diff --git a/Makefile b/Makefile
index 3a3c42cd9723366e5c62544979ed22dcff209df5..8de202fa56f0de52a80f6f8a63e68ab6fe18ef33 100644
--- a/Makefile
+++ b/Makefile
@@ -9,6 +9,7 @@ lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep
lint lint_diff:
poetry run black $(PYTHON_FILES) --check
poetry run ruff .
+ poetry run mypy $(PYTHON_FILES)
test:
poetry run pytest -vv -n 20 --cov=semantic_router --cov-report=term-missing --cov-report=xml --cov-fail-under=100
diff --git a/README.md b/README.md
index 9dac42225f967e558b3cac590e3dd4a0bf7a782b..b4b3c0e3260bea834a352de9ddaedfddfe798f68 100644
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@
<img alt="" src="https://img.shields.io/github/repo-size/aurelio-labs/semantic-router" />
<img alt="GitHub Issues" src="https://img.shields.io/github/issues/aurelio-labs/semantic-router" />
<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/aurelio-labs/semantic-router" />
+<img src="https://codecov.io/gh/aurelio-labs/semantic-router/graph/badge.svg?token=H8OOMV2TUF" />
<img alt="Github License" src="https://img.shields.io/badge/License-MIT-yellow.svg" />
</p>
diff --git a/coverage.xml b/coverage.xml
index 3c9c2e7c7acc2890d77de9dd322f29415a4d39aa..8e6ca91d8e505ae98209a2c0ff3add414c2095e8 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,5 +1,5 @@
<?xml version="1.0" ?>
-<coverage version="7.3.2" timestamp="1702458856280" lines-valid="311" lines-covered="311" line-rate="1" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
+<coverage version="7.3.2" timestamp="1702463592393" lines-valid="334" lines-covered="334" line-rate="1" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
<!-- Generated by coverage.py: https://coverage.readthedocs.io/en/7.3.2 -->
<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
<sources>
@@ -12,10 +12,11 @@
<methods/>
<lines>
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- <line number="3" hits="1"/>
+ <line number="2" hits="1"/>
+ <line number="4" hits="1"/>
</lines>
</class>
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+ <class name="hybrid_layer.py" filename="hybrid_layer.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
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</lines>
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<class name="cohere.py" filename="encoders/cohere.py" complexity="0" line-rate="1" branch-rate="0">
@@ -352,6 +380,10 @@
</package>
<package name="utils" line-rate="1" branch-rate="0" complexity="0">
<classes>
+ <class name="__init__.py" filename="utils/__init__.py" complexity="0" line-rate="1" branch-rate="0">
+ <methods/>
+ <lines/>
+ </class>
<class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="1" branch-rate="0">
<methods/>
<lines>
diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb
index 98fccf6972026d62e6d53a3c96492ca90287207e..8b1da5ae75f0a8a9572996b8e416a282d2c48f1b 100644
--- a/docs/examples/hybrid-layer.ipynb
+++ b/docs/examples/hybrid-layer.ipynb
@@ -11,7 +11,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Hybrid Layer in the Semantic Router library can improve decision making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to decision making based on the keywords contained in our utterances and user queries."
+ "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."
]
},
{
@@ -34,36 +34,37 @@
"metadata": {},
"outputs": [],
"source": [
- "!pip install -qU semantic-router==0.0.5"
+ "!pip install -qU semantic-router==0.0.6"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions."
+ "We start by defining a dictionary mapping s to example phrases that should trigger those s."
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ImportError",
+ "evalue": "cannot import name 'Route' from 'semantic_router.schema' (/Users/jakit/customers/aurelio/semantic-router/.venv/lib/python3.11/site-packages/semantic_router/schema.py)",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb Cell 7\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msemantic_router\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mschema\u001b[39;00m \u001b[39mimport\u001b[39;00m Route\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m politics \u001b[39m=\u001b[39m Route(\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mpolitics\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m utterances\u001b[39m=\u001b[39m[\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m ],\n\u001b[1;32m <a href='vscode-notebook-cell:/Users/jakit/customers/aurelio/semantic-router/docs/examples/hybrid-layer.ipynb#X10sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m )\n",
+ "\u001b[0;31mImportError\u001b[0m: cannot import name 'Route' from 'semantic_router.schema' (/Users/jakit/customers/aurelio/semantic-router/.venv/lib/python3.11/site-packages/semantic_router/schema.py)"
+ ]
+ }
+ ],
+ "source": [
+ "from semantic_router.schema import Route\n",
"\n",
- "os.environ[\"COHERE_API_KEY\"] = \"BQBiUqqjDRsYl1QKKux4JsqKdDkjyInS5T3Z3eJP\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from semantic_router.schema import Decision\n",
- "\n",
- "politics = Decision(\n",
+ "politics = Route(\n",
" name=\"politics\",\n",
" utterances=[\n",
" \"isn't politics the best thing ever\",\n",
@@ -89,7 +90,7 @@
"metadata": {},
"outputs": [],
"source": [
- "chitchat = Decision(\n",
+ "chitchat = Route(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
@@ -100,7 +101,7 @@
" ],\n",
")\n",
"\n",
- "chitchat = Decision(\n",
+ "chitchat = Route(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
@@ -111,7 +112,7 @@
" ],\n",
")\n",
"\n",
- "decisions = [politics, chitchat]"
+ "routes = [politics, chitchat]"
]
},
{
@@ -127,6 +128,7 @@
"metadata": {},
"outputs": [],
"source": [
+ "import os\n",
"from semantic_router.encoders import CohereEncoder\n",
"from getpass import getpass\n",
"\n",
@@ -141,7 +143,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
+ "Now we define the `RouteLayer`. When called, the route layer will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `RouteLayer` we need our `encoder` model and a list of `routes`."
