diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index ea083d41e02a19060f4fcb4da24ec57fd3fb02c8..e9105edd056771eb02c4eeb052bbcc9836c02065 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -1,9 +1,8 @@
name: Release Docs
on:
- push:
- branches:
- - main
+ release:
+ types: [released]
jobs:
build-docs:
diff --git a/Makefile b/Makefile
index 8283b96d927172e92cd768862e603727e04842f5..979a97e522253154794c78688035ab5128dd6dba 100644
--- a/Makefile
+++ b/Makefile
@@ -7,8 +7,8 @@ lint: PYTHON_FILES=.
lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$')
lint lint_diff:
- poetry run black --target-version py39 -l 88 $(PYTHON_FILES) --check
- poetry run ruff .
+ poetry run black --target-version py311 -l 88 $(PYTHON_FILES) --check
+ poetry run ruff check .
poetry run mypy $(PYTHON_FILES)
test:
diff --git a/docs/encoders/aurelio-bm25.ipynb b/docs/encoders/aurelio-bm25.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9e4b785246c3f852d6dc02c5cfc9dba6ceb56ea4
--- /dev/null
+++ b/docs/encoders/aurelio-bm25.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using Aurelio AI BM25 Encoder"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hybrid indexes combine both sparse and dense encodings to produce more accurate results. The dense encoder allows us to search based on semantic meaning, while the sparse encoder allows us to search based on text matches. Merging both dense and sparse into a single hybrid retrieval step allows us to step up our performance beyond what dense-only or sparse-only could achieve."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router. Support for the new `AurelioSparseEncoder` parameter was added in `semantic-router==0.1.0`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU semantic-router==0.1.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router import Route\n",
+ "\n",
+ "politics = Route(\n",
+ " name=\"politics\",\n",
+ " utterances=[\n",
+ " \"isn't politics the best thing ever\",\n",
+ " \"why don't you tell me about your political opinions\",\n",
+ " \"don't you just love the president\",\n",
+ " \"don't you just hate the president\",\n",
+ " \"they're going to destroy this country!\",\n",
+ " \"they will save the country!\",\n",
+ " ],\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define another for good measure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "chitchat = Route(\n",
+ " name=\"chitchat\",\n",
+ " utterances=[\n",
+ " \"how's the weather today?\",\n",
+ " \"how are things going?\",\n",
+ " \"lovely weather today\",\n",
+ " \"the weather is horrendous\",\n",
+ " \"let's go to the chippy\",\n",
+ " ],\n",
+ ")\n",
+ "\n",
+ "routes = [politics, chitchat]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we initialize our embedding models. We are going to use a hybrid index which requires both a dense and sparse encoder. For the sparse encoder we will use the pretrained `bm25` model from the Aurelio Platform and OpenAI's `text-embedding-3-small` for the dense encoder.\n",
+ "\n",
+ "To get an API key for the Aurelio Platform, we head to the [Aurelio Platform](https://platform.aurelio.ai/settings/api-keys)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders.aurelio import AurelioSparseEncoder\n",
+ "\n",
+ "os.environ[\"AURELIO_API_KEY\"] = os.getenv(\"AURELIO_API_KEY\") or getpass(\n",
+ " \"Enter Aurelio API Key: \"\n",
+ ")\n",
+ "\n",
+ "sparse_encoder = AurelioSparseEncoder(name=\"bm25\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sparse encoders return dictionaries containing the the indices and values of the non-zero elements in the sparse matrix."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.encoders import OpenAIEncoder\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API Key: \"\n",
+ ")\n",
+ "\n",
+ "encoder = OpenAIEncoder(name=\"text-embedding-3-small\", score_threshold=0.3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have both our sparse and dense encoders. When using both sparse and dense encoders we need to initialize an index that supports hybrid, such as the `HybridLocalIndex` or `PineconeIndex`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.index.hybrid_local import HybridLocalIndex\n",
+ "\n",
+ "index = HybridLocalIndex()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we define the `HybridRouter`. When called, the router will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `HybridRouter` we need an `encoder`, `sparse_encoder` our `routes`, and the hybrid `index` we just define."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-24 18:12:29 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
+ "2024-11-24 18:12:31 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.routers import HybridRouter\n",
+ "\n",
+ "router = HybridRouter(\n",
+ " encoder=encoder,\n",
+ " sparse_encoder=sparse_encoder,\n",
+ " routes=routes,\n",
+ " index=index,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can check the dimensionality and number of dense vector records by looking at the `index` attribute of the `HybridRouter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(11, 1536)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.index.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To find the number of sparse vector records we can look at the `sparse_index` attribute of the `HybridRouter`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "11"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(router.index.sparse_index)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We do have 256-dimensional vectors. Now let's test them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-24 18:12:34 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='politics', function_call=None, similarity_score=1.2837569119668175)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-24 18:12:36 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=1.856375862827761)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"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": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-24 18:12:37 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-router.ipynb
similarity index 72%
rename from docs/examples/hybrid-layer.ipynb
rename to docs/examples/hybrid-router.ipynb
index 0ea545299a7f17b2ee91cf6b88fde01b9d0375be..71e57ca79cd0b44f2df1396810bfb349c7fedc17 100644
--- a/docs/examples/hybrid-layer.ipynb
+++ b/docs/examples/hybrid-router.ipynb
@@ -11,7 +11,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Hybrid Layer in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to making based on the keywords contained in our utterances and user queries.\n"
+ "The `HybridRouter` in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical.\n",
+ "\n",
+ "It helps us provide more importance to making based on the keywords contained in our utterances and user queries."
]
},
{
@@ -34,7 +36,7 @@
"metadata": {},
"outputs": [],
"source": [
- "#!pip install -qU semantic-router==0.0.11"
+ "#!pip install -qU semantic-router==0.1.0.dev2"
]
},
{
@@ -46,14 +48,14 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_router_3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/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"
]
}
@@ -83,28 +85,10 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 3,
+ "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",
"chitchat = Route(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
@@ -133,14 +117,14 @@
"outputs": [],
"source": [
"import os\n",
- "from semantic_router.encoders import CohereEncoder, BM25Encoder, TfidfEncoder\n",
+ "from semantic_router.encoders import OpenAIEncoder, TfidfEncoder\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",
+ "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API Key: \"\n",
")\n",
"\n",
- "dense_encoder = CohereEncoder()\n",
+ "dense_encoder = OpenAIEncoder()\n",
"# sparse_encoder = BM25Encoder()\n",
"sparse_encoder = TfidfEncoder()"
]
@@ -154,63 +138,65 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "\u001b[32m2024-05-07 21:15:31 INFO semantic_router.utils.logger Creating embeddings for all routes...\u001b[0m\n"
+ "\u001b[32m2024-11-24 00:37:14 INFO semantic_router.utils.logger Downloading and initializing default sBM25 model parameters.\u001b[0m\n",
+ "\u001b[32m2024-11-24 00:37:22 INFO semantic_router.utils.logger Encoding route politics\u001b[0m\n",
+ "\u001b[32m2024-11-24 00:37:23 INFO semantic_router.utils.logger Encoding route chitchat\u001b[0m\n"
]
}
],
"source": [
- "from semantic_router.hybrid_layer import HybridRouteLayer\n",
+ "from semantic_router.routers import HybridRouter\n",
"\n",
- "dl = HybridRouteLayer(\n",
+ "router = HybridRouter(\n",
" encoder=dense_encoder, sparse_encoder=sparse_encoder, routes=routes\n",
")"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'politics'"
+ "RouteChoice(name='politics', function_call=None, similarity_score=1.1909239963848142)"
]
},
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "dl(\"don't you love politics?\")"
+ "router(\"don't you love politics?\")"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'chitchat'"
+ "RouteChoice(name='chitchat', function_call=None, similarity_score=2.0)"
]
},
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "dl(\"how's the weather today?\")"
+ "router(\"how's the weather today?\")"
]
},
{
@@ -237,7 +223,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.4"
+ "version": "3.12.7"
}
},
"nbformat": 4,
diff --git a/docs/examples/pinecone-hybrid.ipynb b/docs/examples/pinecone-hybrid.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b354907f33901c2c8b6d3d89105c30e3ee949c52
--- /dev/null
+++ b/docs/examples/pinecone-hybrid.ipynb
@@ -0,0 +1,495 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Using PineconeIndex for Hybrid Routes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Hybrid indexes combine both sparse and dense encodings to produce more accurate results. The dense encoder allows us to search based on semantic meaning, while the sparse encoder allows us to search based on text matches. Merging both dense and sparse into a single hybrid retrieval step allows us to step up our performance beyond what dense-only or sparse-only could achieve."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Getting Started"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing semantic-router. Support for the new `AurelioSparseEncoder` parameter was added in `semantic-router==0.1.0`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install -qU \"semantic-router[pinecone]==0.1.0\""
+ ]
+ },
+ {
+ "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/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/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 models. We are going to use a hybrid index which requires both a dense and sparse encoder. For the sparse encoder we will use the pretrained `bm25` model from the Aurelio Platform and OpenAI's `text-embedding-3-small` for the dense encoder.\n",
+ "\n",
+ "To get an API key for the Aurelio Platform, we head to the [Aurelio Platform](https://platform.aurelio.ai/settings/api-keys)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from getpass import getpass\n",
+ "from semantic_router.encoders.aurelio import AurelioSparseEncoder\n",
+ "\n",
+ "os.environ[\"AURELIO_API_KEY\"] = os.getenv(\"AURELIO_API_KEY\") or getpass(\n",
+ " \"Enter Aurelio API Key: \"\n",
+ ")\n",
+ "\n",
+ "sparse_encoder = AurelioSparseEncoder(name=\"bm25\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Sparse encoders return dictionaries containing the the indices and values of the non-zero elements in the sparse matrix."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.encoders import OpenAIEncoder\n",
+ "\n",
+ "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n",
+ " \"Enter OpenAI API Key: \"\n",
+ ")\n",
+ "\n",
+ "encoder = OpenAIEncoder(name=\"text-embedding-3-small\", score_threshold=0.3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We now have both our sparse and dense encoders. When using both sparse and dense encoders we need to initialize an index that supports hybrid, such as the `HybridLocalIndex` or `PineconeIndex`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n",
+ "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n",
+ "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.index import PineconeIndex\n",
+ "\n",
+ "os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
+ " \"Enter Pinecone API Key: \"\n",
+ ")\n",
+ "\n",
+ "index = PineconeIndex(\n",
+ " index_name=\"hybrid-test\",\n",
+ " dimensions=1536,\n",
+ " metric=\"dotproduct\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we define the `HybridRouter`. When called, the router will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `HybridRouter` we need an `encoder`, `sparse_encoder` our `routes`, and the hybrid `index` we just define."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantic_router.routers import HybridRouter\n",
+ "\n",
+ "router = HybridRouter(\n",
+ " encoder=encoder,\n",
+ " sparse_encoder=sparse_encoder,\n",
+ " routes=routes,\n",
+ " index=index,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see if our local and remote instances are synchronized..."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "False"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.is_synced()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It seems like our `router` is not synchronized, meaning there are differences between the utterances in our local `HybridRouter` and the remote `PineconeIndex`. We can view the differences by calling `get_utterance_diff()`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[' chitchat: how are things going?',\n",
+ " \" chitchat: how's the weather today?\",\n",
+ " \" chitchat: let's go to the chippy\",\n",
+ " ' chitchat: lovely weather today',\n",
+ " ' chitchat: the weather is horrendous',\n",
+ " \" politics: don't you just hate the president\",\n",
+ " \" politics: don't you just love the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.get_utterance_diff()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "From this, we can see that every utterance is preceeded by a `-` meaning it is unique to the local `HybridRouter`. So it seems our `PineconeIndex` is missing all utterances. We can confirm this further by calling `router.index.get_utterances()` to see all utterances in the remote `PineconeIndex`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.index.get_utterances()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As expected, we have no utterances in the remote `PineconeIndex`. The reason for this is that when initializing our `HybridRouter` we did not specify an `auto_sync` parameter, so `auto_sync` defaulted to `None`. When `auto_sync=None` no synchronization is performed during initialization. Let's try again with `auto_sync=\"local\"`, meaning take what we have locally and overwrite the remote `PineconeIndex` with these local values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "router = HybridRouter(\n",
+ " encoder=encoder,\n",
+ " sparse_encoder=sparse_encoder,\n",
+ " routes=routes,\n",
+ " index=index,\n",
+ " auto_sync=\"local\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now let's check our sync state:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.is_synced()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[' chitchat: how are things going?',\n",
+ " \" chitchat: how's the weather today?\",\n",
+ " \" chitchat: let's go to the chippy\",\n",
+ " ' chitchat: lovely weather today',\n",
+ " ' chitchat: the weather is horrendous',\n",
+ " \" politics: don't you just hate the president\",\n",
+ " \" politics: don't you just love the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router.get_utterance_diff()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "... NEED TO FINISH HERE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-27 15:42:03 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"it's raining cats and dogs today\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-11-27 15:42:06 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "RouteChoice(name=None, function_call=None, similarity_score=None)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "router(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this case, we return `None` because no matches were identified. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "decision-layer",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/indexes/pinecone-sync-routes.ipynb b/docs/indexes/pinecone-sync-routes.ipynb
index 14e78905194b87a20fe768323a00235972408672..0c6dc4e0974e779f560f35dac3774a22b0b556cf 100644
--- a/docs/indexes/pinecone-sync-routes.ipynb
+++ b/docs/indexes/pinecone-sync-routes.ipynb
@@ -6,7 +6,7 @@
"metadata": {},
"outputs": [],
"source": [
- "!pip install -qU \"semantic-router[pinecone]==0.1.0.dev1\""
+ "!pip install -qU \"semantic-router[pinecone]==0.1.0.dev2\""
]
},
{
@@ -30,9 +30,18 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/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",
@@ -68,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -93,7 +102,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -108,8 +117,6 @@
"pc_index = PineconeIndex(\n",
" dimensions=1536,\n",
" init_async_index=True, # enables asynchronous methods, it's optional\n",
- " sync=None, # defines whether we sync between local and remote route layers\n",
- " # when sync is None, no sync is performed\n",
")\n",
"pc_index.index = pc_index._init_index(force_create=True)"
]
@@ -118,26 +125,45 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## RouteLayer"
+ "## SemanticRouter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The `RouteLayer` class supports both sync and async operations by default, so we initialize as usual:"
+ "The `SemanticRouter` class supports both sync and async operations by default, so we initialize as usual:"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m2024-11-23 23:46:42 WARNING semantic_router.utils.logger TEMP | add:\n",
+ "chitchat: how are things going?\n",
+ "chitchat: how's the weather today?\n",
+ "chitchat: let's go to the chippy\n",
+ "chitchat: lovely weather today\n",
+ "chitchat: the weather is horrendous\u001b[0m\n",
+ "\u001b[33m2024-11-23 23:46:50 WARNING semantic_router.utils.logger TEMP | add:\n",
+ "chitchat: how are things going?\n",
+ "chitchat: how's the weather today?\n",
+ "chitchat: let's go to the chippy\n",
+ "chitchat: lovely weather today\n",
+ "chitchat: the weather is horrendous\u001b[0m\n"
+ ]
+ }
+ ],
"source": [
- "from semantic_router.layer import RouteLayer\n",
+ "from semantic_router.routers import SemanticRouter\n",
"import time\n",
"\n",
- "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index)\n",
+ "rl = SemanticRouter(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n",
"# due to pinecone indexing latency we wait 3 seconds\n",
"time.sleep(3)"
]
@@ -151,23 +177,16 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 6,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hash_id: sr_hash#\n"
- ]
- },
{
"data": {
"text/plain": [
- "False"
+ "True"
]
},
- "execution_count": 28,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -185,13 +204,13 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"del rl\n",
"\n",
- "rl = RouteLayer(encoder=encoder, routes=[politics], index=pc_index)\n",
+ "rl = SemanticRouter(encoder=encoder, routes=[politics], index=pc_index)\n",
"time.sleep(3)"
]
},
@@ -204,23 +223,16 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 8,
"metadata": {},
"outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "hash_id: sr_hash#\n"
- ]
- },
{
"data": {
"text/plain": [
"False"
]
},
- "execution_count": 30,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -238,27 +250,26 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[\"- politics: don't you just hate the president\",\n",
- " \"- politics: don't you just love the president\",\n",
- " \"- politics: isn't politics the best thing ever\",\n",
- " '- politics: they will save the country!',\n",
- " \"- politics: they're going to destroy this country!\",\n",
- " \"- politics: why don't you tell me about your political opinions\",\n",
- " '+ Route 1: Hello',\n",
- " '+ Route 1: Hi',\n",
- " '+ Route 2: Au revoir',\n",
- " '+ Route 2: Bye',\n",
- " '+ Route 2: Goodbye',\n",
- " '+ Route 3: Boo']"
+ "['+ chitchat: how are things going?',\n",
+ " \"+ chitchat: how's the weather today?\",\n",
+ " \"+ chitchat: let's go to the chippy\",\n",
+ " '+ chitchat: lovely weather today',\n",
+ " '+ chitchat: the weather is horrendous',\n",
+ " \" politics: don't you just hate the president\",\n",
+ " \" politics: don't you just love the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
]
},
- "execution_count": 31,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -283,9 +294,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"remote_utterances = rl.index.get_utterances()\n",
"remote_utterances"
@@ -293,9 +325,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' ')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"local_utterances = rl.to_config().to_utterances()\n",
"local_utterances"
@@ -330,9 +378,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.diff"
]
@@ -354,9 +423,24 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_tag(\"+\")"
]
@@ -370,9 +454,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_tag(\"-\")"
]
@@ -386,9 +481,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n",
+ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_tag(\" \")"
]
@@ -423,45 +534,142 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'remote': {'upsert': [],\n",
+ " 'delete': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]},\n",
+ " 'local': {'upsert': [], 'delete': []}}"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_sync_strategy(\"local\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'remote': {'upsert': [], 'delete': []},\n",
+ " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n",
+ " 'delete': []}}"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_sync_strategy(\"remote\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'remote': {'upsert': [], 'delete': []},\n",
+ " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n",
+ " 'delete': []}}"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_sync_strategy(\"merge-force-remote\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-11-23 23:47:11 INFO semantic_router.utils.logger local_only_mapper: {}\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "{'remote': {'upsert': [],\n",
+ " 'delete': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]},\n",
+ " 'local': {'upsert': [], 'delete': []}}"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_sync_strategy(\"merge-force-local\")"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'remote': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n",
+ " 'delete': []},\n",
+ " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n",
+ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n",
+ " 'delete': []}}"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"diff.get_sync_strategy(\"merge\")"
]
@@ -477,7 +685,16 @@
"cell_type": "code",
"execution_count": 22,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m2024-11-23 23:47:15 WARNING semantic_router.utils.logger TEMP | _remove_and_sync:\n",
+ "chitchat: ['how are things going?', \"how's the weather today?\", \"let's go to the chippy\", 'lovely weather today', 'the weather is horrendous']\u001b[0m\n"
+ ]
+ }
+ ],
"source": [
"strategy = diff.get_sync_strategy(\"local\")\n",
"rl._execute_sync_strategy(strategy=strategy)"
@@ -485,9 +702,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"time.sleep(3)\n",
"rl.is_synced()"
@@ -502,9 +730,25 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[\" politics: don't you just hate the president\",\n",
+ " \" politics: don't you just love the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"rl.get_utterance_diff()"
]
@@ -520,12 +764,42 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[33m2024-11-23 23:47:23 WARNING semantic_router.utils.logger Local and remote route layers are already synchronized.\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[\" politics: don't you just hate the president\",\n",
+ " \" politics: don't you just love the president\",\n",
+ " \" politics: isn't politics the best thing ever\",\n",
+ " ' politics: they will save the country!',\n",
+ " \" politics: they're going to destroy this country!\",\n",
+ " \" politics: why don't you tell me about your political opinions\"]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"rl.sync(sync_mode=\"local\")"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
}
],
"metadata": {
@@ -544,7 +818,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.12.7"
}
},
"nbformat": 4,
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 2dae787f1cb09b53476cd424cb45108ea15386d0..0340e029dfeb8e4c547313698748e475d1c173d0 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -15,7 +15,7 @@ sys.path.insert(0, os.path.abspath("../..")) # Source code dir relative to this
project = "Semantic Router"
copyright = "2024, Aurelio AI"
author = "Aurelio AI"
-release = "0.1.0.dev1"
+release = "0.1.0.dev2"
# -- General configuration ---------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
diff --git a/poetry.lock b/poetry.lock
index 11dbb7edab640ff3c28ee5c94f287a85d41406fd..19288adbc6b52ed524059c8bcceaf8cbb9156d9a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,15 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+
+[[package]]
+name = "aiofiles"
+version = "24.1.0"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
+ {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
+]
[[package]]
name = "aiohappyeyeballs"
@@ -13,112 +24,98 @@ files = [
[[package]]
name = "aiohttp"
-version = "3.10.8"
+version = "3.11.8"
description = "Async http client/server framework (asyncio)"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "aiohttp-3.10.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a1ba7bc139592339ddeb62c06486d0fa0f4ca61216e14137a40d626c81faf10c"},
- {file = "aiohttp-3.10.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:85e4d7bd05d18e4b348441e7584c681eff646e3bf38f68b2626807f3add21aa2"},
- {file = "aiohttp-3.10.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:69de056022e7abf69cb9fec795515973cc3eeaff51e3ea8d72a77aa933a91c52"},
- {file = "aiohttp-3.10.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ee3587506898d4a404b33bd19689286ccf226c3d44d7a73670c8498cd688e42c"},
- {file = "aiohttp-3.10.8-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fe285a697c851734285369614443451462ce78aac2b77db23567507484b1dc6f"},
- {file = "aiohttp-3.10.8-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:10c7932337285a6bfa3a5fe1fd4da90b66ebfd9d0cbd1544402e1202eb9a8c3e"},
- {file = "aiohttp-3.10.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cd9716ef0224fe0d0336997eb242f40619f9f8c5c57e66b525a1ebf9f1d8cebe"},
- {file = "aiohttp-3.10.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ceacea31f8a55cdba02bc72c93eb2e1b77160e91f8abd605969c168502fd71eb"},
- {file = "aiohttp-3.10.8-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:9721554bfa9e15f6e462da304374c2f1baede3cb06008c36c47fa37ea32f1dc4"},
- {file = "aiohttp-3.10.8-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:22cdeb684d8552490dd2697a5138c4ecb46f844892df437aaf94f7eea99af879"},
- {file = "aiohttp-3.10.8-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:e56bb7e31c4bc79956b866163170bc89fd619e0581ce813330d4ea46921a4881"},
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- {file = "aiohttp-3.10.8-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d82404a0e7b10e0d7f022cf44031b78af8a4f99bd01561ac68f7c24772fed021"},
- {file = "aiohttp-3.10.8-cp310-cp310-win32.whl", hash = "sha256:4e10b04542d27e21538e670156e88766543692a0a883f243ba8fad9ddea82e53"},
- {file = "aiohttp-3.10.8-cp310-cp310-win_amd64.whl", hash = "sha256:680dbcff5adc7f696ccf8bf671d38366a1f620b5616a1d333d0cb33956065395"},
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- {file = "aiohttp-3.10.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6c7efa6616a95e3bd73b8a69691012d2ef1f95f9ea0189e42f338fae080c2fc6"},
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aiosignal = ">=1.1.2"
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attrs = ">=17.3.0"
frozenlist = ">=1.1.1"
multidict = ">=4.5,<7.0"
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speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"]
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name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
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trio = ["trio (>=0.26.1)"]
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[[package]]
name = "async-timeout"
-version = "4.0.3"
+version = "5.0.1"
description = "Timeout context manager for asyncio programs"
optional = false
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tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
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+description = "Aurelio Platform SDK"
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+requests-toolbelt = ">=1.0.0,<2.0.0"
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version = "2.16.0"
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[[package]]
name = "boto3"
-version = "1.35.32"
+version = "1.35.71"
description = "The AWS SDK for Python"
optional = true
python-versions = ">=3.8"
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jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
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[[package]]
name = "botocore"
-version = "1.35.32"
+version = "1.35.71"
description = "Low-level, data-driven core of boto 3."
