diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..98fccf6972026d62e6d53a3c96492ca90287207e
--- /dev/null
+++ b/docs/examples/hybrid-layer.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Semantic Router: Hybrid Layer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Hybrid Layer in the Semantic Router library can improve decision making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to decision making based on the keywords contained in our utterances and user queries."
+   ]
+  },
+  {
+   "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==0.0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"COHERE_API_KEY\"] = \"BQBiUqqjDRsYl1QKKux4JsqKdDkjyInS5T3Z3eJP\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from semantic_router.schema import Decision\n",
+    "\n",
+    "politics = Decision(\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 = Decision(\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",
+    "chitchat = Decision(\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",
+    "decisions = [politics, chitchat]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we initialize our embedding model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from semantic_router.encoders import CohereEncoder\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n",
+    "    \"Enter Cohere API Key: \"\n",
+    ")\n",
+    "\n",
+    "encoder = CohereEncoder()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from semantic_router.layer import HybridDecisionLayer\n",
+    "\n",
+    "dl = HybridDecisionLayer(encoder=encoder, decisions=decisions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dl(\"don't you love politics?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dl(\"how's the weather today?\")"
+   ]
+  },
+  {
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+    {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:237533179d9747080bcaad4d02083ce295c0d2eab3e9e8ce103411a4312991a0"},
+    {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:02ab6006ec3c3463b528374c4cdce86434e7b89ad355e7bf29e2f16b46c7dd6f"},
+    {file = "aiohttp-3.9.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04fa38875e53eb7e354ece1607b1d2fdee2d175ea4e4d745f6ec9f751fe20c7c"},
+    {file = "aiohttp-3.9.1-cp39-cp39-win32.whl", hash = "sha256:82eefaf1a996060602f3cc1112d93ba8b201dbf5d8fd9611227de2003dddb3b7"},
+    {file = "aiohttp-3.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:9b05d33ff8e6b269e30a7957bd3244ffbce2a7a35a81b81c382629b80af1a8bf"},
+    {file = "aiohttp-3.9.1.tar.gz", hash = "sha256:8fc49a87ac269d4529da45871e2ffb6874e87779c3d0e2ccd813c0899221239d"},
 ]
 
 [package.dependencies]
 aiosignal = ">=1.1.2"
-async-timeout = ">=4.0.0a3,<5.0"
+async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""}
 attrs = ">=17.3.0"
-charset-normalizer = ">=2.0,<4.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 yarl = ">=1.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli", "aiodns", "cchardet"]
+speedups = ["Brotli", "aiodns", "brotlicffi"]
 
 [[package]]
 name = "aiosignal"
@@ -235,13 +223,13 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "certifi"
-version = "2023.7.22"
+version = "2023.11.17"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"},
-    {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
+    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
+    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
 ]
 
 [[package]]
@@ -423,19 +411,19 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cohere"
-version = "4.34"
-description = ""
+version = "4.37"
+description = "Python SDK for the Cohere API"
 optional = false
-python-versions = ">=3.7,<4.0"
+python-versions = ">=3.8,<4.0"
 files = [
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-    {file = "cohere-4.34.tar.gz", hash = "sha256:597bb4ea490a8873ba8166b1bd491380595f4bc22b9e1ff8b3bbe3a4e6fd74bb"},
+    {file = "cohere-4.37-py3-none-any.whl", hash = "sha256:f3fad3a0f8d86761d4de851dfd2233a1e5c7634a024102212d850bde9c9bb031"},
+    {file = "cohere-4.37.tar.gz", hash = "sha256:788021d9d992c6c31d1985d95cccb277c7265882c4acd7a49b3e47da77b4bec8"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.0,<4.0"
 backoff = ">=2.0,<3.0"
-fastavro = {version = "1.8.2", markers = "python_version >= \"3.8\""}
+fastavro = ">=1.8,<2.0"
 importlib_metadata = ">=6.0,<7.0"
 requests = ">=2.25.0,<3.0.0"
 urllib3 = ">=1.26,<3"
@@ -575,13 +563,13 @@ files = [
 
 [[package]]
 name = "exceptiongroup"
-version = "1.1.3"
+version = "1.2.0"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.1.3-py3-none-any.whl", hash = "sha256:343280667a4585d195ca1cf9cef84a4e178c4b6cf2274caef9859782b567d5e3"},
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+    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
 ]
 
