diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..85742347a89803823c44a3e44aadfbce934263c9
--- /dev/null
+++ b/.env.example
@@ -0,0 +1 @@
+COHERE_API_KEY=
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index c9982c2eae4f656d31ccfad086ac443fee862d1b..96e79706bb1ee0711d3a0a9f586222a843f53df6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,13 @@
-.env
-mac.env
-
+__pycache__
+*.pyc
+.venv
+.DS_Store
+venv/
+/.vscode
**/__pycache__
+**/*.py[cod]
+
+# local env files
+.env*.local
+.env
+mac.env
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..a477bd488ad05487e6f225e16ddd10692289026e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Aurelio AI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
index 7761ea084613b543abd934f01c702b3f29b7e79d..64652ae08d097ab5e941fcf829a494e20dac3a7f 100644
--- a/README.md
+++ b/README.md
@@ -1 +1 @@
-# Decision Layer
\ No newline at end of file
+# Semantic Router
diff --git a/decision_layer/__init__.py b/decision_layer/__init__.py
deleted file mode 100644
index bb6fc2057c974f7d92a54308f28675c7d64e7575..0000000000000000000000000000000000000000
--- a/decision_layer/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from decision_layer.decision_layer import DecisionLayer
\ No newline at end of file
diff --git a/decision_layer/decision_layer.py b/decision_layer/decision_layer.py
deleted file mode 100644
index 31de29bd2acc810edad348b3d5d6c309f7582659..0000000000000000000000000000000000000000
--- a/decision_layer/decision_layer.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from decision_layer.encoders import BaseEncoder
-from decision_layer.schema import Decision
-import numpy as np
-from numpy.linalg import norm
-
-
-class DecisionLayer:
- index = None
- categories = None
-
- def __init__(self, encoder: BaseEncoder, decisions: list[Decision] = []):
- self.encoder = encoder
- # if decisions list has been passed, we initialize index now
- if decisions:
- # initialize index now
- for decision in decisions:
- self._add_decision(decision=decision)
-
- def __call__(self, text: str):
- results = self._query(text)
- raise NotImplementedError("To implement decision logic based on scores")
-
-
- def add(self, decision: Decision):
- self._add_decision(devision=decision)
-
- def _add_decision(self, decision: Decision):
- # create embeddings
- embeds = self.encoder(decision.utterances)
- # create decision array
- if self.categories is None:
- self.categories = np.array([decision.name]*len(embeds))
- else:
- str_arr = np.array([decision.name]*len(embeds))
- self.categories = np.concatenate([self.categories, str_arr])
- # create utterance array (the index)
- if self.index is None:
- self.index = np.array(embeds)
- else:
- embed_arr = np.array(embeds)
- self.index = np.concatenate([self.index, embed_arr])
-
- 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 query vector
- xq = np.array(self.encoder([text]))
- # calculate cosine similarities
- sim = np.dot(self.index, xq.T) / (norm(self.index)*norm(xq.T))
- # get indices of top_k records
- idx = np.argpartition(sim.T[0], -top_k)[-top_k:]
- scores = sim[idx]
- # get the utterance categories (decision names)
- decisions = self.categories[idx]
- return [
- {"decision": d, "score": s.item()} for d, s in zip(decisions, scores)
- ]
diff --git a/decision_layer/encoders/__init__.py b/decision_layer/encoders/__init__.py
deleted file mode 100644
index 4dcb46c03bde09f3f1216ebefadde52876800d1b..0000000000000000000000000000000000000000
--- a/decision_layer/encoders/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from decision_layer.encoders.base import BaseEncoder
-from decision_layer.encoders.cohere import CohereEncoder
-from decision_layer.encoders.huggingface import HuggingFaceEncoder
-from decision_layer.encoders.openai import OpenAIEncoder
\ No newline at end of file
diff --git a/decision_layer/encoders/cohere.py b/decision_layer/encoders/cohere.py
deleted file mode 100644
index 4700f52bcb0560766dfdfc504fbdbe1d1cfa4047..0000000000000000000000000000000000000000
--- a/decision_layer/encoders/cohere.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from decision_layer.encoders import BaseEncoder
-
-class CohereEncoder(BaseEncoder):