optional = true
python-versions = ">=3.8"
files = [
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]
[package.extras]
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+crt = ["awscrt (==0.22.0)"]
[[package]]
name = "cachetools"
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description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7.0"
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@@ -580,17 +612,16 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "cohere"
-version = "5.10.0"
+version = "5.11.4"
description = ""
optional = true
python-versions = "<4.0,>=3.8"
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name = "httpcore"
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description = "A minimal low-level HTTP client."
optional = false
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@@ -4303,6 +4265,20 @@ requests = ">=2.22,<3"
[package.extras]
fixture = ["fixtures"]
+[[package]]
+name = "requests-toolbelt"
+version = "1.0.0"
+description = "A utility belt for advanced users of python-requests"
+optional = false
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+]
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+ {file = "yarl-1.18.0.tar.gz", hash = "sha256:20d95535e7d833889982bfe7cc321b7f63bf8879788fee982c76ae2b24cfb715"},
]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
+propcache = ">=0.2.0"
[[package]]
name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
- {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+ {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
]
[package.extras]
@@ -5496,12 +5552,11 @@ hybrid = ["pinecone-text"]
local = ["llama-cpp-python", "tokenizers", "torch", "transformers"]
mistralai = ["mistralai"]
pinecone = ["pinecone"]
-postgres = ["psycopg2"]
-processing = ["matplotlib"]
+postgres = []
qdrant = ["qdrant-client"]
vision = ["pillow", "torch", "torchvision", "transformers"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "b0ddd77f2b9a210601eba56f69630eaa6a53cb358cae95bace2ae080d51c7812"
+content-hash = "d649e614d9c7122c642ed351abf8886cc008960f0fd7664483cfb5dde7865808"
diff --git a/pyproject.toml b/pyproject.toml
index 68881c73da1889d6316580a45261065234881e7c..e1eff0c7657e89c66fc3dfe35fab7b9a88406f68 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "semantic-router"
-version = "0.1.0.dev1"
+version = "0.1.0.dev2"
description = "Super fast semantic router for AI decision making"
authors = ["Aurelio AI <hello@aurelio.ai>"]
readme = "README.md"
@@ -16,6 +16,7 @@ mistralai= {version = ">=0.0.12,<0.1.0", optional = true}
numpy = "^1.25.2"
colorlog = "^6.8.0"
pyyaml = "^6.0.1"
+aurelio-sdk = {version = "^0.0.16"}
pinecone-text = {version = ">=0.7.1,<0.10.0", optional = true}
torch = {version = ">=2.1.0,<2.6.0", optional = true}
transformers = {version = ">=4.36.2", optional = true}
@@ -27,24 +28,23 @@ regex = ">=2023.12.25"
torchvision = { version = ">=0.17.0,<0.18.0", optional = true}
pillow = { version = ">=10.2.0,<11.0.0", optional = true}
tiktoken = ">=0.6.0,<1.0.0"
-matplotlib = { version = "^3.8.3", optional = true}
qdrant-client = {version = "^1.11.1", optional = true}
google-cloud-aiplatform = {version = "^1.45.0", optional = true}
requests-mock = "^1.12.1"
boto3 = { version = "^1.34.98", optional = true }
botocore = {version = "^1.34.110", optional = true}
-aiohttp = "^3.9.5"
+aiohttp = "^3.10.11"
fastembed = {version = "^0.3.0", optional = true}
-psycopg2 = {version = "^2.9.9", optional = true}
+psycopg2-binary = {version = "^2.9.9", optional = true}
sphinx = {version = "^7.0.0", optional = true}
sphinxawesome-theme = {version = "^5.2.0", optional = true}
+tornado = {version = "^6.4.2", optional = true}
[tool.poetry.extras]
hybrid = ["pinecone-text"]
local = ["torch", "transformers", "tokenizers", "huggingface-hub", "llama-cpp-python"]
pinecone = ["pinecone"]
vision = ["torch", "torchvision", "transformers", "pillow"]
-processing = ["matplotlib"]
mistralai = ["mistralai"]
qdrant = ["qdrant-client"]
google = ["google-cloud-aiplatform"]
@@ -57,10 +57,11 @@ cohere = ["cohere"]
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.25.0"
ruff = "^0.1.5"
-pytest = "^7.4.3"
+pytest = "^8.2"
pytest-mock = "^3.12.0"
pytest-cov = "^4.1.0"
pytest-xdist = "^3.5.0"
+pytest-asyncio = "^0.24.0"
mypy = "^1.7.1"
types-pyyaml = "^6.0.12.12"
types-requests = "^2.31.0"
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index a2ed0d0718f3ed59947678c574c44805c979afee..4e65f0023f57011e5de3f88ede6da0c002c6cb77 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -1,7 +1,6 @@
-from semantic_router.hybrid_layer import HybridRouteLayer
-from semantic_router.layer import LayerConfig, RouteLayer
+from semantic_router.routers import RouterConfig, SemanticRouter, HybridRouter
from semantic_router.route import Route
-__all__ = ["RouteLayer", "HybridRouteLayer", "Route", "LayerConfig"]
+__all__ = ["SemanticRouter", "HybridRouter", "Route", "RouterConfig"]
-__version__ = "0.1.0.dev1"
+__version__ = "0.1.0.dev2"
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
index a1026240d37fbfecb1ec8b1445d42fc05f04265f..07e468d893afe8fb2b8ecf69d1dcd8bbc2f4d8ee 100644
--- a/semantic_router/encoders/__init__.py
+++ b/semantic_router/encoders/__init__.py
@@ -1,6 +1,7 @@
from typing import List, Optional
-from semantic_router.encoders.base import BaseEncoder
+from semantic_router.encoders.aurelio import AurelioSparseEncoder
+from semantic_router.encoders.base import DenseEncoder, SparseEncoder
from semantic_router.encoders.bedrock import BedrockEncoder
from semantic_router.encoders.bm25 import BM25Encoder
from semantic_router.encoders.clip import CLIPEncoder
@@ -14,10 +15,12 @@ from semantic_router.encoders.openai import OpenAIEncoder
from semantic_router.encoders.tfidf import TfidfEncoder
from semantic_router.encoders.vit import VitEncoder
from semantic_router.encoders.zure import AzureOpenAIEncoder
-from semantic_router.schema import EncoderType
+from semantic_router.schema import EncoderType, SparseEmbedding
__all__ = [
- "BaseEncoder",
+ "AurelioSparseEncoder",
+ "DenseEncoder",
+ "SparseEncoder",
"AzureOpenAIEncoder",
"CohereEncoder",
"OpenAIEncoder",
@@ -37,7 +40,7 @@ __all__ = [
class AutoEncoder:
type: EncoderType
name: Optional[str]
- model: BaseEncoder
+ model: DenseEncoder | SparseEncoder
def __init__(self, type: str, name: Optional[str]):
self.type = EncoderType(type)
@@ -49,6 +52,8 @@ class AutoEncoder:
self.model = CohereEncoder(name=name)
elif self.type == EncoderType.OPENAI:
self.model = OpenAIEncoder(name=name)
+ elif self.type == EncoderType.AURELIO:
+ self.model = AurelioSparseEncoder(name=name)
elif self.type == EncoderType.BM25:
if name is None:
name = "bm25"
@@ -74,5 +79,5 @@ class AutoEncoder:
else:
raise ValueError(f"Encoder type '{type}' not supported")
- def __call__(self, texts: List[str]) -> List[List[float]]:
+ def __call__(self, texts: List[str]) -> List[List[float]] | List[SparseEmbedding]:
return self.model(texts)
diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b2501ba4faa0a22a5a18c5500a300e77fa63f95
--- /dev/null
+++ b/semantic_router/encoders/aurelio.py
@@ -0,0 +1,47 @@
+import os
+from typing import Any, List, Optional
+from pydantic.v1 import Field
+
+from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse
+
+from semantic_router.encoders.base import SparseEncoder
+from semantic_router.schema import SparseEmbedding
+
+
+class AurelioSparseEncoder(SparseEncoder):
+ model: Optional[Any] = None
+ client: AurelioClient = Field(default_factory=AurelioClient, exclude=True)
+ async_client: AsyncAurelioClient = Field(
+ default_factory=AsyncAurelioClient, exclude=True
+ )
+ type: str = "sparse"
+
+ def __init__(
+ self,
+ name: str | None = None,
+ api_key: Optional[str] = None,
+ ):
+ if name is None:
+ name = "bm25"
+ super().__init__(name=name)
+ if api_key is None:
+ api_key = os.getenv("AURELIO_API_KEY")
+ if api_key is None:
+ raise ValueError("AURELIO_API_KEY environment variable is not set.")
+ self.client = AurelioClient(api_key=api_key)
+ self.async_client = AsyncAurelioClient(api_key=api_key)
+
+ def __call__(self, docs: list[str]) -> list[SparseEmbedding]:
+ res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name)
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ async def acall(self, docs: list[str]) -> list[SparseEmbedding]:
+ res: EmbeddingResponse = await self.async_client.embedding(
+ input=docs, model=self.name
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ def fit(self, docs: List[str]):
+ raise NotImplementedError("AurelioSparseEncoder does not support fit.")
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index fcc5734db4cecb730a9202ed1fba944df645b4be..0a25f21050e2e5da1ea610fb5194324cdeef9a5f 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -1,9 +1,12 @@
from typing import Any, Coroutine, List, Optional
from pydantic.v1 import BaseModel, Field, validator
+import numpy as np
+from semantic_router.schema import SparseEmbedding
-class BaseEncoder(BaseModel):
+
+class DenseEncoder(BaseModel):
name: str
score_threshold: Optional[float] = None
type: str = Field(default="base")
@@ -20,3 +23,31 @@ class BaseEncoder(BaseModel):
def acall(self, docs: List[Any]) -> Coroutine[Any, Any, List[List[float]]]:
raise NotImplementedError("Subclasses must implement this method")
+
+
+class SparseEncoder(BaseModel):
+ name: str
+ type: str = Field(default="base")
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def acall(self, docs: List[str]) -> list[SparseEmbedding]:
+ raise NotImplementedError("Subclasses must implement this method")
+
+ def _array_to_sparse_embeddings(
+ self, sparse_arrays: np.ndarray
+ ) -> List[SparseEmbedding]:
+ """Consumes several sparse vectors containing zero-values and returns a compact array."""
+ if sparse_arrays.ndim != 2:
+ raise ValueError(f"Expected a 2D array, got a {sparse_arrays.ndim}D array.")
+ # get coordinates of non-zero values
+ coords = np.nonzero(sparse_arrays)
+ # create compact array
+ compact_array = np.array([coords[0], coords[1], sparse_arrays[coords]]).T
+ arr_range = range(compact_array[:, 0].max().astype(int) + 1)
+ arrs = [compact_array[compact_array[:, 0] == i, :][:, 1:3] for i in arr_range]
+ return [SparseEmbedding.from_compact_array(arr) for arr in arrs]
diff --git a/semantic_router/encoders/bedrock.py b/semantic_router/encoders/bedrock.py
index fed53900f4e0b10810d667e75d72ef51f066e565..5ec3381e31a3be32ed23e2dbb283db899c78c3e6 100644
--- a/semantic_router/encoders/bedrock.py
+++ b/semantic_router/encoders/bedrock.py
@@ -1,7 +1,7 @@
"""
This module provides the BedrockEncoder class for generating embeddings using Amazon's Bedrock Platform.
-The BedrockEncoder class is a subclass of BaseEncoder and utilizes the TextEmbeddingModel from the
+The BedrockEncoder class is a subclass of DenseEncoder and utilizes the TextEmbeddingModel from the
Amazon's Bedrock Platform to generate embeddings for given documents. It requires an AWS Access Key ID
and AWS Secret Access Key and supports customization of the pre-trained model, score threshold, and region.
@@ -21,12 +21,12 @@ from typing import List, Optional, Any
import os
from time import sleep
import tiktoken
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
-class BedrockEncoder(BaseEncoder):
+class BedrockEncoder(DenseEncoder):
client: Any = None
type: str = "bedrock"
input_type: Optional[str] = "search_query"
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index 1965fb6ef1f6fb5032134c562d4c38ac6b22f8fa..f42bf9c28937419b29ae4429151a1fe3120c0487 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -1,21 +1,24 @@
from typing import Any, Dict, List, Optional
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders.tfidf import TfidfEncoder
from semantic_router.utils.logger import logger
+from semantic_router.schema import SparseEmbedding
+from semantic_router.route import Route
-class BM25Encoder(BaseEncoder):
+class BM25Encoder(TfidfEncoder):
model: Optional[Any] = None
idx_mapping: Optional[Dict[int, int]] = None
type: str = "sparse"
def __init__(
self,
- name: str = "bm25",
- score_threshold: float = 0.82,
+ name: str | None = None,
use_default_params: bool = True,
):
- super().__init__(name=name, score_threshold=score_threshold)
+ if name is None:
+ name = "bm25"
+ super().__init__(name=name)
try:
from pinecone_text.sparse import BM25Encoder as encoder
except ImportError:
@@ -32,6 +35,7 @@ class BM25Encoder(BaseEncoder):
self._set_idx_mapping()
def _set_idx_mapping(self):
+ # TODO JB: this is training the model somehow - not sure how...
params = self.model.get_params()
doc_freq = params["doc_freq"]
if isinstance(doc_freq, dict):
@@ -40,8 +44,20 @@ class BM25Encoder(BaseEncoder):
else:
raise TypeError("Expected a dictionary for 'doc_freq'")
- def __call__(self, docs: List[str]) -> List[List[float]]:
- if self.model is None or self.idx_mapping is None:
+ def fit(self, routes: List[Route]):
+ """Trains the encoder weights on the provided routes.
+
+ :param routes: List of routes to train the encoder on.
+ :type routes: List[Route]
+ """
+ self._fit_validate(routes=routes)
+ if self.model is None:
+ raise ValueError("Model is not initialized.")
+ utterances = [utterance for route in routes for utterance in route.utterances]
+ self.model.fit(corpus=utterances)
+
+ def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
+ if self.model is None:
raise ValueError("Model or index mapping is not initialized.")
if len(docs) == 1:
sparse_dicts = self.model.encode_queries(docs)
@@ -50,18 +66,8 @@ class BM25Encoder(BaseEncoder):
else:
raise ValueError("No documents to encode.")
- embeds = [[0.0] * len(self.idx_mapping)] * len(docs)
+ embeds = []
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
+ if isinstance(output, dict):
+ embeds.append(SparseEmbedding.from_pinecone_dict(output))
return embeds
-
- def fit(self, docs: List[str]):
- if self.model is None:
- raise ValueError("Model is not initialized.")