 [package.extras]
@@ -603,42 +591,48 @@ tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipyth
 
 [[package]]
 name = "fastavro"
-version = "1.8.2"
+version = "1.9.0"
 description = "Fast read/write of AVRO files"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "fastavro-1.9.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:215f40921d3f1f229cea89af25533e7be3fde16dd85c55436c15fb1ad067b486"},
+    {file = "fastavro-1.9.0-cp39-cp39-win_amd64.whl", hash = "sha256:0c046ed9759d1100df59dc18452901253cff5a37d9e8e8701d0102116c3202cb"},
+    {file = "fastavro-1.9.0.tar.gz", hash = "sha256:71aad82b17442dc41223f8351b9f28a60dd877a8e5a7525eaf6342f45f6d23e1"},
 ]
 
 [package.extras]
-codecs = ["lz4", "python-snappy", "zstandard"]
+codecs = ["cramjam", "lz4", "zstandard"]
 lz4 = ["lz4"]
-snappy = ["python-snappy"]
+snappy = ["cramjam"]
 zstandard = ["zstandard"]
 
 [[package]]
@@ -713,31 +707,31 @@ files = [
 
 [[package]]
 name = "idna"
-version = "3.4"
+version = "3.6"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.4-py3-none-any.whl", hash = "sha256:90b77e79eaa3eba6de819a0c442c0b4ceefc341a7a2ab77d7562bf49f425c5c2"},
-    {file = "idna-3.4.tar.gz", hash = "sha256:814f528e8dead7d329833b91c5faa87d60bf71824cd12a7530b5526063d02cb4"},
+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
 ]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.8.0"
+version = "6.9.0"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
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+    {file = "importlib_metadata-6.9.0-py3-none-any.whl", hash = "sha256:1c8dc6839ddc9771412596926f24cb5a553bbd40624ee2c7e55e531542bed3b8"},
+    {file = "importlib_metadata-6.9.0.tar.gz", hash = "sha256:e8acb523c335a91822674e149b46c0399ec4d328c4d1f6e49c273da5ff0201b9"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
@@ -754,13 +748,13 @@ files = [
 
 [[package]]
 name = "ipykernel"
-version = "6.26.0"
+version = "6.27.1"
 description = "IPython Kernel for Jupyter"
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "ipykernel-6.26.0.tar.gz", hash = "sha256:553856658eb8430bbe9653ea041a41bff63e9606fc4628873fc92a6cf3abd404"},
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+    {file = "ipykernel-6.27.1.tar.gz", hash = "sha256:7d5d594b6690654b4d299edba5e872dc17bb7396a8d0609c97cb7b8a1c605de6"},
 ]
 
 [package.dependencies]
@@ -787,24 +781,23 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio"
 
 [[package]]
 name = "ipython"
-version = "8.17.2"
+version = "8.18.1"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.9"
 files = [
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+    {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"},
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 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
 exceptiongroup = {version = "*", markers = "python_version < \"3.11\""}
 jedi = ">=0.16"
 matplotlib-inline = "*"
 pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-prompt-toolkit = ">=3.0.30,<3.0.37 || >3.0.37,<3.1.0"
+prompt-toolkit = ">=3.0.41,<3.1.0"
 pygments = ">=2.4.0"
 stack-data = "*"
 traitlets = ">=5"
@@ -841,6 +834,17 @@ docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alab
 qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
 testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
+[[package]]
+name = "joblib"
+version = "1.3.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "jupyter-client"
 version = "8.6.0"
@@ -897,6 +901,50 @@ files = [
 [package.dependencies]
 traitlets = "*"
 
+[[package]]
+name = "mmh3"
+version = "3.1.0"
+description = "Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions."
+optional = false
+python-versions = "*"
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 [[package]]
 name = "multidict"
 version = "6.0.4"
@@ -1002,49 +1050,63 @@ files = [
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 ]
 