]
},
{
@@ -150,9 +152,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from semantic_router.layer import HybridDecisionLayer\n",
+ "from semantic_router.hybrid_layer import HybridRouteLayer\n",
"\n",
- "dl = HybridDecisionLayer(encoder=encoder, decisions=decisions)"
+ "dl = HybridRouteLayer(encoder=encoder, routes=routes)"
]
},
{
@@ -197,7 +199,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
+ "version": "3.11.3"
}
},
"nbformat": 4,
diff --git a/poetry.lock b/poetry.lock
index 3bedc8de3502b726b4fdbc6d765f0c7b1ffacea9..b459e6bab91d1777c8b70090352de26bc1ab70e3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1065,6 +1065,53 @@ files = [
{file = "multidict-6.0.4.tar.gz", hash = "sha256:3666906492efb76453c0e7b97f2cf459b0682e7402c0489a95484965dbc1da49"},
]
+[[package]]
+name = "mypy"
+version = "1.7.1"
+description = "Optional static typing for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "mypy-1.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:12cce78e329838d70a204293e7b29af9faa3ab14899aec397798a4b41be7f340"},
+ {file = "mypy-1.7.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1484b8fa2c10adf4474f016e09d7a159602f3239075c7bf9f1627f5acf40ad49"},
+ {file = "mypy-1.7.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:31902408f4bf54108bbfb2e35369877c01c95adc6192958684473658c322c8a5"},
+ {file = "mypy-1.7.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f2c2521a8e4d6d769e3234350ba7b65ff5d527137cdcde13ff4d99114b0c8e7d"},
+ {file = "mypy-1.7.1-cp310-cp310-win_amd64.whl", hash = "sha256:fcd2572dd4519e8a6642b733cd3a8cfc1ef94bafd0c1ceed9c94fe736cb65b6a"},
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+ {file = "mypy-1.7.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ee5d62d28b854eb61889cde4e1dbc10fbaa5560cb39780c3995f6737f7e82120"},
+ {file = "mypy-1.7.1-cp39-cp39-win_amd64.whl", hash = "sha256:72cf32ce7dd3562373f78bd751f73c96cfb441de147cc2448a92c1a308bd0ca6"},
+ {file = "mypy-1.7.1-py3-none-any.whl", hash = "sha256:f7c5d642db47376a0cc130f0de6d055056e010debdaf0707cd2b0fc7e7ef30ea"},
+ {file = "mypy-1.7.1.tar.gz", hash = "sha256:fcb6d9afb1b6208b4c712af0dafdc650f518836065df0d4fb1d800f5d6773db2"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=1.0.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = ">=4.1.0"
+
+[package.extras]
+dmypy = ["psutil (>=4.0)"]
+install-types = ["pip"]
+mypyc = ["setuptools (>=50)"]
+reports = ["lxml"]
+
[[package]]
name = "mypy-extensions"
version = "1.0.0"
@@ -2055,4 +2102,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.10"
-content-hash = "b17b9fd9486d6c744c41a31ab54f7871daba1e2d4166fda228033c5858f6f9d8"
+content-hash = "58bf19052f05863cb4623e85a73de5758d581ff539cfb69f0920e57f6cb035d0"
diff --git a/pyproject.toml b/pyproject.toml
index b21cd485683a42c26724bba6c32724b197ec518e..5a8e18e06303a4406535d6640045d9cfc3e50768 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "semantic-router"
-version = "0.0.6"
+version = "0.0.7"
description = "Super fast semantic router for AI decision making"
authors = [
"James Briggs <james@aurelio.ai>",
@@ -29,6 +29,7 @@ pytest = "^7.4.3"
pytest-mock = "^3.12.0"
pytest-cov = "^4.1.0"
pytest-xdist = "^3.5.0"
+mypy = "^1.7.1"
[build-system]
requires = ["poetry-core"]
@@ -36,3 +37,6 @@ build-backend = "poetry.core.masonry.api"
[tool.ruff.per-file-ignores]
"*.ipynb" = ["E402"]
+
+[tool.mypy]
+ignore_missing_imports = true
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
index 734906f8554e11898fbefe8c95ef92a555dc5c8d..0c445bea3ff4efd8f3aa8950e2c772277d93b20c 100644
--- a/semantic_router/__init__.py
+++ b/semantic_router/__init__.py
@@ -1,3 +1,4 @@
-from .layer import DecisionLayer, HybridDecisionLayer
+from .hybrid_layer import HybridRouteLayer
+from .layer import RouteLayer
-__all__ = ["DecisionLayer", "HybridDecisionLayer"]
+__all__ = ["RouteLayer", "HybridRouteLayer"]
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index b6de1f89b80f42ea42d3abe76bbf61441936aa6e..632ebc7924a5a74088068bfb329a4e04c68cb6df 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -7,5 +7,5 @@ class BaseEncoder(BaseModel):
class Config:
arbitrary_types_allowed = True
- def __call__(self, docs: list[str]) -> list[float]:
+ def __call__(self, docs: list[str]) -> list[list[float]]:
raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index 0d498197b5e548ad3fd7cc0eade0b457f47d795f..c9da628e1493e53760f6c060dcd64e4dfccdc3d4 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -1,29 +1,36 @@
+from typing import Any
+
from pinecone_text.sparse import BM25Encoder as encoder
from semantic_router.encoders import BaseEncoder
class BM25Encoder(BaseEncoder):
- model: encoder | None = None
+ model: Any | None = None
idx_mapping: dict[int, int] | None = None
def __init__(self, name: str = "bm25"):
super().__init__(name=name)
- # initialize BM25 encoder with default params (trained on MSMarco)
self.model = encoder.default()
- self.idx_mapping = {
- idx: i
- for i, idx in enumerate(self.model.get_params()["doc_freq"]["indices"])
- }
+
+ params = self.model.get_params()
+ doc_freq = params["doc_freq"]
+ if isinstance(doc_freq, dict):
+ indices = doc_freq["indices"]
+ self.idx_mapping = {int(idx): i for i, idx in enumerate(indices)}
+ 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:
+ raise ValueError("Model or index mapping is not initialized.")
if len(docs) == 1:
sparse_dicts = self.model.encode_queries(docs)
elif len(docs) > 1:
sparse_dicts = self.model.encode_documents(docs)
else:
raise ValueError("No documents to encode.")
