diff --git a/docs/encoders/google.ipynb b/docs/encoders/google.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0707864447436e3a15c46512b66119f350a7b3df
--- /dev/null
+++ b/docs/encoders/google.ipynb
@@ -0,0 +1,211 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/google.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/google.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using GoogleEncoder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Google's [Pathways Language Model](https://blog.research.google/2022/04/pathways-language-model-palm-scaling-to.html) (PaLM) is a dense decoder-only model that is trained on a large corpus of text data. The hidden states of the model can be used as embeddings for text data, and Google has released versions of those layers for public use. This notebook demonstrates how to use the GoogleEncoder with the Semantic Router."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router. Support for the new `GoogleEncoder` class was added in `semantic-router==0.0.31`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[google]==0.0.31\""
+ ]
+ },
+ {
+ "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",
+ "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": null,
+ "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. To do this with GoogleEncoder, you'll need to have an active Google Cloud Platform account and a project with the Embeddings API enabled. You can find more information on how to set up a development project in the [Google Cloud documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/start/quickstarts/quickstart-text-embeddings)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.encoders import GoogleEncoder\n",
+ "\n",
+ "PROJECT_ID = \"your-project-id\"\n",
+ "LOCATION = \"us-central1\"\n",
+ "\n",
+ "encoder = GoogleEncoder(project_id=PROJECT_ID, location=LOCATION)"
+ ]
+ },
+ {
+ "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:"
+ ]
+ },
+ {
+ "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(\"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": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index 1979be5fcb4456852473a279824deba313ccd729..0ea7fcd3771dd413c81e4029bdaf36fc1385b9d7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
[[package]]
name = "aiohttp"
@@ -259,6 +259,17 @@ d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
uvloop = ["uvloop (>=0.15.2)"]
+[[package]]
+name = "cachetools"
+version = "5.3.3"
+description = "Extensible memoizing collections and decorators"
+optional = true
+python-versions = ">=3.7"
+files = [
+ {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+ {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
+]
+
[[package]]
name = "certifi"
version = "2024.2.2"
@@ -737,6 +748,17 @@ files = [
{file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
]
+[[package]]
+name = "docstring-parser"
+version = "0.16"
+description = "Parse Python docstrings in reST, Google and Numpydoc format"
+optional = true
+python-versions = ">=3.6,<4.0"
+files = [
+ {file = "docstring_parser-0.16-py3-none-any.whl", hash = "sha256:bf0a1387354d3691d102edef7ec124f219ef639982d096e26e3b60aeffa90637"},
+ {file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"},
+]
+
[[package]]
name = "exceptiongroup"
version = "1.2.0"
@@ -1059,6 +1081,324 @@ smb = ["smbprotocol"]
ssh = ["paramiko"]
tqdm = ["tqdm"]
+[[package]]
+name = "google-api-core"
+version = "2.18.0"
+description = "Google API client core library"
+optional = true
+python-versions = ">=3.7"
+files = [
+ {file = "google-api-core-2.18.0.tar.gz", hash = "sha256:62d97417bfc674d6cef251e5c4d639a9655e00c45528c4364fbfebb478ce72a9"},
+ {file = "google_api_core-2.18.0-py3-none-any.whl", hash = "sha256:5a63aa102e0049abe85b5b88cb9409234c1f70afcda21ce1e40b285b9629c1d6"},
+]
+
+[package.dependencies]
+google-auth = ">=2.14.1,<3.0.dev0"
+googleapis-common-protos = ">=1.56.2,<2.0.dev0"
+grpcio = [