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+description = "Natural Language Toolkit"
+optional = false
+python-versions = ">=3.7"
+files = [
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+
+[package.dependencies]
+click = "*"
+joblib = "*"
+regex = ">=2021.8.3"
+tqdm = "*"
+
+[package.extras]
+all = ["matplotlib", "numpy", "pyparsing", "python-crfsuite", "requests", "scikit-learn", "scipy", "twython"]
+corenlp = ["requests"]
+machine-learning = ["numpy", "python-crfsuite", "scikit-learn", "scipy"]
+plot = ["matplotlib"]
+tgrep = ["pyparsing"]
+twitter = ["twython"]
+
 [[package]]
 name = "numpy"
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+version = "1.25.2"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
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 [[package]]
@@ -1623,18 +1805,18 @@ telegram = ["requests"]
 
 [[package]]
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-version = "5.13.0"
+version = "5.14.0"
 description = "Traitlets Python configuration system"
 optional = false
 python-versions = ">=3.8"
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 [package.extras]
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 [[package]]
 name = "typing-extensions"
@@ -1665,96 +1847,122 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
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+]
+
+[[package]]
+name = "wget"
+version = "3.2"
+description = "pure python download utility"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "yarl-1.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:d92d897cb4b4bf915fbeb5e604c7911021a8456f0964f3b8ebbe7f9188b9eabb"},
+    {file = "yarl-1.9.3-py3-none-any.whl", hash = "sha256:271d63396460b6607b588555ea27a1a02b717ca2e3f2cf53bdde4013d7790929"},
+    {file = "yarl-1.9.3.tar.gz", hash = "sha256:4a14907b597ec55740f63e52d7fee0e9ee09d5b9d57a4f399a7423268e457b57"},
 ]
 
 [package.dependencies]
@@ -1779,4 +1987,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "c0b2fcab1a4add0d43415d41359db59dd15ce87f14f5227e8f0cfb956a679dcd"
+content-hash = "b751e9eced707d903729ec6f473ec547e00bd7ef98e7536da003e5d2f4a80783"
diff --git a/pyproject.toml b/pyproject.toml
index ce9aa8f0c01ecff5159cc03ac288995693e13b22..76ac7539cb2b21a902572467ed188ed871a34573 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,11 +1,12 @@
 [tool.poetry]
 name = "semantic-router"
-version = "0.0.4"
+version = "0.0.5"
 description = "Super fast semantic router for AI decision making"
 authors = [
     "James Briggs <james@aurelio.ai>",
     "Siraj Aizlewood <siraj@aurelio.ai>",
     "Simonas Jakubonis <simonas@aurelio.ai>",
+    "Luca Mannini <luca@aurelio.ai>",
     "Bogdan Buduroiu <bogdan@aurelio.ai>"
 ]
 readme = "README.md"
@@ -15,7 +16,8 @@ python = "^3.10"
 pydantic = "^1.8.2"
 openai = "^0.28.1"
 cohere = "^4.32"
-numpy = "^1.26.2"
+numpy = "^1.25.2"
+pinecone-text = "^0.7.0"
 
 
 [tool.poetry.group.dev.dependencies]
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..734906f8554e11898fbefe8c95ef92a555dc5c8d 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -0,0 +1,3 @@
+from .layer import DecisionLayer, HybridDecisionLayer
+
+__all__ = ["DecisionLayer", "HybridDecisionLayer"]
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index 3fc9481593d725ce2bc91b89dd404d4c5b19d602..0c86ce7c47290d1490565837bb8a98a5631941f2 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -1,5 +1,6 @@
 from .base import BaseEncoder
 from .cohere import CohereEncoder
 from .openai import OpenAIEncoder
+from .bm25 import BM25Encoder
 
-__all__ = ["BaseEncoder", "CohereEncoder", "OpenAIEncoder"]
+__all__ = ["BaseEncoder", "CohereEncoder", "OpenAIEncoder", "BM25Encoder"]
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index 4b5ca40d338e279b5ee8cb14eeb0a1c0c042d08c..b6de1f89b80f42ea42d3abe76bbf61441936aa6e 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -7,5 +7,5 @@ class BaseEncoder(BaseModel):
     class Config:
         arbitrary_types_allowed = True
 