- # convert sparse dict to sparse vector
+
embeds = [[0.0] * len(self.idx_mapping)] * len(docs)
for i, output in enumerate(sparse_dicts):
indices = output["indices"]
@@ -32,9 +39,9 @@ class BM25Encoder(BaseEncoder):
if idx in self.idx_mapping:
position = self.idx_mapping[idx]
embeds[i][position] = val
- else:
- print(idx, "not in encoder.idx_mapping")
return embeds
def fit(self, docs: list[str]):
+ if self.model is None:
+ raise ValueError("Model is not initialized.")
self.model.fit(docs)
diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..dec6336e917d1a21ead95720d50f5f2e582aaa81
--- /dev/null
+++ b/semantic_router/hybrid_layer.py
@@ -0,0 +1,145 @@
+import numpy as np
+from numpy.linalg import norm
+from tqdm.auto import tqdm
+
+from semantic_router.encoders import (
+ BaseEncoder,
+ BM25Encoder,
+ CohereEncoder,
+ OpenAIEncoder,
+)
+from semantic_router.schema import Route
+from semantic_router.utils.logger import logger
+
+
+class HybridRouteLayer:
+ index = None
+ sparse_index = None
+ categories = None
+ score_threshold = 0.82
+
+ def __init__(
+ self, encoder: BaseEncoder, routes: list[Route] = [], alpha: float = 0.3
+ ):
+ self.encoder = encoder
+ self.sparse_encoder = BM25Encoder()
+ self.alpha = alpha
+ # decide on default threshold based on encoder
+ if isinstance(encoder, OpenAIEncoder):
+ self.score_threshold = 0.82
+ elif isinstance(encoder, CohereEncoder):
+ self.score_threshold = 0.3
+ else:
+ self.score_threshold = 0.82
+ # if routes list has been passed, we initialize index now
+ if routes:
+ # initialize index now
+ for route in tqdm(routes):
+ self._add_route(route=route)
+
+ def __call__(self, text: str) -> str | None:
+ results = self._query(text)
+ top_class, top_class_scores = self._semantic_classify(results)
+ passed = self._pass_threshold(top_class_scores, self.score_threshold)
+ if passed:
+ return top_class
+ else:
+ return None
+
+ def add(self, route: Route):
+ self._add_route(route=route)
+
+ def _add_route(self, route: Route):
+ # create embeddings
+ dense_embeds = np.array(self.encoder(route.utterances)) # * self.alpha
+ sparse_embeds = np.array(
+ self.sparse_encoder(route.utterances)
+ ) # * (1 - self.alpha)
+
+ # create route array
+ if self.categories is None:
+ self.categories = np.array([route.name] * len(route.utterances))
+ self.utterances = np.array(route.utterances)
+ else:
+ str_arr = np.array([route.name] * len(route.utterances))
+ self.categories = np.concatenate([self.categories, str_arr])
+ self.utterances = np.concatenate(
+ [self.utterances, np.array(route.utterances)]
+ )
+ # create utterance array (the dense index)
+ if self.index is None:
+ self.index = dense_embeds
+ else:
+ self.index = np.concatenate([self.index, dense_embeds])
+ # create sparse utterance array
+ if self.sparse_index is None:
+ self.sparse_index = sparse_embeds
+ else:
+ self.sparse_index = np.concatenate([self.sparse_index, sparse_embeds])
+
+ def _query(self, text: str, top_k: int = 5):
+ """Given some text, encodes and searches the index vector space to
+ retrieve the top_k most similar records.
+ """
+ # create dense query vector
+ xq_d = np.array(self.encoder([text]))
+ xq_d = np.squeeze(xq_d) # Reduce to 1d array.