- self.model.fit(docs)
- self._set_idx_mapping()
diff --git a/semantic_router/encoders/clip.py b/semantic_router/encoders/clip.py
index 6495c870fa25eb5e55f4ddf93081009dc752597c..65fbdb8f0312d5339d68ae5900542aea2c896693 100644
--- a/semantic_router/encoders/clip.py
+++ b/semantic_router/encoders/clip.py
@@ -3,10 +3,10 @@ from typing import Any, List, Optional
import numpy as np
from pydantic.v1 import PrivateAttr
from typing import Dict
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
-class CLIPEncoder(BaseEncoder):
+class CLIPEncoder(DenseEncoder):
name: str = "openai/clip-vit-base-patch16"
type: str = "huggingface"
score_threshold: float = 0.2
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
index cdc114bbed659abceb58f258dc6ed0f35c767f2d..04b878141a4b33d742d5bb28a5bfc8bcd1347d19 100644
--- a/semantic_router/encoders/cohere.py
+++ b/semantic_router/encoders/cohere.py
@@ -3,11 +3,11 @@ from typing import Any, List, Optional
from pydantic.v1 import PrivateAttr
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
-class CohereEncoder(BaseEncoder):
+class CohereEncoder(DenseEncoder):
_client: Any = PrivateAttr()
_embed_type: Any = PrivateAttr()
type: str = "cohere"
diff --git a/semantic_router/encoders/fastembed.py b/semantic_router/encoders/fastembed.py
index 27590bc398e34fc6f0ed847775db7680f6106d05..5cda5e643746c660cd6e820cb767a6a333981b2d 100644
--- a/semantic_router/encoders/fastembed.py
+++ b/semantic_router/encoders/fastembed.py
@@ -3,10 +3,10 @@ from typing import Any, List, Optional
import numpy as np
from pydantic.v1 import PrivateAttr
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
-class FastEmbedEncoder(BaseEncoder):
+class FastEmbedEncoder(DenseEncoder):
type: str = "fastembed"
name: str = "BAAI/bge-small-en-v1.5"
max_length: int = 512
diff --git a/semantic_router/encoders/google.py b/semantic_router/encoders/google.py
index 088d4bba943360202c455a1bb4fbf1b6dc51b927..5d50a0e14ea573269202e04086ed21f44997e310 100644
--- a/semantic_router/encoders/google.py
+++ b/semantic_router/encoders/google.py
@@ -1,7 +1,7 @@
"""
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
+The GoogleEncoder class is a subclass of DenseEncoder 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.
@@ -19,11 +19,11 @@ Classes:
import os
from typing import Any, List, Optional
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
-class GoogleEncoder(BaseEncoder):
+class GoogleEncoder(DenseEncoder):
"""GoogleEncoder class for generating embeddings using Google's AI Platform.
Attributes:
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index 7ca7580d972238824c5b42c8a2e8e89547cf9522..7c7e56f77f11cf5143437c822ffda338c8f92cf0 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -1,7 +1,7 @@
"""
This module provides the HFEndpointEncoder class to embeddings models using Huggingface's endpoint.
-The HFEndpointEncoder class is a subclass of BaseEncoder and utilizes a specified Huggingface
+The HFEndpointEncoder class is a subclass of DenseEncoder and utilizes a specified Huggingface
endpoint to generate embeddings for given documents. It requires the URL of the Huggingface
API endpoint and an API key for authentication. The class supports customization of the score
threshold for filtering or processing the embeddings.
@@ -27,11 +27,11 @@ from typing import Any, List, Optional, Dict
from pydantic.v1 import PrivateAttr
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.logger import logger
-class HuggingFaceEncoder(BaseEncoder):
+class HuggingFaceEncoder(DenseEncoder):
name: str = "sentence-transformers/all-MiniLM-L6-v2"
type: str = "huggingface"
score_threshold: float = 0.5
@@ -140,7 +140,7 @@ class HuggingFaceEncoder(BaseEncoder):
return self._torch.max(token_embeddings, 1)[0]
-class HFEndpointEncoder(BaseEncoder):
+class HFEndpointEncoder(DenseEncoder):
"""
A class to encode documents using a Hugging Face transformer model endpoint.
diff --git a/semantic_router/encoders/mistral.py b/semantic_router/encoders/mistral.py
index 974f11284a162979420ac897474413c266fff1a5..46bc89aec0ed9ed41fffc5734fa5a7658f6f5cd7 100644
--- a/semantic_router/encoders/mistral.py
+++ b/semantic_router/encoders/mistral.py
@@ -6,11 +6,11 @@ from typing import Any, List, Optional
from pydantic.v1 import PrivateAttr
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
-class MistralEncoder(BaseEncoder):
+class MistralEncoder(DenseEncoder):
"""Class to encode text using MistralAI"""
_client: Any = PrivateAttr()
diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py
index 4bc86ac29b16892536bfc7f5afb8ba3f2540f7f1..fb8a83f097e252a7fcd03a356f648077eb267cda 100644
--- a/semantic_router/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -10,7 +10,7 @@ from openai._types import NotGiven
from openai.types import CreateEmbeddingResponse
import tiktoken
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.schema import EncoderInfo
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
@@ -35,7 +35,7 @@ model_configs = {
}
-class OpenAIEncoder(BaseEncoder):
+class OpenAIEncoder(DenseEncoder):
client: Optional[openai.Client]
async_client: Optional[openai.AsyncClient]
dimensions: Union[int, NotGiven] = NotGiven()
diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py
index 17cc569ae8c9045ed40f3d47b0cb3912df46e4cd..656ee117e54b60a293c0b425452065258bb5db96 100644
--- a/semantic_router/encoders/tfidf.py
+++ b/semantic_router/encoders/tfidf.py
@@ -3,24 +3,25 @@ from collections import Counter
from typing import Dict, List
import numpy as np
-from numpy import ndarray
-from numpy.linalg import norm
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import SparseEncoder
from semantic_router.route import Route
+from semantic_router.schema import SparseEmbedding
-class TfidfEncoder(BaseEncoder):
- idf: ndarray = np.array([])
+class TfidfEncoder(SparseEncoder):
+ idf: np.ndarray = np.array([])
+ # TODO: add option to use default params like with BM25Encoder
word_index: Dict = {}
- def __init__(self, name: str = "tfidf", score_threshold: float = 0.82):
- # TODO default score_threshold not thoroughly tested, should optimize
- super().__init__(name=name, score_threshold=score_threshold)
+ def __init__(self, name: str | None = None):
+ if name is None:
+ name = "tfidf"
+ super().__init__(name=name)
self.word_index = {}
self.idf = np.array([])
- def __call__(self, docs: List[str]) -> List[List[float]]:
+ def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
if len(self.word_index) == 0 or self.idf.size == 0:
raise ValueError("Vectorizer is not initialized.")
if len(docs) == 0:
@@ -29,16 +30,28 @@ class TfidfEncoder(BaseEncoder):
docs = [self._preprocess(doc) for doc in docs]
tf = self._compute_tf(docs)
tfidf = tf * self.idf
- return tfidf.tolist()
+ return self._array_to_sparse_embeddings(tfidf)
def fit(self, routes: List[Route]):
+ """Trains the encoder weights on the provided routes.
+
+ :param routes: List of routes to train the encoder on.
+ :type routes: List[Route]
+ """
+ self._fit_validate(routes=routes)
docs = []
for route in routes:
for doc in route.utterances:
docs.append(self._preprocess(doc)) # type: ignore
self.word_index = self._build_word_index(docs)
+ if len(self.word_index) == 0:
+ raise ValueError(f"Too little data to fit {self.__class__.__name__}.")
self.idf = self._compute_idf(docs)
+ def _fit_validate(self, routes: List[Route]):
+ if not isinstance(routes, list) or not isinstance(routes[0], Route):
+ raise TypeError("`routes` parameter must be a list of Route objects.")
+
def _build_word_index(self, docs: List[str]) -> Dict:
words = set()
for doc in docs:
@@ -57,7 +70,7 @@ class TfidfEncoder(BaseEncoder):
if word in self.word_index:
tf[i, self.word_index[word]] = count
# L2 normalization
- tf = tf / norm(tf, axis=1, keepdims=True)
+ tf = tf / np.linalg.norm(tf, axis=1, keepdims=True)
return tf
def _compute_idf(self, docs: List[str]) -> np.ndarray:
diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py
index 44ae58018635e67a10d37150a1de1c783a4cee97..73cb058281d3c47d452bfa3d5a8e4c92246ae173 100644
--- a/semantic_router/encoders/vit.py
+++ b/semantic_router/encoders/vit.py
@@ -2,10 +2,10 @@ from typing import Any, List, Optional, Dict
from pydantic.v1 import PrivateAttr
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
-class VitEncoder(BaseEncoder):
+class VitEncoder(DenseEncoder):
name: str = "google/vit-base-patch16-224"
type: str = "huggingface"
score_threshold: float = 0.5
diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py
index d6f656609cdcc83ae8b23c2df5e44cf753facfdf..fd8594a986ea5911cd78c4761b304f72fb6b4b2e 100644
--- a/semantic_router/encoders/zure.py
+++ b/semantic_router/encoders/zure.py
@@ -8,12 +8,12 @@ from openai._types import NotGiven
from openai import OpenAIError
from openai.types import CreateEmbeddingResponse
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
from semantic_router.utils.defaults import EncoderDefault
from semantic_router.utils.logger import logger
-class AzureOpenAIEncoder(BaseEncoder):
+class AzureOpenAIEncoder(DenseEncoder):
client: Optional[openai.AzureOpenAI] = None
async_client: Optional[openai.AsyncAzureOpenAI] = None
dimensions: Union[int, NotGiven] = NotGiven()
diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py
deleted file mode 100644
index 2aaf6f17b72b05d6c2d02958c8e098b312d37bf4..0000000000000000000000000000000000000000
--- a/semantic_router/hybrid_layer.py
+++ /dev/null
@@ -1,220 +0,0 @@
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-from numpy.linalg import norm
-
-from semantic_router.encoders import (
- BaseEncoder,
- BM25Encoder,
- TfidfEncoder,
-)
-from semantic_router.route import Route
-from semantic_router.utils.logger import logger
-
-
-class HybridRouteLayer:
- index = None
- sparse_index = None
- categories = None
- score_threshold: float
-
- def __init__(
- self,
- encoder: BaseEncoder,
- sparse_encoder: Optional[BM25Encoder] = None,
- routes: List[Route] = [],
- alpha: float = 0.3,
- top_k: int = 5,
- aggregation: str = "sum",
- ):
- self.encoder = encoder
- if self.encoder.score_threshold is None:
- raise ValueError(
- "No score threshold provided for encoder. Please set the score threshold "
- "in the encoder config."
- )
- self.score_threshold = self.encoder.score_threshold
-
- if sparse_encoder is None:
- logger.warning("No sparse_encoder provided. Using default BM25Encoder.")
- self.sparse_encoder = BM25Encoder()
- else:
- self.sparse_encoder = sparse_encoder
-
- self.alpha = alpha
- self.top_k = top_k
- if self.top_k < 1:
- raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.")
- self.aggregation = aggregation
- if self.aggregation not in ["sum", "mean", "max"]:
- raise ValueError(
- f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'."
- )
- self.aggregation_method = self._set_aggregation_method(self.aggregation)
- self.routes = routes
- if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr(
- self.sparse_encoder, "fit"
- ):
- self.sparse_encoder.fit(routes)
- # if routes list has been passed, we initialize index now
- if routes:
- # initialize index now
- # for route in tqdm(routes):
- # self._add_route(route=route)
- self._add_routes(routes)
-
- def __call__(self, text: str) -> Optional[str]:
- results = self._query(text, self.top_k)
- 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, route: Route):
- self._add_route(route=route)
-
- def _add_route(self, route: Route):
- self.routes += [route]
-
- self.update_dense_embeddings_index(route.utterances)
-
- if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr(
- self.sparse_encoder, "fit"
- ):
- self.sparse_encoder.fit(self.routes)
- # re-build index
- self.sparse_index = None
- all_utterances = [
- utterance for route in self.routes for utterance in route.utterances
- ]
- self.update_sparse_embeddings_index(all_utterances)
- else:
- self.update_sparse_embeddings_index(route.utterances)
-
- # create route array
- if self.categories is None:
- self.categories = np.array([route.name] * len(route.utterances))
- else:
- str_arr = np.array([route.name] * len(route.utterances))
- self.categories = np.concatenate([self.categories, str_arr])
- self.routes.append(route)
-
- def _add_routes(self, routes: List[Route]):
- # create embeddings for all routes
- logger.info("Creating embeddings for all routes...")
- all_utterances = [
- utterance for route in routes for utterance in route.utterances
- ]
- self.update_dense_embeddings_index(all_utterances)
- self.update_sparse_embeddings_index(all_utterances)
-
- # create route array
- route_names = [route.name for route in routes for _ in route.utterances]
- route_array = np.array(route_names)
- self.categories = (
- np.concatenate([self.categories, route_array])
- if self.categories is not None
- else route_array
- )
-
- def update_dense_embeddings_index(self, utterances: list):
- dense_embeds = np.array(self.encoder(utterances))
- # create utterance array (the dense index)
- self.index = (
- np.concatenate([self.index, dense_embeds])
- if self.index is not None
- else dense_embeds
- )
-
- def update_sparse_embeddings_index(self, utterances: list):
- sparse_embeds = np.array(self.sparse_encoder(utterances))
- # create sparse utterance array
- self.sparse_index = (
- np.concatenate([self.sparse_index, sparse_embeds])
- if self.sparse_index is not None
- else 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 and self.sparse_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 (route names)
- routes = self.categories[idx] if self.categories is not None else []
- return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
- else:
- logger.warning("No index found. Please add routes to the layer.")
- return []
-
- def _convex_scaling(self, dense: np.ndarray, sparse: np.ndarray):
- # scale sparse and dense vecs
- dense = np.array(dense) * self.alpha
- sparse = np.array(sparse) * (1 - self.alpha)
- return dense, sparse
-
- def _set_aggregation_method(self, aggregation: str = "sum"):
- if aggregation == "sum":
- return lambda x: sum(x)
- elif aggregation == "mean":
- return lambda x: np.mean(x)
- elif aggregation == "max":
- return lambda x: max(x)
- else:
- raise ValueError(
- f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'."
- )
-
- def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]:
- scores_by_class: Dict[str, List[float]] = {}
- for result in query_results:
- score = result["score"]
- route = result["route"]
- if route in scores_by_class:
- scores_by_class[route].append(score)
- else:
- scores_by_class[route] = [score]
-
- # Calculate total score for each class
- total_scores = {
- route: self.aggregation_method(scores)
- for route, 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
- if top_class is not None:
- return str(top_class), scores_by_class.get(top_class, [])
- else:
- logger.warning("No classification found for semantic classifier.")
- return "", []
-
- def _pass_threshold(self, scores: List[float], threshold: float) -> bool:
- if scores:
- return max(scores) > threshold
- else:
- return False
diff --git a/semantic_router/index/__init__.py b/semantic_router/index/__init__.py
index 9a01b996985414a5b60bd1c4f1280a4488999275..14b60da37d2f809ac3902211a8e77f3a485da550 100644
--- a/semantic_router/index/__init__.py
+++ b/semantic_router/index/__init__.py
@@ -1,11 +1,15 @@
from semantic_router.index.base import BaseIndex
+from semantic_router.index.hybrid_local import HybridLocalIndex
from semantic_router.index.local import LocalIndex
from semantic_router.index.pinecone import PineconeIndex
+from semantic_router.index.postgres import PostgresIndex
from semantic_router.index.qdrant import QdrantIndex
__all__ = [
"BaseIndex",
+ "HybridLocalIndex",
"LocalIndex",
"QdrantIndex",
"PineconeIndex",
+ "PostgresIndex",
]
diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py
index 4893987a78335f2dda3039c9929b2c8503d108d7..97fe3bd4eadc4066f172ed159d0adef7f88f4e5b 100644
--- a/semantic_router/index/base.py
+++ b/semantic_router/index/base.py
@@ -4,7 +4,7 @@ import json
import numpy as np
from pydantic.v1 import BaseModel
-from semantic_router.schema import ConfigParameter, Utterance
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.route import Route
from semantic_router.utils.logger import logger
@@ -18,12 +18,12 @@ class BaseIndex(BaseModel):
# You can define common attributes here if there are any.
# For example, a placeholder for the index attribute:
- index: Optional[Any] = None
routes: Optional[np.ndarray] = None
utterances: Optional[np.ndarray] = None
dimensions: Union[int, None] = None
type: str = "base"
init_async_index: bool = False
+ index: Optional[Any] = None
def add(
self,
@@ -108,6 +108,7 @@ class BaseIndex(BaseModel):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query_vector and return top_k results.
@@ -120,6 +121,7 @@ class BaseIndex(BaseModel):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query_vector and return top_k results.
diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4096edbf59413643e6b4bec72ac218549fde8a9
--- /dev/null
+++ b/semantic_router/index/hybrid_local.py
@@ -0,0 +1,212 @@
+from typing import List, Optional, Tuple, Dict
+
+import numpy as np
+from numpy.linalg import norm
+
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
+from semantic_router.index.local import LocalIndex
+from semantic_router.utils.logger import logger
+from typing import Any
+
+
+class HybridLocalIndex(LocalIndex):
+ type: str = "hybrid_local"
+ sparse_index: Optional[list[dict]] = None
+ route_names: Optional[np.ndarray] = None
+
+ def __init__(self):
+ super().__init__()
+
+ def add(
+ self,
+ embeddings: List[List[float]],
+ routes: List[str],
+ utterances: List[str],
+ function_schemas: Optional[List[Dict[str, Any]]] = None,
+ metadata_list: List[Dict[str, Any]] = [],
+ sparse_embeddings: Optional[List[SparseEmbedding]] = None,
+ ):
+ if sparse_embeddings is None:
+ raise ValueError("Sparse embeddings are required for HybridLocalIndex.")
+ if function_schemas is not None:
+ logger.warning("Function schemas are not supported for HybridLocalIndex.")
+ if metadata_list:
+ logger.warning("Metadata is not supported for HybridLocalIndex.")
+ embeds = np.array(
+ embeddings
+ ) # TODO: we previously had as a array, so switching back and forth seems inefficient
+ routes_arr = np.array(routes)
+ if isinstance(utterances[0], str):
+ utterances_arr = np.array(utterances)
+ else:
+ utterances_arr = np.array(
+ utterances, dtype=object
+ ) # TODO: could we speed up if this were already array?
+ if self.index is None or self.sparse_index is None:
+ self.index = embeds
+ self.sparse_index = [
+ x.to_dict() for x in sparse_embeddings
+ ] # TODO: switch back to using SparseEmbedding later
+ self.routes = routes_arr
+ self.utterances = utterances_arr
+ else:
+ # TODO: we should probably switch to an `upsert` method and standardize elsewhere
+ self.index = np.concatenate([self.index, embeds])
+ self.sparse_index.extend([x.to_dict() for x in sparse_embeddings])
+ self.routes = np.concatenate([self.routes, routes_arr])
+ self.utterances = np.concatenate([self.utterances, utterances_arr])
+
+ def get_utterances(self) -> List[Utterance]:
+ """Gets a list of route and utterance objects currently stored in the index.
+
+ Returns:
+ List[Tuple]: A list of (route_name, utterance) objects.
+ """
+ if self.routes is None or self.utterances is None:
+ return []
+ return [Utterance.from_tuple(x) for x in zip(self.routes, self.utterances)]
+
+ def describe(self) -> Dict:
+ return {
+ "type": self.type,
+ "dimensions": self.index.shape[1] if self.index is not None else 0,
+ "vectors": self.index.shape[0] if self.index is not None else 0,
+ }
+
+ def _sparse_dot_product(
+ self, vec_a: dict[int, float], vec_b: dict[int, float]
+ ) -> float:
+ # switch vecs to ensure first is smallest for more efficiency
+ if len(vec_a) > len(vec_b):
+ vec_a, vec_b = vec_b, vec_a
+ return sum(vec_a[i] * vec_b.get(i, 0) for i in vec_a)
+
+ def _sparse_index_dot_product(self, vec_a: dict[int, float]) -> list[float]:
+ if self.sparse_index is None:
+ raise ValueError("self.sparse_index is not populated.")
+ dot_products = [
+ self._sparse_dot_product(vec_a, vec_b) for vec_b in self.sparse_index
+ ]
+ return dot_products
+
+ def query(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ """Search the index for the query and return top_k results.
+
+ :param vector: The query vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of top results to return, defaults to 5.