-    def __call__(self, texts: list[str]) -> list[float]:
+    def __call__(self, docs: list[str]) -> list[float]:
         raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d498197b5e548ad3fd7cc0eade0b457f47d795f
--- /dev/null
+++ b/semantic_router/encoders/bm25.py
@@ -0,0 +1,40 @@
+from pinecone_text.sparse import BM25Encoder as encoder
+
+from semantic_router.encoders import BaseEncoder
+
+
+class BM25Encoder(BaseEncoder):
+    model: encoder | None = None
+    idx_mapping: dict[int, int] | None = None
+
+    def __init__(self, name: str = "bm25"):
+        super().__init__(name=name)
+        # initialize BM25 encoder with default params (trained on MSMarco)
+        self.model = encoder.default()
+        self.idx_mapping = {
+            idx: i
+            for i, idx in enumerate(self.model.get_params()["doc_freq"]["indices"])
+        }
+
+    def __call__(self, docs: list[str]) -> list[list[float]]:
+        if len(docs) == 1:
+            sparse_dicts = self.model.encode_queries(docs)
+        elif len(docs) > 1:
+            sparse_dicts = self.model.encode_documents(docs)
+        else:
+            raise ValueError("No documents to encode.")
+        # convert sparse dict to sparse vector
+        embeds = [[0.0] * len(self.idx_mapping)] * len(docs)
+        for i, output in enumerate(sparse_dicts):
+            indices = output["indices"]
+            values = output["values"]
+            for idx, val in zip(indices, values):
+                if idx in self.idx_mapping:
+                    position = self.idx_mapping[idx]
+                    embeds[i][position] = val
+                else:
+                    print(idx, "not in encoder.idx_mapping")
+        return embeds
+
+    def fit(self, docs: list[str]):
+        self.model.fit(docs)
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
index bea75ba20a3fd3f137b998e348d51c651ab01038..fd20fa7586da1a4370d541e8659b826b93db9fac 100644
--- a/semantic_router/encoders/cohere.py
+++ b/semantic_router/encoders/cohere.py
@@ -17,8 +17,8 @@ class CohereEncoder(BaseEncoder):
             raise ValueError("Cohere API key cannot be 'None'.")
         self.client = cohere.Client(cohere_api_key)
 
-    def __call__(self, texts: list[str]) -> list[list[float]]:
+    def __call__(self, docs: list[str]) -> list[list[float]]:
         if self.client is None:
             raise ValueError("Cohere client is not initialized.")
-        embeds = self.client.embed(texts, input_type="search_query", model=self.name)
+        embeds = self.client.embed(docs, input_type="search_query", model=self.name)
         return embeds.embeddings
diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py
index 87feec4ca3fdbc93d777e4b6d91d7aa0b16bee07..5700c8003b05bcb7a3be237b7ac655d9b13d56d4 100644
--- a/semantic_router/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -14,7 +14,7 @@ class OpenAIEncoder(BaseEncoder):
         if openai.api_key is None:
             raise ValueError("OpenAI API key cannot be 'None'.")
 
-    def __call__(self, texts: list[str]) -> list[list[float]]:
+    def __call__(self, docs: list[str]) -> list[list[float]]:
         """Encode a list of texts using the OpenAI API. Returns a list of
         vector embeddings.
         """
@@ -22,7 +22,7 @@ class OpenAIEncoder(BaseEncoder):
         # exponential backoff in case of RateLimitError
         for j in range(5):
             try:
-                res = openai.Embedding.create(input=texts, engine=self.name)
+                res = openai.Embedding.create(input=docs, engine=self.name)
                 if isinstance(res, dict) and "data" in res:
                     break
             except RateLimitError:
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
index 5add0784a6b966de926c276cbc89a000b9a5673b..adff961c16cb87965f06975afed8b185d4932f8c 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -1,6 +1,13 @@
 import numpy as np
+from numpy.linalg import norm
+from tqdm.auto import tqdm
 
-from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
+from semantic_router.encoders import (
+    BaseEncoder,
+    CohereEncoder,
+    OpenAIEncoder,
+    BM25Encoder,
+)
 from semantic_router.linear import similarity_matrix, top_scores
 from semantic_router.schema import Decision
 
