diff --git a/docs/encoders/huggingface-endpoint.ipynb b/docs/encoders/huggingface-endpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c5714142e5b204f7c88740aeccebe92db1cdf042
--- /dev/null
+++ b/docs/encoders/huggingface-endpoint.ipynb
@@ -0,0 +1,3354 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface-endpoint.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/huggingface-endpoint.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using Huggingface endpoint"
+ ]
+ },
+ {
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# !pip install -qU semantic-router==0.0.20"
+ ]
+ },
+ {
+ "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": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "d:\\Program_Installation\\anaconda\\envs\\testenv\\Lib\\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"
+ ]
+ }
+ ],
+ "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": 3,
+ "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, we will use the Huggingface endpoint"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "d:\\Program_Installation\\anaconda\\envs\\testenv\\Lib\\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",
+ "\u001b[32m2024-04-14 20:54:07 INFO semantic_router.utils.logger Model Initializing wait for - 26.81s \u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders.huggingface import HFEndpointEncoder\n",
+ "\n",
+ "huggingface_url = os.getenv(\"HF_API_URL\") or getpass(\"Enter HuggingFace API URL: \")\n",
+ "huggingface_api_key = os.getenv(\"HF_API_KEY\") or getpass(\"Enter HuggingFace API Key: \")\n",
+ "\n",
+ "encoder = HFEndpointEncoder(\n",
+ " huggingface_url=huggingface_url,\n",
+ " huggingface_api_key=huggingface_api_key,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 0.2599547207355499,\n",
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+ " 0.20850639045238495,\n",
+ " -0.22161859273910522,\n",
+ " -0.36194825172424316,\n",
+ " 0.2561335563659668,\n",
+ " -0.03479013219475746,\n",
+ " -0.35598382353782654,\n",
+ " -0.2267131507396698,\n",
+ " 0.5062740445137024,\n",
+ " -0.8605638742446899,\n",
+ " 0.3181823790073395,\n",
+ " 0.5258446335792542,\n",
+ " -0.03991806507110596,\n",
+ " -0.6525282263755798,\n",
+ " 0.28436800837516785,\n",
+ " -0.17757639288902283,\n",
+ " ...]]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "encoder(\"Hey\")"
+ ]
+ },
+ {
+ "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": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-04-14 20:02:32 INFO semantic_router.utils.logger local\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check the dimensionality of our vectors by looking at the `index` attribute of the `RouteLayer`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "LocalIndex(index=array([[ 0.37762564, 0.37923592, 0.04006954, ..., 0.2910035 ,\n",
+ " 0.14261879, -0.14989774],\n",
+ " [ 0.14489685, -0.47280183, -0.13473961, ..., -0.184137 ,\n",
+ " -0.44280073, -0.96940869],\n",
+ " [ 1.16709912, 0.38906148, 0.24399863, ..., 0.03619115,\n",
+ " -0.00167309, 0.50425595],\n",
+ " ...,\n",
+ " [-0.64046752, 0.45156148, -0.27317011, ..., -0.64851284,\n",
+ " -0.10258984, 0.15441738],\n",
+ " [-0.11908327, 0.4233726 , -0.29102552, ..., -0.69622546,\n",
+ " 0.27602831, 0.2030668 ],\n",
+ " [-0.06046702, -0.18556708, -0.45608515, ..., -0.86009502,\n",
+ " -0.01424424, -0.489003 ]]), routes=array(['politics', 'politics', 'politics', 'politics', 'politics',\n",
+ " 'politics', 'chitchat', 'chitchat', 'chitchat', 'chitchat',\n",
+ " 'chitchat'], dtype='<U8'), utterances=array([\"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!', \"how's the weather today?\",\n",
+ " 'how are things going?', 'lovely weather today',\n",
+ " 'the weather is horrendous', \"let's go to the chippy\"],\n",
+ " dtype='<U51'), dimensions=None, type='local')"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.index"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We do have 1024-dimensional vectors. Now let's test them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"tell me about your political opinions?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 8,
+ "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": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 9,