+ {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+ {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+]
+grpcio-status = [
+ {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+ {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
+]
+proto-plus = ">=1.22.3,<2.0.0dev"
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+requests = ">=2.18.0,<3.0.0.dev0"
+
+[package.extras]
+grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio (>=1.49.1,<2.0dev)", "grpcio-status (>=1.33.2,<2.0.dev0)", "grpcio-status (>=1.49.1,<2.0.dev0)"]
+grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
+grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"]
+
+[[package]]
+name = "google-auth"
+version = "2.29.0"
+description = "Google Authentication Library"
+optional = true
+python-versions = ">=3.7"
+files = [
+ {file = "google-auth-2.29.0.tar.gz", hash = "sha256:672dff332d073227550ffc7457868ac4218d6c500b155fe6cc17d2b13602c360"},
+ {file = "google_auth-2.29.0-py2.py3-none-any.whl", hash = "sha256:d452ad095688cd52bae0ad6fafe027f6a6d6f560e810fec20914e17a09526415"},
+]
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+[package.dependencies]
+cachetools = ">=2.0.0,<6.0"
+pyasn1-modules = ">=0.2.1"
+rsa = ">=3.1.4,<5"
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.6.2,<4.0.0.dev0)", "requests (>=2.20.0,<3.0.0.dev0)"]
+enterprise-cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"]
+pyopenssl = ["cryptography (>=38.0.3)", "pyopenssl (>=20.0.0)"]
+reauth = ["pyu2f (>=0.1.5)"]
+requests = ["requests (>=2.20.0,<3.0.0.dev0)"]
+
+[[package]]
+name = "google-cloud-aiplatform"
+version = "1.45.0"
+description = "Vertex AI API client library"
+optional = true
+python-versions = ">=3.8"
+files = [
+ {file = "google-cloud-aiplatform-1.45.0.tar.gz", hash = "sha256:8fdc5f79fe9211ccbb9191b92db883798dffdd63995c12cc734bc17fcdbb3846"},
+ {file = "google_cloud_aiplatform-1.45.0-py2.py3-none-any.whl", hash = "sha256:40bf5e2baa9cdb453689c4276eee5e7fe12db2e7723c133f000d35bcca964fb2"},
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+[package.dependencies]
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+[package.extras]
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+cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
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+grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"]
+
+[[package]]
+name = "grpc-google-iam-v1"
+version = "0.13.0"
+description = "IAM API client library"
+optional = true
+python-versions = ">=3.7"
+files = [
+ {file = "grpc-google-iam-v1-0.13.0.tar.gz", hash = "sha256:fad318608b9e093258fbf12529180f400d1c44453698a33509cc6ecf005b294e"},
+ {file = "grpc_google_iam_v1-0.13.0-py2.py3-none-any.whl", hash = "sha256:53902e2af7de8df8c1bd91373d9be55b0743ec267a7428ea638db3775becae89"},
+]
+
+[package.dependencies]
+googleapis-common-protos = {version = ">=1.56.0,<2.0.0dev", extras = ["grpc"]}
+grpcio = ">=1.44.0,<2.0.0dev"
+protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev"
+
[[package]]
name = "grpcio"
version = "1.62.1"
@@ -1125,6 +1465,22 @@ files = [
[package.extras]
protobuf = ["grpcio-tools (>=1.62.1)"]
+[[package]]
+name = "grpcio-status"
+version = "1.62.1"
+description = "Status proto mapping for gRPC"
+optional = true
+python-versions = ">=3.6"
+files = [
+ {file = "grpcio-status-1.62.1.tar.gz", hash = "sha256:3431c8abbab0054912c41df5c72f03ddf3b7a67be8a287bb3c18a3456f96ff77"},
+ {file = "grpcio_status-1.62.1-py3-none-any.whl", hash = "sha256:af0c3ab85da31669f21749e8d53d669c061ebc6ce5637be49a46edcb7aa8ab17"},
+]
+
+[package.dependencies]
+googleapis-common-protos = ">=1.5.5"
+grpcio = ">=1.62.1"
+protobuf = ">=4.21.6"
+
[[package]]
name = "grpcio-tools"
version = "1.62.1"
@@ -2735,6 +3091,23 @@ files = [
[package.dependencies]
wcwidth = "*"
+[[package]]
+name = "proto-plus"
+version = "1.23.0"
+description = "Beautiful, Pythonic protocol buffers."