- def __init__(self, name: str):
- super().__init__(name)
-
- def __call__(self, texts: list[str]) -> list[float]:
- raise NotImplementedError
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 105b180554201e7de7763e7b6d0759f4306b660a..10ca8e278b3cfe4ec040000ee7754d921e8c4578 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
[[package]]
name = "aiohttp"
@@ -133,6 +133,35 @@ files = [
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
]
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+optional = false
+python-versions = "*"
+files = [
+ {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+ {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+
+[[package]]
+name = "asttokens"
+version = "2.4.1"
+description = "Annotate AST trees with source code positions"
+optional = false
+python-versions = "*"
+files = [
+ {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
+ {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+]
+
+[package.dependencies]
+six = ">=1.12.0"
+
+[package.extras]
+astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
+test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
+
[[package]]
name = "async-timeout"
version = "4.0.3"
@@ -173,6 +202,46 @@ files = [
{file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
]
+[[package]]
+name = "black"
+version = "23.11.0"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "black-23.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dbea0bb8575c6b6303cc65017b46351dc5953eea5c0a59d7b7e3a2d2f433a911"},
+ {file = "black-23.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:412f56bab20ac85927f3a959230331de5614aecda1ede14b373083f62ec24e6f"},
+ {file = "black-23.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d136ef5b418c81660ad847efe0e55c58c8208b77a57a28a503a5f345ccf01394"},
+ {file = "black-23.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:6c1cac07e64433f646a9a838cdc00c9768b3c362805afc3fce341af0e6a9ae9f"},
+ {file = "black-23.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cf57719e581cfd48c4efe28543fea3d139c6b6f1238b3f0102a9c73992cbb479"},
+ {file = "black-23.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:698c1e0d5c43354ec5d6f4d914d0d553a9ada56c85415700b81dc90125aac244"},
+ {file = "black-23.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:760415ccc20f9e8747084169110ef75d545f3b0932ee21368f63ac0fee86b221"},
+ {file = "black-23.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:58e5f4d08a205b11800332920e285bd25e1a75c54953e05502052738fe16b3b5"},
+ {file = "black-23.11.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:45aa1d4675964946e53ab81aeec7a37613c1cb71647b5394779e6efb79d6d187"},
+ {file = "black-23.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4c44b7211a3a0570cc097e81135faa5f261264f4dfaa22bd5ee2875a4e773bd6"},
+ {file = "black-23.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2a9acad1451632021ee0d146c8765782a0c3846e0e0ea46659d7c4f89d9b212b"},
+ {file = "black-23.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:fc7f6a44d52747e65a02558e1d807c82df1d66ffa80a601862040a43ec2e3142"},
+ {file = "black-23.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7f622b6822f02bfaf2a5cd31fdb7cd86fcf33dab6ced5185c35f5db98260b055"},
+ {file = "black-23.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:250d7e60f323fcfc8ea6c800d5eba12f7967400eb6c2d21ae85ad31c204fb1f4"},
+ {file = "black-23.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5133f5507007ba08d8b7b263c7aa0f931af5ba88a29beacc4b2dc23fcefe9c06"},
+ {file = "black-23.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:421f3e44aa67138ab1b9bfbc22ee3780b22fa5b291e4db8ab7eee95200726b07"},
+ {file = "black-23.11.0-py3-none-any.whl", hash = "sha256:54caaa703227c6e0c87b76326d0862184729a69b73d3b7305b6288e1d830067e"},
+ {file = "black-23.11.0.tar.gz", hash = "sha256:4c68855825ff432d197229846f971bc4d6666ce90492e5b02013bcaca4d9ab05"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
[[package]]
name = "certifi"
version = "2023.7.22"
@@ -184,6 +253,70 @@ files = [
{file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
]
+[[package]]
+name = "cffi"
+version = "1.16.0"
+description = "Foreign Function Interface for Python calling C code."