+ "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. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ }
+ ],
+ "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": "3.12.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/encoders/huggingface.ipynb b/docs/encoders/huggingface.ipynb
index f0b4b8ba1ecacb2b73db9d75969de71f67b88bd9..cd553a75d261fee9a2327045c898e03e99351dbf 100644
--- a/docs/encoders/huggingface.ipynb
+++ b/docs/encoders/huggingface.ipynb
@@ -53,9 +53,18 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "d:\\Program_Installation\\anaconda\\envs\\testenv\\Lib\\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"
+ ]
+ }
+ ],
"source": [
"from semantic_router import Route\n",
"\n",
@@ -88,7 +97,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -115,21 +124,19 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"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"
+ "tokenizer_config.json: 100%|██████████| 350/350 [00:00<?, ?B/s] \n",
+ "vocab.txt: 100%|██████████| 232k/232k [00:00<00:00, 950kB/s]\n",
+ "tokenizer.json: 100%|██████████| 466k/466k [00:00<00:00, 630kB/s]\n",
+ "special_tokens_map.json: 100%|██████████| 112/112 [00:00<?, ?B/s] \n",
+ "config.json: 100%|██████████| 612/612 [00:00<?, ?B/s] \n",
+ "model.safetensors: 100%|██████████| 90.9M/90.9M [00:03<00:00, 25.8MB/s]\n"
]
}
],
@@ -139,6 +146,409 @@
"encoder = HuggingFaceEncoder()"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ " 0.07984637469053268,\n",
+ " -0.012213138863444328]]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "encoder([\"hey\"])"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -148,14 +558,14 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"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"
+ "\u001b[32m2024-04-14 18:15:59 INFO semantic_router.utils.logger local\u001b[0m\n"
]
}
],
@@ -165,6 +575,48 @@
"rl = RouteLayer(encoder=encoder, routes=routes)"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "LocalIndex(index=array([[-0.00864941, 0.02088905, 0.04548961, ..., -0.00787578,\n",
+ " 0.0252752 , 0.01958269],\n",
+ " [ 0.10101052, -0.05990515, 0.01437028, ..., -0.00809868,\n",
+ " 0.03701495, -0.01487793],\n",
+ " [ 0.01996296, -0.03627442, 0.15291646, ..., 0.06719883,\n",
+ " 0.08079942, -0.03931363],\n",
+ " ...,\n",
+ " [-0.01322144, 0.11396162, 0.14592913, ..., -0.01772162,\n",
+ " -0.09720093, 0.05921701],\n",
+ " [ 0.02863792, 0.09272329, 0.10989423, ..., -0.00030185,\n",
+ " -0.10717052, 0.04849005],\n",
+ " [-0.03355407, -0.04666358, -0.05054352, ..., 0.04337099,\n",
+ " 0.10585055, -0.06144635]]), routes=array(['politics', 'politics', 'politics', 'politics', 'politics',\n",
+ " 'politics', 'chitchat', 'chitchat', 'chitchat', 'chitchat',\n",
+ " 'chitchat'], dtype='<U8'), utterances=array([\"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!', \"how's the weather today?\",\n",
+ " 'how are things going?', 'lovely weather today',\n",
+ " 'the weather is horrendous', \"let's go to the chippy\"],\n",
+ " dtype='<U51'), dimensions=None, type='local')"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.index"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -174,16 +626,118 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
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+ " 3.10892314e-02 3.17056179e-02 4.35627997e-02 -3.98492068e-02]\n"
+ ]
+ },
{
"data": {
"text/plain": [
- "RouteChoice(name='politics', function_call=None)"
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -263,7 +817,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.2"
}
},
"nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index 0ea7fcd3771dd413c81e4029bdaf36fc1385b9d7..53b7b5cd7df3ef496e4c97d39e3beea6cd662bc8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2,87 +2,87 @@
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optional = false
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pillow = ">=8"
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+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d074b07a10c391fc5bbdcb37b2f16f20fcd9e51e10d01652ab298c0d07908ee2"},
+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c69567ddbac186e8c0aadc1f324a60a564cfe25e43ef2ce81bcc4b8c3abffbae"},
