diff --git a/coverage.xml b/coverage.xml
index 52c3943d7d48cc62a290d9e209359537cc8cdf3b..2144ec09333eef0ca6be994a674ef84d04916893 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,12 +1,12 @@
<?xml version="1.0" ?>
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<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.4.3 -->
<!-- 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>
+ <source>/Users/jamesbriggs/Documents/projects/aurelio-labs/semantic-router/semantic_router</source>
</sources>
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+ <package name="." line-rate="0.8981" branch-rate="0" complexity="0">
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<class name="__init__.py" filename="__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
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</lines>
</class>
- <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="0.2115" branch-rate="0">
+ <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="0.9808" branch-rate="0">
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</class>
<class name="zure.py" filename="encoders/zure.py" complexity="0" line-rate="0.9589" branch-rate="0">
@@ -1207,7 +1290,7 @@
</class>
</classes>
</package>
- <package name="index" line-rate="0.4954" branch-rate="0" complexity="0">
+ <package name="index" line-rate="0.4932" branch-rate="0" complexity="0">
<classes>
<class name="__init__.py" filename="index/__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
@@ -1303,7 +1386,7 @@
<line number="108" hits="0"/>
</lines>
</class>
- <class name="pinecone.py" filename="index/pinecone.py" complexity="0" line-rate="0.3022" branch-rate="0">
+ <class name="pinecone.py" filename="index/pinecone.py" complexity="0" line-rate="0.3" branch-rate="0">
<methods/>
<lines>
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@@ -1402,6 +1485,7 @@
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<line number="158" hits="0"/>
<line number="159" hits="0"/>
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<line number="163" hits="0"/>
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@@ -1449,7 +1533,7 @@
</class>
</classes>
</package>
- <package name="llms" line-rate="0.7585" branch-rate="0" complexity="0">
+ <package name="llms" line-rate="0.915" branch-rate="0" complexity="0">
<classes>
<class name="__init__.py" filename="llms/__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
@@ -1463,7 +1547,7 @@
<line number="8" hits="1"/>
</lines>
</class>
- <class name="base.py" filename="llms/base.py" complexity="0" line-rate="0.7317" branch-rate="0">
+ <class name="base.py" filename="llms/base.py" complexity="0" line-rate="0.9756" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
@@ -1487,8 +1571,8 @@
<line number="31" hits="1"/>
<line number="34" hits="1"/>
<line number="35" hits="1"/>
- <line number="36" hits="0"/>
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+ <line number="36" hits="1"/>
+ <line number="37" hits="1"/>
<line number="38" hits="1"/>
<line number="39" hits="1"/>
<line number="40" hits="1"/>
@@ -1498,15 +1582,15 @@
<line number="48" hits="1"/>
<line number="98" hits="1"/>
<line number="99" hits="1"/>
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+ <line number="101" hits="1"/>
<line number="102" hits="0"/>
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- <line number="107" hits="0"/>
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- <line number="110" hits="0"/>
+ <line number="104" hits="1"/>
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+ <line number="106" hits="1"/>
+ <line number="107" hits="1"/>
+ <line number="108" hits="1"/>
+ <line number="109" hits="1"/>
+ <line number="110" hits="1"/>
</lines>