+optional = true
+python-versions = ">=3.6"
+files = [
+ {file = "proto-plus-1.23.0.tar.gz", hash = "sha256:89075171ef11988b3fa157f5dbd8b9cf09d65fffee97e29ce403cd8defba19d2"},
+ {file = "proto_plus-1.23.0-py3-none-any.whl", hash = "sha256:a829c79e619e1cf632de091013a4173deed13a55f326ef84f05af6f50ff4c82c"},
+]
+
+[package.dependencies]
+protobuf = ">=3.19.0,<5.0.0dev"
+
+[package.extras]
+testing = ["google-api-core[grpc] (>=1.31.5)"]
+
[[package]]
name = "protobuf"
version = "4.25.3"
@@ -2808,6 +3181,31 @@ files = [
[package.extras]
tests = ["pytest"]
+[[package]]
+name = "pyasn1"
+version = "0.6.0"
+description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)"
+optional = true
+python-versions = ">=3.8"
+files = [
+ {file = "pyasn1-0.6.0-py2.py3-none-any.whl", hash = "sha256:cca4bb0f2df5504f02f6f8a775b6e416ff9b0b3b16f7ee80b5a3153d9b804473"},
+ {file = "pyasn1-0.6.0.tar.gz", hash = "sha256:3a35ab2c4b5ef98e17dfdec8ab074046fbda76e281c5a706ccd82328cfc8f64c"},
+]
+
+[[package]]
+name = "pyasn1-modules"
+version = "0.4.0"
+description = "A collection of ASN.1-based protocols modules"
+optional = true
+python-versions = ">=3.8"
+files = [
+ {file = "pyasn1_modules-0.4.0-py3-none-any.whl", hash = "sha256:be04f15b66c206eed667e0bb5ab27e2b1855ea54a842e5037738099e8ca4ae0b"},
+ {file = "pyasn1_modules-0.4.0.tar.gz", hash = "sha256:831dbcea1b177b28c9baddf4c6d1013c24c3accd14a1873fffaa6a2e905f17b6"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.4.6,<0.7.0"
+
[[package]]
name = "pycparser"
version = "2.22"
@@ -3397,6 +3795,20 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+[[package]]
+name = "rsa"
+version = "4.9"
+description = "Pure-Python RSA implementation"
+optional = true
+python-versions = ">=3.6,<4"
+files = [
+ {file = "rsa-4.9-py3-none-any.whl", hash = "sha256:90260d9058e514786967344d0ef75fa8727eed8a7d2e43ce9f4bcf1b536174f7"},
+ {file = "rsa-4.9.tar.gz", hash = "sha256:e38464a49c6c85d7f1351b0126661487a7e0a14a50f1675ec50eb34d4f20ef21"},
+]
+
+[package.dependencies]
+pyasn1 = ">=0.1.3"
+
[[package]]
name = "ruff"
version = "0.1.15"
@@ -3571,6 +3983,63 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments
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"]
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]]
+name = "shapely"
+version = "2.0.3"
+description = "Manipulation and analysis of geometric objects"
+optional = true
+python-versions = ">=3.7"
+files = [
+ {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:af7e9abe180b189431b0f490638281b43b84a33a960620e6b2e8d3e3458b61a1"},
+ {file = "shapely-2.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:98040462b36ced9671e266b95c326b97f41290d9d17504a1ee4dc313a7667b9c"},
+ {file = "shapely-2.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:71eb736ef2843f23473c6e37f6180f90f0a35d740ab284321548edf4e55d9a52"},
+ {file = "shapely-2.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:881eb9dbbb4a6419667e91fcb20313bfc1e67f53dbb392c6840ff04793571ed1"},
+ {file = "shapely-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f10d2ccf0554fc0e39fad5886c839e47e207f99fdf09547bc687a2330efda35b"},
+ {file = "shapely-2.0.3-cp310-cp310-win32.whl", hash = "sha256:6dfdc077a6fcaf74d3eab23a1ace5abc50c8bce56ac7747d25eab582c5a2990e"},
+ {file = "shapely-2.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:64c5013dacd2d81b3bb12672098a0b2795c1bf8190cfc2980e380f5ef9d9e4d9"},
+ {file = "shapely-2.0.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:56cee3e4e8159d6f2ce32e421445b8e23154fd02a0ac271d6a6c0b266a8e3cce"},
+ {file = "shapely-2.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:619232c8276fded09527d2a9fd91a7885ff95c0ff9ecd5e3cb1e34fbb676e2ae"},
