ -131,7 +132,7 @@ class LayerConfig:
self.routes.append(route)
logger.info(f"Added route `{route.name}`")
- def get(self, name: str) -> Route | None:
+ def get(self, name: str) -> Optional[Route]:
for route in self.routes:
if route.name == name:
return route
@@ -147,16 +148,17 @@ class LayerConfig:
class RouteLayer:
- index: np.ndarray | None = None
- categories: np.ndarray | None = None
+ index: Optional[np.ndarray] = None
+ categories: Optional[np.ndarray] = None
score_threshold: float
encoder: BaseEncoder
def __init__(
self,
- encoder: BaseEncoder | None = None,
- llm: BaseLLM | None = None,
- routes: list[Route] | None = None,
+ encoder: Optional[BaseEncoder] = None,
+ llm: Optional[BaseLLM] = None,
+ routes: Optional[list[Route]] = None,
+ top_k_routes: int = 3,
):
logger.info("Initializing RouteLayer")
self.index = None
diff --git a/semantic_router/llms/base.py b/semantic_router/llms/base.py
index 51db1fd0e5f317ba7a436cecaac891e8628a161e..bf5f29b6005daaa76abc4674971dc8f775f4af80 100644
--- a/semantic_router/llms/base.py
+++ b/semantic_router/llms/base.py
@@ -1,3 +1,5 @@
+from typing import Optional
+
from pydantic import BaseModel
from semantic_router.schema import Message
@@ -9,5 +11,5 @@ class BaseLLM(BaseModel):
class Config:
arbitrary_types_allowed = True
- def __call__(self, messages: list[Message]) -> str | None:
+ def __call__(self, messages: list[Message]) -> Optional[str]:
raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/llms/cohere.py b/semantic_router/llms/cohere.py
index 775817001f22a4474f8705b2335c697730035d47..0ec21f354c090f0d1d00da7c20b89c5b233c3a89 100644
--- a/semantic_router/llms/cohere.py
+++ b/semantic_router/llms/cohere.py
@@ -1,4 +1,5 @@
import os
+from typing import Optional
import cohere
@@ -7,12 +8,12 @@ from semantic_router.schema import Message
class CohereLLM(BaseLLM):
- client: cohere.Client | None = None
+ client: Optional[cohere.Client] = None
def __init__(
self,
- name: str | None = None,
- cohere_api_key: str | None = None,
+ name: Optional[str] = None,
+ cohere_api_key: Optional[str] = None,
):
if name is None:
name = os.getenv("COHERE_CHAT_MODEL_NAME", "command")
diff --git a/semantic_router/llms/openai.py b/semantic_router/llms/openai.py
index 43ddd642bd42702461da56fd5de87dea01635dfb..8b3442c742c2f268773bf551fb49ce0cd24645af 100644
--- a/semantic_router/llms/openai.py
+++ b/semantic_router/llms/openai.py
@@ -1,4 +1,5 @@
import os
+from typing import Optional
import openai
@@ -8,14 +9,14 @@ from semantic_router.utils.logger import logger
class OpenAILLM(BaseLLM):
- client: openai.OpenAI | None
- temperature: float | None
- max_tokens: int | None
+ client: Optional[openai.OpenAI]
+ temperature: Optional[float]
+ max_tokens: Optional[int]
def __init__(
self,
- name: str | None = None,
- openai_api_key: str | None = None,
+ name: Optional[str] = None,
+ openai_api_key: Optional[str] = None,
temperature: float = 0.01,
max_tokens: int = 200,
):
diff --git a/semantic_router/llms/openrouter.py b/semantic_router/llms/openrouter.py
index 587eeb121e60c4e6f63dac289e390dd5c9086a1a..4cc15d6bfedbfa67fb5957129d1ce901544dcb38 100644
--- a/semantic_router/llms/openrouter.py
+++ b/semantic_router/llms/openrouter.py
@@ -1,4 +1,5 @@
import os
+from typing import Optional
import openai
@@ -8,15 +9,15 @@ from semantic_router.utils.logger import logger
class OpenRouterLLM(BaseLLM):
- client: openai.OpenAI | None
- base_url: str | None
- temperature: float | None
- max_tokens: int | None
+ client: Optional[openai.OpenAI]
+ base_url: Optional[str]
+ temperature: Optional[float]
+ max_tokens: Optional[int]
def __init__(
self,
- name: str | None = None,
- openrouter_api_key: str | None = None,
+ name: Optional[str] = None,
