diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..85742347 --- /dev/null +++ b/.env.example @@ -0,0 +1 @@ +COHERE_API_KEY= \ No newline at end of file diff --git a/.gitignore b/.gitignore index c9982c2e..96e79706 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 00000000..a477bd48 --- /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 7761ea08..64652ae0 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 bb6fc205..00000000 --- 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 31de29bd..00000000 --- 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 4dcb46c0..00000000 --- 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 4700f52b..00000000 --- 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 105b1805..10ca8e27 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. 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"James Briggs ", + "Siraj Aizlewood " +] 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 00000000..e69de29b diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py new file mode 100644 index 00000000..4e65f468 --- /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 7f291a3e..4b5ca40d 100644 --- a/decision_layer/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -8,4 +8,4 @@ class Config: 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 00000000..0ed2ecc0 --- /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 ea39bd2c..258c5037 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 7d79a175..87feec4c 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 @@ def __call__(self, texts: list[str]) -> list[list[float]]: """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 00000000..089f2793 --- /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 530a419f..439f2322 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 @@ def __init__(self, type: str, name: str): def __call__(self, texts: list[str]) -> list[float]: return self.model(texts) + @dataclass class SemanticSpace: id: str @@ -49,4 +54,4 @@ def __init__(self, decisions: list[Decision] = []): 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 700f577c..1b100e86 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