+ {file = "shapely-2.0.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b2a7d256db6f5b4b407dc0c98dd1b2fcf1c9c5814af9416e5498d0a2e4307a4b"},
+ {file = "shapely-2.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e45f0c8cd4583647db3216d965d49363e6548c300c23fd7e57ce17a03f824034"},
+ {file = "shapely-2.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13cb37d3826972a82748a450328fe02a931dcaed10e69a4d83cc20ba021bc85f"},
+ {file = "shapely-2.0.3-cp311-cp311-win32.whl", hash = "sha256:9302d7011e3e376d25acd30d2d9e70d315d93f03cc748784af19b00988fc30b1"},
+ {file = "shapely-2.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:6b464f2666b13902835f201f50e835f2f153f37741db88f68c7f3b932d3505fa"},
+ {file = "shapely-2.0.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:e86e7cb8e331a4850e0c2a8b2d66dc08d7a7b301b8d1d34a13060e3a5b4b3b55"},
+ {file = "shapely-2.0.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:c91981c99ade980fc49e41a544629751a0ccd769f39794ae913e53b07b2f78b9"},
+ {file = "shapely-2.0.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:bd45d456983dc60a42c4db437496d3f08a4201fbf662b69779f535eb969660af"},
+ {file = "shapely-2.0.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:882fb1ffc7577e88c1194f4f1757e277dc484ba096a3b94844319873d14b0f2d"},
+ {file = "shapely-2.0.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9f2d93bff2ea52fa93245798cddb479766a18510ea9b93a4fb9755c79474889"},
+ {file = "shapely-2.0.3-cp312-cp312-win32.whl", hash = "sha256:99abad1fd1303b35d991703432c9481e3242b7b3a393c186cfb02373bf604004"},
+ {file = "shapely-2.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:6f555fe3304a1f40398977789bc4fe3c28a11173196df9ece1e15c5bc75a48db"},
+ {file = "shapely-2.0.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a983cc418c1fa160b7d797cfef0e0c9f8c6d5871e83eae2c5793fce6a837fad9"},
+ {file = "shapely-2.0.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18bddb8c327f392189a8d5d6b9a858945722d0bb95ccbd6a077b8e8fc4c7890d"},
+ {file = "shapely-2.0.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:442f4dcf1eb58c5a4e3428d88e988ae153f97ab69a9f24e07bf4af8038536325"},
+ {file = "shapely-2.0.3-cp37-cp37m-win32.whl", hash = "sha256:31a40b6e3ab00a4fd3a1d44efb2482278642572b8e0451abdc8e0634b787173e"},
+ {file = "shapely-2.0.3-cp37-cp37m-win_amd64.whl", hash = "sha256:59b16976c2473fec85ce65cc9239bef97d4205ab3acead4e6cdcc72aee535679"},
+ {file = "shapely-2.0.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:705efbce1950a31a55b1daa9c6ae1c34f1296de71ca8427974ec2f27d57554e3"},
+ {file = "shapely-2.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:601c5c0058a6192df704cb889439f64994708563f57f99574798721e9777a44b"},
+ {file = "shapely-2.0.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f24ecbb90a45c962b3b60d8d9a387272ed50dc010bfe605f1d16dfc94772d8a1"},
+ {file = "shapely-2.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8c2a2989222c6062f7a0656e16276c01bb308bc7e5d999e54bf4e294ce62e76"},
+ {file = "shapely-2.0.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42bceb9bceb3710a774ce04908fda0f28b291323da2688f928b3f213373b5aee"},
+ {file = "shapely-2.0.3-cp38-cp38-win32.whl", hash = "sha256:54d925c9a311e4d109ec25f6a54a8bd92cc03481a34ae1a6a92c1fe6729b7e01"},
+ {file = "shapely-2.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:300d203b480a4589adefff4c4af0b13919cd6d760ba3cbb1e56275210f96f654"},
+ {file = "shapely-2.0.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:083d026e97b6c1f4a9bd2a9171c7692461092ed5375218170d91705550eecfd5"},