</class>
<class name="cohere.py" filename="llms/cohere.py" complexity="0" line-rate="0.9655" branch-rate="0">
@@ -1543,49 +1627,49 @@
<line number="48" hits="1"/>
</lines>
</class>
- <class name="llamacpp.py" filename="llms/llamacpp.py" complexity="0" line-rate="0" branch-rate="0">
+ <class name="llamacpp.py" filename="llms/llamacpp.py" complexity="0" line-rate="0.9" branch-rate="0">
<methods/>
<lines>
- <line number="1" hits="0"/>
- <line number="2" hits="0"/>
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- <line number="7" hits="0"/>
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- <line number="50" hits="0"/>
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- <line number="53" hits="0"/>
+ <line number="1" hits="1"/>
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+ <line number="60" hits="1"/>
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+ <line number="70" hits="1"/>
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</lines>
</class>
<class name="mistral.py" filename="llms/mistral.py" complexity="0" line-rate="0.8333" branch-rate="0">
@@ -1792,7 +1876,7 @@
</class>
</classes>
</package>
- <package name="splitters" line-rate="0.3607" branch-rate="0" complexity="0">
+ <package name="splitters" line-rate="0.3595" branch-rate="0" complexity="0">
<classes>
<class name="__init__.py" filename="splitters/__init__.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
@@ -1904,7 +1988,7 @@
<line number="73" hits="1"/>
</lines>
</class>
- <class name="rolling_window.py" filename="splitters/rolling_window.py" complexity="0" line-rate="0.1422" branch-rate="0">
+ <class name="rolling_window.py" filename="splitters/rolling_window.py" complexity="0" line-rate="0.1415" branch-rate="0">
<methods/>
<lines>
<line number="1" hits="1"/>
@@ -2075,6 +2159,7 @@
<line number="362" hits="0"/>
<line number="363" hits="0"/>
<line number="364" hits="0"/>
+ <line number="365" hits="0"/>
<line number="372" hits="0"/>
<line number="373" hits="0"/>
<line number="374" hits="0"/>
diff --git a/docs/08-multi-modal.ipynb b/docs/08-multi-modal.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c9cfb4655e26505cbd82f6e2dbd1a9aed4cd1c4d
--- /dev/null
+++ b/docs/08-multi-modal.ipynb
@@ -0,0 +1,239 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/08-multi-modal.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/08-multi-modal.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Multi-Modal Routes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Semantic Router library can also be used for detection of specific images or videos, for example the detection of **N**ot **S**hrek **F**or **W**ork (NSFW) and **S**hrek **F**or **W**ork (SFW) images as we will demonstrate in this walkthrough."
+ ]
+ },
+ {
+ "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 \\\n",
+ " \"semantic-router[vision]==0.0.25\" \\\n",
+ " datasets==2.17.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by downloading a multi-modal dataset, we'll be using the `aurelio-ai/shrek-detection` dataset from Hugging Face."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "data = load_dataset(\"aurelio-ai/shrek-detection\", split=\"train\", trust_remote_code=True)\n",
+ "data[3][\"image\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will grab the images that are labeled with `is_shrek`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "shrek_pics = [d[\"image\"] for d in data if d[\"is_shrek\"]]\n",
+ "not_shrek_pics = [d[\"image\"] for d in data if not d[\"is_shrek\"]]\n",
+ "print(f\"We have {len(shrek_pics)} shrek pics, and {len(not_shrek_pics)} not shrek pics\")"
+ ]
+ },
+ {
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "shrek = Route(\n",
