From 2596bf9f1bf7c6338048078bba16fff797021c63 Mon Sep 17 00:00:00 2001
From: the-anup-das <anup.das17@yahoo.com>
Date: Sun, 14 Apr 2024 22:31:59 +0530
Subject: [PATCH] Added huggingface encoder endpoint
---
docs/09-route-filter.ipynb | 7 +-
docs/encoders/huggingface-endpoint.ipynb | 3354 +++++++++++++++++
docs/encoders/huggingface.ipynb | 590 ++-
docs/indexes/qdrant.ipynb | 1 +
semantic_router/encoders/__init__.py | 2 +
semantic_router/encoders/hfendpointencoder.py | 53 -
semantic_router/encoders/huggingface.py | 163 +
tests/unit/encoders/test_hfendpointencoder.py | 68 +
8 files changed, 4164 insertions(+), 74 deletions(-)
create mode 100644 docs/encoders/huggingface-endpoint.ipynb
delete mode 100644 semantic_router/encoders/hfendpointencoder.py
create mode 100644 tests/unit/encoders/test_hfendpointencoder.py
diff --git a/docs/09-route-filter.ipynb b/docs/09-route-filter.ipynb
index aa885567..9c95e513 100644
--- a/docs/09-route-filter.ipynb
+++ b/docs/09-route-filter.ipynb
@@ -41,7 +41,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pip install -qU semantic-router==0.0.32\n"
+ "!pip install -qU semantic-router==0.0.32"
]
},
{
@@ -124,6 +124,7 @@
"import os\n",
"from getpass import getpass\n",
"from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
+ "\n",
"os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
" \"Enter Cohere API Key: \"\n",
")\n",
@@ -259,7 +260,7 @@
}
],
"source": [
- "rl(\"don't you love politics?\", route_filter=[\"chitchat\"])\n"
+ "rl(\"don't you love politics?\", route_filter=[\"chitchat\"])"
]
},
{
@@ -288,7 +289,7 @@
}
],
"source": [
- "rl(\"how's the weather today?\", route_filter=[\"politics\"])\n"
+ "rl(\"how's the weather today?\", route_filter=[\"politics\"])"
]
},
{
diff --git a/docs/encoders/huggingface-endpoint.ipynb b/docs/encoders/huggingface-endpoint.ipynb
new file mode 100644
index 00000000..a33ab08c
--- /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/fastembed.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/fastembed.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",
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+ " -0.36194825172424316,\n",
+ " 0.2561335563659668,\n",
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+ " -0.35598382353782654,\n",
+ " -0.2267131507396698,\n",
+ " 0.5062740445137024,\n",
+ " -0.8605638742446899,\n",
+ " 0.3181823790073395,\n",
+ " 0.5258446335792542,\n",
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+ " -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 f0b4b8ba..cd553a75 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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+ " 0.010834690183401108,\n",
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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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+ " 4.78513241e-02 -2.57437732e-02 6.86436594e-02 -3.65344249e-02\n",
+ " 9.58712995e-02 -7.33761489e-02 -6.54934272e-02 -7.58369714e-02\n",
+ " 2.16469876e-02 2.11787838e-02 -1.32356793e-01 7.74216946e-33\n",
+ " -5.21602631e-02 -1.12684049e-01 3.75458039e-02 2.14454755e-02\n",
+ " -1.59239266e-02 -2.00403482e-02 2.00451282e-03 -7.60207623e-02\n",
+ " 6.17395993e-03 9.82744992e-02 -5.70474076e-05 -5.46450354e-02\n",
+ " -8.58346745e-03 3.54736857e-02 9.09018740e-02 -7.34343156e-02\n",
+ " 5.37787378e-02 2.34990641e-02 -7.36654252e-02 -2.96295825e-02\n",
+ " -4.20701243e-02 4.01046425e-02 -1.05566114e-01 8.53969455e-02\n",
+ " -9.78117287e-02 3.11244652e-02 -8.60458147e-03 -5.80112636e-02\n",
+ " 8.06473009e-03 2.32330374e-02 1.29305897e-02 -1.45072332e-02\n",
+ " -6.42017797e-02 -1.80655587e-02 -3.71037386e-02 7.91664515e-03\n",
+ " -2.76426841e-02 -4.71186750e-02 3.25874314e-02 -2.59979051e-02\n",
+ " -2.75685731e-02 -5.33751510e-02 2.11490206e-02 2.41934936e-02\n",
+ " -7.74724558e-02 -5.48699610e-02 -3.02678696e-03 2.51382738e-02\n",
+ " 7.51809077e-03 -1.04018506e-02 -5.87963313e-02 4.60062809e-02\n",
+ " 3.05394977e-02 -2.98662260e-02 4.60343696e-02 -2.43270658e-02\n",
+ " -1.80021673e-02 8.80592465e-02 -3.52736451e-02 3.42088193e-02\n",
+ " -1.84306744e-02 1.83841325e-02 -1.26974359e-02 -5.30652609e-03\n",
+ " 1.65736768e-02 -1.89048573e-02 -2.92793736e-02 -7.48898089e-02\n",
+ " 9.58707631e-02 4.02442832e-03 2.81538311e-02 -5.81194721e-02\n",