+ # create sparse query vector
+ xq_s = np.array(self.sparse_encoder([text]))
+ xq_s = np.squeeze(xq_s)
+ # convex scaling
+ xq_d, xq_s = self._convex_scaling(xq_d, xq_s)
+
+ if self.index is not None 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 _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: sum(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/layer.py b/semantic_router/layer.py
index 1bb900fb78884becca16a37988751786d356a408..cb408c5c5f452b78e9750976d7c669a01028450a 100644
--- a/semantic_router/layer.py
+++ b/semantic_router/layer.py
@@ -1,23 +1,21 @@
import numpy as np
-from numpy.linalg import norm
-from tqdm.auto import tqdm
from semantic_router.encoders import (
BaseEncoder,
- BM25Encoder,
CohereEncoder,
OpenAIEncoder,
)
from semantic_router.linear import similarity_matrix, top_scores
-from semantic_router.schema import Decision
+from semantic_router.schema import Route
+from semantic_router.utils.logger import logger
-class DecisionLayer:
+class RouteLayer:
index = None
categories = None
score_threshold = 0.82
- def __init__(self, encoder: BaseEncoder, decisions: list[Decision] = []):
+ def __init__(self, encoder: BaseEncoder, routes: list[Route] = []):
self.encoder = encoder
# decide on default threshold based on encoder
if isinstance(encoder, OpenAIEncoder):
@@ -26,10 +24,10 @@ class DecisionLayer:
self.score_threshold = 0.3
else:
self.score_threshold = 0.82
- # if decisions list has been passed, we initialize index now
- if decisions:
+ # if routes list has been passed, we initialize index now
+ if routes:
# initialize index now
- self.add_decisions(decisions=decisions)
+ self.add_routes(routes=routes)
def __call__(self, text: str) -> str | None:
results = self._query(text)
@@ -40,18 +38,15 @@ class DecisionLayer:
else:
return None
- # def add(self, decision: Decision):
- # self.add_decision(decision=decision)
-
- def add_decision(self, decision: Decision):
+ def add_route(self, route: Route):
# create embeddings
- embeds = self.encoder(decision.utterances)
+ embeds = self.encoder(route.utterances)
- # create decision array
+ # create route array
if self.categories is None:
- self.categories = np.array([decision.name] * len(embeds))
+ self.categories = np.array([route.name] * len(embeds))
else:
- str_arr = np.array([decision.name] * len(embeds))
+ str_arr = np.array([route.name] * len(embeds))
self.categories = np.concatenate([self.categories, str_arr])
# create utterance array (the index)
if self.index is None:
@@ -60,22 +55,20 @@ class DecisionLayer:
embed_arr = np.array(embeds)
self.index = np.concatenate([self.index, embed_arr])
- def add_decisions(self, decisions: list[Decision]):
- # create embeddings for all decisions
+ def add_routes(self, routes: list[Route]):
+ # create embeddings for all routes
all_utterances = [
- utterance for decision in decisions for utterance in decision.utterances
+ utterance for route in routes for utterance in route.utterances
]
embedded_utterance = self.encoder(all_utterances)
- # create decision array
- decision_names = [
- decision.name for decision in decisions for _ in decision.utterances
- ]
- decision_array = np.array(decision_names)
+ # 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, decision_array])
+ np.concatenate([self.categories, route_array])
if self.categories is not None
- else decision_array
+ else route_array
)
# create utterance array (the index)
@@ -98,164 +91,33 @@ class DecisionLayer:
# calculate similarity matrix
sim = similarity_matrix(xq, self.index)
scores, idx = top_scores(sim, top_k)
- # get the utterance categories (decision names)
- decisions = self.categories[idx] if self.categories is not None else []
- return [
- {"decision": d, "score": s.item()} for d, s in zip(decisions, scores)
- ]
+ # 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 for route layer.")
return []
def _semantic_classify(self, query_results: list[dict]) -> tuple[str, list[float]]:
- scores_by_class = {}
+ scores_by_class: dict[str, list[float]] = {}
for result in query_results:
score = result["score"]
- decision = result["decision"]
- if decision in scores_by_class:
- scores_by_class[decision].append(score)
+ route = result["route"]
+ if route in scores_by_class:
+ scores_by_class[route].append(score)
else:
- scores_by_class[decision] = [score]
+ scores_by_class[route] = [score]
# Calculate total score for each class
- total_scores = {
- decision: sum(scores) for decision, scores in scores_by_class.items()
- }
+ total_scores = {route: sum(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
- return str(top_class), scores_by_class.get(top_class, [])
-
- def _pass_threshold(self, scores: list[float], threshold: float) -> bool:
- if scores:
- return max(scores) > threshold
+ if top_class is not None:
+ return str(top_class), scores_by_class.get(top_class, [])
else:
- return False
-
-
-class HybridDecisionLayer:
- index = None
- sparse_index = None
- categories = None
- score_threshold = 0.82
-
- def __init__(
- self, encoder: BaseEncoder, decisions: list[Decision] = [], alpha: float = 0.3
- ):
- self.encoder = encoder
- self.sparse_encoder = BM25Encoder()
- self.alpha = alpha
- # decide on default threshold based on encoder
- if isinstance(encoder, OpenAIEncoder):
- self.score_threshold = 0.82
- elif isinstance(encoder, CohereEncoder):
- self.score_threshold = 0.3
- else:
- self.score_threshold = 0.82
- # if decisions list has been passed, we initialize index now
- if decisions:
- # initialize index now
- for decision in tqdm(decisions):
- self._add_decision(decision=decision)
-
- def __call__(self, text: str) -> str | None:
- results = self._query(text)
- top_class, top_class_scores = self._semantic_classify(results)
- passed = self._pass_threshold(top_class_scores, self.score_threshold)
- if passed:
- return top_class
- else:
- return None
-
- def add(self, decision: Decision):
- self._add_decision(decision=decision)
-
- def _add_decision(self, decision: Decision):
- # create embeddings
- dense_embeds = np.array(self.encoder(decision.utterances)) # * self.alpha
- sparse_embeds = np.array(
- self.sparse_encoder(decision.utterances)
- ) # * (1 - self.alpha)
-
- # create decision array
- if self.categories is None:
- self.categories = np.array([decision.name] * len(decision.utterances))
- self.utterances = np.array(decision.utterances)
- else:
- str_arr = np.array([decision.name] * len(decision.utterances))
- self.categories = np.concatenate([self.categories, str_arr])
- self.utterances = np.concatenate(
- [self.utterances, np.array(decision.utterances)]
- )
- # create utterance array (the dense index)
- if self.index is None:
- self.index = dense_embeds
- else:
- self.index = np.concatenate([self.index, dense_embeds])
- # create sparse utterance array
- if self.sparse_index is None:
- self.sparse_index = sparse_embeds
- else:
- self.sparse_index = np.concatenate([self.sparse_index, sparse_embeds])
-
- def _query(self, text: str, top_k: int = 5):
- """Given some text, encodes and searches the index vector space to
- retrieve the top_k most similar records.