+ " name=\"shrek\",\n",
+ " utterances=shrek_pics,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "not_shrek = Route(\n",
+ " name=\"not_shrek\",\n",
+ " utterances=not_shrek_pics,\n",
+ ")\n",
+ "\n",
+ "routes = [shrek, not_shrek]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.encoders.clip import CLIPEncoder\n",
+ "\n",
+ "encoder = CLIPEncoder()"
+ ]
+ },
+ {
+ "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": null,
+ "metadata": {},
+ "outputs": [],
+ "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 with _text_ to see if we hit the routes that we defined with images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"shrek\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"dwayne the rock johnson\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Everything is being classified accurately, let's pull in some images that we haven't seen before and see if we can classify them as NSFW or SFW."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "test_data = load_dataset(\n",
+ " \"aurelio-ai/shrek-detection\", split=\"test\", trust_remote_code=True\n",
+ ")\n",
+ "test_data"
+ ]
+ }
+ ],
+ "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/video-splitter.ipynb b/docs/examples/video-splitter.ipynb
index 5b44f85eacd58e4aa9e206a0354681c995315c8b..4fd07b431d3a85f4e4e06e7738385bf291748bd7 100644
--- a/docs/examples/video-splitter.ipynb
+++ b/docs/examples/video-splitter.ipynb
@@ -1,5 +1,13 @@
{
"cells": [
+ {
+ "cell_type": "markdown",
+ "id": "dce8c304",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/examples/video-splitter.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/examples/video-splitter.ipynb)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "b3e65ca8-60c5-4936-bbbd-ccdb7fe3f0f4",
@@ -15,6 +23,19 @@
"Let's start by loading a test video and splitting it into frames"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8772039d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \\\n",
+ " \"semantic-router[vision]==0.0.25\" \\\n",
+ " opencv-python \\\n",
+ " matplotlib"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 1,
diff --git a/poetry.lock b/poetry.lock
index fff4911a18d411f8d16f02740c7becdf57f58d02..8e64ef1d97c81c96f2e3d8bc7e9a70c242e50649 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -449,13 +449,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cohere"
-version = "4.49"
+version = "4.50"
description = "Python SDK for the Cohere API"
optional = false
python-versions = ">=3.8,<4.0"
files = [
- {file = "cohere-4.49-py3-none-any.whl", hash = "sha256:145fdf55e44c50bc69d892d124cf47534686a30e3d83bd94c350513196d19def"},
- {file = "cohere-4.49.tar.gz", hash = "sha256:509e81c44f8e1b4eb31b27d8ea388e79e04c5bee8308ac1a337a6053dd3f5a87"},
+ {file = "cohere-4.50-py3-none-any.whl", hash = "sha256:790744034c76cabd9c3d8e05c0b2e85733caee64e695e5a9904c4527202c913e"},
+ {file = "cohere-4.50.tar.gz", hash = "sha256:64908677069fee23bb5dc968d576eab3ed644278e800bb7dcc3e5a336e5fc206"},
]
[package.dependencies]
@@ -593,63 +593,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
[[package]]
name = "coverage"
-version = "7.4.2"
+version = "7.4.3"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.8"
files = [
- {file = "coverage-7.4.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bf54c3e089179d9d23900e3efc86d46e4431188d9a657f345410eecdd0151f50"},
- {file = "coverage-7.4.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fe6e43c8b510719b48af7db9631b5fbac910ade4bd90e6378c85ac5ac706382c"},
- {file = "coverage-7.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b98c89db1b150d851a7840142d60d01d07677a18f0f46836e691c38134ed18b"},