@@ -8,27 +15,27 @@ from semantic_router.schema import Decision
 class DecisionLayer:
     index = None
     categories = None
-    similarity_threshold = 0.82
+    score_threshold = 0.82
 
     def __init__(self, encoder: BaseEncoder, decisions: list[Decision] = []):
         self.encoder = encoder
         # decide on default threshold based on encoder
         if isinstance(encoder, OpenAIEncoder):
-            self.similarity_threshold = 0.82
+            self.score_threshold = 0.82
         elif isinstance(encoder, CohereEncoder):
-            self.similarity_threshold = 0.3
+            self.score_threshold = 0.3
         else:
-            self.similarity_threshold = 0.82
+            self.score_threshold = 0.82
         # if decisions list has been passed, we initialize index now
         if decisions:
             # initialize index now
-            for decision in decisions:
+            for decision in tqdm(decisions):
                 self._add_decision(decision=decision)
 
     def __call__(self, text: str) -> str | None:
         results = self._query(text)
         top_class, top_class_scores = self._semantic_classify(results)
-        passed = self._pass_threshold(top_class_scores, self.similarity_threshold)
+        passed = self._pass_threshold(top_class_scores, self.score_threshold)
         if passed:
             return top_class
         else:
@@ -98,3 +105,135 @@ class DecisionLayer:
             return max(scores) > threshold
         else:
             return False
+
+
+class HybridDecisionLayer:
+    index = None
+    sparse_index = None
+    categories = None
+    score_threshold = 0.82
+
+    def __init__(
+        self, encoder: BaseEncoder, decisions: list[Decision] = [], alpha: float = 0.3
+    ):
+        self.encoder = encoder
+        self.sparse_encoder = BM25Encoder()
+        self.alpha = alpha
+        # decide on default threshold based on encoder
+        if isinstance(encoder, OpenAIEncoder):
+            self.score_threshold = 0.82
+        elif isinstance(encoder, CohereEncoder):
+            self.score_threshold = 0.3
+        else:
+            self.score_threshold = 0.82
+        # if decisions list has been passed, we initialize index now
+        if decisions:
+            # initialize index now
+            for decision in tqdm(decisions):
+                self._add_decision(decision=decision)
+
+    def __call__(self, text: str) -> str | None:
+        results = self._query(text)
+        top_class, top_class_scores = self._semantic_classify(results)
+        passed = self._pass_threshold(top_class_scores, self.score_threshold)
+        if passed:
+            return top_class
+        else:
+            return None
+
+    def add(self, decision: Decision):
+        self._add_decision(decision=decision)
+
+    def _add_decision(self, decision: Decision):
+        # create embeddings
+        dense_embeds = np.array(self.encoder(decision.utterances))  # * self.alpha
+        sparse_embeds = np.array(
+            self.sparse_encoder(decision.utterances)
+        )  # * (1 - self.alpha)
+
+        # create decision array
+        if self.categories is None:
+            self.categories = np.array([decision.name] * len(decision.utterances))
+            self.utterances = np.array(decision.utterances)
+        else:
+            str_arr = np.array([decision.name] * len(decision.utterances))
+            self.categories = np.concatenate([self.categories, str_arr])
+            self.utterances = np.concatenate(
+                [self.utterances, np.array(decision.utterances)]
+            )
+        # create utterance array (the dense index)
+        if self.index is None:
+            self.index = dense_embeds
+        else:
+            self.index = np.concatenate([self.index, dense_embeds])
+        # create sparse utterance array
+        if self.sparse_index is None:
+            self.sparse_index = sparse_embeds
+        else:
+            self.sparse_index = np.concatenate([self.sparse_index, sparse_embeds])
+
+    def _query(self, text: str, top_k: int = 5):
+        """Given some text, encodes and searches the index vector space to
+        retrieve the top_k most similar records.
+        """
+        # create dense query vector
+        xq_d = np.array(self.encoder([text]))
+        xq_d = np.squeeze(xq_d)  # Reduce to 1d array.
+        # create sparse query vector
+        xq_s = np.array(self.sparse_encoder([text]))
+        xq_s = np.squeeze(xq_s)
+        # convex scaling
+        xq_d, xq_s = self._convex_scaling(xq_d, xq_s)
+
+        if self.index is not None:
+            # calculate dense vec similarity
+            index_norm = norm(self.index, axis=1)
+            xq_d_norm = norm(xq_d.T)
+            sim_d = np.dot(self.index, xq_d.T) / (index_norm * xq_d_norm)
+            # calculate sparse vec similarity
+            sparse_norm = norm(self.sparse_index, axis=1)
+            xq_s_norm = norm(xq_s.T)
+            sim_s = np.dot(self.sparse_index, xq_s.T) / (sparse_norm * xq_s_norm)
+            total_sim = sim_d + sim_s
+            # get indices of top_k records
+            top_k = min(top_k, total_sim.shape[0])
+            idx = np.argpartition(total_sim, -top_k)[-top_k:]
+            scores = total_sim[idx]
+            # get the utterance categories (decision names)
+            decisions = self.categories[idx] if self.categories is not None else []
+            return [
+                {"decision": d, "score": s.item()} for d, s in zip(decisions, scores)
+            ]
+        else:
+            return []
+
+    def _convex_scaling(self, dense: list[float], sparse: list[float]):
+        # scale sparse and dense vecs
+        dense = np.array(dense) * self.alpha
+        sparse = np.array(sparse) * (1 - self.alpha)
+        return dense, sparse
+
+    def _semantic_classify(self, query_results: list[dict]) -> tuple[str, list[float]]:
+        scores_by_class = {}
+        for result in query_results:
+            score = result["score"]
+            decision = result["decision"]
+            if decision in scores_by_class:
+                scores_by_class[decision].append(score)
+            else:
+                scores_by_class[decision] = [score]
+
+        # Calculate total score for each class
+        total_scores = {
+            decision: sum(scores) for decision, scores in scores_by_class.items()
+        }
+        top_class = max(total_scores, key=lambda x: total_scores[x], default=None)
+
+        # Return the top class and its associated scores
+        return str(top_class), scores_by_class.get(top_class, [])
+
+    def _pass_threshold(self, scores: list[float], threshold: float) -> bool:
+        if scores:
+            return max(scores) > threshold
+        else:
+            return False
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index b0de34d01da3fe17fcecab900accbbf5daba3c45..37a43dd41007634706caedc1b5c5dadd6d734bba 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -17,9 +17,9 @@ class Decision(BaseModel):
 