+ :type top_k: int, optional
+ :param route_filter: A list of route names to filter the search results, defaults to None.
+ :type route_filter: Optional[List[str]], optional
+ :param sparse_vector: The sparse vector to search for, must be provided.
+ :type sparse_vector: dict[int, float]
+ """
+ if route_filter:
+ raise ValueError("Route filter is not supported for HybridLocalIndex.")
+
+ xq_d = vector.copy()
+ # align sparse vector type
+ if isinstance(sparse_vector, SparseEmbedding):
+ xq_s = sparse_vector.to_dict()
+ elif isinstance(sparse_vector, dict):
+ xq_s = sparse_vector
+ else:
+ raise ValueError("Sparse vector must be a SparseEmbedding or dict.")
+
+ if self.index is not None and self.sparse_index is not None:
+ # calculate dense vec similarity
+ index_norm = norm(self.index, axis=1)
+ xq_d_norm = norm(xq_d) # TODO: this used to be xq_d.T, should work without
+ sim_d = np.squeeze(np.dot(self.index, xq_d.T)) / (index_norm * xq_d_norm)
+ # calculate sparse vec similarity
+ sim_s = np.array(self._sparse_index_dot_product(xq_s))
+ 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 (route names)
+ route_names = self.routes[idx] if self.routes is not None else []
+ return scores, route_names
+ else:
+ logger.warning("Index or sparse index is not populated.")
+ return np.array([]), []
+
+ async def aquery(
+ self,
+ vector: np.ndarray,
+ top_k: int = 5,
+ route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
+ ) -> Tuple[np.ndarray, List[str]]:
+ """Search the index for the query and return top_k results. This method calls the
+ sync `query` method as everything uses numpy computations which is CPU-bound
+ and so no benefit can be gained from making this async.
+
+ :param vector: The query vector to search for.
+ :type vector: np.ndarray
+ :param top_k: The number of top results to return, defaults to 5.
+ :type top_k: int, optional
+ :param route_filter: A list of route names to filter the search results, defaults to None.
+ :type route_filter: Optional[List[str]], optional
+ :param sparse_vector: The sparse vector to search for, must be provided.
+ :type sparse_vector: dict[int, float]
+ """
+ return self.query(
+ vector=vector,
+ top_k=top_k,
+ route_filter=route_filter,
+ sparse_vector=sparse_vector,
+ )
+
+ def aget_routes(self):
+ logger.error(f"Sync remove is not implemented for {self.__class__.__name__}.")
+
+ def _write_config(self, config: ConfigParameter):
+ logger.warning(f"No config is written for {self.__class__.__name__}.")
+
+ def delete(self, route_name: str):
+ """
+ Delete all records of a specific route from the index.
+ """
+ if (
+ self.index is not None
+ and self.routes is not None
+ and self.utterances is not None
+ ):
+ delete_idx = self._get_indices_for_route(route_name=route_name)
+ self.index = np.delete(self.index, delete_idx, axis=0)
+ self.routes = np.delete(self.routes, delete_idx, axis=0)
+ self.utterances = np.delete(self.utterances, delete_idx, axis=0)
+ else:
+ raise ValueError(
+ "Attempted to delete route records but either index, routes or "
+ "utterances is None."
+ )
+
+ def delete_index(self):
+ """
+ Deletes the index, effectively clearing it and setting it to None.
+ """
+ self.index = None
+ self.routes = None
+ self.utterances = None
+
+ def _get_indices_for_route(self, route_name: str):
+ """Gets an array of indices for a specific route."""
+ if self.routes is None:
+ raise ValueError("Routes are not populated.")
+ idx = [i for i, route in enumerate(self.routes) if route == route_name]
+ return idx
+
+ def __len__(self):
+ if self.index is not None:
+ return self.index.shape[0]
+ else:
+ return 0
diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py
index 509e0fdfded4b3021b328b14d0bf7ffdd55ccb31..76d44d82b50108136f5a6776621d75aca428c451 100644
--- a/semantic_router/index/local.py
+++ b/semantic_router/index/local.py
@@ -2,7 +2,7 @@ from typing import List, Optional, Tuple, Dict
import numpy as np
-from semantic_router.schema import ConfigParameter, Utterance
+from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance
from semantic_router.index.base import BaseIndex
from semantic_router.linear import similarity_matrix, top_scores
from semantic_router.utils.logger import logger
@@ -10,14 +10,10 @@ from typing import Any
class LocalIndex(BaseIndex):
- def __init__(
- self,
- index: Optional[np.ndarray] = None,
- routes: Optional[np.ndarray] = None,
- utterances: Optional[np.ndarray] = None,
- ):
- super().__init__(index=index, routes=routes, utterances=utterances)
- self.type = "local"
+ type: str = "local"
+
+ def __init__(self):
+ super().__init__()
class Config:
# Stop pydantic from complaining about Optional[np.ndarray]type hints.
@@ -46,8 +42,26 @@ class LocalIndex(BaseIndex):
self.routes = np.concatenate([self.routes, routes_arr])
self.utterances = np.concatenate([self.utterances, utterances_arr])
- def _remove_and_sync(self, routes_to_delete: dict):
- logger.warning("Sync remove is not implemented for LocalIndex.")
+ def _remove_and_sync(self, routes_to_delete: dict) -> np.ndarray:
+ if self.index is None or self.routes is None or self.utterances is None:
+ raise ValueError("Index, routes, or utterances are not populated.")
+ # TODO JB: implement routes and utterances as a numpy array
+ route_utterances = np.array([self.routes, self.utterances]).T
+ # initialize our mask with all true values (ie keep all)
+ mask = np.ones(len(route_utterances), dtype=bool)
+ for route, utterances in routes_to_delete.items():
+ # TODO JB: we should be able to vectorize this?
+ for utterance in utterances:
+ mask &= ~(
+ (route_utterances[:, 0] == route)
+ & (route_utterances[:, 1] == utterance)
+ )
+ # apply the mask to index, routes, and utterances
+ self.index = self.index[mask]
+ self.routes = self.routes[mask]
+ self.utterances = self.utterances[mask]
+ # return what was removed
+ return route_utterances[~mask]
def get_utterances(self) -> List[Utterance]:
"""
@@ -72,6 +86,7 @@ class LocalIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query and return top_k results.
@@ -101,6 +116,7 @@ class LocalIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Search the index for the query and return top_k results.
diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py
index 0a3856d816173ba0a085f28ef9eaca5c768c587d..303b47157533fbe71a74231ce550a2485d1f8e30 100644
--- a/semantic_router/index/pinecone.py
+++ b/semantic_router/index/pinecone.py
@@ -11,7 +11,7 @@ import numpy as np
from pydantic.v1 import BaseModel, Field
from semantic_router.index.base import BaseIndex
-from semantic_router.schema import ConfigParameter
+from semantic_router.schema import ConfigParameter, SparseEmbedding
from semantic_router.utils.logger import logger
@@ -22,6 +22,7 @@ def clean_route_name(route_name: str) -> str:
class PineconeRecord(BaseModel):
id: str = ""
values: List[float]
+ sparse_values: Optional[dict[int, float]] = None
route: str
utterance: str
function_schema: str = "{}"
@@ -42,11 +43,17 @@ class PineconeRecord(BaseModel):
)
def to_dict(self):
- return {
+ d = {
"id": self.id,
"values": self.values,
"metadata": self.metadata,
}
+ if self.sparse_values:
+ d["sparse_values"] = {
+ "indices": list(self.sparse_values.keys()),
+ "values": list(self.sparse_values.values()),
+ }
+ return d
class PineconeIndex(BaseIndex):
@@ -54,7 +61,7 @@ class PineconeIndex(BaseIndex):
api_key: Optional[str] = None
index_name: str = "index"
dimensions: Union[int, None] = None
- metric: str = "cosine"
+ metric: str = "dotproduct"
cloud: str = "aws"
region: str = "us-west-2"
host: str = ""
@@ -70,7 +77,7 @@ class PineconeIndex(BaseIndex):
api_key: Optional[str] = None,
index_name: str = "index",
dimensions: Optional[int] = None,
- metric: str = "cosine",
+ metric: str = "dotproduct",
cloud: str = "aws",
region: str = "us-west-2",
host: str = "",
@@ -216,7 +223,7 @@ class PineconeIndex(BaseIndex):
# if the index doesn't exist and we don't have the dimensions
# we raise warning
logger.warning("Index could not be initialized.")
- self.host = index_stats["host"] if index_stats else None
+ self.host = index_stats["host"] if index_stats else ""
def _batch_upsert(self, batch: List[Dict]):
"""Helper method for upserting a single batch of records."""
@@ -230,27 +237,35 @@ class PineconeIndex(BaseIndex):
embeddings: List[List[float]],
routes: List[str],
utterances: List[str],
- function_schemas: Optional[List[Dict[str, Any]]] = None,
+ function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None,
metadata_list: List[Dict[str, Any]] = [],
batch_size: int = 100,
+ sparse_embeddings: Optional[Optional[List[dict[int, float]]]] = None,
):
"""Add vectors to Pinecone in batches."""
- temp = "\n".join([f"{x[0]}: {x[1]}" for x in zip(routes, utterances)])
- logger.warning("TEMP | add:\n" + temp)
if self.index is None:
self.dimensions = self.dimensions or len(embeddings[0])
self.index = self._init_index(force_create=True)
-
+ if function_schemas is None:
+ function_schemas = [{}] * len(embeddings)
+ if sparse_embeddings is None:
+ sparse_embeddings = [{}] * len(embeddings)
vectors_to_upsert = [
PineconeRecord(
values=vector,
+ sparse_values=sparse_dict,
route=route,
utterance=utterance,
function_schema=json.dumps(function_schema),
metadata=metadata,
).to_dict()
- for vector, route, utterance, function_schema, metadata in zip(
- embeddings, routes, utterances, function_schemas, metadata_list # type: ignore
+ for vector, route, utterance, function_schema, metadata, sparse_dict in zip(
+ embeddings,
+ routes,
+ utterances,
+ function_schemas,
+ metadata_list,
+ sparse_embeddings,
)
]
@@ -259,10 +274,6 @@ class PineconeIndex(BaseIndex):
self._batch_upsert(batch)
def _remove_and_sync(self, routes_to_delete: dict):
- temp = "\n".join(
- [f"{route}: {utterances}" for route, utterances in routes_to_delete.items()]
- )
- logger.warning("TEMP | _remove_and_sync:\n" + temp)
for route, utterances in routes_to_delete.items():
remote_routes = self._get_routes_with_ids(route_name=route)
ids_to_delete = [
@@ -351,7 +362,7 @@ class PineconeIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
- **kwargs: Any,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""Search the index for the query vector and return the top_k results.
@@ -361,10 +372,10 @@ class PineconeIndex(BaseIndex):
:type top_k: int, optional
:param route_filter: A list of route names to filter the search results, defaults to None.
:type route_filter: Optional[List[str]], optional
+ :param sparse_vector: An optional sparse vector to include in the query.
+ :type sparse_vector: Optional[SparseEmbedding]
:param kwargs: Additional keyword arguments for the query, including sparse_vector.
:type kwargs: Any
- :keyword sparse_vector: An optional sparse vector to include in the query.
- :type sparse_vector: Optional[dict]
:return: A tuple containing an array of scores and a list of route names.
:rtype: Tuple[np.ndarray, List[str]]
:raises ValueError: If the index is not populated.
@@ -376,9 +387,15 @@ class PineconeIndex(BaseIndex):
filter_query = {"sr_route": {"$in": route_filter}}
else:
filter_query = None
+ if sparse_vector is not None:
+ if isinstance(sparse_vector, dict):
+ sparse_vector = SparseEmbedding.from_dict(sparse_vector)
+ if isinstance(sparse_vector, SparseEmbedding):
+ # unnecessary if-statement but mypy didn't like this otherwise
+ sparse_vector = sparse_vector.to_pinecone()
results = self.index.query(
vector=[query_vector_list],
- sparse_vector=kwargs.get("sparse_vector", None),
+ sparse_vector=sparse_vector,
top_k=top_k,
filter=filter_query,
include_metadata=True,
@@ -435,7 +452,7 @@ class PineconeIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
- **kwargs: Any,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Asynchronously search the index for the query vector and return the top_k results.
@@ -454,16 +471,24 @@ class PineconeIndex(BaseIndex):
:rtype: Tuple[np.ndarray, List[str]]
:raises ValueError: If the index is not populated.
"""
- if self.async_client is None or self.host is None:
+ if self.async_client is None or self.host == "":
raise ValueError("Async client or host are not initialized.")
query_vector_list = vector.tolist()
if route_filter is not None:
filter_query = {"sr_route": {"$in": route_filter}}
else:
filter_query = None
+ # set sparse_vector_obj
+ sparse_vector_obj: dict[str, Any] | None = None
+ if sparse_vector is not None:
+ if isinstance(sparse_vector, dict):
+ sparse_vector_obj = SparseEmbedding.from_dict(sparse_vector)
+ if isinstance(sparse_vector, SparseEmbedding):
+ # unnecessary if-statement but mypy didn't like this otherwise
+ sparse_vector_obj = sparse_vector.to_pinecone()
results = await self._async_query(
vector=query_vector_list,
- sparse_vector=kwargs.get("sparse_vector", None),
+ sparse_vector=sparse_vector_obj,
namespace=self.namespace or "",
filter=filter_query,
top_k=top_k,
@@ -480,7 +505,7 @@ class PineconeIndex(BaseIndex):
:return: A list of (route_name, utterance) objects.
:rtype: List[Tuple]
"""
- if self.async_client is None or self.host is None:
+ if self.async_client is None or self.host == "":
raise ValueError("Async client or host are not initialized.")
return await self._async_get_routes()
@@ -493,7 +518,7 @@ class PineconeIndex(BaseIndex):
async def _async_query(
self,
vector: list[float],
- sparse_vector: Optional[dict] = None,
+ sparse_vector: dict[str, Any] | None = None,
namespace: str = "",
filter: Optional[dict] = None,
top_k: int = 5,
@@ -507,6 +532,8 @@ class PineconeIndex(BaseIndex):
"top_k": top_k,
"include_metadata": include_metadata,
}
+ if self.host == "":
+ raise ValueError("self.host is not initialized.")
async with self.async_client.post(
f"https://{self.host}/query",
json=params,
@@ -523,7 +550,7 @@ class PineconeIndex(BaseIndex):
dimension: int,
cloud: str,
region: str,
- metric: str = "cosine",
+ metric: str = "dotproduct",
):
params = {
"name": name,
@@ -557,6 +584,8 @@ class PineconeIndex(BaseIndex):
"""
if self.index is None:
raise ValueError("Index is None, could not retrieve vector IDs.")
+ if self.host == "":
+ raise ValueError("self.host is not initialized.")
all_vector_ids = []
next_page_token = None
@@ -611,6 +640,8 @@ class PineconeIndex(BaseIndex):
:return: A dictionary containing the metadata for the vector.
:rtype: dict
"""
+ if self.host == "":
+ raise ValueError("self.host is not initialized.")
url = f"https://{self.host}/vectors/fetch"
params = {
@@ -640,6 +671,10 @@ class PineconeIndex(BaseIndex):
)
def __len__(self):
- return self.index.describe_index_stats()["namespaces"][self.namespace][
- "vector_count"
- ]
+ namespace_stats = self.index.describe_index_stats()["namespaces"].get(
+ self.namespace
+ )
+ if namespace_stats:
+ return namespace_stats["vector_count"]
+ else:
+ return 0
diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py
index 0d80d745df528962b12776629868546670bb2d5f..6d445f2bedabeba63a24712db955d6d4757647a2 100644
--- a/semantic_router/index/postgres.py
+++ b/semantic_router/index/postgres.py
@@ -1,16 +1,18 @@
import os
import uuid
from enum import Enum
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
import numpy as np
-import psycopg2
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
from semantic_router.index.base import BaseIndex
-from semantic_router.schema import ConfigParameter, Metric
+from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding
from semantic_router.utils.logger import logger
+if TYPE_CHECKING:
+ import psycopg2
+
class MetricPgVecOperatorMap(Enum):
"""
@@ -104,8 +106,9 @@ class PostgresIndex(BaseIndex):
dimensions: int = 1536
metric: Metric = Metric.COSINE
namespace: Optional[str] = ""
- conn: Optional[psycopg2.extensions.connection] = None
+ conn: Optional["psycopg2.extensions.connection"] = None
type: str = "postgres"
+ pg2: Any = Field(default=None, exclude=True)
def __init__(
self,
@@ -133,6 +136,14 @@ class PostgresIndex(BaseIndex):
:type namespace: Optional[str]
"""
super().__init__()
+ # try and import psycopg2
+ try:
+ import psycopg2
+ except ImportError:
+ raise ImportError(
+ "Please install psycopg2 to use PostgresIndex. "
+ "You can install it with: `pip install 'semantic-router[postgres]'`"
+ )
if connection_string:
self.connection_string = connection_string
else:
@@ -340,6 +351,7 @@ class PostgresIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
"""
Searches the index for the query vector and returns the top_k results.
diff --git a/semantic_router/index/qdrant.py b/semantic_router/index/qdrant.py
index eb7dc688df1e5d0de7a770cfdcbf00f568e983c9..0da5c25eb9dd8891a5d1aa9ef1338b8a07c0caf3 100644
--- a/semantic_router/index/qdrant.py
+++ b/semantic_router/index/qdrant.py
@@ -4,7 +4,7 @@ import numpy as np
from pydantic.v1 import Field
from semantic_router.index.base import BaseIndex
-from semantic_router.schema import ConfigParameter, Metric, Utterance
+from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding, Utterance
from semantic_router.utils.logger import logger
DEFAULT_COLLECTION_NAME = "semantic-router-index"
@@ -259,6 +259,7 @@ class QdrantIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
from qdrant_client import QdrantClient, models
@@ -292,6 +293,7 @@ class QdrantIndex(BaseIndex):
vector: np.ndarray,
top_k: int = 5,
route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
) -> Tuple[np.ndarray, List[str]]:
from qdrant_client import AsyncQdrantClient, models
diff --git a/semantic_router/routers/__init__.py b/semantic_router/routers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..79987406ed8c430993f714f433e6df892b53e299
--- /dev/null
+++ b/semantic_router/routers/__init__.py
@@ -0,0 +1,10 @@
+from semantic_router.routers.base import BaseRouter, RouterConfig
+from semantic_router.routers.semantic import SemanticRouter
+from semantic_router.routers.hybrid import HybridRouter
+
+__all__ = [
+ "BaseRouter",
+ "RouterConfig",
+ "SemanticRouter",
+ "HybridRouter",
+]
diff --git a/semantic_router/layer.py b/semantic_router/routers/base.py
similarity index 79%
rename from semantic_router/layer.py
rename to semantic_router/routers/base.py
index ec051633364696ebf3c0361bd229e4a445a1f392..e4376b421b1d9a87b964443795c2ccd4d2e1cab5 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/routers/base.py
@@ -3,13 +3,14 @@ import json
import os
import random
import hashlib
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from pydantic.v1 import BaseModel, Field
import numpy as np
import yaml # type: ignore
from tqdm.auto import tqdm
-from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder
+from semantic_router.encoders import AutoEncoder, DenseEncoder, OpenAIEncoder
from semantic_router.index.base import BaseIndex
from semantic_router.index.local import LocalIndex
from semantic_router.index.pinecone import PineconeIndex
@@ -56,14 +57,17 @@ def is_valid(layer_config: str) -> bool:
return False
-class LayerConfig:
+class RouterConfig:
"""
- Generates a LayerConfig object that can be used for initializing a
- RouteLayer.
+ Generates a RouterConfig object that can be used for initializing a
+ Routers.