- {file = "coverage-7.4.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c5f9683be6a5b19cd776ee4e2f2ffb411424819c69afab6b2db3a0a364ec6642"},
- {file = "coverage-7.4.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:78cdcbf7b9cb83fe047ee09298e25b1cd1636824067166dc97ad0543b079d22f"},
- {file = "coverage-7.4.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:2599972b21911111114100d362aea9e70a88b258400672626efa2b9e2179609c"},
- {file = "coverage-7.4.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:ef00d31b7569ed3cb2036f26565f1984b9fc08541731ce01012b02a4c238bf03"},
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description = "Python bindings for the llama.cpp library"
optional = true
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[package.dependencies]
@@ -3595,50 +3603,54 @@ nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linu
nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.18.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
-triton = {version = "2.1.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-typing-extensions = "*"
+triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
+typing-extensions = ">=4.8.0"
[package.extras]
-dynamo = ["jinja2"]
opt-einsum = ["opt-einsum (>=3.3)"]
+optree = ["optree (>=0.9.1)"]
[[package]]
name = "torchvision"
-version = "0.16.2"
+version = "0.17.1"
description = "image and video datasets and models for torch deep learning"
optional = true
python-versions = ">=3.8"
files = [
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[package.dependencies]
numpy = "*"
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-requests = "*"
-torch = "2.1.2"
+torch = "2.2.1"
[package.extras]
scipy = ["scipy"]
@@ -3768,28 +3780,26 @@ vision = ["Pillow (>=10.0.1,<=15.0)"]
[[package]]
name = "triton"
-version = "2.1.0"
+version = "2.2.0"
description = "A language and compiler for custom Deep Learning operations"
optional = true
python-versions = "*"
files = [
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+ {file = "triton-2.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:227cc6f357c5efcb357f3867ac2a8e7ecea2298cd4606a8ba1e931d1d5a947df"},
]
[package.dependencies]
filelock = "*"
[package.extras]
-build = ["cmake (>=3.18)", "lit"]
-tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
-tutorials = ["matplotlib", "pandas", "tabulate"]
+build = ["cmake (>=3.20)", "lit"]
+tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"]
+tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
[[package]]
name = "types-pyyaml"
@@ -3994,4 +4004,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "067185d9d8b058fbcf9bd00e3c9a0b5b3ada33cbfa7f77e81d164692d98ae22b"
+content-hash = "7e533decdcadb8bed91697d492129d5f6136d10624b9c1f5322b6b7c26c47bc7"
diff --git a/pyproject.toml b/pyproject.toml
index 74d8c368e05a04bb95bdfab75e08143758305e42..6d366978ab00d2a13d15abbe35c35546f4c380c0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ black = "^23.12.1"
colorama = "^0.4.6"
pinecone-client = {version="^3.0.0", optional = true}
regex = "^2023.12.25"
-torchvision = { version = "^0.16.2", optional = true}
+torchvision = { version = "^0.17.0", optional = true}
pillow = { version= "^10.2.0", optional = true}
tiktoken = "^0.6.0"
matplotlib = { version="^3.8.3", optional = true}
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 9e74bb1a6a85388bf0f659348a32d76461df1b67..d351028e1a35b141beca18189f28fdb272444643 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -1,5 +1,6 @@
from semantic_router.encoders.base import BaseEncoder
from semantic_router.encoders.bm25 import BM25Encoder
+from semantic_router.encoders.clip import CLIPEncoder
from semantic_router.encoders.cohere import CohereEncoder
from semantic_router.encoders.fastembed import FastEmbedEncoder