+optional = false
+python-versions = ">=3.8"
+files = [
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+ {file = "pyzmq-25.1.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fedbdc753827cf014c01dbbee9c3be17e5a208dcd1bf8641ce2cd29580d1f0d4"},
+ {file = "pyzmq-25.1.1-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bc16ac425cc927d0a57d242589f87ee093884ea4804c05a13834d07c20db203c"},
+ {file = "pyzmq-25.1.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11c1d2aed9079c6b0c9550a7257a836b4a637feb334904610f06d70eb44c56d2"},
+ {file = "pyzmq-25.1.1-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:e8a701123029cc240cea61dd2d16ad57cab4691804143ce80ecd9286b464d180"},
+ {file = "pyzmq-25.1.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:61706a6b6c24bdece85ff177fec393545a3191eeda35b07aaa1458a027ad1304"},
+ {file = "pyzmq-25.1.1.tar.gz", hash = "sha256:259c22485b71abacdfa8bf79720cd7bcf4b9d128b30ea554f01ae71fdbfdaa23"},
+]
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+
[[package]]
name = "regex"
version = "2023.10.3"
@@ -1001,6 +1662,32 @@ urllib3 = ">=1.21.1,<3"
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+[[package]]
+name = "ruff"
+version = "0.1.5"
+description = "An extremely fast Python linter and code formatter, written in Rust."
+optional = false
+python-versions = ">=3.7"
+files = [
+ {file = "ruff-0.1.5-py3-none-macosx_10_7_x86_64.whl", hash = "sha256:32d47fc69261c21a4c48916f16ca272bf2f273eb635d91c65d5cd548bf1f3d96"},
+ {file = "ruff-0.1.5-py3-none-macosx_10_9_x86_64.macosx_11_0_arm64.macosx_10_9_universal2.whl", hash = "sha256:171276c1df6c07fa0597fb946139ced1c2978f4f0b8254f201281729981f3c17"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17ef33cd0bb7316ca65649fc748acc1406dfa4da96a3d0cde6d52f2e866c7b39"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:b2c205827b3f8c13b4a432e9585750b93fd907986fe1aec62b2a02cf4401eee6"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bb408e3a2ad8f6881d0f2e7ad70cddb3ed9f200eb3517a91a245bbe27101d379"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:f20dc5e5905ddb407060ca27267c7174f532375c08076d1a953cf7bb016f5a24"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aafb9d2b671ed934998e881e2c0f5845a4295e84e719359c71c39a5363cccc91"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a4894dddb476597a0ba4473d72a23151b8b3b0b5f958f2cf4d3f1c572cdb7af7"},
+ {file = "ruff-0.1.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a00a7ec893f665ed60008c70fe9eeb58d210e6b4d83ec6654a9904871f982a2a"},
+ {file = "ruff-0.1.5-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:a8c11206b47f283cbda399a654fd0178d7a389e631f19f51da15cbe631480c5b"},
+ {file = "ruff-0.1.5-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:fa29e67b3284b9a79b1a85ee66e293a94ac6b7bb068b307a8a373c3d343aa8ec"},
+ {file = "ruff-0.1.5-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9b97fd6da44d6cceb188147b68db69a5741fbc736465b5cea3928fdac0bc1aeb"},
+ {file = "ruff-0.1.5-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:721f4b9d3b4161df8dc9f09aa8562e39d14e55a4dbaa451a8e55bdc9590e20f4"},
+ {file = "ruff-0.1.5-py3-none-win32.whl", hash = "sha256:f80c73bba6bc69e4fdc73b3991db0b546ce641bdcd5b07210b8ad6f64c79f1ab"},
+ {file = "ruff-0.1.5-py3-none-win_amd64.whl", hash = "sha256:c21fe20ee7d76206d290a76271c1af7a5096bc4c73ab9383ed2ad35f852a0087"},
+ {file = "ruff-0.1.5-py3-none-win_arm64.whl", hash = "sha256:82bfcb9927e88c1ed50f49ac6c9728dab3ea451212693fe40d08d314663e412f"},
+ {file = "ruff-0.1.5.tar.gz", hash = "sha256:5cbec0ef2ae1748fb194f420fb03fb2c25c3258c86129af7172ff8f198f125ab"},
+]
+
[[package]]
name = "safetensors"
version = "0.4.0"
@@ -1120,6 +1807,36 @@ tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
torch = ["safetensors[numpy]", "torch (>=1.10)"]
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+ {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+ {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "stack-data"
+version = "0.6.3"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+optional = false