- """
- # create dense query vector
- xq_d = np.array(self.encoder([text]))
- xq_d = np.squeeze(xq_d) # Reduce to 1d array.
- # create sparse query vector
- xq_s = np.array(self.sparse_encoder([text]))
- xq_s = np.squeeze(xq_s)
- # convex scaling
- xq_d, xq_s = self._convex_scaling(xq_d, xq_s)
-
- if self.index is not None:
- # calculate dense vec similarity
- index_norm = norm(self.index, axis=1)
- xq_d_norm = norm(xq_d.T)
- sim_d = np.dot(self.index, xq_d.T) / (index_norm * xq_d_norm)
- # calculate sparse vec similarity
- sparse_norm = norm(self.sparse_index, axis=1)
- xq_s_norm = norm(xq_s.T)
- sim_s = np.dot(self.sparse_index, xq_s.T) / (sparse_norm * xq_s_norm)
- total_sim = sim_d + sim_s
- # get indices of top_k records
- top_k = min(top_k, total_sim.shape[0])
- idx = np.argpartition(total_sim, -top_k)[-top_k:]
- scores = total_sim[idx]
- # get the utterance categories (decision names)
- decisions = self.categories[idx] if self.categories is not None else []
- return [
- {"decision": d, "score": s.item()} for d, s in zip(decisions, scores)
- ]
- else:
- return []
-
- def _convex_scaling(self, dense: list[float], sparse: list[float]):
- # scale sparse and dense vecs
- dense = np.array(dense) * self.alpha
- sparse = np.array(sparse) * (1 - self.alpha)
- return dense, sparse
-
- def _semantic_classify(self, query_results: list[dict]) -> tuple[str, list[float]]:
- scores_by_class = {}
- for result in query_results:
- score = result["score"]
- decision = result["decision"]
- if decision in scores_by_class:
- scores_by_class[decision].append(score)
- else:
- scores_by_class[decision] = [score]
-
- # Calculate total score for each class
- total_scores = {
- decision: sum(scores) for decision, scores in scores_by_class.items()
- }
- top_class = max(total_scores, key=lambda x: total_scores[x], default=None)
-
- # Return the top class and its associated scores
- return str(top_class), scores_by_class.get(top_class, [])
+ logger.warning("No classification found for semantic classifier.")
+ return "", []
def _pass_threshold(self, scores: list[float], threshold: float) -> bool:
if scores:
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index 37a43dd41007634706caedc1b5c5dadd6d734bba..007cddcbeb2c9e464e02a6c7f6cd12d2e9769cbc 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -10,7 +10,7 @@ from semantic_router.encoders import (
)
-class Decision(BaseModel):
+class Route(BaseModel):
name: str
utterances: list[str]
description: str | None = None
@@ -38,19 +38,19 @@ class Encoder:
elif self.type == EncoderType.COHERE:
self.model = CohereEncoder(name)
- def __call__(self, texts: list[str]) -> list[float]:
+ def __call__(self, texts: list[str]) -> list[list[float]]:
return self.model(texts)
@dataclass
class SemanticSpace:
id: str
- decisions: list[Decision]
+ routes: list[Route]
encoder: str = ""
- def __init__(self, decisions: list[Decision] = []):
+ def __init__(self, routes: list[Route] = []):
self.id = ""
- self.decisions = decisions
+ self.routes = routes
- def add(self, decision: Decision):
- self.decisions.append(decision)
+ def add(self, route: Route):
+ self.routes.append(route)
diff --git a/semantic_router/utils/__init__.py b/semantic_router/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
index c198715133829ff4869167b80117c40dba72d4fb..e654d7bbc98070b6db16249c3164d515852e28e5 100644
--- a/tests/unit/encoders/test_bm25.py
+++ b/tests/unit/encoders/test_bm25.py
@@ -33,3 +33,22 @@ class TestBM25Encoder:
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()
+
+ 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):
+ bm25_encoder.model = None
+ with pytest.raises(ValueError):
+ bm25_encoder.fit(["test"])
diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..94720cd8d9567b19e78a016a03c2cc90b8f62d40
--- /dev/null
+++ b/tests/unit/test_hybrid_layer.py
@@ -0,0 +1,118 @@
+import pytest
+
+from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
+from semantic_router.hybrid_layer import HybridRouteLayer
+from semantic_router.schema import Route
+
+
+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],
+ }
+ return [mock_responses.get(u, [0, 0, 0]) for u in utterances]
+
+
+@pytest.fixture
+def base_encoder():
+ return BaseEncoder(name="test-encoder")
+
+
+@pytest.fixture
+def cohere_encoder(mocker):
+ mocker.patch.object(CohereEncoder, "__call__", side_effect=mock_encoder_call)
+ return CohereEncoder(name="test-cohere-encoder", cohere_api_key="test_api_key")
+
+
+@pytest.fixture
+def openai_encoder(mocker):
+ mocker.patch.object(OpenAIEncoder, "__call__", side_effect=mock_encoder_call)
+ return OpenAIEncoder(name="test-openai-encoder", openai_api_key="test_api_key")
+
+
+@pytest.fixture
+def routes():
+ return [
+ Route(name="Route 1", utterances=["Hello", "Hi"]),
+ Route(name="Route 2", utterances=["Goodbye", "Bye", "Au revoir"]),
+ ]
+
+
+class TestHybridRouteLayer:
+ def test_initialization(self, openai_encoder, routes):
+ route_layer = HybridRouteLayer(encoder=openai_encoder, routes=routes)
+ assert route_layer.index is not None and route_layer.categories is not None
+ assert route_layer.score_threshold == 0.82
+ assert len(route_layer.index) == 5
+ assert len(set(route_layer.categories)) == 2
+
+ def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