+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:baf1c7b78cddb5af00971ad5294a4583188bda1495b13760d9f03c9483bb6203"},
+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:2684a94fdfd1b146ff10689c6e4e815f6a01141781c493b97342cdc5b06f4d5d"},
+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:73c1bc8a86a5c9e8721a088df234265317692d0b5cd9e86e975ce3bc3db62a59"},
+ {file = "pydantic_core-2.18.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:e60defc3c15defb70bb38dd605ff7e0fae5f6c9c7cbfe0ad7868582cb7e844a6"},
+ {file = "pydantic_core-2.18.1.tar.gz", hash = "sha256:de9d3e8717560eb05e28739d1b35e4eac2e458553a52a301e51352a7ffc86a35"},
]
[package.dependencies]
@@ -3506,7 +3508,6 @@ files = [
{file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
{file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
- {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
{file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
{file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
{file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3795,6 +3796,23 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+[[package]]
+name = "requests-mock"
+version = "1.12.1"
+description = "Mock out responses from the requests package"
+optional = false
+python-versions = ">=3.5"
+files = [
+ {file = "requests-mock-1.12.1.tar.gz", hash = "sha256:e9e12e333b525156e82a3c852f22016b9158220d2f47454de9cae8a77d371401"},
+ {file = "requests_mock-1.12.1-py2.py3-none-any.whl", hash = "sha256:b1e37054004cdd5e56c84454cc7df12b25f90f382159087f4b6915aaeef39563"},
+]
+
+[package.dependencies]
+requests = ">=2.22,<3"
+
+[package.extras]
+fixture = ["fixtures"]
+
[[package]]
name = "rsa"
version = "4.9"
@@ -3969,18 +3987,18 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
[[package]]
name = "setuptools"
-version = "69.2.0"
+version = "69.5.1"
description = "Easily download, build, install, upgrade, and uninstall Python packages"
optional = true
python-versions = ">=3.8"
files = [
- {file = "setuptools-69.2.0-py3-none-any.whl", hash = "sha256:c21c49fb1042386df081cb5d86759792ab89efca84cf114889191cd09aacc80c"},
- {file = "setuptools-69.2.0.tar.gz", hash = "sha256:0ff4183f8f42cd8fa3acea16c45205521a4ef28f73c6391d8a25e92893134f2e"},
+ {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
+ {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
-testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.2)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
[[package]]
@@ -4442,13 +4460,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "transformers"
-version = "4.39.2"
+version = "4.39.3"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = true
python-versions = ">=3.8.0"
files = [
- {file = "transformers-4.39.2-py3-none-any.whl", hash = "sha256:8388a4ae1d91ade935f5c5b36dc47aa1a352b092c30595e3337b49a5f7e71b4e"},
- {file = "transformers-4.39.2.tar.gz", hash = "sha256:be0c7392cb92ab48efab2656f1cfd1cbda33b2b8a2917a18bd1196707dbebe14"},
+ {file = "transformers-4.39.3-py3-none-any.whl", hash = "sha256:7838034a12cca3168247f9d2d1dba6724c9de3ae0f73a108258c6b8fc5912601"},
+ {file = "transformers-4.39.3.tar.gz", hash = "sha256:2586e5ff4150f122716fc40f5530e92871befc051848fbe82600969c535b762d"},
]
[package.dependencies]
@@ -4544,13 +4562,13 @@ files = [
[[package]]
name = "types-requests"
-version = "2.31.0.20240402"
+version = "2.31.0.20240406"
description = "Typing stubs for requests"
optional = false
python-versions = ">=3.8"
files = [
- {file = "types-requests-2.31.0.20240402.tar.gz", hash = "sha256:e5c09a202f8ae79cd6ffbbba2203b6c3775a83126283bb2a6abbc129abc02a12"},
- {file = "types_requests-2.31.0.20240402-py3-none-any.whl", hash = "sha256:bd7eb7102168d4b5b489f15cdd9842b63ab7fe56aa82a0589fa595b94195acf4"},
+ {file = "types-requests-2.31.0.20240406.tar.gz", hash = "sha256:4428df33c5503945c74b3f42e82b181e86ec7b724620419a2966e2de604ce1a1"},
+ {file = "types_requests-2.31.0.20240406-py3-none-any.whl", hash = "sha256:6216cdac377c6b9a040ac1c0404f7284bd13199c0e1bb235f4324627e8898cf5"},
]
[package.dependencies]
@@ -4558,13 +4576,13 @@ urllib3 = ">=2"
[[package]]
name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
