diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index ea083d41..e9105edd 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 8283b96d..979a97e5 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 00000000..9e4b7852 --- /dev/null +++ b/docs/encoders/aurelio-bm25.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](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 0ea54529..71e57ca7 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 00000000..b354907f --- /dev/null +++ b/docs/examples/pinecone-hybrid.ipynb @@ -0,0 +1,495 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](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 14e78905..0c6dc4e0 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 2dae787f..0340e029 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,7 @@ 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 11dbb7ed..19288adb 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 = 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"d649e614d9c7122c642ed351abf8886cc008960f0fd7664483cfb5dde7865808" diff --git a/pyproject.toml b/pyproject.toml index 68881c73..e1eff0c7 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 "] 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 a2ed0d07..4e65f002 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 a1026240..07e468d8 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.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 @@ 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 @@ def __init__(self, type: str, name: Optional[str]): 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 @@ def __init__(self, type: str, name: Optional[str]): 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 00000000..8b2501ba --- /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 fcc5734d..0a25f210 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 @@ def __call__(self, docs: List[Any]) -> List[List[float]]: 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 fed53900..5ec3381e 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 @@ 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 1965fb6e..f42bf9c2 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 @@ def __init__( 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 @@ def _set_idx_mapping(self): 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 @@ def __call__(self, docs: List[str]) -> List[List[float]]: 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 6495c870..65fbdb8f 100644 --- a/semantic_router/encoders/clip.py +++ b/semantic_router/encoders/clip.py @@ -3,10 +3,10 @@ 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 cdc114bb..04b87814 100644 --- a/semantic_router/encoders/cohere.py +++ b/semantic_router/encoders/cohere.py @@ -3,11 +3,11 @@ 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 27590bc3..5cda5e64 100644 --- a/semantic_router/encoders/fastembed.py +++ b/semantic_router/encoders/fastembed.py @@ -3,10 +3,10 @@ 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 088d4bba..5d50a0e1 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 @@ 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 7ca7580d..7c7e56f7 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 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 @@ def _max_pooling(self, model_output, attention_mask): 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 974f1128..46bc89ae 100644 --- a/semantic_router/encoders/mistral.py +++ b/semantic_router/encoders/mistral.py @@ -6,11 +6,11 @@ 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 4bc86ac2..fb8a83f0 100644 --- a/semantic_router/encoders/openai.py +++ b/semantic_router/encoders/openai.py @@ -10,7 +10,7 @@ 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 @@ } -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 17cc569a..656ee117 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -3,24 +3,25 @@ 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 @@ def __call__(self, docs: List[str]) -> List[List[float]]: 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 @@ def _compute_tf(self, docs: List[str]) -> np.ndarray: 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 44ae5801..73cb0582 100644 --- a/semantic_router/encoders/vit.py +++ b/semantic_router/encoders/vit.py @@ -2,10 +2,10 @@ 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 d6f65660..fd8594a9 100644 --- a/semantic_router/encoders/zure.py +++ b/semantic_router/encoders/zure.py @@ -8,12 +8,12 @@ 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 2aaf6f17..00000000 --- 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 9a01b996..14b60da3 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 4893987a..97fe3bd4 100644 --- a/semantic_router/index/base.py +++ b/semantic_router/index/base.py @@ -4,7 +4,7 @@ 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 @@ def query( 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 @@ async def aquery( 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 00000000..d4096edb --- /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 509e0fdf..76d44d82 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -2,7 +2,7 @@ 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 @@ 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 @@ def add( 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 @@ def query( 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 @@ async def aquery( 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 0a3856d8..303b4715 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -11,7 +11,7 @@ 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 @@ def __init__(self, **data): ) 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 @@ def __init__( 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 @@ async def _init_async_index(self, force_create: bool = False): # 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 @@ def add( 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 @@ def add( 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 @@ def query( 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 @@ def query( :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 @@ def query( 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 @@ async def aquery( 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 @@ async def aquery( :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 @@ async def aget_routes(self) -> list[tuple]: :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 @@ def delete_index(self): 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 @@ async def _async_query( "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 @@ async def _async_create_index( dimension: int, cloud: str, region: str, - metric: str = "cosine", + metric: str = "dotproduct", ): params = { "name": name, @@ -557,6 +584,8 @@ async def _async_get_all( """ 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 @@ async def _async_fetch_metadata(self, vector_id: str) -> dict: :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 @@ async def _async_fetch_metadata(self, vector_id: str) -> dict: ) 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 0d80d745..6d445f2b 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 @@ def __init__( :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 @@ def query( 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 eb7dc688..0da5c25e 100644 --- a/semantic_router/index/qdrant.py +++ b/semantic_router/index/qdrant.py @@ -4,7 +4,7 @@ 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 @@ def query( 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 @@ async def aquery( 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 00000000..79987406 --- /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 ec051633..e4376b42 100644 --- a/semantic_router/layer.py +++ b/semantic_router/routers/base.py @@ -3,13 +3,14 @@ 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 @@ def __init__( 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 @@ def __init__( 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 @@ def from_tuples( 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 @@ def from_index( 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 @@ def to_utterances(self) -> List[Utterance]: 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 @@ def get_hash(self) -> ConfigParameter: ) -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 @@ def __init__( 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 @@ def __init__( 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 @@ def __call__( 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 @@ async def acall( 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 @@ def retrieve_multiple_routes( 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 @@ def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]] 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 @@ def _local_upsert(self, utterances: List[Utterance]): 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 @@ def _local_delete(self, utterances: List[Utterance]): 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 @@ def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: 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 @@ def update( 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 @@ def update( 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 @@ def delete(self, route_name: str): 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 @@ def delete(self, route_name: str): 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 @@ def _refresh_routes(self): 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 @@ def get_utterance_diff(self, include_metadata: bool = False) -> List[str]: ) 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 @@ def _extract_routes_details( 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 @@ async def _async_retrieve( 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 @@ def _set_aggregation_method(self, aggregation: str = "sum"): ) 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 @@ def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float 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 @@ async def async_group_scores_by_class( 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 @@ def to_yaml(self, file_path: str): 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 @@ def fit( 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 @@ def fit( 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 @@ def _get_route_names(self) -> List[str]: 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 00000000..36ccd8f9 --- /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 00000000..33af2a32 --- /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 ef2b2190..c9e63943 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 47e617a5..c7bd8d9a 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 @@ @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 4d4b87ae..53a62331 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 73e52d55..a22b0cc2 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 @@ def test_call_method_no_word(self, bm25_encoder): result = bm25_encoder(["doc with fake word gta5jabcxyz"]) assert isinstance(result, list), "Result should be a list" assert all( - isinstance(sublist, list) for sublist in result - ), "Each item in result should be a list" - - 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 7664433d..94beec9f 100644 --- a/tests/unit/encoders/test_tfidf.py +++ b/tests/unit/encoders/test_tfidf.py @@ -37,8 +37,8 @@ def test_call_method(self, tfidf_encoder): 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 @@ def test_call_method_no_word(self, tfidf_encoder): 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 fbf14566..b12ea2f5 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -2,32 +2,41 @@ 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 @@ def test_semantic_classify(self, openai_encoder, routes): 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 @@ def test_semantic_classify_multiple_routes(self, openai_encoder, routes): 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 @@ def test_semantic_classify_multiple_routes_with_different_aggregation( {"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 ead3f6d3..ef36e0ab 100644 --- a/tests/unit/test_layer.py +++ b/tests/unit/test_router.py @@ -6,11 +6,11 @@ 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 @@ def layer_yaml(): @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 @@ def test_initialization(self, routes, openai_encoder, index_cls, encoder_cls): 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 @@ def test_initialization_dynamic_route( 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 @@ def test_delete_index(self, openai_encoder, routes, index_cls): 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 @@ def test_add_route(self, routes, openai_encoder, index_cls): 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 @@ def test_add_route(self, routes, openai_encoder, index_cls): 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 @@ def test_add_route(self, routes, openai_encoder, index_cls): 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 @@ def test_list_route_names(self, openai_encoder, routes, index_cls): 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 @@ def test_delete_route(self, openai_encoder, routes, index_cls): 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 @@ def test_remove_route_not_found(self, openai_encoder, routes, index_cls): 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 @@ def test_add_multiple_routes(self, openai_encoder, routes, index_cls): 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 @@ def test_query_and_classification(self, openai_encoder, routes, index_cls): 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 @@ def test_query_filter(self, openai_encoder, routes, index_cls): 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 @@ def test_query_filter_pinecone(self, openai_encoder, routes, index_cls): 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 @@ def test_namespace_pinecone_index(self, openai_encoder, routes, index_cls): 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 @@ def test_query_with_vector(self, openai_encoder, routes, index_cls): 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 @@ def test_semantic_classify(self, openai_encoder, routes, index_cls): 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 @@ def test_query_no_text_dynamic_route( 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 @@ def test_query_no_text_dynamic_route( 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 @@ def test_pass_threshold(self, openai_encoder, index_cls): 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 @@ def test_json(self, openai_encoder, routes, index_cls): 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 @@ def test_json(self, openai_encoder, routes, index_cls): ) 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 @@ def test_yaml(self, openai_encoder, routes, index_cls): 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 @@ def test_yaml(self, openai_encoder, routes, index_cls): ) 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 @@ def test_from_file_json(openai_encoder, tmp_path, index_cls): 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 @@ def test_from_file_yaml(openai_encoder, tmp_path, index_cls): 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 @@ def test_from_file_yaml(openai_encoder, tmp_path, index_cls): 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 @@ def test_from_file_unsupported_type(self, tmp_path, index_cls): 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 @@ def test_from_file_invalid_config(self, tmp_path, index_cls): 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 @@ def test_from_file_with_llm(self, tmp_path, index_cls): 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 @@ def test_from_file_with_llm(self, tmp_path, index_cls): 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 @@ def test_config(self, openai_encoder, routes, index_cls): 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 @@ def test_with_multiple_routes_passing_threshold( {"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 @@ def test_with_multiple_routes_passing_threshold( 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 @@ def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cl 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 @@ def test_with_no_query_results(self, openai_encoder, routes, index_cls): 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 @@ def test_with_unrecognized_route(self, openai_encoder, routes, index_cls): 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 @@ def test_retrieve_with_text(self, openai_encoder, routes, index_cls): 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 @@ def test_retrieve_with_vector(self, openai_encoder, routes, index_cls): 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 @@ def test_retrieve_without_text_or_vector(self, openai_encoder, routes, index_cls 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 @@ def test_retrieve_with_text_for_multiple_matches( 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 @@ def test_set_aggregation_method_with_unsupported_value( 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 @@ def test_set_aggregation_method_with_unsupported_value( 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 @@ def test_refresh_routes_not_implemented(self, openai_encoder, routes, index_cls) 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 @@ def test_update_threshold(self, openai_encoder, routes, index_cls): 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 @@ def test_update_non_existent_route(self, openai_encoder, routes, index_cls): 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 @@ def test_update_without_parameters(self, openai_encoder, routes, index_cls): 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 @@ def test_update_utterances_not_implemented(self, openai_encoder, routes, index_c 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 @@ def test_eval(self, openai_encoder, routes, test_data): 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 @@ def test_fit(self, openai_encoder, routes, test_data): # 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 @@ def test_add(self): 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 @@ def test_semantic_classify_multiple_routes_with_different_aggregation( {"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 6235088f..2f19236b 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -4,15 +4,21 @@ 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 @@ def layer_yaml(): """ +# 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 @@ def test_initialization(self, openai_encoder, routes, index_cls): ) 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 @@ def test_second_initialization_not_synced( 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 @@ def test_second_initialization_not_synced( ) 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 @@ def test_auto_sync_local(self, openai_encoder, routes, routes_2, index_cls): 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 @@ def test_auto_sync_remote(self, openai_encoder, routes, routes_2, index_cls): 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 @@ def test_auto_sync_merge_force_local( 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 @@ def test_auto_sync_merge_force_local( # 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 @@ def test_auto_sync_merge_force_remote( 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 @@ def test_auto_sync_merge_force_remote( # 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 @@ def test_auto_sync_merge_force_remote( 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 @@ def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls): 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 @@ def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls): # 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"}