+ openrouter_api_key: Optional[str] = None,
base_url: str = "https://openrouter.ai/api/v1",
temperature: float = 0.01,
max_tokens: int = 200,
diff --git a/semantic_router/route.py b/semantic_router/route.py
index 0d8269f0a24df70cb71da172781154de64bf1425..2289825071a39652aeaa467395103d71dc623140 100644
--- a/semantic_router/route.py
+++ b/semantic_router/route.py
@@ -1,6 +1,6 @@
import json
import re
-from typing import Any, Callable, Union
+from typing import Any, Callable, Optional, Union
from pydantic import BaseModel
@@ -41,9 +41,9 @@ def is_valid(route_config: str) -> bool:
class Route(BaseModel):
name: str
utterances: list[str]
- description: str | None = None
- function_schema: dict[str, Any] | None = None
- llm: BaseLLM | None = None
+ description: Optional[str] = None
+ function_schema: Optional[dict[str, Any]] = None
+ llm: Optional[BaseLLM] = None
def __call__(self, query: str) -> RouteChoice:
if self.function_schema:
@@ -96,29 +96,29 @@ class Route(BaseModel):
logger.info("Generating dynamic route...")
prompt = f"""
- You are tasked to generate a JSON configuration based on the provided
- function schema. Please follow the template below, no other tokens allowed:
-
- <config>
- {{
- "name": "<function_name>",
- "utterances": [
- "<example_utterance_1>",
- "<example_utterance_2>",
- "<example_utterance_3>",
- "<example_utterance_4>",
- "<example_utterance_5>"]
- }}
- </config>
-
- Only include the "name" and "utterances" keys in your answer.
- The "name" should match the function name and the "utterances"
- should comprise a list of 5 example phrases that could be used to invoke
- the function. Use real values instead of placeholders.
-
- Input schema:
- {function_schema}
- """
+You are tasked to generate a JSON configuration based on the provided
+function schema. Please follow the template below, no other tokens allowed:
+
+<config>
+{{
+ "name": "<function_name>",
+ "utterances": [
+ "<example_utterance_1>",
+ "<example_utterance_2>",
+ "<example_utterance_3>",
+ "<example_utterance_4>",
+ "<example_utterance_5>"]
+}}
+</config>
+
+Only include the "name" and "utterances" keys in your answer.
+The "name" should match the function name and the "utterances"
+should comprise a list of 5 example phrases that could be used to invoke
+the function. Use real values instead of placeholders.
+
+Input schema:
+{function_schema}
+"""
llm_input = [Message(role="user", content=prompt)]
output = llm(llm_input)
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 5e94c23b13f8c9d9359609e46c790e18dd860ab4..bb1a4c6ac26819ef7b13bec1f293fe6e51a66a7a 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -1,4 +1,5 @@
from enum import Enum
+from typing import Optional
from pydantic import BaseModel
from pydantic.dataclasses import dataclass
@@ -20,17 +21,19 @@ class EncoderType(Enum):
class RouteChoice(BaseModel):
- name: str | None = None
- function_call: dict | None = None
+ name: Optional[str] = None
+ function_call: Optional[dict] = None
+ similarity_score: Optional[float] = None
+ trigger: Optional[bool] = None
@dataclass
class Encoder:
type: EncoderType
- name: str | None
+ name: Optional[str]
model: BaseEncoder
- def __init__(self, type: str, name: str | None):
+ def __init__(self, type: str, name: Optional[str]):
self.type = EncoderType(type)
self.name = name
if self.type == EncoderType.HUGGINGFACE:
diff --git a/semantic_router/utils/function_call.py b/semantic_router/utils/function_call.py
index cedd9b6ecd86131b630cf6d4921848604dc88fa0..3c8b3277b3b4cdd8d3c2ebc1849ff3da4cbd1ca7 100644
--- a/semantic_router/utils/function_call.py
+++ b/semantic_router/utils/function_call.py
@@ -47,33 +47,36 @@ def extract_function_inputs(
logger.info("Extracting function input...")