+ route_layer_cohere = HybridRouteLayer(encoder=cohere_encoder)
+ assert route_layer_cohere.score_threshold == 0.3
+
+ route_layer_openai = HybridRouteLayer(encoder=openai_encoder)
+ assert route_layer_openai.score_threshold == 0.82
+
+ def test_add_route(self, openai_encoder):
+ route_layer = HybridRouteLayer(encoder=openai_encoder)
+ route = Route(name="Route 3", utterances=["Yes", "No"])
+ route_layer.add(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
+
+ def test_add_multiple_routes(self, openai_encoder, routes):
+ route_layer = HybridRouteLayer(encoder=openai_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
+
+ def test_query_and_classification(self, openai_encoder, routes):
+ route_layer = HybridRouteLayer(encoder=openai_encoder, routes=routes)
+ query_result = route_layer("Hello")
+ assert query_result in ["Route 1", "Route 2"]
+
+ def test_query_with_no_index(self, openai_encoder):
+ route_layer = HybridRouteLayer(encoder=openai_encoder)
+ assert route_layer("Anything") is None
+
+ def test_semantic_classify(self, openai_encoder, routes):
+ route_layer = HybridRouteLayer(encoder=openai_encoder, routes=routes)
+ classification, score = route_layer._semantic_classify(
+ [
+ {"route": "Route 1", "score": 0.9},
+ {"route": "Route 2", "score": 0.1},
+ ]
+ )
+ assert classification == "Route 1"
+ assert score == [0.9]
+
+ def test_semantic_classify_multiple_routes(self, openai_encoder, routes):
+ route_layer = HybridRouteLayer(encoder=openai_encoder, routes=routes)
+ classification, score = route_layer._semantic_classify(
+ [
+ {"route": "Route 1", "score": 0.9},
+ {"route": "Route 2", "score": 0.1},
+ {"route": "Route 1", "score": 0.8},
+ ]
+ )
+ assert classification == "Route 1"
+ assert score == [0.9, 0.8]
+
+ def test_pass_threshold(self, openai_encoder):
+ route_layer = HybridRouteLayer(encoder=openai_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)
+ assert route_layer.score_threshold == 0.82
+
+
+# Add more tests for edge cases and error handling as needed.
diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py
index 4d919f912ae635269e9f7dca93ac85030f9dd7cd..66e0d53bb9350c77578682f9ea0742b1d3dfe0b2 100644
--- a/tests/unit/test_layer.py
+++ b/tests/unit/test_layer.py
@@ -1,12 +1,8 @@
import pytest
from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
-from semantic_router.layer import (
- DecisionLayer,
- HybridDecisionLayer,
-)
-
-from semantic_router.schema import Decision
+from semantic_router.layer import RouteLayer
+from semantic_router.schema import Route
def mock_encoder_call(utterances):
@@ -39,185 +35,94 @@ def openai_encoder(mocker):
@pytest.fixture
-def decisions():
+def routes():
return [
- Decision(name="Decision 1", utterances=["Hello", "Hi"]),
- Decision(name="Decision 2", utterances=["Goodbye", "Bye", "Au revoir"]),
+ Route(name="Route 1", utterances=["Hello", "Hi"]),
+ Route(name="Route 2", utterances=["Goodbye", "Bye", "Au revoir"]),
]
-class TestDecisionLayer:
- def test_initialization(self, openai_encoder, decisions):
- decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions)
- assert decision_layer.score_threshold == 0.82
- assert len(decision_layer.index) if decision_layer.index is not None else 0 == 5
+class TestRouteLayer:
+ def test_initialization(self, openai_encoder, routes):
+ route_layer = RouteLayer(encoder=openai_encoder, routes=routes)
+ assert route_layer.score_threshold == 0.82
+ assert len(route_layer.index) if route_layer.index is not None else 0 == 5
assert (
- len(set(decision_layer.categories))
- if decision_layer.categories is not None
+ len(set(route_layer.categories))
+ if route_layer.categories is not None
else 0 == 2
)
def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
- decision_layer_cohere = DecisionLayer(encoder=cohere_encoder)
- assert decision_layer_cohere.score_threshold == 0.3
-
- decision_layer_openai = DecisionLayer(encoder=openai_encoder)
- assert decision_layer_openai.score_threshold == 0.82
-
- def test_add_decision(self, openai_encoder):
- decision_layer = DecisionLayer(encoder=openai_encoder)
- decision1 = Decision(name="Decision 1", utterances=["Yes", "No"])
- decision2 = Decision(name="Decision 2", utterances=["Maybe", "Sure"])
-
- decision_layer.add_decision(decision=decision1)
- assert (
- decision_layer.index is not None and decision_layer.categories is not None
- )
- assert len(decision_layer.index) == 2
- assert len(set(decision_layer.categories)) == 1
- assert set(decision_layer.categories) == {"Decision 1"}
-
- decision_layer.add_decision(decision=decision2)
- assert len(decision_layer.index) == 4
- assert len(set(decision_layer.categories)) == 2
- assert set(decision_layer.categories) == {"Decision 1", "Decision 2"}
-
- def test_add_multiple_decisions(self, openai_encoder, decisions):
- decision_layer = DecisionLayer(encoder=openai_encoder)
- decision_layer.add_decisions(decisions=decisions)
- assert (
- decision_layer.index is not None and decision_layer.categories is not None
- )
- assert len(decision_layer.index) == 5
- assert len(set(decision_layer.categories)) == 2
-
- def test_query_and_classification(self, openai_encoder, decisions):
- decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions)