description = "Backported and Experimental Type Hints for Python 3.8+"
optional = false
python-versions = ">=3.8"
files = [
- {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
- {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+ {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+ {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
]
[[package]]
@@ -4751,4 +4769,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "5e662aecf752131b9985184767c251e864a51add59d3dd267c8f70d14cbefcae"
+content-hash = "9f308d2dd1c067185f9d84721b25d81e7d1e72a239059863bad1f4439a7a26cc"
diff --git a/pyproject.toml b/pyproject.toml
index b0ec4253ef1f428a2a5e314c51c275c110978c8a..2c64a23a119d90baf7b8117aa57e0d8e9b0da9e1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,6 +37,7 @@ tiktoken = "^0.6.0"
matplotlib = { version = "^3.8.3", optional = true}
qdrant-client = {version = "^1.8.0", optional = true}
google-cloud-aiplatform = {version = "^1.45.0", optional = true}
+requests-mock = "^1.12.1"
[tool.poetry.extras]
hybrid = ["pinecone-text"]
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index b53e5be2f78b2f7246488fdb9427c9142e5e00e9..a79fa60595315943591ad5ec4eb2a34e39a8c50c 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -5,6 +5,7 @@ from semantic_router.encoders.cohere import CohereEncoder
from semantic_router.encoders.fastembed import FastEmbedEncoder
from semantic_router.encoders.google import GoogleEncoder
from semantic_router.encoders.huggingface import HuggingFaceEncoder
+from semantic_router.encoders.huggingface import HFEndpointEncoder
from semantic_router.encoders.mistral import MistralEncoder
from semantic_router.encoders.openai import OpenAIEncoder
from semantic_router.encoders.tfidf import TfidfEncoder
@@ -20,6 +21,7 @@ __all__ = [
"TfidfEncoder",
"FastEmbedEncoder",
"HuggingFaceEncoder",
+ "HFEndpointEncoder",
"MistralEncoder",
"VitEncoder",
"CLIPEncoder",
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index 64b183ff13506d3f21abff147a487c581edbc88f..24bd5cd044c09bd8c2de2c5836ad8d79fc6204f8 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -1,8 +1,34 @@
+"""
+This module provides the HFEndpointEncoder class to embeddings models using Huggingface's endpoint.
+
+The HFEndpointEncoder class is a subclass of BaseEncoder and utilizes a specified Huggingface
+endpoint to generate embeddings for given documents. It requires the URL of the Huggingface
+API endpoint and an API key for authentication. The class supports customization of the score
+threshold for filtering or processing the embeddings.
+
+Example usage:
+
+ from semantic_router.encoders.hfendpointencoder import HFEndpointEncoder
+
+ encoder = HFEndpointEncoder(
+ huggingface_url="https://api-inference.huggingface.co/models/BAAI/bge-large-en-v1.5",
+ huggingface_api_key="your-hugging-face-api-key"
+ )
+ embeddings = encoder(["document1", "document2"])
+
+Classes:
+ HFEndpointEncoder: A class for generating embeddings using a Huggingface endpoint.
+"""
+
+import requests
+import time
+import os
from typing import Any, List, Optional
from pydantic.v1 import PrivateAttr
from semantic_router.encoders import BaseEncoder
+from semantic_router.utils.logger import logger
class HuggingFaceEncoder(BaseEncoder):
@@ -112,3 +138,138 @@ class HuggingFaceEncoder(BaseEncoder):
)
token_embeddings[input_mask_expanded == 0] = -1e9
return self._torch.max(token_embeddings, 1)[0]
+
+
+class HFEndpointEncoder(BaseEncoder):
+ """
+ A class to encode documents using a Hugging Face transformer model endpoint.
+
+ Attributes:
+ huggingface_url (str): The URL of the Hugging Face API endpoint.
+ huggingface_api_key (str): The API key for authenticating with the Hugging Face API.
+ score_threshold (float): A threshold value used for filtering or processing the embeddings.
+ """
+
+ name: str = "hugging_face_custom_endpoint"
+ huggingface_url: Optional[str] = None
+ huggingface_api_key: Optional[str] = None
+ score_threshold: float = 0.8
+
+ def __init__(
+ self,
+ name: Optional[str] = "hugging_face_custom_endpoint",
+ huggingface_url: Optional[str] = None,
+ huggingface_api_key: Optional[str] = None,
+ score_threshold: float = 0.8,
+ ):
+ """
+ Initializes the HFEndpointEncoder with the specified parameters.