+python-versions = "*"
+files = [
+ {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"},
+ {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"},
+]
+
+[package.dependencies]
+asttokens = ">=2.1.0"
+executing = ">=1.2.0"
+pure-eval = "*"
+
+[package.extras]
+tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
+
[[package]]
name = "tokenizers"
version = "0.14.1"
@@ -1235,6 +1952,26 @@ dev = ["tokenizers[testing]"]
docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
+[[package]]
+name = "tornado"
+version = "6.3.3"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+optional = false
+python-versions = ">= 3.8"
+files = [
+ {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:502fba735c84450974fec147340016ad928d29f1e91f49be168c0a4c18181e1d"},
+ {file = "tornado-6.3.3-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:805d507b1f588320c26f7f097108eb4023bbaa984d63176d1652e184ba24270a"},
+ {file = "tornado-6.3.3-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1bd19ca6c16882e4d37368e0152f99c099bad93e0950ce55e71daed74045908f"},
+ {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ac51f42808cca9b3613f51ffe2a965c8525cb1b00b7b2d56828b8045354f76a"},
+ {file = "tornado-6.3.3-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:71a8db65160a3c55d61839b7302a9a400074c9c753040455494e2af74e2501f2"},
+ {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:ceb917a50cd35882b57600709dd5421a418c29ddc852da8bcdab1f0db33406b0"},
+ {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_i686.whl", hash = "sha256:7d01abc57ea0dbb51ddfed477dfe22719d376119844e33c661d873bf9c0e4a16"},
+ {file = "tornado-6.3.3-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:9dc4444c0defcd3929d5c1eb5706cbe1b116e762ff3e0deca8b715d14bf6ec17"},
+ {file = "tornado-6.3.3-cp38-abi3-win32.whl", hash = "sha256:65ceca9500383fbdf33a98c0087cb975b2ef3bfb874cb35b8de8740cf7f41bd3"},
+ {file = "tornado-6.3.3-cp38-abi3-win_amd64.whl", hash = "sha256:22d3c2fa10b5793da13c807e6fc38ff49a4f6e1e3868b0a6f4164768bb8e20f5"},
+ {file = "tornado-6.3.3.tar.gz", hash = "sha256:e7d8db41c0181c80d76c982aacc442c0783a2c54d6400fe028954201a2e032fe"},
+]
+
[[package]]
name = "tqdm"
version = "4.66.1"
@@ -1255,6 +1992,21 @@ notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
+[[package]]
+name = "traitlets"
+version = "5.13.0"
+description = "Traitlets Python configuration system"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "traitlets-5.13.0-py3-none-any.whl", hash = "sha256:baf991e61542da48fe8aef8b779a9ea0aa38d8a54166ee250d5af5ecf4486619"},
+ {file = "traitlets-5.13.0.tar.gz", hash = "sha256:9b232b9430c8f57288c1024b34a8f0251ddcc47268927367a0dd3eeaca40deb5"},
+]
+
+[package.extras]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"]
+test = ["argcomplete (>=3.0.3)", "mypy (>=1.6.0)", "pre-commit", "pytest (>=7.0,<7.5)", "pytest-mock", "pytest-mypy-testing"]
+
[[package]]
name = "transformers"
version = "4.34.1"
@@ -1352,6 +2104,17 @@ secure = ["certifi", "cryptography (>=1.9)", "idna (>=2.0.0)", "pyopenssl (>=17.
socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
zstd = ["zstandard (>=0.18.0)"]
+[[package]]
+name = "wcwidth"
+version = "0.2.9"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+ {file = "wcwidth-0.2.9-py2.py3-none-any.whl", hash = "sha256:9a929bd8380f6cd9571a968a9c8f4353ca58d7cd812a4822bba831f8d685b223"},
+ {file = "wcwidth-0.2.9.tar.gz", hash = "sha256:a675d1a4a2d24ef67096a04b85b02deeecd8e226f57b5e3a72dbb9ed99d27da8"},
+]
+
[[package]]
name = "yarl"
version = "1.9.2"
@@ -1457,4 +2220,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
[metadata]
lock-version = "2.0"
python-versions = "^3.11"
-content-hash = "39d35371d73e4114e1cbccb7a1f4fc0ed98797c5a175acf4fe24444927b511d4"
+content-hash = "2fa8cf964dc36d9571bf4e08ce9e46a09942de868c541c43a2c2d84404f1abdc"
diff --git a/pyproject.toml b/pyproject.toml
index 011cc62674989b4ee88fe7d851542f7d99244a80..3ab4f232c2b1e5435b59a2a4d66b1161a509d7b8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,8 +1,11 @@
[tool.poetry]
-name = "decision-layer"