- query_result = decision_layer("Hello")
- assert query_result in ["Decision 1", "Decision 2"]
+ route_layer_cohere = RouteLayer(encoder=cohere_encoder)
+ assert route_layer_cohere.score_threshold == 0.3
+
+ route_layer_openai = RouteLayer(encoder=openai_encoder)
+ assert route_layer_openai.score_threshold == 0.82
+
+ def test_add_route(self, openai_encoder):
+ route_layer = RouteLayer(encoder=openai_encoder)
+ route1 = Route(name="Route 1", utterances=["Yes", "No"])
+ route2 = Route(name="Route 2", utterances=["Maybe", "Sure"])
+
+ route_layer.add_route(route=route1)
+ 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
+ assert set(route_layer.categories) == {"Route 1"}
+
+ route_layer.add_route(route=route2)
+ assert len(route_layer.index) == 4
+ assert len(set(route_layer.categories)) == 2
+ assert set(route_layer.categories) == {"Route 1", "Route 2"}
+
+ def test_add_multiple_routes(self, openai_encoder, routes):
+ route_layer = RouteLayer(encoder=openai_encoder)
+ route_layer.add_routes(routes=routes)
+ 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
+
+ def test_query_and_classification(self, openai_encoder, routes):
+ route_layer = RouteLayer(encoder=openai_encoder, routes=routes)
+ query_result = route_layer("Hello")
+ assert query_result in ["Route 1", "Route 2"]
def test_query_with_no_index(self, openai_encoder):
- decision_layer = DecisionLayer(encoder=openai_encoder)
- assert decision_layer("Anything") is None
+ route_layer = RouteLayer(encoder=openai_encoder)
+ assert route_layer("Anything") is None
- def test_semantic_classify(self, openai_encoder, decisions):
- decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions)
- classification, score = decision_layer._semantic_classify(
+ def test_semantic_classify(self, openai_encoder, routes):
+ route_layer = RouteLayer(encoder=openai_encoder, routes=routes)
+ classification, score = route_layer._semantic_classify(
[
- {"decision": "Decision 1", "score": 0.9},
- {"decision": "Decision 2", "score": 0.1},
+ {"route": "Route 1", "score": 0.9},
+ {"route": "Route 2", "score": 0.1},
]
)
- assert classification == "Decision 1"
+ assert classification == "Route 1"
assert score == [0.9]
- def test_semantic_classify_multiple_decisions(self, openai_encoder, decisions):
- decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions)
- classification, score = decision_layer._semantic_classify(
- [
- {"decision": "Decision 1", "score": 0.9},
- {"decision": "Decision 2", "score": 0.1},
- {"decision": "Decision 1", "score": 0.8},
- ]
- )
- assert classification == "Decision 1"
- assert score == [0.9, 0.8]
-
- def test_pass_threshold(self, openai_encoder):
- decision_layer = DecisionLayer(encoder=openai_encoder)
- assert not decision_layer._pass_threshold([], 0.5)
- assert decision_layer._pass_threshold([0.6, 0.7], 0.5)
-
- def test_failover_score_threshold(self, base_encoder):
- decision_layer = DecisionLayer(encoder=base_encoder)
- assert decision_layer.score_threshold == 0.82
-
-
-class TestHybridDecisionLayer:
- def test_initialization(self, openai_encoder, decisions):
- decision_layer = HybridDecisionLayer(
- encoder=openai_encoder, decisions=decisions
- )
- assert decision_layer.score_threshold == 0.82
- assert (
- decision_layer.index is not None and decision_layer.categories is not None
- )
- assert len(decision_layer.index) == 5
- assert len(set(decision_layer.categories)) == 2
-
- def test_initialization_different_encoders(self, cohere_encoder, openai_encoder):
- decision_layer_cohere = HybridDecisionLayer(encoder=cohere_encoder)
- assert decision_layer_cohere.score_threshold == 0.3
-
- decision_layer_openai = HybridDecisionLayer(encoder=openai_encoder)
- assert decision_layer_openai.score_threshold == 0.82
-
- def test_add_decision(self, openai_encoder):
- decision_layer = HybridDecisionLayer(encoder=openai_encoder)
- decision = Decision(name="Decision 3", utterances=["Yes", "No"])
- decision_layer.add(decision)
- assert (
- decision_layer.index is not None and decision_layer.categories is not None
- )
- assert len(decision_layer.index) == 2
- assert len(set(decision_layer.categories)) == 1
-
- def test_add_multiple_decisions(self, openai_encoder, decisions):
- decision_layer = HybridDecisionLayer(encoder=openai_encoder)
- for decision in decisions:
- decision_layer.add(decision)
- assert (
- decision_layer.index is not None and decision_layer.categories is not None
- )
- assert len(decision_layer.index) == 5
- assert len(set(decision_layer.categories)) == 2
-
- def test_query_and_classification(self, openai_encoder, decisions):
- decision_layer = HybridDecisionLayer(
- encoder=openai_encoder, decisions=decisions
- )
- query_result = decision_layer("Hello")
- assert query_result in ["Decision 1", "Decision 2"]
-
- def test_query_with_no_index(self, openai_encoder):
- decision_layer = HybridDecisionLayer(encoder=openai_encoder)
- assert decision_layer("Anything") is None
-
- def test_semantic_classify(self, openai_encoder, decisions):
- decision_layer = HybridDecisionLayer(
- encoder=openai_encoder, decisions=decisions
- )
- classification, score = decision_layer._semantic_classify(
- [
- {"decision": "Decision 1", "score": 0.9},
- {"decision": "Decision 2", "score": 0.1},
- ]