from semantic_router.encoders.huggingface import HuggingFaceEncoder
@@ -20,4 +21,5 @@ __all__ = [
"HuggingFaceEncoder",
"MistralEncoder",
"VitEncoder",
+ "CLIPEncoder",
]
diff --git a/semantic_router/encoders/clip.py b/semantic_router/encoders/clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..89a85cf3ff7cce83c11456ecee5345d532da6c37
--- /dev/null
+++ b/semantic_router/encoders/clip.py
@@ -0,0 +1,126 @@
+from typing import Any, List, Optional
+
+import numpy as np
+from pydantic.v1 import PrivateAttr
+
+from semantic_router.encoders import BaseEncoder
+
+
+class CLIPEncoder(BaseEncoder):
+ name: str = "openai/clip-vit-base-patch16"
+ type: str = "huggingface"
+ score_threshold: float = 0.2
+ tokenizer_kwargs: dict = {}
+ processor_kwargs: dict = {}
+ model_kwargs: dict = {}
+ device: Optional[str] = None
+ _tokenizer: Any = PrivateAttr()
+ _processor: Any = PrivateAttr()
+ _model: Any = PrivateAttr()
+ _torch: Any = PrivateAttr()
+ _Image: Any = PrivateAttr()
+
+ def __init__(self, **data):
+ super().__init__(**data)
+ self._tokenizer, self._processor, self._model = self._initialize_hf_model()
+
+ def __call__(
+ self,
+ docs: List[Any],
+ batch_size: int = 32,
+ normalize_embeddings: bool = True,
+ ) -> List[List[float]]:
+ all_embeddings = []
+ if isinstance(docs[0], str):
+ text = True
+ else:
+ text = False
+ for i in range(0, len(docs), batch_size):
+ batch_docs = docs[i : i + batch_size]
+ if text:
+ embeddings = self._encode_text(docs=batch_docs)
+ else:
+ embeddings = self._encode_image(images=batch_docs)
+ if normalize_embeddings:
+ embeddings = embeddings / np.linalg.norm(embeddings, axis=0)
+
+ embeddings = embeddings.tolist()
+ all_embeddings.extend(embeddings)
+ return all_embeddings
+
+ def _initialize_hf_model(self):
+ try:
+ from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
+ except ImportError:
+ raise ImportError(
+ "Please install transformers to use CLIPEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[vision]`"
+ )
+
+ try:
+ import torch
+ except ImportError:
+ raise ImportError(
+ "Please install Pytorch to use CLIPEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[vision]`"
+ )
+
+ try:
+ from PIL import Image
+ except ImportError:
+ raise ImportError(
+ "Please install PIL to use HuggingFaceEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[vision]`"
+ )
+
+ self._torch = torch
+ self._Image = Image
+
+ tokenizer = CLIPTokenizerFast.from_pretrained(
+ self.name,
+ **self.tokenizer_kwargs,
+ )
+ processor = CLIPProcessor.from_pretrained(self.name)
+ model = CLIPModel.from_pretrained(self.name, **self.model_kwargs)
+
+ self.device = self._get_device()
+ model.to(self.device)
+ return tokenizer, processor, model
+
+ def _get_device(self) -> str:
+ if self.device:
+ device = self.device
+ elif self._torch.cuda.is_available():
+ device = "cuda"
+ elif self._torch.backends.mps.is_available():
+ device = "mps"
+ else:
+ device = "cpu"
+ return device
+
+ def _encode_text(self, docs: List[str]) -> Any:
+ inputs = self._tokenizer(
+ docs, return_tensors="pt", padding=True, truncation=True
+ ).to(self.device)
+ with self._torch.no_grad():
+ embeds = self._model.get_text_features(**inputs)
+ embeds = embeds.squeeze(0).cpu().detach().numpy()
+ return embeds
+
+ def _encode_image(self, images: List[Any]) -> Any:
+ rgb_images = [self._ensure_rgb(img) for img in images]
+ inputs = self._processor(text=None, images=rgb_images, return_tensors="pt")[
+ "pixel_values"
+ ].to(self.device)
+ with self._torch.no_grad():
+ embeds = self._model.get_image_features(pixel_values=inputs)
+ embeds = embeds.squeeze(0).cpu().detach().numpy()
+ return embeds
+
+ def _ensure_rgb(self, img: Any):
+ rgbimg = self._Image.new("RGB", img.size)