prompt = f"""
- You are a helpful assistant designed to output JSON.
- Given the following function schema
- << {function_schema} >>
- and query
- << {query} >>
- extract the parameters values from the query, in a valid JSON format.
- Example:
- Input:
- query: "How is the weather in Hawaii right now in International units?"
- schema:
- {{
- "name": "get_weather",
- "description": "Useful to get the weather in a specific location",
- "signature": "(location: str, degree: str) -> str",
- "output": "<class 'str'>",
- }}
-
- Result: {{
- "location": "London",
- "degree": "Celsius",
- }}
-
- Input:
- query: {query}
- schema: {function_schema}
- Result:
- """
+You are a helpful assistant designed to output JSON.
+Given the following function schema
+<< {function_schema} >>
+and query
+<< {query} >>
+extract the parameters values from the query, in a valid JSON format.
+Example:
+Input:
+query: "How is the weather in Hawaii right now in International units?"
+schema:
+{{
+ "name": "get_weather",
+ "description": "Useful to get the weather in a specific location",
+ "signature": "(location: str, degree: str) -> float",
+ "output": "<class 'float'>",
+}}
+
+Result:
+{{
+ "location": "Hawaii",
+ "degree": "Kelvin",
+}}
+
+Input:
+query: \"{query}\"
+schema:
+{json.dumps(function_schema, indent=4)}
+
+Result:
+"""
llm_input = [Message(role="user", content=prompt)]
output = llm(llm_input)
if not output:
diff --git a/semantic_router/utils/llm.py b/semantic_router/utils/llm.py
index e92c1bcf7752b5fce5a071dde41da4a24d0851a9..4f89566f79df700ec3ae8103b5b8cdfd84700627 100644
--- a/semantic_router/utils/llm.py
+++ b/semantic_router/utils/llm.py
@@ -1,11 +1,12 @@
import os
+from typing import Optional
import openai
from semantic_router.utils.logger import logger
-def llm(prompt: str) -> str | None:
+def llm(prompt: str) -> Optional[str]:
try:
client = openai.OpenAI(
base_url="https://openrouter.ai/api/v1",
@@ -35,7 +36,7 @@ def llm(prompt: str) -> str | None:
# TODO integrate async LLM function
-# async def allm(prompt: str) -> str | None:
+# async def allm(prompt: str) -> Optional[str]:
# try:
# client = openai.AsyncOpenAI(
# base_url="https://openrouter.ai/api/v1",
diff --git a/test_output.txt b/test_output.txt
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
index 9c28ab2d6f32bc31b7cd5d6e2252ad9c41880b96..73e52d5585a3391488d7198d64543a0b2f0dcc5c 100644
--- a/tests/unit/encoders/test_bm25.py
+++ b/tests/unit/encoders/test_bm25.py
@@ -5,7 +5,11 @@ from semantic_router.encoders import BM25Encoder
@pytest.fixture
def bm25_encoder():
- return BM25Encoder()
+ sparse_encoder = BM25Encoder(use_default_params=False)
+ sparse_encoder.fit(
+ ["The quick brown fox", "jumps over the lazy dog", "Hello, world!"]
