diff --git a/docs/09-route-filter.ipynb b/docs/09-route-filter.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..bfddcad4254e7c05edb371abfa5b03874e42ab93
--- /dev/null
+++ b/docs/09-route-filter.ipynb
@@ -0,0 +1,324 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/00-introduction.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Semantic Router Filter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The Semantic Router library can be used as a super fast route making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make routes. Cutting route making time down from seconds to milliseconds."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing the library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU semantic-router==0.0.29\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/zahidsyed/anaconda3/envs/semantic_router/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"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\",\n",
+ " \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chitchat = Route(\n",
+ " name=\"chitchat\",\n",
+ " utterances=[\n",
+ " \"how's the weather today?\",\n",
+ " \"how are things going?\",\n",
+ " \"lovely weather today\",\n",
+ " \"the weather is horrendous\",\n",
+ " \"let's go to the chippy\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import CohereEncoder, OpenAIEncoder\n",
+ "os.environ[\"COHERE_API_KEY\"] = os.getenv(\"COHERE_API_KEY\") or getpass(\n",
+ " \"Enter Cohere API Key: \"\n",
+ ")\n",
+ "# os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
+ "# \"Enter OpenAI API Key: \"\n",
+ "# )\n",
+ "\n",
+ "encoder = CohereEncoder()\n",
+ "# encoder = OpenAIEncoder()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we define the `RouteLayer`. When called, the route layer will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `RouteLayer` we need our `encoder` model and a list of `routes`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-03-28 14:24:37 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": [
+ "Now we can test it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "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": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 6,
+ "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."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Demonstrating the Filter Feature\n",
+ "\n",
+ "Now, let's demonstrate the filter feature. We can specify a subset of routes to consider when making a classification. This can be useful if we want to restrict the scope of possible routes based on some context.\n",
+ "\n",
+ "For example, let's say we only want to consider the \"chitchat\" route for a particular query:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"don't you love politics?\", route_filter=[\"chitchat\"])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Even though the query might be more related to the \"politics\" route, it will be classified as \"chitchat\" because we've restricted the routes to consider.\n",
+ "\n",
+ "Similarly, we can restrict it to the \"politics\" route:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\", route_filter=[\"politics\"])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, it will return None because the query doesn't match the \"politics\" route well enough to pass the threshold.\n",
+ "\n"
+ ]
+ }
+ ],
+ "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": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/indexes/qdrant.ipynb b/docs/indexes/qdrant.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..56427753ae88a9ab725f81cad7062f48b8486a68
--- /dev/null
+++ b/docs/indexes/qdrant.ipynb
@@ -0,0 +1,325 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[qdrant]\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "# we could use this as a guide for our chatbot to avoid political conversations\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\" \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# this could be used as an indicator to our chatbot to switch to a more\n",
+ "# conversational prompt\n",
+ "chitchat = Route(\n",
+ " name=\"chitchat\",\n",
+ " utterances=[\n",
+ " \"how's the weather today?\",\n",
+ " \"how are things going?\",\n",
+ " \"lovely weather today\",\n",
+ " \"the weather is horrendous\",\n",
+ " \"let's go to the chippy\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "# we place both of our decisions together into single list\n",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders import CohereEncoder\n",
+ "os.environ[\"COHERE_API_KEY\"] = os.environ.get(\"COHERE_API_KEY\") or getpass(\n",
+ " \"Enter COHERE API key: \"\n",
+ ")\n",
+ "encoder = CohereEncoder()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.index.qdrant import QdrantIndex\n",
+ "\n",
+ "\n",