+name = "semantic-router"
version = "0.0.1"
-description = "Super fast decision layer for AI"
-authors = ["James Briggs <james@aurelio.ai>"]
+description = "Super fast semantic router for AI decision making"
+authors = [
+ "James Briggs <james@aurelio.ai>",
+ "Siraj Aizlewood <siraj@aurelio.ai>"
+]
readme = "README.md"
[tool.poetry.dependencies]
@@ -15,6 +18,12 @@ openai = "^0.28.1"
transformers = "^4.34.1"
cohere = "^4.32"
+
+[tool.poetry.group.dev.dependencies]
+ipykernel = "^6.26.0"
+ruff = "^0.1.5"
+black = "^23.11.0"
+
[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"
diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e65f468bb83c972c67438b2017743ac8d96cab2
--- /dev/null
+++ b/semantic_router/encoders/__init__.py
@@ -0,0 +1,6 @@
+from .base import BaseEncoder
+from .cohere import CohereEncoder
+from .huggingface import HuggingFaceEncoder
+from .openai import OpenAIEncoder
+
+__all__ = ["BaseEncoder", "CohereEncoder", "HuggingFaceEncoder", "OpenAIEncoder"]
diff --git a/decision_layer/encoders/base.py b/semantic_router/encoders/base.py
similarity index 72%
rename from decision_layer/encoders/base.py
rename to semantic_router/encoders/base.py
index 7f291a3e2d4a1188dafae3250a341623e1d59042..4b5ca40d338e279b5ee8cb14eeb0a1c0c042d08c 100644
--- a/decision_layer/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -8,4 +8,4 @@ class BaseEncoder(BaseModel):
arbitrary_types_allowed = True
def __call__(self, texts: list[str]) -> list[float]:
- pass
+ raise NotImplementedError("Subclasses must implement this method")
diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ed2ecc0c87e9e1aa28990faeeff4c437b088cc4
--- /dev/null
+++ b/semantic_router/encoders/cohere.py
@@ -0,0 +1,28 @@
+import os
+
+import cohere
+
+from semantic_router.encoders import BaseEncoder
+
+
+class CohereEncoder(BaseEncoder):
+ client: cohere.Client | None
+
+ def __init__(
+ self, name: str = "embed-english-v3.0", cohere_api_key: str | None = None
+ ):
+ super().__init__(name=name)
+ cohere_api_key = cohere_api_key or os.getenv("COHERE_API_KEY")
+ if cohere_api_key is None:
+ raise ValueError("Cohere API key cannot be 'None'.")
+ self.client = cohere.Client(cohere_api_key)
+
+ def __call__(self, texts: list[str]) -> list[list[float]]:
+ if self.client is None:
+ raise ValueError("Cohere client is not initialized.")
+ if len(texts) == 1:
+ input_type = "search_query"
+ else:
+ input_type = "search_document"
+ embeds = self.client.embed(texts, input_type=input_type, model=self.name)
+ return embeds.embeddings
diff --git a/decision_layer/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
similarity index 54%
rename from decision_layer/encoders/huggingface.py
rename to semantic_router/encoders/huggingface.py
index ea39bd2c854ce795eff0e776f09281fb461c67c2..258c5037a822d5b0054755722a997c0feae749cd 100644
--- a/decision_layer/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -1,9 +1,9 @@
-from decision_layer.encoders import BaseEncoder
+from semantic_router.encoders import BaseEncoder
class HuggingFaceEncoder(BaseEncoder):
def __init__(self, name: str):
- super().__init__(name)
+ self.name = name
def __call__(self, texts: list[str]) -> list[float]:
- raise NotImplementedError
\ No newline at end of file
+ raise NotImplementedError
diff --git a/decision_layer/encoders/openai.py b/semantic_router/encoders/openai.py
similarity index 59%
rename from decision_layer/encoders/openai.py
rename to semantic_router/encoders/openai.py
index 7d79a1750342c3fa12b5665ea86b741dd19c18cf..87feec4ca3fdbc93d777e4b6d91d7aa0b16bee07 100644
--- a/decision_layer/encoders/openai.py
+++ b/semantic_router/encoders/openai.py
@@ -1,8 +1,10 @@
import os
+from time import sleep
-from decision_layer.encoders import BaseEncoder
import openai
-from time import time
+from openai.error import RateLimitError
+
+from semantic_router.encoders import BaseEncoder
class OpenAIEncoder(BaseEncoder):
@@ -16,20 +18,18 @@ class OpenAIEncoder(BaseEncoder):
"""Encode a list of texts using the OpenAI API. Returns a list of
vector embeddings.