+ )
+ return sparse_encoder
class TestBM25Encoder:
diff --git a/tests/unit/encoders/test_huggingface.py b/tests/unit/encoders/test_huggingface.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e916f3eca2956b32c6147db6cf5c42695720f14
--- /dev/null
+++ b/tests/unit/encoders/test_huggingface.py
@@ -0,0 +1,63 @@
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+
+from semantic_router.encoders.huggingface import HuggingFaceEncoder
+
+encoder = HuggingFaceEncoder()
+
+
+class TestHuggingFaceEncoder:
+ def test_huggingface_encoder_import_errors_transformers(self):
+ with patch.dict("sys.modules", {"transformers": None}):
+ with pytest.raises(ImportError) as error:
+ HuggingFaceEncoder()
+
+ assert "Please install transformers to use HuggingFaceEncoder" in str(
+ error.value
+ )
+
+ def test_huggingface_encoder_import_errors_torch(self):
+ with patch.dict("sys.modules", {"torch": None}):
+ with pytest.raises(ImportError) as error:
+ HuggingFaceEncoder()
+
+ assert "Please install Pytorch to use HuggingFaceEncoder" in str(error.value)
+
+ def test_huggingface_encoder_mean_pooling(self):
+ test_docs = ["This is a test", "This is another test"]
+ embeddings = encoder(test_docs, pooling_strategy="mean")
+ assert isinstance(embeddings, list)
+ assert len(embeddings) == len(test_docs)
+ assert all(isinstance(embedding, list) for embedding in embeddings)
+ assert all(len(embedding) > 0 for embedding in embeddings)
+
+ def test_huggingface_encoder_max_pooling(self):
+ test_docs = ["This is a test", "This is another test"]
+ embeddings = encoder(test_docs, pooling_strategy="max")
+ assert isinstance(embeddings, list)
+ assert len(embeddings) == len(test_docs)
+ assert all(isinstance(embedding, list) for embedding in embeddings)
+ assert all(len(embedding) > 0 for embedding in embeddings)
+
+ def test_huggingface_encoder_normalized_embeddings(self):
+ docs = ["This is a test document.", "Another test document."]
+ unnormalized_embeddings = encoder(docs, normalize_embeddings=False)
+ normalized_embeddings = encoder(docs, normalize_embeddings=True)
+ assert len(unnormalized_embeddings) == len(normalized_embeddings)
+
+ for unnormalized, normalized in zip(
+ unnormalized_embeddings, normalized_embeddings
+ ):
+ norm_unnormalized = np.linalg.norm(unnormalized, ord=2)
+ norm_normalized = np.linalg.norm(normalized, ord=2)
+ # Ensure the norm of the normalized embeddings is approximately 1
+ assert np.isclose(norm_normalized, 1.0)
+ # Ensure the normalized embeddings are actually normalized versions of unnormalized embeddings
+ np.testing.assert_allclose(
+ normalized,
+ np.divide(unnormalized, norm_unnormalized),
+ rtol=1e-5,
+ atol=1e-5, # Adjust tolerance levels
+ )
diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py
index 323912c284e338502d138e1894111d68f540d12f..cf1e66c3bfa11a60d257a6dd0eb9099d45d37e59 100644
--- a/tests/unit/test_hybrid_layer.py
+++ b/tests/unit/test_hybrid_layer.py
@@ -2,9 +2,9 @@ import pytest
from semantic_router.encoders import (
BaseEncoder,
+ BM25Encoder,
CohereEncoder,
OpenAIEncoder,
- BM25Encoder,
TfidfEncoder,
)
from semantic_router.hybrid_layer import HybridRouteLayer
@@ -62,10 +62,14 @@ def routes():
]
+sparse_encoder = BM25Encoder(use_default_params=False)
+sparse_encoder.fit(["The quick brown fox", "jumps over the lazy dog", "Hello, world!"])