+ " -9.39897671e-02 3.95648554e-02 1.23225600e-02 5.75946681e-02\n",
+ " 1.96183030e-03 6.95726126e-02 -2.68521514e-02 2.91528199e-02\n",
+ " 2.64277216e-02 -1.06734969e-02 1.18107386e-02 -7.41961002e-02\n",
+ " 2.32443679e-02 5.63910119e-02 -2.35880818e-02 6.71420619e-02\n",
+ " -2.24174298e-02 5.93297668e-02 5.77776693e-02 3.75099629e-02\n",
+ " 5.69688855e-03 -2.56761108e-02 6.62673786e-02 -1.96984313e-08\n",
+ " 3.92213389e-02 1.70475300e-02 4.09772508e-02 -6.42266218e-03\n",
+ " -6.23241104e-02 -4.91327532e-02 -1.97951663e-02 -4.40760069e-02\n",
+ " -2.72826254e-02 -1.60910953e-02 1.15375720e-01 4.63905372e-02\n",
+ " -6.51486441e-02 -1.31851183e-02 7.16804564e-02 1.75282490e-02\n",
+ " 6.10029511e-02 2.75316294e-02 -1.92716401e-02 -2.04059891e-02\n",
+ " -1.46359978e-02 4.85771447e-02 -1.65706128e-02 8.66604224e-02\n",
+ " 2.96872873e-02 6.26259074e-02 2.56345142e-02 -3.19722369e-02\n",
+ " 8.13637450e-02 -3.31001319e-02 1.27992919e-02 3.62702198e-02\n",
+ " -6.38388470e-02 4.30489294e-02 5.40841487e-04 -3.80511861e-03\n",
+ " 4.21091588e-03 -1.12978499e-02 -7.65859755e-03 -7.39676924e-03\n",
+ " -1.59889944e-02 1.02662235e-01 5.57321496e-02 -2.99020577e-02\n",
+ " -6.92943633e-02 1.64305847e-02 -1.08625945e-02 2.04215292e-02\n",
+ " 8.24088156e-02 1.39690740e-02 -2.41274685e-02 7.50095770e-02\n",
+ " 8.87042657e-03 1.05826482e-02 9.90147814e-02 -2.97501963e-02\n",
+ " 3.43261287e-02 2.33199596e-02 3.74692902e-02 -8.54534374e-05\n",
+ " 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/docs/indexes/qdrant.ipynb b/docs/indexes/qdrant.ipynb
index 56427753..9f32fb87 100644
--- a/docs/indexes/qdrant.ipynb
+++ b/docs/indexes/qdrant.ipynb
@@ -55,6 +55,7 @@
"import os\n",
"from getpass import getpass\n",
"from semantic_router.encoders import CohereEncoder\n",
+ "\n",
"os.environ[\"COHERE_API_KEY\"] = os.environ.get(\"COHERE_API_KEY\") or getpass(\n",
" \"Enter COHERE API key: \"\n",
")\n",
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index b53e5be2..a79fa605 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/hfendpointencoder.py b/semantic_router/encoders/hfendpointencoder.py
deleted file mode 100644
index 4a5260da..00000000
--- a/semantic_router/encoders/hfendpointencoder.py
+++ /dev/null
@@ -1,53 +0,0 @@
-from typing import List, Optional
-
-import requests
-
-from semantic_router.encoders import BaseEncoder
-
-class HFEndpointEncoder(BaseEncoder):
- 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):
-
- super().__init__(name = name, huggingface_url=huggingface_url, huggingface_api_key=huggingface_api_key, score_threshold=score_threshold)
- huggingface_url = huggingface_url
- huggingface_api_key = huggingface_api_key
- score_threshold = score_threshold
-
- 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'.")
-
- try:
- self.query({"inputs": "Hello World!", "parameters": {} })
- pass
-
- 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]]:
- embeddings = []
- for d in docs:
- try:
- output = self.query({"inputs": d, "parameters": {} })
- embeddings.append(output[0])
- except Exception as e:
- raise ValueError(f"No embeddings returned. Error!")
- return embeddings
-
-
- def query(self, payload):
- API_URL = self.huggingface_url
- headers = {
- "Accept" : "application/json",
- "Authorization": f"Bearer {self.huggingface_api_key}",
- "Content-Type": "application/json"
- }
- response = requests.post(API_URL, headers=headers, json=payload)
- return response.json()
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index 64b183ff..56a3b685 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 +138,140 @@ 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: Optional[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")
+
+ 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'.")
+
+ super().__init__(
+ name=name,
+ huggingface_url=huggingface_url,
+ huggingface_api_key=huggingface_api_key,
+ score_threshold=score_threshold,
+ )
+
+ 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/tests/unit/encoders/test_hfendpointencoder.py b/tests/unit/encoders/test_hfendpointencoder.py
new file mode 100644
index 00000000..cb8dd16a
--- /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]]
--
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