+ {file = "shapely-2.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:27b6e1910094d93e9627f2664121e0e35613262fc037051680a08270f6058daf"},
+ {file = "shapely-2.0.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:71b2de56a9e8c0e5920ae5ddb23b923490557ac50cb0b7fa752761bf4851acde"},
+ {file = "shapely-2.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d279e56bbb68d218d63f3efc80c819cedcceef0e64efbf058a1df89dc57201b"},
+ {file = "shapely-2.0.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88566d01a30f0453f7d038db46bc83ce125e38e47c5f6bfd4c9c287010e9bf74"},
+ {file = "shapely-2.0.3-cp39-cp39-win32.whl", hash = "sha256:58afbba12c42c6ed44c4270bc0e22f3dadff5656d711b0ad335c315e02d04707"},
+ {file = "shapely-2.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:5026b30433a70911979d390009261b8c4021ff87c7c3cbd825e62bb2ffa181bc"},
+ {file = "shapely-2.0.3.tar.gz", hash = "sha256:4d65d0aa7910af71efa72fd6447e02a8e5dd44da81a983de9d736d6e6ccbe674"},
+]
+
+[package.dependencies]
+numpy = ">=1.14,<2"
+
+[package.extras]
+docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"]
+test = ["pytest", "pytest-cov"]
+
[[package]]
name = "six"
version = "1.16.0"
@@ -4270,6 +4739,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[extras]
fastembed = ["fastembed"]
+google = ["google-cloud-aiplatform"]
hybrid = ["pinecone-text"]
local = ["llama-cpp-python", "torch", "transformers"]
mistralai = ["mistralai"]
@@ -4281,4 +4751,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "a233cd0df597c010d9bef5703977730c752e53d5854d942f630f53a186198afe"
+content-hash = "5e662aecf752131b9985184767c251e864a51add59d3dd267c8f70d14cbefcae"
diff --git a/pyproject.toml b/pyproject.toml
index a2f0678150c6539eea35509a90bb3932aeb892f2..78a5fcc0d08054e87bbd60f520246b4f8cb2b219 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,10 +32,11 @@ colorama = "^0.4.6"
pinecone-client = {version="^3.0.0", optional = true}
regex = "^2023.12.25"
torchvision = { version = "^0.17.0", optional = true}
-pillow = { version= "^10.2.0", optional = true}
+pillow = { version = "^10.2.0", optional = true}
tiktoken = "^0.6.0"
-matplotlib = { version="^3.8.3", optional = true}
-qdrant-client = {version="^1.8.0", optional = true}
+matplotlib = { version = "^3.8.3", optional = true}
+qdrant-client = {version = "^1.8.0", optional = true}
+google-cloud-aiplatform = {version = "^1.45.0", optional = true}
[tool.poetry.extras]
hybrid = ["pinecone-text"]
@@ -46,6 +47,7 @@ vision = ["torch", "torchvision", "transformers", "pillow"]
processing = ["matplotlib"]
mistralai = ["mistralai"]
qdrant = ["qdrant-client"]
+google = ["google-cloud-aiplatform"]
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.25.0"
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index d351028e1a35b141beca18189f28fdb272444643..b53e5be2f78b2f7246488fdb9427c9142e5e00e9 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -3,6 +3,7 @@ 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.google import GoogleEncoder
from semantic_router.encoders.huggingface import HuggingFaceEncoder
from semantic_router.encoders.mistral import MistralEncoder
from semantic_router.encoders.openai import OpenAIEncoder
@@ -22,4 +23,5 @@ __all__ = [
"MistralEncoder",
"VitEncoder",
"CLIPEncoder",
+ "GoogleEncoder",
]
diff --git a/semantic_router/encoders/google.py b/semantic_router/encoders/google.py
new file mode 100644
index 0000000000000000000000000000000000000000..42996dee51f640d269ab5bc8908b8a753045e71d
--- /dev/null
+++ b/semantic_router/encoders/google.py
@@ -0,0 +1,104 @@
+"""
+This module provides the GoogleEncoder class for generating embeddings using Google's AI Platform.