+ rgbimg.paste(img)
+ return rgbimg
diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py
index 5b9da669796e25315c71a98afb2cd3223abb75c6..65de365ac6abca6a941e5e51e6e538f1a8449a34 100644
--- a/semantic_router/encoders/vit.py
+++ b/semantic_router/encoders/vit.py
@@ -3,12 +3,6 @@ from typing import Any, List, Optional
from pydantic.v1 import PrivateAttr
from semantic_router.encoders import BaseEncoder
-from semantic_router.utils.logger import logger
-
-try:
- from PIL import Image
-except ImportError:
- logger.warning("Pillow is not installed. Install it with `pip install pillow`")
class VitEncoder(BaseEncoder):
@@ -22,6 +16,7 @@ class VitEncoder(BaseEncoder):
_model: Any = PrivateAttr()
_torch: Any = PrivateAttr()
_T: Any = PrivateAttr()
+ _Image: Any = PrivateAttr()
def __init__(self, **data):
super().__init__(**data)
@@ -34,7 +29,7 @@ class VitEncoder(BaseEncoder):
raise ImportError(
"Please install transformers to use HuggingFaceEncoder. "
"You can install it with: "
- "`pip install semantic-router[local]`"
+ "`pip install semantic-router[vision]`"
)
try:
@@ -44,10 +39,20 @@ class VitEncoder(BaseEncoder):
raise ImportError(
"Please install Pytorch to use HuggingFaceEncoder. "
"You can install it with: "
- "`pip install semantic-router[local]`"
+ "`pip install semantic-router[vision]`"
+ )
+
+ try:
+ from PIL import Image
+ except ImportError:
+ raise ImportError(
+ "Please install PIL to use HuggingFaceEncoder. "
+ "You can install it with: "
+ "`pip install semantic-router[vision]`"
)
self._torch = torch
+ self._Image = Image
self._T = T
processor = ViTImageProcessor.from_pretrained(
@@ -56,16 +61,22 @@ class VitEncoder(BaseEncoder):
model = ViTModel.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
+ self.device = self._get_device()
+ model.to(self.device)
return processor, model
+ def _get_device(self) -> str:
+ if self.device:
+ device = self.device
+ elif self._torch.cuda.is_available():
+ device = "cuda"
+ elif self._torch.backends.mps.is_available():
+ device = "mps"
+ else:
+ device = "cpu"
+ return device
+
def _process_images(self, images: List[Any]):
rgb_images = [self._ensure_rgb(img) for img in images]
processed_images = self._processor(images=rgb_images, return_tensors="pt")
@@ -73,7 +84,7 @@ class VitEncoder(BaseEncoder):
return processed_images
def _ensure_rgb(self, img: Any):
- rgbimg = Image.new("RGB", img.size)
+ rgbimg = self._Image.new("RGB", img.size)
rgbimg.paste(img)
return rgbimg
diff --git a/tests/unit/encoders/test_clip.py b/tests/unit/encoders/test_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebbb80e50c8833fd5923599a2474b9dcc09f13f0
--- /dev/null
+++ b/tests/unit/encoders/test_clip.py
@@ -0,0 +1,78 @@
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+
+from semantic_router.encoders import CLIPEncoder
+
+test_model_name = "hf-internal-testing/tiny-random-CLIPModel"
+clip_encoder = CLIPEncoder(name=test_model_name)
+embed_dim = 64
+
+if torch.cuda.is_available():
+ device = "cuda"
+elif torch.backends.mps.is_available():
+ device = "mps"
+else:
+ device = "cpu"
+
+
+@pytest.fixture()
+def dummy_pil_image():
+ return Image.fromarray(np.random.rand(512, 224, 3).astype(np.uint8))
+
+
+@pytest.fixture()
+def dummy_black_and_white_img():
+ return Image.fromarray(np.random.rand(224, 224, 2).astype(np.uint8))
+
+
+@pytest.fixture()
+def misshaped_pil_image():
+ return Image.fromarray(np.random.rand(64, 64, 3).astype(np.uint8))
+
+
+class TestVitEncoder:
+ def test_clip_encoder__import_errors_transformers(self, mocker):
+ mocker.patch.dict("sys.modules", {"transformers": None})
+ with pytest.raises(ImportError):
+ CLIPEncoder()
+
+ def test_clip_encoder__import_errors_torch(self, mocker):