"""
- passed = False
+ res = None
# exponential backoff in case of RateLimitError
for j in range(5):
try:
- # create embeddings
- res = openai.Embedding.create(
- input=texts, engine=self.name
- )
- passed = True
- except openai.error.RateLimitError:
- time.sleep(2 ** j)
- if not passed:
- raise openai.error.RateLimitError
+ res = openai.Embedding.create(input=texts, engine=self.name)
+ if isinstance(res, dict) and "data" in res:
+ break
+ except RateLimitError:
+ sleep(2**j)
+ if not res or not isinstance(res, dict) or "data" not in res:
+ raise ValueError("Failed to create embeddings.")
+
# get embeddings
embeds = [r["embedding"] for r in res["data"]]
return embeds
-
\ No newline at end of file
diff --git a/semantic_router/layer.py b/semantic_router/layer.py
new file mode 100644
index 0000000000000000000000000000000000000000..089f2793703c4320b6c01f94764f16b2dbeaff41
--- /dev/null
+++ b/semantic_router/layer.py
@@ -0,0 +1,104 @@
+import numpy as np
+from numpy.linalg import norm
+
+from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder
+from semantic_router.schema import Decision
+
+
+class DecisionLayer:
+ index = None
+ categories = None
+ similarity_threshold = 0.82
+
+ def __init__(self, encoder: BaseEncoder, decisions: list[Decision] = []):
+ self.encoder = encoder
+ # decide on default threshold based on encoder
+ if isinstance(encoder, OpenAIEncoder):
+ self.similarity_threshold = 0.82
+ elif isinstance(encoder, CohereEncoder):
+ self.similarity_threshold = 0.3
+ else:
+ self.similarity_threshold = 0.82
+ # if decisions list has been passed, we initialize index now
+ if decisions:
+ # initialize index now
+ for decision in decisions:
+ self._add_decision(decision=decision)
+
+ def __call__(self, text: str) -> str | None:
+ results = self._query(text)
+ top_class, top_class_scores = self._semantic_classify(results)
+ passed = self._pass_threshold(top_class_scores, self.similarity_threshold)
+ 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
+ embeds = self.encoder(decision.utterances)
+
+ # create decision array
+ if self.categories is None:
+ self.categories = np.array([decision.name] * len(embeds))
+ else:
+ str_arr = np.array([decision.name] * len(embeds))
+ self.categories = np.concatenate([self.categories, str_arr])
+ # create utterance array (the index)
+ if self.index is None:
+ self.index = np.array(embeds)
+ else:
+ embed_arr = np.array(embeds)
+ self.index = np.concatenate([self.index, embed_arr])
+
+ 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 query vector
+ xq = np.array(self.encoder([text]))
+ xq = np.squeeze(xq) # Reduce to 1d array.
+
+ if self.index is not None:
+ index_norm = norm(self.index, axis=1)
+ xq_norm = norm(xq.T)
+ sim = np.dot(self.index, xq.T) / (index_norm * xq_norm)
+ # get indices of top_k records
+ top_k = min(top_k, sim.shape[0])
+ idx = np.argpartition(sim, -top_k)[-top_k:]
+ scores = 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 _semantic_classify(self, query_results: list[dict]) -> tuple[str, list[float]]:
+ scores_by_class = {}
+ for result in query_results:
+ score = result["score"]
+ decision = result["decision"]
+ if decision in scores_by_class:
+ scores_by_class[decision].append(score)
+ else:
+ scores_by_class[decision] = [score]
+
+ # Calculate total score for each class
+ total_scores = {
+ decision: sum(scores) for decision, scores in scores_by_class.items()
+ }
+ top_class = max(total_scores, key=lambda x: total_scores[x], default=None)
+
+ # Return the top class and its associated scores
+ return str(top_class), scores_by_class.get(top_class, [])
+
+ def _pass_threshold(self, scores: list[float], threshold: float) -> bool:
+ if scores:
+ return max(scores) > threshold
+ else:
+ return False
diff --git a/decision_layer/schema.py b/semantic_router/schema.py
similarity index 93%
rename from decision_layer/schema.py
rename to semantic_router/schema.py
index 530a419f572325282ab9a8a860d2f01b3c42a71d..439f232280e02eb26325d193f1e1ebab766af11a 100644
--- a/decision_layer/schema.py
+++ b/semantic_router/schema.py
@@ -1,11 +1,13 @@
from enum import Enum
+
from pydantic import BaseModel
from pydantic.dataclasses import dataclass
-from decision_layer.encoders import (
+
+from semantic_router.encoders import (
BaseEncoder,
+ CohereEncoder,
HuggingFaceEncoder,
OpenAIEncoder,
- CohereEncoder,
)