"""
routes: List[Route] = []
+ class Config:
+ arbitrary_types_allowed = True
+
def __init__(
self,
routes: List[Route] = [],
@@ -76,7 +80,7 @@ class LayerConfig:
if encode_type.value == self.encoder_type:
if self.encoder_type == EncoderType.HUGGINGFACE.value:
raise NotImplementedError(
- "HuggingFace encoder not supported by LayerConfig yet."
+ "HuggingFace encoder not supported by RouterConfig yet."
)
encoder_name = EncoderDefault[encode_type.name].value[
"embedding_model"
@@ -87,7 +91,7 @@ class LayerConfig:
self.routes = routes
@classmethod
- def from_file(cls, path: str) -> "LayerConfig":
+ def from_file(cls, path: str) -> "RouterConfig":
logger.info(f"Loading route config from {path}")
_, ext = os.path.splitext(path)
with open(path, "r") as f:
@@ -139,7 +143,7 @@ class LayerConfig:
encoder_type: str = "openai",
encoder_name: Optional[str] = None,
):
- """Initialize a LayerConfig from a list of tuples of routes and
+ """Initialize a RouterConfig from a list of tuples of routes and
utterances.
:param route_tuples: A list of tuples, each containing a route name and an
@@ -178,9 +182,9 @@ class LayerConfig:
encoder_type: str = "openai",
encoder_name: Optional[str] = None,
):
- """Initialize a LayerConfig from a BaseIndex object.
+ """Initialize a RouterConfig from a BaseIndex object.
- :param index: The index to initialize the LayerConfig from.
+ :param index: The index to initialize the RouterConfig from.
:type index: BaseIndex
:param encoder_type: The type of encoder to use, defaults to "openai".
:type encoder_type: str, optional
@@ -246,6 +250,11 @@ class LayerConfig:
return utterances
def add(self, route: Route):
+ """Add a route to the RouterConfig.
+
+ :param route: The route to add.
+ :type route: Route
+ """
self.routes.append(route)
logger.info(f"Added route `{route.name}`")
@@ -271,38 +280,46 @@ class LayerConfig:
)
-class RouteLayer:
- score_threshold: float
- encoder: BaseEncoder
- index: BaseIndex
+class BaseRouter(BaseModel):
+ encoder: DenseEncoder = Field(default_factory=OpenAIEncoder)
+ index: BaseIndex = Field(default_factory=BaseIndex)
+ score_threshold: Optional[float] = Field(default=None)
+ routes: List[Route] = []
+ llm: Optional[BaseLLM] = None
+ top_k: int = 5
+ aggregation: str = "mean"
+ aggregation_method: Optional[Callable] = None
+ auto_sync: Optional[str] = None
+
+ class Config:
+ arbitrary_types_allowed = True
def __init__(
self,
- encoder: Optional[BaseEncoder] = None,
+ encoder: Optional[DenseEncoder] = None,
llm: Optional[BaseLLM] = None,
- routes: Optional[List[Route]] = None,
+ routes: List[Route] = [],
index: Optional[BaseIndex] = None, # type: ignore
top_k: int = 5,
- aggregation: str = "sum",
+ aggregation: str = "mean",
auto_sync: Optional[str] = None,
):
- self.index: BaseIndex = index if index is not None else LocalIndex()
- if encoder is None:
- logger.warning(
- "No encoder provided. Using default OpenAIEncoder. Ensure "
- "that you have set OPENAI_API_KEY in your environment."
- )
- self.encoder = OpenAIEncoder()
- else:
- self.encoder = encoder
+ super().__init__(
+ encoder=encoder,
+ llm=llm,
+ routes=routes,
+ index=index,
+ top_k=top_k,
+ aggregation=aggregation,
+ auto_sync=auto_sync,
+ )
+ self.encoder = self._get_encoder(encoder=encoder)
self.llm = llm
- self.routes = routes if routes else []
- if self.encoder.score_threshold is None:
- raise ValueError(
- "No score threshold provided for encoder. Please set the score threshold "
- "in the encoder config."
- )
- self.score_threshold = self.encoder.score_threshold
+ self.routes = routes.copy() if routes else []
+ # initialize index
+ self.index = self._get_index(index=index)
+ # set score threshold using default method
+ self._set_score_threshold()
self.top_k = top_k
if self.top_k < 1:
raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.")
@@ -318,15 +335,34 @@ class RouteLayer:
for route in self.routes:
if route.score_threshold is None:
route.score_threshold = self.score_threshold
- # if routes list has been passed, we initialize index now
+
+ def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex:
+ if index is None:
+ logger.warning("No index provided. Using default LocalIndex.")
+ index = LocalIndex()
+ else:
+ index = index
+ return index
+
+ def _get_encoder(self, encoder: Optional[DenseEncoder]) -> DenseEncoder:
+ if encoder is None:
+ logger.warning("No encoder provided. Using default OpenAIEncoder.")
+ encoder = OpenAIEncoder()
+ else:
+ encoder = encoder
+ return encoder
+
+ def _init_index_state(self):
+ """Initializes an index (where required) and runs auto_sync if active."""
+ # initialize index now, check if we need dimensions
+ if self.index.dimensions is None:
+ dims = len(self.encoder(["test"])[0])
+ self.index.dimensions = dims
+ # now init index
+ if isinstance(self.index, PineconeIndex):
+ self.index.index = self.index._init_index(force_create=True)
+ # run auto sync if active
if self.auto_sync:
- # initialize index now, check if we need dimensions
- if self.index.dimensions is None:
- dims = len(self.encoder(["test"])[0])
- self.index.dimensions = dims
- # now init index
- if isinstance(self.index, PineconeIndex):
- self.index.index = self.index._init_index(force_create=True)
local_utterances = self.to_config().to_utterances()
remote_utterances = self.index.get_utterances()
diff = UtteranceDiff.from_utterances(
@@ -336,6 +372,21 @@ class RouteLayer:
sync_strategy = diff.get_sync_strategy(self.auto_sync)
self._execute_sync_strategy(sync_strategy)
+ def _set_score_threshold(self):
+ """Set the score threshold for the layer based on the encoder
+ score threshold.
+
+ When no score threshold is used a default `None` value
+ is used, which means that a route will always be returned when
+ the layer is called."""
+ if self.encoder.score_threshold is not None:
+ self.score_threshold = self.encoder.score_threshold
+ if self.score_threshold is None:
+ logger.warning(
+ "No score threshold value found in encoder. Using the default "
+ "'None' value can lead to unexpected results."
+ )
+
def check_for_matching_routes(self, top_class: str) -> Optional[Route]:
matching_route = next(
(route for route in self.routes if route.name == top_class), None
@@ -359,7 +410,7 @@ class RouteLayer:
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector = self._encode(text=text)
+ vector = self._encode(text=[text])
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:
@@ -401,7 +452,7 @@ class RouteLayer:
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector = await self._async_encode(text=text)
+ vector = await self._async_encode(text=[text])
route, top_class_scores = await self._async_retrieve_top_route(
vector, route_filter
@@ -440,23 +491,57 @@ class RouteLayer:
if vector is None:
if text is None:
raise ValueError("Either text or vector must be provided")
- vector_arr = self._encode(text=text)
+ vector_arr = self._encode(text=[text])
else:
vector_arr = np.array(vector)
# get relevant utterances
results = self._retrieve(xq=vector_arr)
-
# decide most relevant routes
categories_with_scores = self._semantic_classify_multiple_routes(results)
+ return [
+ RouteChoice(name=category, similarity_score=score)
+ for category, score in categories_with_scores
+ ]
+
+ # route_choices = []
+ # TODO JB: do we need this check? Maybe we should be returning directly
+ # for category, score in categories_with_scores:
+ # route = self.check_for_matching_routes(category)
+ # if route:
+ # route_choice = RouteChoice(name=route.name, similarity_score=score)
+ # route_choices.append(route_choice)
+
+ # return route_choices
- route_choices = []
- for category, score in categories_with_scores:
- route = self.check_for_matching_routes(category)
- if route:
- route_choice = RouteChoice(name=route.name, similarity_score=score)
- route_choices.append(route_choice)
+ def _retrieve_top_route(
+ 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, route_filter=route_filter
+ )
+ # decide most relevant routes
+ top_class, top_class_scores = self._semantic_classify(results)
+ # TODO do we need this check?
+ route = self.check_for_matching_routes(top_class)
+ return route, top_class_scores
- return route_choices
+ async def _async_retrieve_top_route(
+ self, vector: List[float], route_filter: Optional[List[str]] = None
+ ) -> Tuple[Optional[Route], List[float]]:
+ # get relevant results (scores and routes)
+ results = await self._async_retrieve(
+ xq=np.array(vector), top_k=self.top_k, route_filter=route_filter
+ )
+ # decide most relevant routes
+ top_class, top_class_scores = await self._async_semantic_classify(results)
+ # TODO do we need this check?
+ route = self.check_for_matching_routes(top_class)
+ return route, top_class_scores
def sync(self, sync_mode: str, force: bool = False) -> List[str]:
"""Runs a sync of the local routes with the remote index.
@@ -525,9 +610,9 @@ class RouteLayer:
self._write_hash()
def _local_upsert(self, utterances: List[Utterance]):
- """Adds new routes to the RouteLayer.
+ """Adds new routes to the SemanticRouter.
- :param utterances: The utterances to add to the local RouteLayer.
+ :param utterances: The utterances to add to the local SemanticRouter.
:type utterances: List[Utterance]
"""
new_routes = {route.name: route for route in self.routes}
@@ -544,22 +629,18 @@ class RouteLayer:
new_routes[utt_obj.route].utterances.append(utt_obj.utterance)
new_routes[utt_obj.route].function_schemas = utt_obj.function_schemas
new_routes[utt_obj.route].metadata = utt_obj.metadata
- temp = "\n".join([f"{name}: {r.utterances}" for name, r in new_routes.items()])
- logger.warning("TEMP | _local_upsert:\n" + temp)
self.routes = list(new_routes.values())
def _local_delete(self, utterances: List[Utterance]):
- """Deletes routes from the local RouteLayer.
+ """Deletes routes from the local SemanticRouter.
- :param utterances: The utterances to delete from the local RouteLayer.
+ :param utterances: The utterances to delete from the local SemanticRouter.
:type utterances: List[Utterance]
"""
# create dictionary of route names to utterances
route_dict: dict[str, List[str]] = {}
for utt in utterances:
route_dict.setdefault(utt.route, []).append(utt.utterance)
- temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()])
- logger.warning("TEMP | _local_delete:\n" + temp)
# iterate over current routes and delete specific utterance if found
new_routes = []
for route in self.routes:
@@ -581,54 +662,17 @@ class RouteLayer:
metadata=route.metadata,
)
)
- logger.warning(
- f"TEMP | _local_delete OLD | {route.name}: {route.utterances}"
- )
- logger.warning(
- f"TEMP | _local_delete NEW | {route.name}: {new_routes[-1].utterances}"
- )
else:
# the route is not in the route_dict, so we keep it as is
new_routes.append(route)
- temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()])
- logger.warning("TEMP | _local_delete:\n" + temp)
self.routes = new_routes
- def _retrieve_top_route(
- 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, route_filter=route_filter
- )
- # decide most relevant routes
- top_class, top_class_scores = self._semantic_classify(results)
- # TODO do we need this check?
- route = self.check_for_matching_routes(top_class)
- return route, top_class_scores
-
- async def _async_retrieve_top_route(
- self, vector: List[float], route_filter: Optional[List[str]] = None
- ) -> Tuple[Optional[Route], List[float]]:
- # get relevant results (scores and routes)
- results = await self._async_retrieve(
- xq=np.array(vector), top_k=self.top_k, route_filter=route_filter
- )
- # decide most relevant routes
- top_class, top_class_scores = await self._async_semantic_classify(results)
- # TODO do we need this check?
- route = self.check_for_matching_routes(top_class)
- return route, top_class_scores
-
def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool:
"""
Check if the route's score passes the specified threshold.
"""
+ # TODO JB: do we need this?
if route is None:
return False
threshold = (
@@ -640,62 +684,44 @@ class RouteLayer:
def __str__(self):
return (
- f"RouteLayer(encoder={self.encoder}, "
+ f"{self.__class__.__name__}(encoder={self.encoder}, "
f"score_threshold={self.score_threshold}, "
f"routes={self.routes})"
)
@classmethod
def from_json(cls, file_path: str):
- config = LayerConfig.from_file(file_path)
+ config = RouterConfig.from_file(file_path)
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
- return cls(encoder=encoder, routes=config.routes)
+ if isinstance(encoder, DenseEncoder):
+ return cls(encoder=encoder, routes=config.routes)
+ else:
+ raise ValueError(f"{type(encoder)} not supported for loading from JSON.")
@classmethod
def from_yaml(cls, file_path: str):
- config = LayerConfig.from_file(file_path)
+ config = RouterConfig.from_file(file_path)
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
- return cls(encoder=encoder, routes=config.routes)
+ if isinstance(encoder, DenseEncoder):
+ return cls(encoder=encoder, routes=config.routes)
+ else:
+ raise ValueError(f"{type(encoder)} not supported for loading from YAML.")
@classmethod
- def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None):
+ def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None):
encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model
- return cls(encoder=encoder, routes=config.routes, index=index)
+ if isinstance(encoder, DenseEncoder):
+ return cls(encoder=encoder, routes=config.routes, index=index)
+ else:
+ raise ValueError(f"{type(encoder)} not supported for loading from config.")
- def add(self, route: Route):
- """Add a route to the local RouteLayer and index.
+ def add(self, routes: List[Route] | Route):
+ """Add a route to the local SemanticRouter and index.
:param route: The route to add.
:type route: Route
"""
- current_local_hash = self._get_hash()
- current_remote_hash = self.index._read_hash()
- if current_remote_hash.value == "":
- # if remote hash is empty, the index is to be initialized
- current_remote_hash = current_local_hash
- embedded_utterances = self.encoder(route.utterances)
- self.index.add(
- embeddings=embedded_utterances,
- routes=[route.name] * len(route.utterances),
- utterances=route.utterances,
- function_schemas=(
- route.function_schemas * len(route.utterances)
- if route.function_schemas
- else [{}] * len(route.utterances)
- ),
- metadata_list=[route.metadata if route.metadata else {}]
- * len(route.utterances),
- )
-
- self.routes.append(route)
- if current_local_hash.value == current_remote_hash.value:
- self._write_hash() # update current hash in index
- else:
- logger.warning(
- "Local and remote route layers were not aligned. Remote hash "
- "not updated. Use `RouteLayer.get_utterance_diff()` to see "
- "details."
- )
+ raise NotImplementedError("This method must be implemented by subclasses.")
def list_route_names(self) -> List[str]:
return [route.name for route in self.routes]
@@ -711,7 +737,7 @@ class RouteLayer:
threshold or utterances parameters, those fields are not updated.
If neither field is provided raises a ValueError.
- The name must exist within the local RouteLayer, if not a
+ The name must exist within the local SemanticRouter, if not a
KeyError will be raised.
"""
current_local_hash = self._get_hash()
@@ -745,8 +771,8 @@ class RouteLayer:
else:
logger.warning(
"Local and remote route layers were not aligned. Remote hash "
- "not updated. Use `RouteLayer.get_utterance_diff()` to see "
- "details."
+ f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` "
+ "to see details."
)
def delete(self, route_name: str):
@@ -762,7 +788,7 @@ class RouteLayer:
current_remote_hash = current_local_hash
if route_name not in [route.name for route in self.routes]:
- err_msg = f"Route `{route_name}` not found in RouteLayer"
+ err_msg = f"Route `{route_name}` not found in {self.__class__.__name__}"
logger.warning(err_msg)
try:
self.index.delete(route_name=route_name)
@@ -777,8 +803,8 @@ class RouteLayer:
else:
logger.warning(
"Local and remote route layers were not aligned. Remote hash "
- "not updated. Use `RouteLayer.get_utterance_diff()` to see "
- "details."
+ f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` "
+ "to see details."
)
def _refresh_routes(self):
@@ -798,43 +824,6 @@ class RouteLayer:
route = route_mapping[route_name]
self.routes.append(route)
- def _add_routes(self, routes: List[Route]):
- current_local_hash = self._get_hash()
- current_remote_hash = self.index._read_hash()
- if current_remote_hash.value == "":
- # if remote hash is empty, the index is to be initialized
- current_remote_hash = current_local_hash
-
- if not routes:
- logger.warning("No routes provided to add.")
- return
- # create embeddings for all routes
- route_names, all_utterances, all_function_schemas, all_metadata = (
- self._extract_routes_details(routes, include_metadata=True)
- )
- embedded_utterances = self.encoder(all_utterances)
- try:
- # Batch insertion into the index
- self.index.add(
- embeddings=embedded_utterances,
- routes=route_names,
- utterances=all_utterances,
- function_schemas=all_function_schemas,
- metadata_list=all_metadata,
- )
- except Exception as e:
- logger.error(f"Failed to add routes to the index: {e}")
- raise Exception("Indexing error occurred") from e
-
- if current_local_hash.value == current_remote_hash.value:
- self._write_hash() # update current hash in index
- else:
- logger.warning(
- "Local and remote route layers were not aligned. Remote hash "
- "not updated. Use `RouteLayer.get_utterance_diff()` to see "
- "details."
- )
-
def _get_hash(self) -> ConfigParameter:
config = self.to_config()
return config.get_hash()
@@ -891,20 +880,6 @@ class RouteLayer:
)
return diff_obj.to_utterance_str(include_metadata=include_metadata)
- def _add_and_sync_routes(self, routes: List[Route]):
- self.routes.extend(routes)
- # first we get remote and local utterances
- remote_utterances = self.index.get_utterances()
- local_utterances = self.to_config().to_utterances()
-
- diff_obj = UtteranceDiff.from_utterances(
- local_utterances=local_utterances, remote_utterances=remote_utterances
- )
- sync_strategy = diff_obj.get_sync_strategy(sync_mode=self.auto_sync)
- self._execute_sync_strategy(strategy=sync_strategy)
- # update remote hash
- self._write_hash()
-
def _extract_routes_details(
self, routes: List[Route], include_metadata: bool = False
) -> Tuple:
@@ -921,19 +896,31 @@ class RouteLayer:
return route_names, utterances, function_schemas, metadata
return route_names, utterances, function_schemas
- def _encode(self, text: str) -> Any:
- """Given some text, encode it."""
- # create query vector
- xq = np.array(self.encoder([text]))
- xq = np.squeeze(xq) # Reduce to 1d array.
- return xq
+ def _encode(self, text: list[str]) -> Any:
+ """Generates embeddings for a given text.
+
+ Must be implemented by a subclass.
+
+ :param text: The text to encode.
+ :type text: list[str]
+ :return: The embeddings of the text.
+ :rtype: Any
+ """
+ # TODO: should encode "content" rather than text
+ raise NotImplementedError("This method should be implemented by subclasses.")
+
+ async def _async_encode(self, text: list[str]) -> Any:
+ """Asynchronously generates embeddings for a given text.
- async def _async_encode(self, text: str) -> Any:
- """Given some text, encode it."""
- # create query vector
- xq = np.array(await self.encoder.acall(docs=[text]))
- xq = np.squeeze(xq) # Reduce to 1d array.
- return xq
+ Must be implemented by a subclass.
+
+ :param text: The text to encode.
+ :type text: list[str]
+ :return: The embeddings of the text.
+ :rtype: Any
+ """
+ # TODO: should encode "content" rather than text
+ raise NotImplementedError("This method should be implemented by subclasses.")
def _retrieve(
self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None
@@ -956,6 +943,7 @@ class RouteLayer:
return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)]
def _set_aggregation_method(self, aggregation: str = "sum"):
+ # TODO is this really needed?
if aggregation == "sum":
return lambda x: sum(x)
elif aggregation == "mean":
@@ -968,8 +956,20 @@ class RouteLayer:
)
def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]:
+ """Classify the query results into a single class based on the highest total score.