+ "qd_index = QdrantIndex(location=\":memory:\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-03-27 18:22:42 INFO semantic_router.utils.logger local\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.layer import RouteLayer\n",
+ "\n",
+ "rl = RouteLayer(encoder=encoder, routes=routes, index=qd_index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check our route layer and index information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['politics', 'chitchat']"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.list_route_names()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And query:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'politics'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"don't you love politics?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'chitchat'"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl(\"how's the weather today?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"I'm interested in learning about llama 2\").name"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can delete or update routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import time\n",
+ "\n",
+ "rl.delete(route_name=\"chitchat\")\n",
+ "time.sleep(1)\n",
+ "len(rl.index)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "rl(\"how's the weather today?\").name"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[('politics', 'they will save the country!'),\n",
+ " ('politics', \"isn't politics the best thing ever\"),\n",
+ " ('politics', \"why don't you tell me about your political opinions\"),\n",
+ " ('politics', \"they're going to destroy this country!\"),\n",
+ " ('politics',\n",
+ " \"don't you just love the presidentdon't you just hate the president\")]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.index.get_routes()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'type': 'qdrant', 'dimensions': 1024, 'vectors': 5}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "rl.index.describe()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "semantic_router_1",
+ "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": "3.11.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/poetry.lock b/poetry.lock
index db50db68f40569e06cf4b6ffcc9c295e483741b9..c14dd23b3d9c4187b3d1837166756edf8677e03a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
@@ -449,13 +449,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cohere"
-version = "4.54"
+version = "4.57"
description = "Python SDK for the Cohere API"
optional = false
python-versions = ">=3.8,<4.0"
files = [
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- {file = "cohere-4.54.tar.gz", hash = "sha256:c4bd84f0d766575430db91262e3ba0f4b655e40fec8a08fde98652e49a18608d"},
+ {file = "cohere-4.57-py3-none-any.whl", hash = "sha256:479bdea81ae119e53f671f1ae808fcff9df88211780525d7ef2f7b99dfb32e59"},
+ {file = "cohere-4.57.tar.gz", hash = "sha256:71ace0204a92d1a2a8d4b949b88b353b4f22fc645486851924284cc5a0eb700d"},
]
[package.dependencies]
@@ -593,63 +593,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
[[package]]
name = "coverage"
-version = "7.4.3"
+version = "7.4.4"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.8"
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@@ -1379,7 +1379,7 @@ zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["jaraco.collections", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
+testing = ["jaraco.test (>=5.4)", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)", "zipp (>=3.17)"]
[[package]]
name = "iniconfig"
@@ -1667,12 +1667,12 @@ files = [
[[package]]
name = "llama-cpp-python"
-version = "0.2.56"
+version = "0.2.57"
description = "Python bindings for the llama.cpp library"
optional = true
python-versions = ">=3.8"
files = [
- {file = "llama_cpp_python-0.2.56.tar.gz", hash = "sha256:9c82db80e929ae93c2ab069a76a8a52aac82479cf9d0523c3550af48554cc785"},
+ {file = "llama_cpp_python-0.2.57.tar.gz", hash = "sha256:bd81dbc4bc03b7deca3be0496330705d4c53bba726f7c3a47d556c7ec2452304"},
]
[package.dependencies]
@@ -2296,7 +2296,6 @@ description = "Nvidia JIT LTO Library"
optional = true
python-versions = ">=3"
files = [
- {file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_aarch64.whl", hash = "sha256:75d6498c96d9adb9435f2bbdbddb479805ddfb97b5c1b32395c694185c20ca57"},
{file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl", hash = "sha256:c6428836d20fe7e327191c175791d38570e10762edc588fb46749217cd444c74"},
{file = "nvidia_nvjitlink_cu12-12.4.99-py3-none-win_amd64.whl", hash = "sha256:991905ffa2144cb603d8ca7962d75c35334ae82bf92820b6ba78157277da1ad2"},
]
@@ -2397,13 +2396,13 @@ sympy = "*"
[[package]]
name = "openai"
-version = "1.13.3"
+version = "1.14.2"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
- {file = "openai-1.13.3-py3-none-any.whl", hash = "sha256:5769b62abd02f350a8dd1a3a242d8972c947860654466171d60fb0972ae0a41c"},