@@ -14,11 +16,13 @@ class Decision(BaseModel):
utterances: list[str]
description: str | None = None
+
class EncoderType(Enum):
HUGGINGFACE = "huggingface"
OPENAI = "openai"
COHERE = "cohere"
+
@dataclass
class Encoder:
type: EncoderType
@@ -38,6 +42,7 @@ class Encoder:
def __call__(self, texts: list[str]) -> list[float]:
return self.model(texts)
+
@dataclass
class SemanticSpace:
id: str
@@ -49,4 +54,4 @@ class SemanticSpace:
self.decisions = decisions
def add(self, decision: Decision):
- self.decisions.append(decision)
\ No newline at end of file
+ self.decisions.append(decision)
diff --git a/walkthrough.ipynb b/walkthrough.ipynb
index 700f577c818e319479c9de8d88c0b5e7af59a879..1b100e867e3e58d9f8a5c6df51f044463769e34e 100644
--- a/walkthrough.ipynb
+++ b/walkthrough.ipynb
@@ -1,41 +1,43 @@
{
"cells": [
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Decision Layer Walkthrough"
+ "# Semantic Router Walkthrough"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
- "The decision layer library can be used as a super fast decision making layer on top of LLMs. That means that 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 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."
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting Started"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We start by installing the library:"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "!pip install -qU \\\n",
- " decision-layer"
+ "!pip install -qU semantic-router==0.0.1"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -44,11 +46,11 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "from decision_layer.schema import Decision\n",
+ "from semantic_router.schema import Decision\n",
"\n",
"politics = Decision(\n",
" name=\"politics\",\n",
@@ -72,7 +74,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -94,71 +96,89 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we initialize our embedding model (we will add support for Hugging Face):"
+ "Now we initialize our embedding model:"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "from decision_layer.encoders import OpenAIEncoder\n",
+ "from semantic_router.encoders import CohereEncoder\n",
+ "from getpass import getpass\n",
+ "\n",
"import os\n",
"\n",
- "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n",
- "encoder = OpenAIEncoder(name=\"text-embedding-ada-002\")"
+ "# os.environ[\"COHERE_API_KEY\"] = getpass(\"Enter Cohere API Key: \")\n",
+ "os.environ[\"COHERE_API_KEY\"]\n",
+ "encoder = CohereEncoder()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — for now we can only `_query` and get the most similar `Decision` `utterances`."
+ "Now we define the `DecisionLayer`. When called, the decision layer will consume text (a query) and output the category (`Decision`) it belongs to — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`."
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "from decision_layer import DecisionLayer\n",
+ "from semantic_router import DecisionLayer\n",
"\n",
"dl = DecisionLayer(encoder=encoder, decisions=decisions)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we can test it:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"don't you love politics?\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"how's the weather today?\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Both are classified accurately, what if we send a query that is unrelated to our existing `Decision` objects?"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'decision': 'politics', 'score': 0.24968127755063652},\n",
- " {'decision': 'politics', 'score': 0.2536216026530966},\n",
- " {'decision': 'politics', 'score': 0.27568433588684954},\n",
- " {'decision': 'politics', 'score': 0.27732789989574913},\n",
- " {'decision': 'politics', 'score': 0.28110307885950714}]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "out = dl._query(\"don't you love politics?\")\n",
- "out"
- ]
- },
- {
- "attachments": {},
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dl(\"I'm interested in learning about llama 2\")"
+ ]
+ },
+ {
"cell_type": "markdown",
"metadata": {},
"source": [
- "---"
+ "In this case, we return `None` because no matches were identified."
]
}
],
@@ -178,9 +198,8 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.5"
- },
- "orig_nbformat": 4
+ "version": "3.11.3"
+ }
},
"nbformat": 4,
"nbformat_minor": 2