 
 class EncoderType(Enum):
+    HUGGINGFACE = "huggingface"
     OPENAI = "openai"
     COHERE = "cohere"
-    HUGGINGFACE = "huggingface"
 
 
 @dataclass
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
new file mode 100644
index 0000000000000000000000000000000000000000..c198715133829ff4869167b80117c40dba72d4fb
--- /dev/null
+++ b/tests/unit/encoders/test_bm25.py
@@ -0,0 +1,35 @@
+import pytest
+
+from semantic_router.encoders import BM25Encoder
+
+
+@pytest.fixture
+def bm25_encoder():
+    return BM25Encoder()
+
+
+class TestBM25Encoder:
+    def test_initialization(self, bm25_encoder):
+        assert len(bm25_encoder.idx_mapping) != 0
+
+    def test_fit(self, bm25_encoder):
+        bm25_encoder.fit(["some docs", "and more docs", "and even more docs"])
+        assert len(bm25_encoder.idx_mapping) != 0
+
+    def test_call_method(self, bm25_encoder):
+        result = bm25_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"
+
+    def test_call_method_no_docs(self, bm25_encoder):
+        with pytest.raises(ValueError):
+            bm25_encoder([])
+
+    def test_call_method_no_word(self, bm25_encoder):
+        result = bm25_encoder(["doc with fake word gta5jabcxyz"])
+        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"
diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py
index 96e06a086469b78c03dde08ff4508c33ea548095..a746c4ec8f220f0896abb5c415749cb7b62a5796 100644
--- a/tests/unit/test_layer.py
+++ b/tests/unit/test_layer.py
@@ -1,7 +1,10 @@
 import pytest
 
 from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
-from semantic_router.layer import DecisionLayer  # Replace with the actual module name
+from semantic_router.layer import (
+    DecisionLayer,
+    HybridDecisionLayer,
+)  # Replace with the actual module name
 from semantic_router.schema import Decision
 