+
+ Args:
+ name (str, optional): The name of the encoder. Defaults to
+ "hugging_face_custom_endpoint".
+ huggingface_url (str, optional): The URL of the Hugging Face API endpoint.
+ Cannot be None.
+ huggingface_api_key (str, optional): The API key for the Hugging Face API.
+ Cannot be None.
+ score_threshold (float, optional): A threshold for processing the embeddings.
+ Defaults to 0.8.
+
+ Raises:
+ ValueError: If either `huggingface_url` or `huggingface_api_key` is None.
+ """
+ huggingface_url = huggingface_url or os.getenv("HF_API_URL")
+ huggingface_api_key = huggingface_api_key or os.getenv("HF_API_KEY")
+
+ super().__init__(name=name, score_threshold=score_threshold) # type: ignore
+
+ if huggingface_url is None:
+ raise ValueError("HuggingFace endpoint url cannot be 'None'.")
+ if huggingface_api_key is None:
+ raise ValueError("HuggingFace API key cannot be 'None'.")
+
+ self.huggingface_url = huggingface_url or os.getenv("HF_API_URL")
+ self.huggingface_api_key = huggingface_api_key or os.getenv("HF_API_KEY")
+
+ try:
+ self.query({"inputs": "Hello World!", "parameters": {}})
+ except Exception as e:
+ raise ValueError(
+ f"HuggingFace endpoint client failed to initialize. Error: {e}"
+ ) from e
+
+ def __call__(self, docs: List[str]) -> List[List[float]]:
+ """
+ Encodes a list of documents into embeddings using the Hugging Face API.
+
+ Args:
+ docs (List[str]): A list of documents to encode.
+
+ Returns:
+ List[List[float]]: A list of embeddings for the given documents.
+
+ Raises:
+ ValueError: If no embeddings are returned for a document.
+ """
+ embeddings = []
+ for d in docs:
+ try:
+ output = self.query({"inputs": d, "parameters": {}})
+ if not output or len(output) == 0:
+ raise ValueError("No embeddings returned from the query.")
+ embeddings.append(output)
+
+ except Exception as e:
+ raise ValueError(
+ f"No embeddings returned for document. Error: {e}"
+ ) from e
+ return embeddings
+
+ def query(self, payload, max_retries=3, retry_interval=5):
+ """
+ Sends a query to the Hugging Face API and returns the response.
+
+ Args:
+ payload (dict): The payload to send in the request.
+
+ Returns:
+ dict: The response from the Hugging Face API.
+
+ Raises:
+ ValueError: If the query fails or the response status is not 200.
+ """
+ headers = {
+ "Accept": "application/json",
+ "Authorization": f"Bearer {self.huggingface_api_key}",
+ "Content-Type": "application/json",
+ }
+ for attempt in range(1, max_retries + 1):
+ try:
+ response = requests.post(
+ self.huggingface_url,
+ headers=headers,
+ json=payload,
+ # timeout=timeout_seconds,
+ )
+ if response.status_code == 503:
+ estimated_time = response.json().get("estimated_time", "")
+ if estimated_time:
+ logger.info(
+ f"Model Initializing wait for - {estimated_time:.2f}s "
+ )
+ time.sleep(estimated_time)
+ continue
+ else:
+ response.raise_for_status()
+
+ except requests.exceptions.RequestException:
+ if attempt < max_retries - 1:
+ logger.info(f"Retrying attempt: {attempt} for payload: {payload} ")
+ time.sleep(retry_interval)
+ retry_interval += attempt
+ else:
+ raise ValueError(
+ f"Query failed with status {response.status_code}: {response.text}"
+ )
+
+ return response.json()
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index 58f7876f359ddad52e6c986bf7eb10f27f0d5852..21c0da2766cd265a632d0e92e370c2a46a509bbd 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -5,7 +5,7 @@ import random
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
-import yaml
+import yaml # type: ignore
from tqdm.auto import tqdm
from semantic_router.encoders import BaseEncoder, OpenAIEncoder
@@ -328,7 +328,7 @@ class RouteLayer:
def add(self, route: Route):
logger.info(f"Adding `{route.name}` route")