- {file = "openai-1.13.3.tar.gz", hash = "sha256:ff6c6b3bc7327e715e4b3592a923a5a1c7519ff5dd764a83d69f633d49e77a7b"},
+ {file = "openai-1.14.2-py3-none-any.whl", hash = "sha256:a48b3c4d635b603952189ac5a0c0c9b06c025b80eb2900396939f02bb2104ac3"},
+ {file = "openai-1.14.2.tar.gz", hash = "sha256:e5642f7c02cf21994b08477d7bb2c1e46d8f335d72c26f0396c5f89b15b5b153"},
]
[package.dependencies]
@@ -3003,17 +3002,17 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale
[[package]]
name = "pytest-mock"
-version = "3.12.0"
+version = "3.13.0"
description = "Thin-wrapper around the mock package for easier use with pytest"
optional = false
python-versions = ">=3.8"
files = [
- {file = "pytest-mock-3.12.0.tar.gz", hash = "sha256:31a40f038c22cad32287bb43932054451ff5583ff094bca6f675df2f8bc1a6e9"},
- {file = "pytest_mock-3.12.0-py3-none-any.whl", hash = "sha256:0972719a7263072da3a21c7f4773069bcc7486027d7e8e1f81d98a47e701bc4f"},
+ {file = "pytest-mock-3.13.0.tar.gz", hash = "sha256:58c73e7eae1fc05aebe7bd4b3b59eb5beb5c0b644c7e2dd3cc652a749f0056e3"},
+ {file = "pytest_mock-3.13.0-py3-none-any.whl", hash = "sha256:158462fe2124763df267f20c81f098bdccf8928e2649e5e1093955ad1e515f80"},
]
[package.dependencies]
-pytest = ">=5.0"
+pytest = ">=6.2.5"
[package.extras]
dev = ["pre-commit", "pytest-asyncio", "tox"]
@@ -3965,13 +3964,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]]
name = "transformers"
-version = "4.38.2"
+version = "4.39.0"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = true
python-versions = ">=3.8.0"
files = [
- {file = "transformers-4.38.2-py3-none-any.whl", hash = "sha256:c4029cb9f01b3dd335e52f364c52d2b37c65b4c78e02e6a08b1919c5c928573e"},
- {file = "transformers-4.38.2.tar.gz", hash = "sha256:c5fc7ad682b8a50a48b2a4c05d4ea2de5567adb1bdd00053619dbe5960857dd5"},
+ {file = "transformers-4.39.0-py3-none-any.whl", hash = "sha256:7801785b1f016d667467e8c372c1c3653c18fe32ba97952059e3bea79ba22b08"},
+ {file = "transformers-4.39.0.tar.gz", hash = "sha256:517a13cd633b10bea01c92ab0b3059762872c7c29da3d223db9d28e926fe330d"},
]
[package.dependencies]
@@ -4247,18 +4246,18 @@ multidict = ">=4.0"
[[package]]
name = "zipp"
-version = "3.17.0"
+version = "3.18.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
- {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
+ {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
+ {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[extras]
fastembed = ["fastembed"]
diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py
index 5271b897298af0d99efb5e6f23f87c94da4e7b12..f8a965df015139cd20afb3bbf3a6b9b0f87531cc 100644
--- a/semantic_router/index/base.py
+++ b/semantic_router/index/base.py
@@ -43,7 +43,12 @@ class BaseIndex(BaseModel):
"""
raise NotImplementedError("This method should be implemented by subclasses.")
- def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query_vector and return top_k results.
This method should be implemented by subclasses.
diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py
index 81da71d1d6114cb58849ec364a0fa175254020c6..6d322f07c456a41afd54f0fa4e8571d9c30623ec 100644
--- a/semantic_router/index/local.py
+++ b/semantic_router/index/local.py
@@ -56,17 +56,33 @@ class LocalIndex(BaseIndex):
"vectors": self.index.shape[0] if self.index is not None else 0,
}
- def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query and return top_k results.
"""
if self.index is None or self.routes is None:
raise ValueError("Index or routes are not populated.")
- sim = similarity_matrix(vector, self.index)
- # extract the index values of top scoring vectors
- scores, idx = top_scores(sim, top_k)
- # get routes from index values
- route_names = self.routes[idx].copy()
+ if route_filter is not None:
+ filtered_index = []
+ filtered_routes = []
+ for route, vec in zip(self.routes, self.index):
+ if route in route_filter:
+ filtered_index.append(vec)
+ filtered_routes.append(route)
+ if not filtered_routes:
+ raise ValueError("No routes found matching the filter criteria.")
+ sim = similarity_matrix(vector, np.array(filtered_index))
+ scores, idx = top_scores(sim, top_k)
+ route_names = [filtered_routes[i] for i in idx]
+ else:
+ sim = similarity_matrix(vector, self.index)
+ scores, idx = top_scores(sim, top_k)
+ route_names = [self.routes[i] for i in idx]
return scores, route_names
def delete(self, route_name: str):
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index 321dee3045d373354a313f84e52db22eefa06689..07d519f0c17bfc6aec45d6b501bd6197cf4a57a4 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -219,13 +219,23 @@ class PineconeIndex(BaseIndex):
else:
raise ValueError("Index is None, cannot describe index stats.")
- def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
if self.index is None:
raise ValueError("Index is not populated.")