+
+The GoogleEncoder class is a subclass of BaseEncoder and utilizes the TextEmbeddingModel from the
+Google AI Platform to generate embeddings for given documents. It requires a Google Cloud project ID
+and supports customization of the pre-trained model, score threshold, location, and API endpoint.
+
+Example usage:
+
+ from semantic_router.encoders.google_encoder import GoogleEncoder
+
+ encoder = GoogleEncoder(project_id="your-project-id")
+ embeddings = encoder(["document1", "document2"])
+
+Classes:
+ GoogleEncoder: A class for generating embeddings using Google's AI Platform.
+"""
+
+import os
+from typing import List, Optional
+
+from google.cloud import aiplatform
+from vertexai.language_models import TextEmbeddingModel
+
+from semantic_router.encoders import BaseEncoder
+from semantic_router.utils.defaults import EncoderDefault
+
+
+class GoogleEncoder(BaseEncoder):
+ """GoogleEncoder class for generating embeddings using Google's AI Platform.
+
+ Attributes:
+ client: An instance of the TextEmbeddingModel client.
+ type: The type of the encoder, which is "google".
+ """
+
+ client: Optional[TextEmbeddingModel] = None
+ type: str = "google"
+
+ def __init__(
+ self,
+ name: Optional[str] = None,
+ score_threshold: float = 0.75,
+ project_id: Optional[str] = None,
+ location: Optional[str] = None,
+ api_endpoint: Optional[str] = None,
+ ):
+ """Initializes the GoogleEncoder.
+
+ Args:
+ model_name: The name of the pre-trained model to use for embedding.
+ If not provided, the default model specified in EncoderDefault will be used.
+ score_threshold: The threshold for similarity scores. Default is 0.3.
+ project_id: The Google Cloud project ID.
+ If not provided, it will be retrieved from the GOOGLE_PROJECT_ID environment variable.
+ location: The location of the AI Platform resources.
+ If not provided, it will be retrieved from the GOOGLE_LOCATION environment variable,
+ defaulting to "us-central1".
+ api_endpoint: The API endpoint for the AI Platform.
+ If not provided, it will be retrieved from the GOOGLE_API_ENDPOINT environment variable.
+
+ Raises:
+ ValueError: If the Google Project ID is not provided or if the AI Platform client fails to initialize.
+ """
+ if name is None:
+ name = EncoderDefault.GOOGLE.value["embedding_model"]
+
+ super().__init__(name=name, score_threshold=score_threshold)
+
+ project_id = project_id or os.getenv("GOOGLE_PROJECT_ID")
+ location = location or os.getenv("GOOGLE_LOCATION", "us-central1")
+ api_endpoint = api_endpoint or os.getenv("GOOGLE_API_ENDPOINT")
+
+ if project_id is None:
+ raise ValueError("Google Project ID cannot be 'None'.")