+
+
class TestHybridRouteLayer:
- def test_initialization(self, openai_encoder, bm25_encoder, routes):
+ def test_initialization(self, openai_encoder, routes):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder, routes=routes
+ encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
assert route_layer.index is not None and route_layer.categories is not None
assert openai_encoder.score_threshold == 0.82
@@ -73,22 +77,20 @@ class TestHybridRouteLayer:
assert len(route_layer.index) == 5
assert len(set(route_layer.categories)) == 2
- def test_initialization_different_encoders(
- self, cohere_encoder, openai_encoder, bm25_encoder
- ):
+ def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
route_layer_cohere = HybridRouteLayer(
- dense_encoder=cohere_encoder, sparse_encoder=bm25_encoder
+ encoder=cohere_encoder, sparse_encoder=sparse_encoder
)
assert route_layer_cohere.score_threshold == 0.3
route_layer_openai = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder
+ encoder=openai_encoder, sparse_encoder=sparse_encoder
)
assert route_layer_openai.score_threshold == 0.82
- def test_add_route(self, openai_encoder, bm25_encoder):
+ def test_add_route(self, openai_encoder):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder
+ encoder=openai_encoder, sparse_encoder=sparse_encoder
)
route = Route(name="Route 3", utterances=["Yes", "No"])
route_layer._add_routes([route])
@@ -96,9 +98,9 @@ class TestHybridRouteLayer:
assert len(route_layer.index) == 2
assert len(set(route_layer.categories)) == 1
- def test_add_multiple_routes(self, openai_encoder, bm25_encoder, routes):
+ def test_add_multiple_routes(self, openai_encoder, routes):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder
+ encoder=openai_encoder, sparse_encoder=sparse_encoder
)
for route in routes:
route_layer.add(route)
@@ -106,22 +108,22 @@ class TestHybridRouteLayer:
assert len(route_layer.index) == 5
assert len(set(route_layer.categories)) == 2
- def test_query_and_classification(self, openai_encoder, bm25_encoder, routes):
+ def test_query_and_classification(self, openai_encoder, routes):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder, routes=routes
+ encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
query_result = route_layer("Hello")
assert query_result in ["Route 1", "Route 2"]
- def test_query_with_no_index(self, openai_encoder, bm25_encoder):
+ def test_query_with_no_index(self, openai_encoder):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder
+ encoder=openai_encoder, sparse_encoder=sparse_encoder
)
assert route_layer("Anything") is None
- def test_semantic_classify(self, openai_encoder, bm25_encoder, routes):
+ def test_semantic_classify(self, openai_encoder, routes):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder, routes=routes
+ encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
classification, score = route_layer._semantic_classify(
[
@@ -132,11 +134,9 @@ class TestHybridRouteLayer:
assert classification == "Route 1"
assert score == [0.9]
- def test_semantic_classify_multiple_routes(
- self, openai_encoder, bm25_encoder, routes
- ):
+ def test_semantic_classify_multiple_routes(self, openai_encoder, routes):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder, routes=routes
+ encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
classification, score = route_layer._semantic_classify(
[
@@ -148,16 +148,16 @@ class TestHybridRouteLayer:
assert classification == "Route 1"
assert score == [0.9, 0.8]
- def test_pass_threshold(self, openai_encoder, bm25_encoder):
+ def test_pass_threshold(self, openai_encoder):
route_layer = HybridRouteLayer(
- dense_encoder=openai_encoder, sparse_encoder=bm25_encoder
+ encoder=openai_encoder, sparse_encoder=sparse_encoder
)
assert not route_layer._pass_threshold([], 0.5)
assert route_layer._pass_threshold([0.6, 0.7], 0.5)
def test_failover_score_threshold(self, base_encoder):
route_layer = HybridRouteLayer(
- dense_encoder=base_encoder, sparse_encoder=base_encoder
+ encoder=base_encoder, sparse_encoder=sparse_encoder
)
assert base_encoder.score_threshold == 0.50
assert route_layer.score_threshold == 0.50