- )
- assert classification == "Decision 1"
- assert score == [0.9]
-
- def test_semantic_classify_multiple_decisions(self, openai_encoder, decisions):
- decision_layer = HybridDecisionLayer(
- encoder=openai_encoder, decisions=decisions
- )
- classification, score = decision_layer._semantic_classify(
+ def test_semantic_classify_multiple_routes(self, openai_encoder, routes):
+ route_layer = RouteLayer(encoder=openai_encoder, routes=routes)
+ classification, score = route_layer._semantic_classify(
[
- {"decision": "Decision 1", "score": 0.9},
- {"decision": "Decision 2", "score": 0.1},
- {"decision": "Decision 1", "score": 0.8},
+ {"route": "Route 1", "score": 0.9},
+ {"route": "Route 2", "score": 0.1},
+ {"route": "Route 1", "score": 0.8},
]
)
- assert classification == "Decision 1"
+ assert classification == "Route 1"
assert score == [0.9, 0.8]
def test_pass_threshold(self, openai_encoder):
- decision_layer = HybridDecisionLayer(encoder=openai_encoder)
- assert not decision_layer._pass_threshold([], 0.5)
- assert decision_layer._pass_threshold([0.6, 0.7], 0.5)
+ route_layer = RouteLayer(encoder=openai_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):
- decision_layer = HybridDecisionLayer(encoder=base_encoder)
- assert decision_layer.score_threshold == 0.82
+ route_layer = RouteLayer(encoder=base_encoder)
+ assert route_layer.score_threshold == 0.82
# Add more tests for edge cases and error handling as needed.
diff --git a/tests/unit/test_schema.py b/tests/unit/test_schema.py
index 2563bf0bc21b7769f96d1119fdc29b6afab66d1a..f471755c35796d33ac9329a2ddb3a20816230cda 100644
--- a/tests/unit/test_schema.py
+++ b/tests/unit/test_schema.py
@@ -2,10 +2,10 @@ import pytest
from semantic_router.schema import (
CohereEncoder,
- Decision,
Encoder,
EncoderType,
OpenAIEncoder,
+ Route,
SemanticSpace,
)
@@ -46,16 +46,14 @@ class TestSemanticSpaceDataclass:
def test_semanticspace_initialization(self):
semantic_space = SemanticSpace()
assert semantic_space.id == ""
- assert semantic_space.decisions == []
+ assert semantic_space.routes == []
- def test_semanticspace_add_decision(self):
- decision = Decision(
- name="test", utterances=["hello", "hi"], description="greeting"
- )
+ def test_semanticspace_add_route(self):
+ route = Route(name="test", utterances=["hello", "hi"], description="greeting")
semantic_space = SemanticSpace()
- semantic_space.add(decision)
+ semantic_space.add(route)
- assert len(semantic_space.decisions) == 1
- assert semantic_space.decisions[0].name == "test"
- assert semantic_space.decisions[0].utterances == ["hello", "hi"]
- assert semantic_space.decisions[0].description == "greeting"
+ assert len(semantic_space.routes) == 1
+ assert semantic_space.routes[0].name == "test"
+ assert semantic_space.routes[0].utterances == ["hello", "hi"]
+ assert semantic_space.routes[0].description == "greeting"
diff --git a/walkthrough.ipynb b/walkthrough.ipynb
index 2e9570b881948caab8ac1ef25be4795147f858ca..d31a88dc0da38dec04e019100bfc28815adf7906 100644
--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -11,7 +11,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The Semantic Router library can be used as a super fast decision making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make decisions. Cutting decision making time down from seconds to milliseconds."
+ "The Semantic Router library can be used as a super fast route making layer on top of LLMs. That means rather than waiting on a slow agent to decide what to do, we can use the magic of semantic vector space to make routes. Cutting route making time down from seconds to milliseconds."
]
},
{
@@ -41,7 +41,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions."
+ "We start by defining a dictionary mapping routes to example phrases that should trigger those routes."
]
},
{
@@ -50,9 +50,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from semantic_router.schema import Decision\n",
+ "from semantic_router.schema import Route\n",
"\n",
- "politics = Decision(\n",
+ "politics = Route(\n",
" name=\"politics\",\n",
" utterances=[\n",
" \"isn't politics the best thing ever\",\n",
@@ -77,7 +77,7 @@
"metadata": {},
"outputs": [],
"source": [
- "chitchat = Decision(\n",
+ "chitchat = Route(\n",
" name=\"chitchat\",\n",
" utterances=[\n",
" \"how's the weather today?\",\n",
@@ -88,7 +88,7 @@
" ],\n",
")\n",
"\n",
- "decisions = [politics, chitchat]"
+ "routes = [politics, chitchat]"
]
},
{
@@ -119,7 +119,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
+ "Now we define the `RouteLayer`. When called, the route layer will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `RouteLayer` we need our `encoder` model and a list of `routes`."
]
},
{
@@ -128,9 +128,9 @@
"metadata": {},
"outputs": [],
"source": [
- "from semantic_router.layer import DecisionLayer\n",
+ "from semantic_router.router import RouteLayer\n",
"\n",
- "dl = DecisionLayer(encoder=encoder, decisions=decisions)"
+ "dl = RouteLayer(encoder=encoder, routes=routes)"
]
},
{
@@ -162,7 +162,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Both are classified accurately, what if we send a query that is unrelated to our existing `Decision` objects?"
+ "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?"
]
},
{