query_vector_list = vector.tolist()
+ if route_filter is not None:
+ filter_query = {"sr_route": {"$in": route_filter}}
+ else:
+ filter_query = None
results = self.index.query(
vector=[query_vector_list],
top_k=top_k,
+ filter=filter_query,
include_metadata=True,
)
scores = [result["score"] for result in results["matches"]]
diff --git a/semantic_router/index/qdrant.py b/semantic_router/index/qdrant.py
index e1339afc033aae4bf9701675583a03aaa4acf225..ea90e46e4529f54320dfcfe71dc1d739200fcde2 100644
--- a/semantic_router/index/qdrant.py
+++ b/semantic_router/index/qdrant.py
@@ -217,10 +217,35 @@ class QdrantIndex(BaseIndex):
"vectors": collection_info.points_count,
}
- def query(self, vector: np.ndarray, top_k: int = 5) -> Tuple[np.ndarray, List[str]]:
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ from qdrant_client import models
+
results = self.client.search(
self.index_name, query_vector=vector, limit=top_k, with_payload=True
)
+ filter = None
+ if route_filter is not None:
+ filter = models.Filter(
+ must=[
+ models.FieldCondition(
+ key=SR_ROUTE_PAYLOAD_KEY,
+ match=models.MatchAny(any=route_filter),
+ )
+ ]
+ )
+
+ results = self.client.search(
+ self.index_name,
+ query_vector=vector,
+ limit=top_k,
+ with_payload=True,
+ query_filter=filter,
+ )
scores = [result.score for result in results]
route_names = [result.payload[SR_ROUTE_PAYLOAD_KEY] for result in results]
return np.array(scores), route_names
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index 851fc1b6d8438d9b7526a8827773f9755890752e..39232c69955ece223221fe8ce22da85d07cbb18d 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -232,6 +232,7 @@ class RouteLayer:
text: Optional[str] = None,
vector: Optional[List[float]] = None,
simulate_static: bool = False,
+ route_filter: Optional[List[str]] = None,
) -> RouteChoice:
# if no vector provided, encode text to get vector
if vector is None:
@@ -239,7 +240,7 @@ class RouteLayer:
raise ValueError("Either text or vector must be provided")
vector = self._encode(text=text)
- route, top_class_scores = self._retrieve_top_route(vector)
+ route, top_class_scores = self._retrieve_top_route(vector, route_filter)
passed = self._check_threshold(top_class_scores, route)
if passed and route is not None and not simulate_static:
@@ -271,14 +272,16 @@ class RouteLayer:
return RouteChoice()
def _retrieve_top_route(
- self, vector: List[float]
+ self, vector: List[float], route_filter: Optional[List[str]] = None
) -> Tuple[Optional[Route], List[float]]:
"""
Retrieve the top matching route based on the given vector.
Returns a tuple of the route (if any) and the scores of the top class.
"""
# get relevant results (scores and routes)
- results = self._retrieve(xq=np.array(vector), top_k=self.top_k)
+ results = self._retrieve(
+ xq=np.array(vector), top_k=self.top_k, route_filter=route_filter
+ )
# decide most relevant routes
top_class, top_class_scores = self._semantic_classify(results)
# TODO do we need this check?
@@ -397,10 +400,14 @@ class RouteLayer:
xq = np.squeeze(xq) # Reduce to 1d array.
return xq
- def _retrieve(self, xq: Any, top_k: int = 5) -> List[dict]:
+ def _retrieve(
+ self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None
+ ) -> List[dict]:
"""Given a query vector, retrieve the top_k most similar records."""
# get scores and routes
- scores, routes = self.index.query(vector=xq, top_k=top_k)
+ scores, routes = self.index.query(
+ vector=xq, top_k=top_k, route_filter=route_filter
+ )
return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
def _set_aggregation_method(self, aggregation: str = "sum"):
diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py
index 1f5bce5a776ef58a0cc70800a78c7c57d473f942..8af6e1cc1135c2c1256913044550de8f1a24fe18 100644
--- a/tests/unit/test_layer.py
+++ b/tests/unit/test_layer.py
@@ -7,6 +7,7 @@ import pytest
from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
from semantic_router.index.local import LocalIndex
+from semantic_router.index.pinecone import PineconeIndex
from semantic_router.index.qdrant import QdrantIndex
from semantic_router.layer import LayerConfig, RouteLayer
from semantic_router.llms.base import BaseLLM
@@ -126,7 +127,6 @@ def get_test_indexes():
if importlib.util.find_spec("qdrant_client") is not None:
indexes.append(QdrantIndex)
-
return indexes
@@ -244,6 +244,33 @@ class TestRouteLayer:
query_result = route_layer(text="Hello").name
assert query_result in ["Route 1", "Route 2"]
+ def test_query_filter(self, openai_encoder, routes, index_cls):
+ route_layer = RouteLayer(
+ encoder=openai_encoder, routes=routes, index=index_cls()
+ )
+ query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
+
+ try:
+ route_layer(text="Hello", route_filter=["Route 8"]).name
+ except ValueError:
+ assert True
+
+ assert query_result in ["Route 1"]
+
+ def test_query_filter_pinecone(self, openai_encoder, routes, index_cls):
+ pineconeindex = PineconeIndex()
+ route_layer = RouteLayer(
+ encoder=openai_encoder, routes=routes, index=pineconeindex
+ )
+ query_result = route_layer(text="Hello", route_filter=["Route 1"]).name
+
+ try:
+ route_layer(text="Hello", route_filter=["Route 8"]).name
+ except ValueError:
+ assert True
+
+ assert query_result in ["Route 1"]
+
def test_query_with_no_index(self, openai_encoder, index_cls):
route_layer = RouteLayer(encoder=openai_encoder, index=index_cls())
with pytest.raises(ValueError):