+ If no classification is found, return an empty string and an empty list.
+
+ :param query_results: The query results to classify. Expected format is a list of
+ dictionaries with "route" and "score" keys.
+ :type query_results: List[Dict]
+ :return: A tuple containing the top class and its associated scores.
+ :rtype: Tuple[str, List[float]]
+ """
scores_by_class = self.group_scores_by_class(query_results)
+ if self.aggregation_method is None:
+ raise ValueError("self.aggregation_method is not set.")
+
# Calculate total score for each class
total_scores = {
route: self.aggregation_method(scores)
@@ -987,8 +987,20 @@ class RouteLayer:
async def _async_semantic_classify(
self, query_results: List[Dict]
) -> Tuple[str, List[float]]:
+ """Classify the query results into a single class based on the highest total score.
+ If no classification is found, return an empty string and an empty list.
+
+ :param query_results: The query results to classify. Expected format is a list of
+ dictionaries with "route" and "score" keys.
+ :type query_results: List[Dict]
+ :return: A tuple containing the top class and its associated scores.
+ :rtype: Tuple[str, List[float]]
+ """
scores_by_class = await self.async_group_scores_by_class(query_results)
+ if self.aggregation_method is None:
+ raise ValueError("self.aggregation_method is not set.")
+
# Calculate total score for each class
total_scores = {
route: self.aggregation_method(scores)
@@ -1059,24 +1071,62 @@ class RouteLayer:
scores_by_class[route] = [score]
return scores_by_class
- def _pass_threshold(self, scores: List[float], threshold: float) -> bool:
+ def _pass_threshold(self, scores: List[float], threshold: float | None) -> bool:
+ """Test if the route score passes the minimum threshold. A threshold of None defaults
+ to 0.0, so the route will always pass no matter how low it scores.
+
+ :param scores: The scores to test.
+ :type scores: List[float]
+ :param threshold: The minimum threshold to pass.
+ :type threshold: float | None
+ :return: True if the route passes the threshold, False otherwise.
+ :rtype: bool
+ """
+ if threshold is None:
+ return True
if scores:
return max(scores) > threshold
else:
return False
- def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None):
+ def _update_thresholds(self, route_thresholds: Optional[Dict[str, float]] = None):
+ """Update the score thresholds for each route using a dictionary of
+ route names and thresholds.
+
+ :param route_thresholds: A dictionary of route names and thresholds.
+ :type route_thresholds: Dict[str, float] | None
"""
- Update the score thresholds for each route.
+ if route_thresholds:
+ for route, threshold in route_thresholds.items():
+ self.set_threshold(
+ threshold=threshold,
+ route_name=route,
+ )
+
+ def set_threshold(self, threshold: float, route_name: str | None = None):
+ """Set the score threshold for a specific route or all routes. A `threshold` of 0.0
+ will mean that the route will be returned no matter how low it scores whereas
+ a threshold of 1.0 will mean that a route must contain an exact utterance match
+ to be returned.
+
+ :param threshold: The threshold to set.
+ :type threshold: float
+ :param route_name: The name of the route to set the threshold for. If None, the
+ threshold will be set for all routes.
+ :type route_name: str | None
"""
- if score_thresholds:
+ if route_name is None:
for route in self.routes:
- route.score_threshold = score_thresholds.get(
- route.name, self.score_threshold
- )
+ route.score_threshold = threshold
+ else:
+ route_get: Route | None = self.get(route_name)
+ if route_get is not None:
+ route_get.score_threshold = threshold
+ else:
+ logger.error(f"Route `{route_name}` not found")
- def to_config(self) -> LayerConfig:
- return LayerConfig(
+ def to_config(self) -> RouterConfig:
+ return RouterConfig(
encoder_type=self.encoder.type,
encoder_name=self.encoder.name,
routes=self.routes,
@@ -1091,9 +1141,8 @@ class RouteLayer:
config.to_file(file_path)
def get_thresholds(self) -> Dict[str, float]:
- # TODO: float() below is hacky fix for lint, fix this with new type?
thresholds = {
- route.name: float(route.score_threshold or self.score_threshold)
+ route.name: route.score_threshold or self.score_threshold or 0.0
for route in self.routes
}
return thresholds
@@ -1142,7 +1191,7 @@ class RouteLayer:
search_range=0.8,
)
# update current route layer
- self._update_thresholds(score_thresholds=thresholds)
+ self._update_thresholds(route_thresholds=thresholds)
# evaluate
acc = self._vec_evaluate(Xq=Xq, y=y)
# update best
@@ -1150,7 +1199,7 @@ class RouteLayer:
best_acc = acc
best_thresholds = thresholds
# update route layer to best thresholds
- self._update_thresholds(score_thresholds=best_thresholds)
+ self._update_thresholds(route_thresholds=best_thresholds)
if local_execution:
# Switch back to the original index
@@ -1186,7 +1235,7 @@ class RouteLayer:
def threshold_random_search(
- route_layer: RouteLayer,
+ route_layer: BaseRouter,
search_range: Union[int, float],
) -> Dict[str, float]:
"""Performs a random search iteration given a route layer and a search range."""
diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py
new file mode 100644
index 0000000000000000000000000000000000000000..36ccd8f94c5e4aa1360462a34ad10382f0c92171
--- /dev/null
+++ b/semantic_router/routers/hybrid.py
@@ -0,0 +1,254 @@
+from typing import Dict, List, Optional
+import asyncio
+from pydantic.v1 import Field
+
+import numpy as np
+
+from semantic_router.encoders import (
+ DenseEncoder,
+ SparseEncoder,
+ BM25Encoder,
+ TfidfEncoder,
+)
+from semantic_router.route import Route
+from semantic_router.index import BaseIndex, HybridLocalIndex
+from semantic_router.schema import RouteChoice, SparseEmbedding, Utterance
+from semantic_router.utils.logger import logger
+from semantic_router.routers.base import BaseRouter
+from semantic_router.llms import BaseLLM
+
+
+class HybridRouter(BaseRouter):
+ """A hybrid layer that uses both dense and sparse embeddings to classify routes."""
+
+ # there are a few additional attributes for hybrid
+ sparse_encoder: Optional[SparseEncoder] = Field(default=None)
+ alpha: float = 0.3
+
+ def __init__(
+ self,
+ encoder: DenseEncoder,
+ sparse_encoder: Optional[SparseEncoder] = None,
+ llm: Optional[BaseLLM] = None,
+ routes: List[Route] = [],
+ index: Optional[HybridLocalIndex] = None,
+ top_k: int = 5,
+ aggregation: str = "mean",
+ auto_sync: Optional[str] = None,
+ alpha: float = 0.3,
+ ):
+ if index is None:
+ logger.warning("No index provided. Using default HybridLocalIndex.")
+ index = HybridLocalIndex()
+ encoder = self._get_encoder(encoder=encoder)
+ super().__init__(
+ encoder=encoder,
+ llm=llm,
+ routes=routes,
+ index=index,
+ top_k=top_k,
+ aggregation=aggregation,
+ auto_sync=auto_sync,
+ )
+ # initialize sparse encoder
+ self.sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder)
+ # set alpha
+ self.alpha = alpha
+ # fit sparse encoder if needed
+ if (
+ isinstance(self.sparse_encoder, TfidfEncoder)
+ and hasattr(self.sparse_encoder, "fit")
+ and self.routes
+ ):
+ self.sparse_encoder.fit(self.routes)
+ # run initialize index now if auto sync is active
+ if self.auto_sync:
+ self._init_index_state()
+
+ def add(self, routes: List[Route] | Route):
+ """Add a route to the local HybridRouter and index.
+
+ :param route: The route to add.
+ :type route: Route
+ """
+ # TODO: merge into single method within BaseRouter
+ current_local_hash = self._get_hash()
+ current_remote_hash = self.index._read_hash()
+ if current_remote_hash.value == "":
+ # if remote hash is empty, the index is to be initialized
+ current_remote_hash = current_local_hash
+ if isinstance(routes, Route):
+ routes = [routes]
+ # create embeddings for all routes
+ route_names, all_utterances, all_function_schemas, all_metadata = (
+ self._extract_routes_details(routes, include_metadata=True)
+ )
+ # TODO: to merge, self._encode should probably output a special
+ # TODO Embedding type that can be either dense or hybrid
+ dense_emb, sparse_emb = self._encode(all_utterances)
+ self.index.add(
+ embeddings=dense_emb.tolist(),
+ routes=route_names,
+ utterances=all_utterances,
+ function_schemas=all_function_schemas,
+ metadata_list=all_metadata,
+ sparse_embeddings=sparse_emb, # type: ignore
+ )
+
+ self.routes.extend(routes)
+ if current_local_hash.value == current_remote_hash.value:
+ self._write_hash() # update current hash in index
+ else:
+ logger.warning(
+ "Local and remote route layers were not aligned. Remote hash "
+ f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` "
+ "to see details."
+ )
+
+ def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]]):
+ """Executes the provided sync strategy, either deleting or upserting
+ routes from the local and remote instances as defined in the strategy.
+
+ :param strategy: The sync strategy to execute.
+ :type strategy: Dict[str, Dict[str, List[Utterance]]]
+ """
+ if strategy["remote"]["delete"]:
+ data_to_delete = {} # type: ignore
+ for utt_obj in strategy["remote"]["delete"]:
+ data_to_delete.setdefault(utt_obj.route, []).append(utt_obj.utterance)
+ # TODO: switch to remove without sync??
+ self.index._remove_and_sync(data_to_delete)
+ if strategy["remote"]["upsert"]:
+ utterances_text = [utt.utterance for utt in strategy["remote"]["upsert"]]
+ dense_emb, sparse_emb = self._encode(utterances_text)
+ self.index.add(
+ embeddings=dense_emb.tolist(),
+ routes=[utt.route for utt in strategy["remote"]["upsert"]],
+ utterances=utterances_text,
+ function_schemas=[
+ utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore
+ ],
+ metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]],
+ sparse_embeddings=sparse_emb, # type: ignore
+ )
+ if strategy["local"]["delete"]:
+ self._local_delete(utterances=strategy["local"]["delete"])
+ if strategy["local"]["upsert"]:
+ self._local_upsert(utterances=strategy["local"]["upsert"])
+ # update hash
+ self._write_hash()
+
+ def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex:
+ if index is None:
+ logger.warning("No index provided. Using default HybridLocalIndex.")
+ index = HybridLocalIndex()
+ else:
+ index = index
+ return index
+
+ def _get_sparse_encoder(
+ self, sparse_encoder: Optional[SparseEncoder]
+ ) -> SparseEncoder:
+ if sparse_encoder is None:
+ logger.warning("No sparse_encoder provided. Using default BM25Encoder.")
+ sparse_encoder = BM25Encoder()
+ else:
+ sparse_encoder = sparse_encoder
+ return sparse_encoder
+
+ def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ """Given some text, generates dense and sparse embeddings, then scales them
+ using the chosen alpha value.
+ """
+ if self.sparse_encoder is None:
+ raise ValueError("self.sparse_encoder is not set.")
+ # TODO: should encode "content" rather than text
+ # TODO: add alpha as a parameter
+ # 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 dict
+ xq_s = self.sparse_encoder(text)
+ # xq_s = np.squeeze(xq_s)
+ # convex scaling
+ xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s)
+ return xq_d, xq_s
+
+ async def _async_encode(
+ self, text: List[str]
+ ) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ """Given some text, generates dense and sparse embeddings, then scales them
+ using the chosen alpha value.
+ """
+ if self.sparse_encoder is None:
+ raise ValueError("self.sparse_encoder is not set.")
+ # TODO: should encode "content" rather than text
+ # TODO: add alpha as a parameter
+ # async encode both dense and sparse
+ dense_coro = self.encoder.acall(text)
+ sparse_coro = self.sparse_encoder.acall(text)
+ dense_vec, xq_s = await asyncio.gather(dense_coro, sparse_coro)
+ # create dense query vector
+ xq_d = np.array(dense_vec)
+ # convex scaling
+ xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s)
+ return xq_d, xq_s
+
+ def __call__(
+ self,
+ text: Optional[str] = None,
+ vector: Optional[List[float]] = None,
+ simulate_static: bool = False,
+ route_filter: Optional[List[str]] = None,
+ sparse_vector: dict[int, float] | SparseEmbedding | None = None,
+ ) -> RouteChoice:
+ vector_arr: np.ndarray | None = None
+ potential_sparse_vector: List[SparseEmbedding] | None = None
+ # if no vector provided, encode text to get vector
+ if vector is None:
+ if text is None:
+ raise ValueError("Either text or vector must be provided")
+ vector_arr, potential_sparse_vector = self._encode(text=[text])
+ if sparse_vector is None:
+ if text is None:
+ raise ValueError("Either text or sparse_vector must be provided")
+ sparse_vector = (
+ potential_sparse_vector[0] if potential_sparse_vector else None
+ )
+ if sparse_vector is None:
+ raise ValueError("Sparse vector is required for HybridLocalIndex.")
+ vector_arr = vector_arr if vector_arr is not None else np.array(vector)
+ # TODO: add alpha as a parameter
+ scores, route_names = self.index.query(
+ vector=vector_arr,
+ top_k=self.top_k,
+ route_filter=route_filter,
+ sparse_vector=sparse_vector,
+ )
+ top_class, top_class_scores = self._semantic_classify(
+ [
+ {"score": score, "route": route}
+ for score, route in zip(scores, route_names)
+ ]
+ )
+ passed = self._pass_threshold(top_class_scores, self.score_threshold)
+ if passed:
+ return RouteChoice(name=top_class, similarity_score=max(top_class_scores))
+ else:
+ return RouteChoice()
+
+ def _convex_scaling(
+ self, dense: np.ndarray, sparse: list[SparseEmbedding]
+ ) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ # TODO: better way to do this?
+ sparse_dicts = [sparse_vec.to_dict() for sparse_vec in sparse]
+ # scale sparse and dense vecs
+ scaled_dense = np.array(dense) * self.alpha
+ scaled_sparse = []
+ for sparse_dict in sparse_dicts:
+ scaled_sparse.append(
+ SparseEmbedding.from_dict(
+ {k: v * (1 - self.alpha) for k, v in sparse_dict.items()}
+ )
+ )
+ return scaled_dense, scaled_sparse
diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py
new file mode 100644
index 0000000000000000000000000000000000000000..33af2a32da0bff9942512cc83ee04e6ef9342037
--- /dev/null
+++ b/semantic_router/routers/semantic.py
@@ -0,0 +1,87 @@
+from typing import Any, List, Optional
+
+import numpy as np
+
+from semantic_router.encoders import DenseEncoder
+from semantic_router.index.base import BaseIndex
+from semantic_router.llms import BaseLLM
+from semantic_router.utils.logger import logger
+from semantic_router.route import Route
+from semantic_router.routers.base import BaseRouter
+
+
+class SemanticRouter(BaseRouter):
+ def __init__(
+ self,
+ encoder: Optional[DenseEncoder] = None,
+ llm: Optional[BaseLLM] = None,
+ routes: Optional[List[Route]] = None,
+ index: Optional[BaseIndex] = None, # type: ignore
+ top_k: int = 5,
+ aggregation: str = "mean",
+ auto_sync: Optional[str] = None,
+ ):
+ index = self._get_index(index=index)
+ encoder = self._get_encoder(encoder=encoder)
+ super().__init__(
+ encoder=encoder,
+ llm=llm,
+ routes=routes if routes else [],
+ index=index,
+ top_k=top_k,
+ aggregation=aggregation,
+ auto_sync=auto_sync,
+ )
+ # run initialize index now if auto sync is active
+ if self.auto_sync:
+ self._init_index_state()
+
+ def _encode(self, text: list[str]) -> Any:
+ """Given some text, encode it."""
+ # create query vector
+ xq = np.array(self.encoder(text))
+ xq = np.squeeze(xq) # Reduce to 1d array.
+ return xq
+
+ async def _async_encode(self, text: list[str]) -> Any:
+ """Given some text, encode it."""
+ # create query vector
+ xq = np.array(await self.encoder.acall(docs=text))
+ xq = np.squeeze(xq) # Reduce to 1d array.
+ return xq
+
+ def add(self, routes: List[Route] | Route):
+ """Add a route to the local SemanticRouter and index.
+
+ :param route: The route to add.
+ :type route: Route
+ """
+ current_local_hash = self._get_hash()
+ current_remote_hash = self.index._read_hash()
+ if current_remote_hash.value == "":
+ # if remote hash is empty, the index is to be initialized
+ current_remote_hash = current_local_hash
+ if isinstance(routes, Route):
+ routes = [routes]
+ # create embeddings for all routes
+ route_names, all_utterances, all_function_schemas, all_metadata = (
+ self._extract_routes_details(routes, include_metadata=True)
+ )
+ dense_emb = self._encode(all_utterances)
+ self.index.add(
+ embeddings=dense_emb.tolist(),
+ routes=route_names,
+ utterances=all_utterances,
+ function_schemas=all_function_schemas,
+ metadata_list=all_metadata,
+ )
+
+ self.routes.extend(routes)
+ if current_local_hash.value == current_remote_hash.value:
+ self._write_hash() # update current hash in index
+ else:
+ logger.warning(
+ "Local and remote route layers were not aligned. Remote hash "
+ f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` "
+ "to see details."
+ )
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index ef2b219062ee175869cb65796c2f957064e4becd..c9e63943823aef590f993c0a2910197d2b78a0f9 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -1,12 +1,15 @@
from datetime import datetime
from difflib import Differ
from enum import Enum
+import numpy as np
from typing import List, Optional, Union, Any, Dict, Tuple
from pydantic.v1 import BaseModel, Field
from semantic_router.utils.logger import logger
+from aurelio_sdk.schema import BM25Embedding
class EncoderType(Enum):
+ AURELIO = "aurelio"
AZURE = "azure"
COHERE = "cohere"
OPENAI = "openai"
@@ -403,3 +406,63 @@ class Metric(Enum):
DOTPRODUCT = "dotproduct"
EUCLIDEAN = "euclidean"
MANHATTAN = "manhattan"
+
+
+class SparseEmbedding(BaseModel):
+ """Sparse embedding interface. Primarily uses numpy operations for faster
+ operations.
+ """
+
+ embedding: np.ndarray
+
+ class Config:
+ arbitrary_types_allowed = True
+
+ @classmethod
+ def from_compact_array(cls, array: np.ndarray):
+ if array.ndim != 2 or array.shape[1] != 2:
+ raise ValueError(
+ f"Expected a 2D array with 2 columns, got a {array.ndim}D array with {array.shape[1]} columns. "
+ "Column 0 should contain index positions, and column 1 should contain respective values."
+ )
+ return cls(embedding=array)
+
+ @classmethod
+ def from_vector(cls, vector: np.ndarray):
+ """Consumes an array of sparse vectors containing zero-values."""
+ if vector.ndim != 1:
+ raise ValueError(f"Expected a 1D array, got a {vector.ndim}D array.")