 
@@ -45,16 +48,16 @@ def decisions():
 class TestDecisionLayer:
     def test_initialization(self, openai_encoder, decisions):
         decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions)
-        assert decision_layer.similarity_threshold == 0.82
+        assert decision_layer.score_threshold == 0.82
         assert len(decision_layer.index) == 5
         assert len(set(decision_layer.categories)) == 2
 
     def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
         decision_layer_cohere = DecisionLayer(encoder=cohere_encoder)
-        assert decision_layer_cohere.similarity_threshold == 0.3
+        assert decision_layer_cohere.score_threshold == 0.3
 
         decision_layer_openai = DecisionLayer(encoder=openai_encoder)
-        assert decision_layer_openai.similarity_threshold == 0.82
+        assert decision_layer_openai.score_threshold == 0.82
 
     def test_add_decision(self, openai_encoder):
         decision_layer = DecisionLayer(encoder=openai_encoder)
@@ -107,9 +110,87 @@ class TestDecisionLayer:
         assert not decision_layer._pass_threshold([], 0.5)
         assert decision_layer._pass_threshold([0.6, 0.7], 0.5)
 
-    def test_failover_similarity_threshold(self, base_encoder):
+    def test_failover_score_threshold(self, base_encoder):
         decision_layer = DecisionLayer(encoder=base_encoder)
-        assert decision_layer.similarity_threshold == 0.82
+        assert decision_layer.score_threshold == 0.82
+
+
+class TestHybridDecisionLayer:
+    def test_initialization(self, openai_encoder, decisions):
+        decision_layer = HybridDecisionLayer(
+            encoder=openai_encoder, decisions=decisions
+        )
+        assert decision_layer.score_threshold == 0.82
+        assert len(decision_layer.index) == 5
+        assert len(set(decision_layer.categories)) == 2
+
+    def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
+        decision_layer_cohere = HybridDecisionLayer(encoder=cohere_encoder)
+        assert decision_layer_cohere.score_threshold == 0.3
+
+        decision_layer_openai = HybridDecisionLayer(encoder=openai_encoder)
+        assert decision_layer_openai.score_threshold == 0.82
+
+    def test_add_decision(self, openai_encoder):
+        decision_layer = HybridDecisionLayer(encoder=openai_encoder)
+        decision = Decision(name="Decision 3", utterances=["Yes", "No"])
+        decision_layer.add(decision)
+        assert len(decision_layer.index) == 2
+        assert len(set(decision_layer.categories)) == 1
+
+    def test_add_multiple_decisions(self, openai_encoder, decisions):
+        decision_layer = HybridDecisionLayer(encoder=openai_encoder)
+        for decision in decisions:
+            decision_layer.add(decision)
+        assert len(decision_layer.index) == 5
+        assert len(set(decision_layer.categories)) == 2
+
+    def test_query_and_classification(self, openai_encoder, decisions):
+        decision_layer = HybridDecisionLayer(
+            encoder=openai_encoder, decisions=decisions
+        )
+        query_result = decision_layer("Hello")
+        assert query_result in ["Decision 1", "Decision 2"]
+
+    def test_query_with_no_index(self, openai_encoder):
+        decision_layer = HybridDecisionLayer(encoder=openai_encoder)
+        assert decision_layer("Anything") is None
+
+    def test_semantic_classify(self, openai_encoder, decisions):
+        decision_layer = HybridDecisionLayer(
+            encoder=openai_encoder, decisions=decisions
+        )
+        classification, score = decision_layer._semantic_classify(
+            [
+                {"decision": "Decision 1", "score": 0.9},
+                {"decision": "Decision 2", "score": 0.1},
+            ]
+        )
+        assert classification == "Decision 1"
+        assert score == [0.9]
+
+    def test_semantic_classify_multiple_decisions(self, openai_encoder, decisions):
+        decision_layer = HybridDecisionLayer(
+            encoder=openai_encoder, decisions=decisions
+        )
+        classification, score = decision_layer._semantic_classify(
+            [
+                {"decision": "Decision 1", "score": 0.9},
+                {"decision": "Decision 2", "score": 0.1},
+                {"decision": "Decision 1", "score": 0.8},
+            ]
+        )
+        assert classification == "Decision 1"
+        assert score == [0.9, 0.8]
+
+    def test_pass_threshold(self, openai_encoder):
+        decision_layer = HybridDecisionLayer(encoder=openai_encoder)
+        assert not decision_layer._pass_threshold([], 0.5)
+        assert decision_layer._pass_threshold([0.6, 0.7], 0.5)
+
+    def test_failover_score_threshold(self, base_encoder):
+        decision_layer = HybridDecisionLayer(encoder=base_encoder)
+        assert decision_layer.score_threshold == 0.82
 
 
 # Add more tests for edge cases and error handling as needed.