# create embeddings
- embeds = self.encoder(route.utterances) # type:ignore
+ embeds = self.encoder(route.utterances) # type: ignore
# if route has no score_threshold, use default
if route.score_threshold is None:
route.score_threshold = self.score_threshold
@@ -337,7 +337,7 @@ class RouteLayer:
self.index.add(
embeddings=embeds,
routes=[route.name] * len(route.utterances),
- utterances=route.utterances, # type:ignore
+ utterances=route.utterances, # type: ignore
)
self.routes.append(route)
@@ -383,14 +383,14 @@ class RouteLayer:
all_utterances = [
utterance for route in routes for utterance in route.utterances
]
- embedded_utterances = self.encoder(all_utterances) # type:ignore
+ embedded_utterances = self.encoder(all_utterances) # type: ignore
# create route array
route_names = [route.name for route in routes for _ in route.utterances]
# add everything to the index
self.index.add(
embeddings=embedded_utterances,
routes=route_names,
- utterances=all_utterances, # type:ignore
+ utterances=all_utterances, # type: ignore
)
def _encode(self, text: str) -> Any:
diff --git a/tests/unit/encoders/test_hfendpointencoder.py b/tests/unit/encoders/test_hfendpointencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb8dd16a661c4194a76935d51390ed4f5b3d3fcc
--- /dev/null
+++ b/tests/unit/encoders/test_hfendpointencoder.py
@@ -0,0 +1,68 @@
+import pytest
+from semantic_router.encoders.huggingface import HFEndpointEncoder
+
+
+@pytest.fixture
+def encoder(requests_mock):
+ # Mock the HTTP request made during HFEndpointEncoder initialization
+ requests_mock.post(
+ "https://api-inference.huggingface.co/models/bert-base-uncased",
+ json=[0.1, 0.2, 0.3],
+ status_code=200,
+ )
+ # Now, when HFEndpointEncoder is initialized, it will use the mocked response above
+ return HFEndpointEncoder(
+ huggingface_url="https://api-inference.huggingface.co/models/bert-base-uncased",
+ huggingface_api_key="test-api-key",
+ score_threshold=0.8,
+ )
+
+
+class TestHFEndpointEncoder:
+ def test_initialization(self, encoder):
+ assert (
+ encoder.huggingface_url
+ == "https://api-inference.huggingface.co/models/bert-base-uncased"
+ )
+ assert encoder.huggingface_api_key == "test-api-key"
+ assert encoder.score_threshold == 0.8
+
+ def test_initialization_failure_no_api_key(self):
+ with pytest.raises(ValueError) as exc_info:
+ HFEndpointEncoder(
+ huggingface_url="https://api-inference.huggingface.co/models/bert-base-uncased"
+ )
+ assert "HuggingFace API key cannot be 'None'" in str(exc_info.value)
+
+ def test_initialization_failure_no_url(self):
+ with pytest.raises(ValueError) as exc_info:
+ HFEndpointEncoder(huggingface_api_key="test-api-key")
+ assert "HuggingFace endpoint url cannot be 'None'" in str(exc_info.value)
+
+ def test_query_success(self, encoder, requests_mock):
+ requests_mock.post(
+ "https://api-inference.huggingface.co/models/bert-base-uncased",
+ json=[0.1, 0.2, 0.3],
+ status_code=200,
+ )
+ response = encoder.query({"inputs": "Hello World!", "parameters": {}})
+ assert response == [0.1, 0.2, 0.3]
+
+ def test_query_failure(self, encoder, requests_mock):
+ requests_mock.post(
+ "https://api-inference.huggingface.co/models/bert-base-uncased",
+ text="Error",
+ status_code=400,
+ )
+ with pytest.raises(ValueError) as exc_info:
+ encoder.query({"inputs": "Hello World!", "parameters": {}})
+ assert "Query failed with status 400: Error" in str(exc_info.value)
+
+ def test_encode_documents_success(self, encoder, requests_mock):
+ requests_mock.post(
+ "https://api-inference.huggingface.co/models/bert-base-uncased",
+ json=[0.1, 0.2, 0.3],
+ status_code=200,
+ )
+ embeddings = encoder(["Hello World!"])
+ assert embeddings == [[0.1, 0.2, 0.3]]