+ return cls.from_compact_array(np.array([np.arange(len(vector)), vector]).T)
+
+ @classmethod
+ def from_aurelio(cls, embedding: BM25Embedding):
+ arr = np.array([embedding.indices, embedding.values]).T
+ return cls.from_compact_array(arr)
+
+ @classmethod
+ def from_dict(cls, sparse_dict: dict):
+ arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T
+ return cls.from_compact_array(arr)
+
+ @classmethod
+ def from_pinecone_dict(cls, sparse_dict: dict):
+ arr = np.array([sparse_dict["indices"], sparse_dict["values"]]).T
+ return cls.from_compact_array(arr)
+
+ def to_dict(self):
+ return {
+ i: v for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1])
+ }
+
+ def to_pinecone(self):
+ return {
+ "indices": self.embedding[:, 0].astype(int).tolist(),
+ "values": self.embedding[:, 1].tolist(),
+ }
+
+ # dictionary interface
+ def items(self):
+ return [
+ (i, v)
+ for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1])
+ ]
diff --git a/tests/integration/encoders/test_openai_integration.py b/tests/integration/encoders/test_openai_integration.py
index 47e617a5b931f02e25346c8c9e21b3607bc27f9e..c7bd8d9add219c14a9937ba2325c34ebf5b679e7 100644
--- a/tests/integration/encoders/test_openai_integration.py
+++ b/tests/integration/encoders/test_openai_integration.py
@@ -1,7 +1,7 @@
import os
import pytest
from openai import OpenAIError
-from semantic_router.encoders.base import BaseEncoder
+from semantic_router.encoders.base import DenseEncoder
from semantic_router.encoders.openai import OpenAIEncoder
with open("tests/integration/57640.4032.txt", "r") as fp:
@@ -11,7 +11,7 @@ with open("tests/integration/57640.4032.txt", "r") as fp:
@pytest.fixture
def openai_encoder():
if os.environ.get("OPENAI_API_KEY") is None:
- return BaseEncoder()
+ return DenseEncoder()
else:
return OpenAIEncoder()
diff --git a/tests/unit/encoders/test_base.py b/tests/unit/encoders/test_base.py
index 4d4b87aecee1b69494d09c8c1a25cde085081d5e..53a6233183254406a3d8be7889d5dca9c93f46cf 100644
--- a/tests/unit/encoders/test_base.py
+++ b/tests/unit/encoders/test_base.py
@@ -1,12 +1,12 @@
import pytest
-from semantic_router.encoders import BaseEncoder
+from semantic_router.encoders import DenseEncoder
-class TestBaseEncoder:
+class TestDenseEncoder:
@pytest.fixture
def base_encoder(self):
- return BaseEncoder(name="TestEncoder", score_threshold=0.5)
+ return DenseEncoder(name="TestEncoder", score_threshold=0.5)
def test_base_encoder_initialization(self, base_encoder):
assert base_encoder.name == "TestEncoder", "Initialization of name failed"
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
index 73e52d5585a3391488d7198d64543a0b2f0dcc5c..a22b0cc2eb5c9e13c8a31038b76b18fe0b6e152c 100644
--- a/tests/unit/encoders/test_bm25.py
+++ b/tests/unit/encoders/test_bm25.py
@@ -1,31 +1,66 @@
import pytest
+import numpy as np
from semantic_router.encoders import BM25Encoder
+from semantic_router.route import Route
+import nltk
+
+nltk.download("punkt_tab")
+
+UTTERANCES = [
+ "Hello we need this text to be a little longer for our sparse encoders",
+ "In this case they need to learn from recurring tokens, ie words.",
+ "We give ourselves several examples from our encoders to learn from.",
+ "But given this is only an example we don't need too many",
+ "Just enough to test that our sparse encoders work as expected",
+]
@pytest.fixture
def bm25_encoder():
sparse_encoder = BM25Encoder(use_default_params=False)
sparse_encoder.fit(
- ["The quick brown fox", "jumps over the lazy dog", "Hello, world!"]
+ [
+ Route(
+ name="test_route",
+ utterances=[
+ "The quick brown fox",
+ "jumps over the lazy dog",
+ "Hello, world!",
+ ],
+ )
+ ]
)
return sparse_encoder
+@pytest.fixture
+def routes():
+ return [
+ Route(name="Route 1", utterances=[UTTERANCES[0], UTTERANCES[1]]),
+ Route(name="Route 2", utterances=[UTTERANCES[2], UTTERANCES[3], UTTERANCES[4]]),
+ ]
+
+
class TestBM25Encoder:
def test_initialization(self, bm25_encoder):
- assert len(bm25_encoder.idx_mapping) != 0
+ assert bm25_encoder.model is not None
+
+ def test_fit(self, bm25_encoder, routes):
+ bm25_encoder.fit(routes)
+ assert bm25_encoder.model is not None
- 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_fit_with_strings(self, bm25_encoder):
+ route_strings = ["test a", "test b", "test c"]
+ with pytest.raises(TypeError):
+ bm25_encoder.fit(route_strings)
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"
+ isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result
+ ), "Each item in result should be an array"
def test_call_method_no_docs_bm25_encoder(self, bm25_encoder):
with pytest.raises(ValueError):
@@ -35,24 +70,15 @@ class TestBM25Encoder:
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"
-
- def test_init_with_non_dict_doc_freq(self, mocker):
- mock_encoder = mocker.MagicMock()
- mock_encoder.get_params.return_value = {"doc_freq": "not a dict"}
- mocker.patch(
- "pinecone_text.sparse.BM25Encoder.default", return_value=mock_encoder
- )
- with pytest.raises(TypeError):
- BM25Encoder()
+ isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result
+ ), "Each item in result should be an array"
def test_call_method_with_uninitialized_model_or_mapping(self, bm25_encoder):
bm25_encoder.model = None
with pytest.raises(ValueError):
bm25_encoder(["test"])
- def test_fit_with_uninitialized_model(self, bm25_encoder):
+ def test_fit_with_uninitialized_model(self, bm25_encoder, routes):
bm25_encoder.model = None
with pytest.raises(ValueError):
- bm25_encoder.fit(["test"])
+ bm25_encoder.fit(routes)
diff --git a/tests/unit/encoders/test_tfidf.py b/tests/unit/encoders/test_tfidf.py
index 7664433d070d74d06c00b7e0fb0336fa6fb7bc6e..94beec9fa99f1a8d73fe77ffa46c7b9b949fa2ab 100644
--- a/tests/unit/encoders/test_tfidf.py
+++ b/tests/unit/encoders/test_tfidf.py
@@ -37,8 +37,8 @@ class TestTfidfEncoder:
result = tfidf_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"
+ isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result
+ ), "Each item in result should be an array"
def test_call_method_no_docs_tfidf_encoder(self, tfidf_encoder):
with pytest.raises(ValueError):
@@ -55,8 +55,13 @@ class TestTfidfEncoder:
result = tfidf_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"
+ isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result
+ ), "Each item in result should be an array"
+
+ def test_fit_with_strings(self, tfidf_encoder):
+ routes = ["test a", "test b", "test c"]
+ with pytest.raises(TypeError):
+ tfidf_encoder.fit(routes)
def test_call_method_with_uninitialized_model(self, tfidf_encoder):
with pytest.raises(ValueError):
diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py
index fbf14566e3c9051331a3825fe7d952213ec38985..b12ea2f5b12d199f809664a8b56630b0da9ae2a1 100644
--- a/tests/unit/test_hybrid_layer.py
+++ b/tests/unit/test_hybrid_layer.py
@@ -2,32 +2,41 @@ import pytest
from semantic_router.encoders import (
AzureOpenAIEncoder,
- BaseEncoder,
+ DenseEncoder,
BM25Encoder,
CohereEncoder,
OpenAIEncoder,
TfidfEncoder,
)
-from semantic_router.hybrid_layer import HybridRouteLayer
+from semantic_router.routers import HybridRouter
from semantic_router.route import Route
+UTTERANCES = [
+ "Hello we need this text to be a little longer for our sparse encoders",
+ "In this case they need to learn from recurring tokens, ie words.",
+ "We give ourselves several examples from our encoders to learn from.",
+ "But given this is only an example we don't need too many",
+ "Just enough to test that our sparse encoders work as expected",
+]
+
+
def mock_encoder_call(utterances):
# Define a mapping of utterances to return values
mock_responses = {
- "Hello": [0.1, 0.2, 0.3],
- "Hi": [0.4, 0.5, 0.6],
- "Goodbye": [0.7, 0.8, 0.9],
- "Bye": [1.0, 1.1, 1.2],
- "Au revoir": [1.3, 1.4, 1.5],
+ UTTERANCES[0]: [0.1, 0.2, 0.3],
+ UTTERANCES[1]: [0.4, 0.5, 0.6],
+ UTTERANCES[2]: [0.7, 0.8, 0.9],
+ UTTERANCES[3]: [1.0, 1.1, 1.2],
+ UTTERANCES[4]: [1.3, 1.4, 1.5],
}
return [mock_responses.get(u, [0, 0, 0]) for u in utterances]
@pytest.fixture
def base_encoder(mocker):
- mock_base_encoder = BaseEncoder(name="test-encoder", score_threshold=0.5)
- mocker.patch.object(BaseEncoder, "__call__", return_value=[[0.1, 0.2, 0.3]])
+ mock_base_encoder = DenseEncoder(name="test-encoder", score_threshold=0.5)
+ mocker.patch.object(DenseEncoder, "__call__", return_value=[[0.1, 0.2, 0.3]])
return mock_base_encoder
@@ -55,92 +64,112 @@ def azure_encoder(mocker):
)
-def bm25_encoder(mocker):
- mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call)
+@pytest.fixture
+def bm25_encoder():
+ # mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call)
return BM25Encoder(name="test-bm25-encoder")
@pytest.fixture
-def tfidf_encoder(mocker):
- mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call)
+def tfidf_encoder():
+ # mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call)
return TfidfEncoder(name="test-tfidf-encoder")
@pytest.fixture
def routes():
return [
- Route(name="Route 1", utterances=["Hello", "Hi"]),
- Route(name="Route 2", utterances=["Goodbye", "Bye", "Au revoir"]),
+ Route(name="Route 1", utterances=[UTTERANCES[0], UTTERANCES[1]]),
+ Route(name="Route 2", utterances=[UTTERANCES[2], UTTERANCES[3], UTTERANCES[4]]),
]
-sparse_encoder = BM25Encoder(use_default_params=False)
-sparse_encoder.fit(["The quick brown fox", "jumps over the lazy dog", "Hello, world!"])
+sparse_encoder = TfidfEncoder()
+sparse_encoder.fit(
+ [
+ Route(
+ name="Route 1",
+ utterances=[
+ "The quick brown fox jumps over the lazy dog",
+ "some other useful text containing words like fox and dog",
+ ],
+ ),
+ Route(name="Route 2", utterances=["Hello, world!"]),
+ ]
+)
-class TestHybridRouteLayer:
+class TestHybridRouter:
def test_initialization(self, openai_encoder, routes):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder,
sparse_encoder=sparse_encoder,
routes=routes,
top_k=10,
alpha=0.8,
)
- assert route_layer.index is not None and route_layer.categories is not None
+ assert route_layer.index is not None and route_layer.routes is not None
assert openai_encoder.score_threshold == 0.3
assert route_layer.score_threshold == 0.3
assert route_layer.top_k == 10
assert route_layer.alpha == 0.8
- assert len(route_layer.index) == 5
- assert len(set(route_layer.categories)) == 2
+ assert route_layer.index.route_names is None
+ assert len(route_layer.routes) == 2
def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
- route_layer_cohere = HybridRouteLayer(
+ route_layer_cohere = HybridRouter(
encoder=cohere_encoder, sparse_encoder=sparse_encoder
)
assert route_layer_cohere.score_threshold == 0.3
- route_layer_openai = HybridRouteLayer(
+ route_layer_openai = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder
)
assert route_layer_openai.score_threshold == 0.3
- def test_add_route(self, openai_encoder):
- route_layer = HybridRouteLayer(
+ def test_add_route(self, openai_encoder, routes):
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder
)
- route = Route(name="Route 3", utterances=["Yes", "No"])
- route_layer._add_routes([route])
- assert route_layer.index is not None and route_layer.categories is not None
- assert len(route_layer.index) == 2
- assert len(set(route_layer.categories)) == 1
+ route_layer.add(routes=routes[0])
+ assert route_layer.index is not None, "route_layer.index is None"
+ assert route_layer.routes is not None, "route_layer.routes is None"
+ assert len(route_layer.routes) == 1, "route_layer.routes is not 1"
def test_add_multiple_routes(self, openai_encoder, routes):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder
)
- for route in routes:
- route_layer.add(route)
- assert route_layer.index is not None and route_layer.categories is not None
- assert len(route_layer.index) == 5
- assert len(set(route_layer.categories)) == 2
+ route_layer.add(routes=routes)
+ assert route_layer.index is not None, "route_layer.index is None"
+ assert route_layer.routes is not None, "route_layer.routes is None"
+ assert len(route_layer.routes) == 2, "route_layer.routes is not 2"
def test_query_and_classification(self, openai_encoder, routes):
- route_layer = HybridRouteLayer(
- encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
+ route_layer = HybridRouter(
+ encoder=openai_encoder,
+ sparse_encoder=sparse_encoder,
+ routes=routes,
+ auto_sync="local",
)
- query_result = route_layer("Hello")
- assert query_result in ["Route 1", "Route 2"]
+ route_layer.set_threshold(0.0)
+ query_result = route_layer(UTTERANCES[0])
+ assert query_result.name in ["Route 1", "Route 2"]
def test_query_with_no_index(self, openai_encoder):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder
)
- assert route_layer("Anything") is None
+ assert isinstance(route_layer.sparse_encoder, BM25Encoder) or isinstance(
+ route_layer.sparse_encoder, TfidfEncoder
+ ), (
+ f"route_layer.sparse_encoder is {route_layer.sparse_encoder.__class__.__name__} "
+ "not BM25Encoder or TfidfEncoder"
+ )
+ assert route_layer("Anything").name is None
def test_semantic_classify(self, openai_encoder, routes):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
classification, score = route_layer._semantic_classify(
@@ -153,7 +182,7 @@ class TestHybridRouteLayer:
assert score == [0.9]
def test_semantic_classify_multiple_routes(self, openai_encoder, routes):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes
)
classification, score = route_layer._semantic_classify(
@@ -167,35 +196,36 @@ class TestHybridRouteLayer:
assert score == [0.9, 0.8]
def test_pass_threshold(self, openai_encoder):
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder, sparse_encoder=sparse_encoder
)
assert not route_layer._pass_threshold([], 0.5)
assert route_layer._pass_threshold([0.6, 0.7], 0.5)
def test_failover_score_threshold(self, base_encoder):
- route_layer = HybridRouteLayer(
- encoder=base_encoder, sparse_encoder=sparse_encoder
- )
+ route_layer = HybridRouter(encoder=base_encoder, sparse_encoder=sparse_encoder)
assert base_encoder.score_threshold == 0.50
assert route_layer.score_threshold == 0.50
def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes):
- hybrid_route_layer = HybridRouteLayer(
+ hybrid_route_layer = HybridRouter(
encoder=cohere_encoder,
sparse_encoder=tfidf_encoder,
routes=routes[:-1],
+ auto_sync="local",
)
- hybrid_route_layer.add(routes[-1])
+ hybrid_route_layer.add(routes=routes[-1])
all_utterances = [
utterance for route in routes for utterance in route.utterances
]
- assert hybrid_route_layer.sparse_index is not None
- assert len(hybrid_route_layer.sparse_index) == len(all_utterances)
+ assert hybrid_route_layer.index.sparse_index is not None, "sparse_index is None"
+ assert len(hybrid_route_layer.index.sparse_index) == len(
+ all_utterances
+ ), "sparse_index length mismatch"
def test_setting_aggregation_methods(self, openai_encoder, routes):
for agg in ["sum", "mean", "max"]:
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder,
sparse_encoder=sparse_encoder,
routes=routes,
@@ -218,7 +248,7 @@ class TestHybridRouteLayer:
{"route": "Route 3", "score": 1.0},
]
for agg in ["sum", "mean", "max"]:
- route_layer = HybridRouteLayer(
+ route_layer = HybridRouter(
encoder=openai_encoder,
sparse_encoder=sparse_encoder,
routes=routes,
diff --git a/tests/unit/test_layer.py b/tests/unit/test_router.py
similarity index 87%
rename from tests/unit/test_layer.py
rename to tests/unit/test_router.py
index ead3f6d39204d9873785791a4b05fb9562441f1b..ef36e0ab4b4180b3ed5e5746d984691efd2b5150 100644
--- a/tests/unit/test_layer.py
+++ b/tests/unit/test_router.py
@@ -6,11 +6,11 @@ from datetime import datetime
import pytest
import time
from typing import Optional
-from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
+from semantic_router.encoders import DenseEncoder, 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.routers import RouterConfig, SemanticRouter
from semantic_router.llms.base import BaseLLM
from semantic_router.route import Route
from platform import python_version
@@ -102,7 +102,7 @@ routes:
@pytest.fixture
def base_encoder():
- return BaseEncoder(name="test-encoder", score_threshold=0.5)
+ return DenseEncoder(name="test-encoder", score_threshold=0.5)
@pytest.fixture
@@ -205,7 +205,7 @@ def get_test_encoders():
class TestIndexEncoders:
def test_initialization(self, routes, openai_encoder, index_cls, encoder_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=encoder_cls(),
routes=routes,
index=index,
@@ -228,22 +228,22 @@ class TestIndexEncoders:
def test_initialization_different_encoders(self, encoder_cls, index_cls):
index = init_index(index_cls)
encoder = encoder_cls()
- route_layer = RouteLayer(encoder=encoder, index=index)
+ route_layer = SemanticRouter(encoder=encoder, index=index)
assert route_layer.score_threshold == encoder.score_threshold
def test_initialization_no_encoder(self, openai_encoder, index_cls, encoder_cls):
os.environ["OPENAI_API_KEY"] = "test_api_key"
- route_layer_none = RouteLayer(encoder=None)
+ route_layer_none = SemanticRouter(encoder=None)
assert route_layer_none.score_threshold == openai_encoder.score_threshold
@pytest.mark.parametrize("index_cls", get_test_indexes())
-class TestRouteLayer:
+class TestSemanticRouter:
def test_initialization_dynamic_route(
self, dynamic_routes, openai_encoder, index_cls
):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=dynamic_routes,
index=index,
@@ -254,7 +254,7 @@ class TestRouteLayer:
def test_delete_index(self, openai_encoder, routes, index_cls):
# TODO merge .delete_index() and .delete_all() and get working
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -269,7 +269,7 @@ class TestRouteLayer:
def test_add_route(self, routes, openai_encoder, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder, routes=[], index=index, auto_sync="local"
)
if index_cls is PineconeIndex:
@@ -281,7 +281,7 @@ class TestRouteLayer:
assert route_layer.index.get_utterances() == []
# Add route1 and check
- route_layer.add(route=routes[0])
+ route_layer.add(routes=routes[0])
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert route_layer.routes == [routes[0]]
@@ -289,7 +289,7 @@ class TestRouteLayer:
assert len(route_layer.index.get_utterances()) == 2
# Add route2 and check
- route_layer.add(route=routes[1])
+ route_layer.add(routes=routes[1])
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert route_layer.routes == [routes[0], routes[1]]
@@ -297,7 +297,7 @@ class TestRouteLayer:
def test_list_route_names(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -312,7 +312,7 @@ class TestRouteLayer:
def test_delete_route(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -337,7 +337,7 @@ class TestRouteLayer:
def test_remove_route_not_found(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP)
# Attempt to remove a route that does not exist
@@ -347,14 +347,14 @@ class TestRouteLayer:
def test_add_multiple_routes(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
index=index,
auto_sync="local",
)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP)
- route_layer._add_routes(routes=routes)
+ route_layer.add(routes=routes)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert route_layer.index is not None
@@ -362,7 +362,7 @@ class TestRouteLayer:
def test_query_and_classification(self, openai_encoder, routes, index_cls):
index = init_index(index_cls, dimensions=3)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -375,7 +375,7 @@ class TestRouteLayer:
def test_query_filter(self, openai_encoder, routes, index_cls):
index = init_index(index_cls, dimensions=3)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -398,7 +398,7 @@ class TestRouteLayer:
def test_query_filter_pinecone(self, openai_encoder, routes, index_cls):
if index_cls is PineconeIndex:
pineconeindex = init_index(index_cls, dimensions=3)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pineconeindex,
@@ -420,7 +420,7 @@ class TestRouteLayer:
def test_namespace_pinecone_index(self, openai_encoder, routes, index_cls):
if index_cls is PineconeIndex:
pineconeindex = init_index(index_cls, namespace="test")
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pineconeindex,
@@ -438,13 +438,13 @@ class TestRouteLayer:
route_layer.index.index.delete(namespace="test", delete_all=True)
def test_query_with_no_index(self, openai_encoder, index_cls):
- route_layer = RouteLayer(encoder=openai_encoder)
+ route_layer = SemanticRouter(encoder=openai_encoder)
with pytest.raises(ValueError):
assert route_layer(text="Anything").name is None
def test_query_with_vector(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -458,13 +458,13 @@ class TestRouteLayer:
def test_query_with_no_text_or_vector(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
with pytest.raises(ValueError):
route_layer()
def test_semantic_classify(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -483,7 +483,7 @@ class TestRouteLayer:
def test_semantic_classify_multiple_routes(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -505,7 +505,7 @@ class TestRouteLayer:
self, openai_encoder, dynamic_routes, index_cls
):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder, routes=dynamic_routes, index=index
)
vector = [0.1, 0.2, 0.3]
@@ -514,7 +514,7 @@ class TestRouteLayer:
def test_pass_threshold(self, openai_encoder, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
index=index,
auto_sync="local",
@@ -524,7 +524,7 @@ class TestRouteLayer:
def test_failover_score_threshold(self, openai_encoder, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
index=index,
auto_sync="local",
@@ -538,7 +538,7 @@ class TestRouteLayer:
temp.close() # Close the file to ensure it can be opened again on Windows
os.environ["OPENAI_API_KEY"] = "test_api_key"
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -546,7 +546,7 @@ class TestRouteLayer:
)
route_layer.to_json(temp_path)
assert os.path.exists(temp_path)
- route_layer_from_file = RouteLayer.from_json(temp_path)
+ route_layer_from_file = SemanticRouter.from_json(temp_path)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
@@ -563,7 +563,7 @@ class TestRouteLayer:
temp.close() # Close the file to ensure it can be opened again on Windows
os.environ["OPENAI_API_KEY"] = "test_api_key"
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -571,7 +571,7 @@ class TestRouteLayer:
)
route_layer.to_yaml(temp_path)
assert os.path.exists(temp_path)
- route_layer_from_file = RouteLayer.from_yaml(temp_path)
+ route_layer_from_file = SemanticRouter.from_yaml(temp_path)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
@@ -588,8 +588,8 @@ class TestRouteLayer:
layer_json()
) # Assuming layer_json() returns a valid JSON string
- # Load the LayerConfig from the temporary file
- layer_config = LayerConfig.from_file(str(config_path))
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
# Assertions to verify the loaded configuration
assert layer_config.encoder_type == "cohere"
@@ -604,8 +604,8 @@ class TestRouteLayer:
layer_yaml()
) # Assuming layer_yaml() returns a valid YAML string
- # Load the LayerConfig from the temporary file
- layer_config = LayerConfig.from_file(str(config_path))
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
# Assertions to verify the loaded configuration
assert layer_config.encoder_type == "cohere"
@@ -615,7 +615,7 @@ class TestRouteLayer:
def test_from_file_invalid_path(self, index_cls):
with pytest.raises(FileNotFoundError) as excinfo:
- LayerConfig.from_file("nonexistent_path.json")
+ RouterConfig.from_file("nonexistent_path.json")
assert "[Errno 2] No such file or directory: 'nonexistent_path.json'" in str(
excinfo.value
)
@@ -626,7 +626,7 @@ class TestRouteLayer:
config_path.write_text(layer_json())
with pytest.raises(ValueError) as excinfo:
- LayerConfig.from_file(str(config_path))
+ RouterConfig.from_file(str(config_path))
assert "Unsupported file type" in str(excinfo.value)
def test_from_file_invalid_config(self, tmp_path, index_cls):
@@ -644,11 +644,11 @@ class TestRouteLayer:
file.write(invalid_config_json)
# Patch the is_valid function to return False for this test
- with patch("semantic_router.layer.is_valid", return_value=False):
- # Attempt to load the LayerConfig from the temporary file
+ with patch("semantic_router.routers.base.is_valid", return_value=False):
+ # Attempt to load the RouterConfig from the temporary file
# and assert that it raises an exception due to invalid configuration
with pytest.raises(Exception) as excinfo:
- LayerConfig.from_file(str(config_path))
+ RouterConfig.from_file(str(config_path))
assert "Invalid config JSON or YAML" in str(
excinfo.value
), "Loading an invalid configuration should raise an exception."