+ try:
+ aiplatform.init(
+ project=project_id, location=location, api_endpoint=api_endpoint
+ )
+ self.client = TextEmbeddingModel.from_pretrained(self.name)
+ except Exception as e:
+ raise ValueError(
+ f"Google AI Platform client failed to initialize. Error: {e}"
+ ) from e
+
+ def __call__(self, docs: List[str]) -> List[List[float]]:
+ """Generates embeddings for the given documents.
+
+ Args:
+ docs: A list of strings representing the documents to embed.
+
+ Returns:
+ A list of lists, where each inner list contains the embedding values for a document.
+
+ Raises:
+ ValueError: If the Google AI Platform client is not initialized or if the API call fails.
+ """
+ if self.client is None:
+ raise ValueError("Google AI Platform client is not initialized.")
+ try:
+ embeddings = self.client.get_embeddings(docs)
+ return [embedding.values for embedding in embeddings]
+ except Exception as e:
+ raise ValueError(f"Google AI Platform API call failed. Error: {e}") from e
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 85d428ef0494a882cd84b8c9ea8caf7c914de517..035ca8a00b77ca7b3681d833f5e18c19bff0054b 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -10,6 +10,7 @@ from semantic_router.encoders import (
FastEmbedEncoder,
MistralEncoder,
OpenAIEncoder,
+ GoogleEncoder,
)
@@ -19,6 +20,7 @@ class EncoderType(Enum):
OPENAI = "openai"
COHERE = "cohere"
MISTRAL = "mistral"
+ GOOGLE = "google"
class RouteChoice(BaseModel):
@@ -46,6 +48,8 @@ class Encoder:
self.model = CohereEncoder(name=name)
elif self.type == EncoderType.MISTRAL:
self.model = MistralEncoder(name=name)
+ elif self.type == EncoderType.GOOGLE:
+ self.model = GoogleEncoder(name=name)
else:
raise ValueError
diff --git a/semantic_router/utils/defaults.py b/semantic_router/utils/defaults.py
index 7b5a3b5f9485ab5fe4b1f31d9fd391e944da09b6..3c9cbb2dd1010f5b861c49fcafad389c591fe9cb 100644
--- a/semantic_router/utils/defaults.py
+++ b/semantic_router/utils/defaults.py
@@ -26,3 +26,8 @@ class EncoderDefault(Enum):
"AZURE_OPENAI_DEPLOYMENT_NAME", "text-embedding-ada-002"
),
}
+ GOOGLE = {
+ "embedding_model": os.getenv(
+ "GOOGLE_EMBEDDING_MODEL", "textembedding-gecko@003"
+ ),
+ }
diff --git a/tests/unit/encoders/test_google.py b/tests/unit/encoders/test_google.py
new file mode 100644
index 0000000000000000000000000000000000000000..11c19cc81c04890b0ac76da7234df2663bd570a8
--- /dev/null
+++ b/tests/unit/encoders/test_google.py
@@ -0,0 +1,120 @@
+import pytest
+from vertexai.language_models import TextEmbedding
+from vertexai.language_models._language_models import TextEmbeddingStatistics
+from google.api_core.exceptions import GoogleAPICallError
+
+from semantic_router.encoders import GoogleEncoder
+
+
+@pytest.fixture
+def google_encoder(mocker):
+ mocker.patch("google.cloud.aiplatform.init")
+ mocker.patch("vertexai.language_models.TextEmbeddingModel.from_pretrained")
+ return GoogleEncoder(project_id="test_project_id")
+
+
+class TestGoogleEncoder:
+ def test_initialization_with_project_id(self, google_encoder):
+ assert google_encoder.client is not None, "Client should be initialized"
+ assert (
+ google_encoder.name == "textembedding-gecko@003"
+ ), "Default name not set correctly"
+
+ def test_initialization_without_project_id(self, mocker, monkeypatch):