+ mocker.patch.dict("sys.modules", {"torch": None})
+ with pytest.raises(ImportError):
+ CLIPEncoder()
+
+ def test_clip_encoder_initialization(self):
+ assert clip_encoder.name == test_model_name
+ assert clip_encoder.type == "huggingface"
+ assert clip_encoder.score_threshold == 0.2
+ assert clip_encoder.device == device
+
+ def test_clip_encoder_call_text(self):
+ embeddings = clip_encoder(["hello", "world"])
+
+ assert len(embeddings) == 2
+ assert len(embeddings[0]) == embed_dim
+
+ def test_clip_encoder_call_image(self, dummy_pil_image):
+ encoded_images = clip_encoder([dummy_pil_image] * 3)
+
+ assert len(encoded_images) == 3
+ assert set(map(len, encoded_images)) == {embed_dim}
+
+ def test_clip_encoder_call_misshaped(self, dummy_pil_image, misshaped_pil_image):
+ encoded_images = clip_encoder([dummy_pil_image, misshaped_pil_image])
+
+ assert len(encoded_images) == 2
+ assert set(map(len, encoded_images)) == {embed_dim}
+
+ def test_clip_device(self):
+ device = clip_encoder._model.device.type
+ assert device == device
+
+ def test_clip_encoder_ensure_rgb(self, dummy_black_and_white_img):
+ rgb_image = clip_encoder._ensure_rgb(dummy_black_and_white_img)
+
+ assert rgb_image.mode == "RGB"
+ assert np.array(rgb_image).shape == (224, 224, 3)
diff --git a/tests/unit/encoders/test_huggingface.py b/tests/unit/encoders/test_huggingface.py
index 1e916f3eca2956b32c6147db6cf5c42695720f14..7c17f98f4e2cea17205a7543e577cb56b763a1af 100644
--- a/tests/unit/encoders/test_huggingface.py
+++ b/tests/unit/encoders/test_huggingface.py
@@ -5,7 +5,8 @@ import pytest
from semantic_router.encoders.huggingface import HuggingFaceEncoder
-encoder = HuggingFaceEncoder()
+test_model_name = "hf-internal-testing/tiny-bert"
+encoder = HuggingFaceEncoder(name=test_model_name)
class TestHuggingFaceEncoder:
diff --git a/tests/unit/encoders/test_vit.py b/tests/unit/encoders/test_vit.py
index 0435058c0cdbcf776b5a44356a085d82c9c30439..72d01b349bc83a0b824e2f2f746d29c0553c94ce 100644
--- a/tests/unit/encoders/test_vit.py
+++ b/tests/unit/encoders/test_vit.py
@@ -1,10 +1,20 @@
import numpy as np
import pytest
+import torch
from PIL import Image
from semantic_router.encoders import VitEncoder
-vit_encoder = VitEncoder()
+test_model_name = "hf-internal-testing/tiny-random-vit"
+vit_encoder = VitEncoder(name=test_model_name)
+embed_dim = 32
+
+if torch.cuda.is_available():
+ device = "cuda"
+elif torch.backends.mps.is_available():
+ device = "mps"
+else:
+ device = "cpu"
@pytest.fixture()
@@ -39,27 +49,27 @@ class TestVitEncoder:
VitEncoder()
def test_vit_encoder_initialization(self):
- assert vit_encoder.name == "google/vit-base-patch16-224"
+ assert vit_encoder.name == test_model_name
assert vit_encoder.type == "huggingface"
assert vit_encoder.score_threshold == 0.5
- assert vit_encoder.device == "cpu"
+ assert vit_encoder.device == device
def test_vit_encoder_call(self, dummy_pil_image):
encoded_images = vit_encoder([dummy_pil_image] * 3)
assert len(encoded_images) == 3
- assert set(map(len, encoded_images)) == {768}
+ assert set(map(len, encoded_images)) == {embed_dim}
def test_vit_encoder_call_misshaped(self, dummy_pil_image, misshaped_pil_image):
encoded_images = vit_encoder([dummy_pil_image, misshaped_pil_image])
assert len(encoded_images) == 2
- assert set(map(len, encoded_images)) == {768}
+ assert set(map(len, encoded_images)) == {embed_dim}
def test_vit_encoder_process_images_device(self, dummy_pil_image):
imgs = vit_encoder._process_images([dummy_pil_image] * 3)["pixel_values"]
- assert imgs.device.type == "cpu"
+ assert imgs.device.type == device
def test_vit_encoder_ensure_rgb(self, dummy_black_and_white_img):
rgb_image = vit_encoder._ensure_rgb(dummy_black_and_white_img)