@@ -675,8 +675,8 @@ class TestRouteLayer:
with open(config_path, "w") as file:
file.write(llm_config_json)
- # Load the LayerConfig from the temporary file
- layer_config = LayerConfig.from_file(str(config_path))
+ # Load the RouterConfig from the temporary file
+ layer_config = RouterConfig.from_file(str(config_path))
# Using BaseLLM because trying to create a usable Mock LLM is a nightmare.
assert isinstance(
@@ -690,12 +690,12 @@ class TestRouteLayer:
def test_config(self, openai_encoder, routes, index_cls):
os.environ["OPENAI_API_KEY"] = "test_api_key"
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
# confirm route creation functions as expected
layer_config = route_layer.to_config()
assert layer_config.routes == route_layer.routes
# now load from config and confirm it's the same
- route_layer_from_config = RouteLayer.from_config(layer_config, index)
+ route_layer_from_config = SemanticRouter.from_config(layer_config, index)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert (
@@ -705,14 +705,14 @@ class TestRouteLayer:
def test_get_thresholds(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
assert route_layer.get_thresholds() == {"Route 1": 0.3, "Route 2": 0.3}
def test_with_multiple_routes_passing_threshold(
self, openai_encoder, routes, index_cls
):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
route_layer.score_threshold = 0.5 # Set the score_threshold if needed
# Assuming route_layer is already set up with routes "Route 1" and "Route 2"
query_results = [
@@ -720,8 +720,6 @@ class TestRouteLayer:
{"route": "Route 2", "score": 0.7},
{"route": "Route 1", "score": 0.8},
]
- # Override _pass_threshold to always return True for this test
- route_layer._pass_threshold = lambda scores, threshold: True
expected = [("Route 1", 0.8), ("Route 2", 0.7)]
results = route_layer._semantic_classify_multiple_routes(query_results)
assert sorted(results) == sorted(
@@ -730,10 +728,9 @@ class TestRouteLayer:
def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
- route_layer.score_threshold = 0.5
- # Override _pass_threshold to always return False for this test
- route_layer._pass_threshold = lambda scores, threshold: False
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
+ # set threshold to 1.0 so that no routes pass
+ route_layer.score_threshold = 1.0
query_results = [
{"route": "Route 1", "score": 0.3},
{"route": "Route 2", "score": 0.2},
@@ -746,7 +743,7 @@ class TestRouteLayer:
def test_with_no_query_results(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
route_layer.score_threshold = 0.5
query_results = []
expected = []
@@ -757,7 +754,7 @@ class TestRouteLayer:
def test_with_unrecognized_route(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
route_layer.score_threshold = 0.5
# Test with a route name that does not exist in the route_layer's routes
query_results = [{"route": "UnrecognizedRoute", "score": 0.9}]
@@ -767,7 +764,7 @@ class TestRouteLayer:
def test_retrieve_with_text(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -782,7 +779,7 @@ class TestRouteLayer:
def test_retrieve_with_vector(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -797,7 +794,7 @@ class TestRouteLayer:
def test_retrieve_without_text_or_vector(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
@@ -808,25 +805,29 @@ class TestRouteLayer:
def test_retrieve_no_matches(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=index,
auto_sync="local",
)
text = "Asparagus"
+ if index_cls is PineconeIndex:
+ time.sleep(PINECONE_SLEEP)
results = route_layer.retrieve_multiple_routes(text=text)
assert len(results) == 0, f"Expected no results, but got {len(results)}"
def test_retrieve_one_match(self, openai_encoder, routes_3, index_cls):
- index = init_index(index_cls)
- route_layer = RouteLayer(
+ index = init_index(index_cls, dimensions=3)
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_3,
index=index,
auto_sync="local",
)
text = "Hello"
+ # set low threshold
+ route_layer.set_threshold(threshold=0.1, route_name="Route 1")
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP)
results = route_layer.retrieve_multiple_routes(text=text)
@@ -838,13 +839,14 @@ class TestRouteLayer:
self, openai_encoder, routes_2, index_cls
):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=index,
auto_sync="local",
)
text = "Hello"
+ route_layer.set_threshold(threshold=0.01, route_name=None)
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP)
results = route_layer.retrieve_multiple_routes(text=text)
@@ -857,7 +859,7 @@ class TestRouteLayer:
self, openai_encoder, routes, index_cls
):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
unsupported_aggregation = "unsupported_aggregation_method"
with pytest.raises(
ValueError,
@@ -867,7 +869,7 @@ class TestRouteLayer:
def test_refresh_routes_not_implemented(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
with pytest.raises(
NotImplementedError, match="This method has not yet been implemented."
):
@@ -875,7 +877,7 @@ class TestRouteLayer:
def test_update_threshold(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
route_name = "Route 1"
new_threshold = 0.8
route_layer.update(name=route_name, threshold=new_threshold)
@@ -886,7 +888,7 @@ class TestRouteLayer:
def test_update_non_existent_route(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
non_existent_route = "Non-existent Route"
with pytest.raises(
ValueError,
@@ -896,7 +898,7 @@ class TestRouteLayer:
def test_update_without_parameters(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
with pytest.raises(
ValueError,
match="At least one of 'threshold' or 'utterances' must be provided.",
@@ -905,7 +907,7 @@ class TestRouteLayer:
def test_update_utterances_not_implemented(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index)
+ route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index)
with pytest.raises(
NotImplementedError,
match="The update method cannot be used for updating utterances yet.",
@@ -915,7 +917,7 @@ class TestRouteLayer:
class TestLayerFit:
def test_eval(self, openai_encoder, routes, test_data):
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
auto_sync="local",
@@ -926,7 +928,7 @@ class TestLayerFit:
route_layer.evaluate(X=X, y=y, batch_size=int(len(test_data) / 5))
def test_fit(self, openai_encoder, routes, test_data):
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
auto_sync="local",
@@ -939,61 +941,61 @@ class TestLayerFit:
# Add more tests for edge cases and error handling as needed.
-class TestLayerConfig:
+class TestRouterConfig:
def test_init(self):
- layer_config = LayerConfig()
+ layer_config = RouterConfig()
assert layer_config.routes == []
def test_to_file_json(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
with patch("builtins.open", mock_open()) as mocked_open:
layer_config.to_file("data/test_output.json")
mocked_open.assert_called_once_with("data/test_output.json", "w")
def test_to_file_yaml(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
with patch("builtins.open", mock_open()) as mocked_open:
layer_config.to_file("data/test_output.yaml")
mocked_open.assert_called_once_with("data/test_output.yaml", "w")
def test_to_file_invalid(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
with pytest.raises(ValueError):
layer_config.to_file("test_output.txt")
def test_from_file_json(self):
mock_json_data = layer_json()
with patch("builtins.open", mock_open(read_data=mock_json_data)) as mocked_open:
- layer_config = LayerConfig.from_file("data/test.json")
+ layer_config = RouterConfig.from_file("data/test.json")
mocked_open.assert_called_once_with("data/test.json", "r")
- assert isinstance(layer_config, LayerConfig)
+ assert isinstance(layer_config, RouterConfig)
def test_from_file_yaml(self):
mock_yaml_data = layer_yaml()
with patch("builtins.open", mock_open(read_data=mock_yaml_data)) as mocked_open:
- layer_config = LayerConfig.from_file("data/test.yaml")
+ layer_config = RouterConfig.from_file("data/test.yaml")
mocked_open.assert_called_once_with("data/test.yaml", "r")
- assert isinstance(layer_config, LayerConfig)
+ assert isinstance(layer_config, RouterConfig)
def test_from_file_invalid(self):
with open("test.txt", "w") as f:
f.write("dummy content")
with pytest.raises(ValueError):
- LayerConfig.from_file("test.txt")
+ RouterConfig.from_file("test.txt")
os.remove("test.txt")
def test_to_dict(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
assert layer_config.to_dict()["routes"] == [route.to_dict()]
def test_add(self):
route = Route(name="test", utterances=["utterance"])
route2 = Route(name="test2", utterances=["utterance2"])
- layer_config = LayerConfig()
+ layer_config = RouterConfig()
layer_config.add(route)
# confirm route added
assert layer_config.routes == [route]
@@ -1003,23 +1005,23 @@ class TestLayerConfig:
def test_get(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
assert layer_config.get("test") == route
def test_get_not_found(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
assert layer_config.get("not_found") is None
def test_remove(self):
route = Route(name="test", utterances=["utterance"])
- layer_config = LayerConfig(routes=[route])
+ layer_config = RouterConfig(routes=[route])
layer_config.remove("test")
assert layer_config.routes == []
def test_setting_aggregation_methods(self, openai_encoder, routes):
for agg in ["sum", "mean", "max"]:
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
aggregation=agg,
@@ -1041,7 +1043,7 @@ class TestLayerConfig:
{"route": "Route 3", "score": 1.0},
]
for agg in ["sum", "mean", "max"]:
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
aggregation=agg,
diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py
index 6235088fcbf4dca05afafccdd4e0a35a397cbc41..2f19236b5e8063f1768e4f26eb43cc9f480db377 100644
--- a/tests/unit/test_sync.py
+++ b/tests/unit/test_sync.py
@@ -4,15 +4,21 @@ from datetime import datetime
import pytest
import time
from typing import Optional
-from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
-from semantic_router.index.pinecone import PineconeIndex
+from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder
+from semantic_router.index import (
+ PineconeIndex,
+ HybridLocalIndex,
+ LocalIndex,
+ QdrantIndex,
+ PostgresIndex,
+)
from semantic_router.schema import Utterance
-from semantic_router.layer import RouteLayer
+from semantic_router.routers import SemanticRouter
from semantic_router.route import Route
from platform import python_version
-PINECONE_SLEEP = 12
+PINECONE_SLEEP = 6
def mock_encoder_call(utterances):
@@ -98,9 +104,42 @@ routes:
"""
+# not all indexes support metadata, so we map the feature here
+INCLUDE_METADATA_MAP = {
+ PineconeIndex: True,
+ HybridLocalIndex: False,
+ LocalIndex: False,
+ QdrantIndex: False,
+ PostgresIndex: False,
+}
+
+
+def include_metadata(index_cls):
+ return INCLUDE_METADATA_MAP.get(index_cls, False)
+
+
+MERGE_FORCE_LOCAL_RESULT_WITH_METADATA = [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 1", utterance="Hi"),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+]
+
+MERGE_FORCE_LOCAL_RESULT_WITHOUT_METADATA = [
+ Utterance(route="Route 1", utterance="Hello"),
+ Utterance(route="Route 1", utterance="Hi"),
+ Utterance(route="Route 2", utterance="Au revoir"),
+ Utterance(route="Route 2", utterance="Bye"),
+ Utterance(route="Route 2", utterance="Goodbye"),
+ Utterance(route="Route 2", utterance="Hi"),
+]
+
+
@pytest.fixture
def base_encoder():
- return BaseEncoder(name="test-encoder", score_threshold=0.5)
+ return DenseEncoder(name="test-encoder", score_threshold=0.5)
@pytest.fixture
@@ -187,13 +226,13 @@ def get_test_indexes():
@pytest.mark.parametrize("index_cls", get_test_indexes())
-class TestRouteLayer:
+class TestSemanticRouter:
@pytest.mark.skipif(
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_initialization(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- _ = RouteLayer(
+ _ = SemanticRouter(
encoder=openai_encoder,
routes=routes,
top_k=10,
@@ -206,7 +245,7 @@ class TestRouteLayer:
)
def test_second_initialization_sync(self, openai_encoder, routes, index_cls):
index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
if index_cls is PineconeIndex:
@@ -220,10 +259,12 @@ class TestRouteLayer:
self, openai_encoder, routes, routes_2, index_cls
):
index = init_index(index_cls)
- _ = RouteLayer(
+ _ = SemanticRouter(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- route_layer = RouteLayer(encoder=openai_encoder, routes=routes_2, index=index)
+ route_layer = SemanticRouter(
+ encoder=openai_encoder, routes=routes_2, index=index
+ )
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
assert route_layer.is_synced() is False
@@ -233,10 +274,12 @@ class TestRouteLayer:
)
def test_utterance_diff(self, openai_encoder, routes, routes_2, index_cls):
index = init_index(index_cls)
- _ = RouteLayer(
+ _ = SemanticRouter(
encoder=openai_encoder, routes=routes, index=index, auto_sync="local"
)
- route_layer_2 = RouteLayer(encoder=openai_encoder, routes=routes_2, index=index)
+ route_layer_2 = SemanticRouter(
+ encoder=openai_encoder, routes=routes_2, index=index
+ )
if index_cls is PineconeIndex:
time.sleep(PINECONE_SLEEP) # allow for index to be populated
diff = route_layer_2.get_utterance_diff(include_metadata=True)
@@ -256,13 +299,13 @@ class TestRouteLayer:
if index_cls is PineconeIndex:
# TEST LOCAL
pinecone_index = init_index(index_cls)
- _ = RouteLayer(
+ _ = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
@@ -281,14 +324,14 @@ class TestRouteLayer:
if index_cls is PineconeIndex:
# TEST REMOTE
pinecone_index = init_index(index_cls)
- _ = RouteLayer(
+ _ = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
@@ -309,14 +352,14 @@ class TestRouteLayer:
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
pinecone_index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
@@ -328,7 +371,8 @@ class TestRouteLayer:
# now confirm utterances are correct
local_utterances = route_layer.index.get_utterances()
# we sort to ensure order is the same
- local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ # TODO JB: there is a bug here where if we include_metadata=True it fails
+ local_utterances.sort(key=lambda x: x.to_str(include_metadata=False))
assert local_utterances == [
Utterance(route="Route 1", utterance="Hello"),
Utterance(route="Route 1", utterance="Hi"),
@@ -347,14 +391,14 @@ class TestRouteLayer:
if index_cls is PineconeIndex:
# TEST MERGE FORCE LOCAL
pinecone_index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
@@ -366,7 +410,9 @@ class TestRouteLayer:
# now confirm utterances are correct
local_utterances = route_layer.index.get_utterances()
# we sort to ensure order is the same
- local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ local_utterances.sort(
+ key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
+ )
assert local_utterances == [
Utterance(
route="Route 1", utterance="Hello", metadata={"type": "default"}
@@ -385,7 +431,7 @@ class TestRouteLayer:
os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required"
)
def test_sync(self, openai_encoder, index_cls):
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=[],
index=init_index(index_cls),
@@ -403,14 +449,14 @@ class TestRouteLayer:
if index_cls is PineconeIndex:
# TEST MERGE
pinecone_index = init_index(index_cls)
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes_2,
index=pinecone_index,
auto_sync="local",
)
time.sleep(PINECONE_SLEEP) # allow for index to be populated
- route_layer = RouteLayer(
+ route_layer = SemanticRouter(
encoder=openai_encoder,
routes=routes,
index=pinecone_index,
@@ -422,7 +468,9 @@ class TestRouteLayer:
# now confirm utterances are correct
local_utterances = route_layer.index.get_utterances()
# we sort to ensure order is the same
- local_utterances.sort(key=lambda x: x.to_str(include_metadata=True))
+ local_utterances.sort(
+ key=lambda x: x.to_str(include_metadata=include_metadata(index_cls))
+ )
assert local_utterances == [
Utterance(
route="Route 1", utterance="Hello", metadata={"type": "default"}