+ monkeypatch.delenv("GOOGLE_PROJECT_ID", raising=False)
+ mocker.patch("google.cloud.aiplatform.init")
+ mocker.patch("vertexai.language_models.TextEmbeddingModel.from_pretrained")
+ with pytest.raises(ValueError):
+ GoogleEncoder()
+
+ def test_call_method(self, google_encoder, mocker):
+ mock_embeddings = [
+ TextEmbedding(
+ values=[0.1, 0.2, 0.3],
+ statistics=TextEmbeddingStatistics(token_count=5, truncated=False),
+ )
+ ]
+ mocker.patch.object(
+ google_encoder.client, "get_embeddings", return_value=mock_embeddings
+ )
+
+ result = google_encoder(["test"])
+ assert isinstance(result, list), "Result should be a list"
+ assert all(
+ isinstance(sublist, list) for sublist in result
+ ), "Each item in result should be a list"
+ google_encoder.client.get_embeddings.assert_called_once()
+
+ def test_returns_list_of_embeddings_for_valid_input(self, google_encoder, mocker):
+ mock_embeddings = [
+ TextEmbedding(
+ values=[0.1, 0.2, 0.3],
+ statistics=TextEmbeddingStatistics(token_count=5, truncated=False),
+ )
+ ]
+ mocker.patch.object(
+ google_encoder.client, "get_embeddings", return_value=mock_embeddings
+ )
+
+ result = google_encoder(["test"])
+ assert isinstance(result, list), "Result should be a list"
+ assert all(
+ isinstance(sublist, list) for sublist in result
+ ), "Each item in result should be a list"
+ google_encoder.client.get_embeddings.assert_called_once()
+
+ def test_handles_multiple_inputs_correctly(self, google_encoder, mocker):
+ mock_embeddings = [
+ TextEmbedding(
+ values=[0.1, 0.2, 0.3],
+ statistics=TextEmbeddingStatistics(token_count=5, truncated=False),
+ ),
+ TextEmbedding(
+ values=[0.4, 0.5, 0.6],
+ statistics=TextEmbeddingStatistics(token_count=6, truncated=False),
+ ),
+ ]
+ mocker.patch.object(
+ google_encoder.client, "get_embeddings", return_value=mock_embeddings
+ )
+
+ result = google_encoder(["test1", "test2"])
+ assert isinstance(result, list), "Result should be a list"
+ assert all(
+ isinstance(sublist, list) for sublist in result
+ ), "Each item in result should be a list"
+ google_encoder.client.get_embeddings.assert_called_once()
+
+ def test_raises_value_error_if_project_id_is_none(self, mocker, monkeypatch):
+ monkeypatch.delenv("GOOGLE_PROJECT_ID", raising=False)
+ mocker.patch("google.cloud.aiplatform.init")
+ mocker.patch("vertexai.language_models.TextEmbeddingModel.from_pretrained")
+ with pytest.raises(ValueError):
+ GoogleEncoder()
+
+ def test_raises_value_error_if_google_client_fails_to_initialize(self, mocker):
+ mocker.patch(
+ "google.cloud.aiplatform.init",
+ side_effect=Exception("Failed to initialize client"),
+ )
+ with pytest.raises(ValueError):
+ GoogleEncoder(project_id="test_project_id")
+
+ def test_raises_value_error_if_google_client_is_not_initialized(self, mocker):
+ mocker.patch("google.cloud.aiplatform.init")
+ mocker.patch(
+ "vertexai.language_models.TextEmbeddingModel.from_pretrained",
+ return_value=None,
+ )
+ encoder = GoogleEncoder(project_id="test_project_id")
+ with pytest.raises(ValueError):
+ encoder(["test"])
+
+ def test_call_method_raises_error_on_api_failure(self, google_encoder, mocker):
+ mocker.patch.object(
+ google_encoder.client,
+ "get_embeddings",
+ side_effect=GoogleAPICallError("API call failed"),
+ )
+ with pytest.raises(ValueError):
+ google_encoder(["test"])