diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb new file mode 100644 index 00000000..98fccf69 --- /dev/null +++ b/docs/examples/hybrid-layer.ipynb @@ -0,0 +1,205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Semantic Router: Hybrid Layer" + ] + }, + { + "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." + ] + }, + { + "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 semantic-router==0.0.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by defining a dictionary mapping decisions to example phrases that should trigger those decisions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\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", + " name=\"politics\",\n", + " utterances=[\n", + " \"isn't politics the best thing ever\",\n", + " \"why don't you tell me about your political opinions\",\n", + " \"don't you just love the president\",\n", + " \"don't you just hate the president\",\n", + " \"they're going to destroy this country!\",\n", + " \"they will save the country!\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define another for good measure:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chitchat = Decision(\n", + " name=\"chitchat\",\n", + " utterances=[\n", + " \"how's the weather today?\",\n", + " \"how are things going?\",\n", + " \"lovely weather today\",\n", + " \"the weather is horrendous\",\n", + " \"let's go to the chippy\",\n", + " ],\n", + ")\n", + "\n", + "chitchat = Decision(\n", + " name=\"chitchat\",\n", + " utterances=[\n", + " \"how's the weather today?\",\n", + " \"how are things going?\",\n", + " \"lovely weather today\",\n", + " \"the weather is horrendous\",\n", + " \"let's go to the chippy\",\n", + " ],\n", + ")\n", + "\n", + "decisions = [politics, chitchat]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we initialize our embedding model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.encoders import CohereEncoder\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n", + " \"Enter Cohere API Key: \"\n", + ")\n", + "\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 — to initialize a `DecisionLayer` we need our `encoder` model and a list of `decisions`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.layer import HybridDecisionLayer\n", + "\n", + "dl = HybridDecisionLayer(encoder=encoder, decisions=decisions)" + ] + }, + { + "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": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "decision-layer", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index c748530e..8aaee95e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,112 +1,100 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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hash = "sha256:271d63396460b6607b588555ea27a1a02b717ca2e3f2cf53bdde4013d7790929"}, + {file = "yarl-1.9.3.tar.gz", hash = "sha256:4a14907b597ec55740f63e52d7fee0e9ee09d5b9d57a4f399a7423268e457b57"}, ] [package.dependencies] @@ -1779,4 +1987,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "c0b2fcab1a4add0d43415d41359db59dd15ce87f14f5227e8f0cfb956a679dcd" +content-hash = "b751e9eced707d903729ec6f473ec547e00bd7ef98e7536da003e5d2f4a80783" diff --git a/pyproject.toml b/pyproject.toml index ce9aa8f0..76ac7539 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,12 @@ [tool.poetry] name = "semantic-router" -version = "0.0.4" +version = "0.0.5" description = "Super fast semantic router for AI decision making" authors = [ "James Briggs ", "Siraj Aizlewood ", "Simonas Jakubonis ", + "Luca Mannini ", "Bogdan Buduroiu " ] readme = "README.md" @@ -15,7 +16,8 @@ python = "^3.10" pydantic = "^1.8.2" openai = "^0.28.1" cohere = "^4.32" -numpy = "^1.26.2" +numpy = "^1.25.2" +pinecone-text = "^0.7.0" [tool.poetry.group.dev.dependencies] diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py index e69de29b..734906f8 100644 --- a/semantic_router/__init__.py +++ b/semantic_router/__init__.py @@ -0,0 +1,3 @@ +from .layer import DecisionLayer, HybridDecisionLayer + +__all__ = ["DecisionLayer", "HybridDecisionLayer"] diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py index 3fc94815..0c86ce7c 100644 --- a/semantic_router/encoders/__init__.py +++ b/semantic_router/encoders/__init__.py @@ -1,5 +1,6 @@ from .base import BaseEncoder from .cohere import CohereEncoder from .openai import OpenAIEncoder +from .bm25 import BM25Encoder -__all__ = ["BaseEncoder", "CohereEncoder", "OpenAIEncoder"] +__all__ = ["BaseEncoder", "CohereEncoder", "OpenAIEncoder", "BM25Encoder"] diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index 4b5ca40d..b6de1f89 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, texts: list[str]) -> list[float]: + def __call__(self, docs: list[str]) -> list[float]: raise NotImplementedError("Subclasses must implement this method") diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py new file mode 100644 index 00000000..0d498197 --- /dev/null +++ b/semantic_router/encoders/bm25.py @@ -0,0 +1,40 @@ +from pinecone_text.sparse import BM25Encoder as encoder + +from semantic_router.encoders import BaseEncoder + + +class BM25Encoder(BaseEncoder): + model: encoder | 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"]) + } + + def __call__(self, docs: list[str]) -> list[list[float]]: + 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"] + values = output["values"] + for idx, val in zip(indices, values): + if idx in self.idx_mapping: + position = self.idx_mapping[idx] + embeds[i][position] = val + else: + print(idx, "not in encoder.idx_mapping") + return embeds + + def fit(self, docs: list[str]): + self.model.fit(docs) diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py index bea75ba2..fd20fa75 100644 --- a/semantic_router/encoders/cohere.py +++ b/semantic_router/encoders/cohere.py @@ -17,8 +17,8 @@ def __init__( raise ValueError("Cohere API key cannot be 'None'.") self.client = cohere.Client(cohere_api_key) - def __call__(self, texts: list[str]) -> list[list[float]]: + def __call__(self, docs: list[str]) -> list[list[float]]: if self.client is None: raise ValueError("Cohere client is not initialized.") - embeds = self.client.embed(texts, input_type="search_query", model=self.name) + embeds = self.client.embed(docs, input_type="search_query", model=self.name) return embeds.embeddings diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py index 87feec4c..5700c800 100644 --- a/semantic_router/encoders/openai.py +++ b/semantic_router/encoders/openai.py @@ -14,7 +14,7 @@ def __init__(self, name: str, openai_api_key: str | None = None): if openai.api_key is None: raise ValueError("OpenAI API key cannot be 'None'.") - def __call__(self, texts: list[str]) -> list[list[float]]: + def __call__(self, docs: list[str]) -> list[list[float]]: """Encode a list of texts using the OpenAI API. Returns a list of vector embeddings. """ @@ -22,7 +22,7 @@ def __call__(self, texts: list[str]) -> list[list[float]]: # exponential backoff in case of RateLimitError for j in range(5): try: - res = openai.Embedding.create(input=texts, engine=self.name) + res = openai.Embedding.create(input=docs, engine=self.name) if isinstance(res, dict) and "data" in res: break except RateLimitError: diff --git a/semantic_router/layer.py b/semantic_router/layer.py index 5add0784..adff961c 100644 --- a/semantic_router/layer.py +++ b/semantic_router/layer.py @@ -1,6 +1,13 @@ import numpy as np +from numpy.linalg import norm +from tqdm.auto import tqdm -from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder +from semantic_router.encoders import ( + BaseEncoder, + CohereEncoder, + OpenAIEncoder, + BM25Encoder, +) from semantic_router.linear import similarity_matrix, top_scores from semantic_router.schema import Decision @@ -8,27 +15,27 @@ class DecisionLayer: index = None categories = None - similarity_threshold = 0.82 + score_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 + self.score_threshold = 0.82 elif isinstance(encoder, CohereEncoder): - self.similarity_threshold = 0.3 + self.score_threshold = 0.3 else: - self.similarity_threshold = 0.82 + self.score_threshold = 0.82 # if decisions list has been passed, we initialize index now if decisions: # initialize index now - for decision in decisions: + 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.similarity_threshold) + passed = self._pass_threshold(top_class_scores, self.score_threshold) if passed: return top_class else: @@ -98,3 +105,135 @@ def _pass_threshold(self, scores: list[float], threshold: float) -> bool: return max(scores) > threshold 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, []) + + def _pass_threshold(self, scores: list[float], threshold: float) -> bool: + if scores: + return max(scores) > threshold + else: + return False diff --git a/semantic_router/schema.py b/semantic_router/schema.py index b0de34d0..37a43dd4 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -17,9 +17,9 @@ class Decision(BaseModel): class EncoderType(Enum): + HUGGINGFACE = "huggingface" OPENAI = "openai" COHERE = "cohere" - HUGGINGFACE = "huggingface" @dataclass diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py new file mode 100644 index 00000000..c1987151 --- /dev/null +++ b/tests/unit/encoders/test_bm25.py @@ -0,0 +1,35 @@ +import pytest + +from semantic_router.encoders import BM25Encoder + + +@pytest.fixture +def bm25_encoder(): + return BM25Encoder() + + +class TestBM25Encoder: + def test_initialization(self, bm25_encoder): + assert len(bm25_encoder.idx_mapping) != 0 + + def test_fit(self, bm25_encoder): + bm25_encoder.fit(["some docs", "and more docs", "and even more docs"]) + assert len(bm25_encoder.idx_mapping) != 0 + + def test_call_method(self, bm25_encoder): + result = bm25_encoder(["test"]) + assert isinstance(result, list), "Result should be a list" + assert all( + isinstance(sublist, list) for sublist in result + ), "Each item in result should be a list" + + def test_call_method_no_docs(self, bm25_encoder): + with pytest.raises(ValueError): + bm25_encoder([]) + + def test_call_method_no_word(self, bm25_encoder): + result = bm25_encoder(["doc with fake word gta5jabcxyz"]) + assert isinstance(result, list), "Result should be a list" + assert all( + isinstance(sublist, list) for sublist in result + ), "Each item in result should be a list" diff --git a/tests/unit/test_layer.py b/tests/unit/test_layer.py index 96e06a08..a746c4ec 100644 --- a/tests/unit/test_layer.py +++ b/tests/unit/test_layer.py @@ -1,7 +1,10 @@ import pytest from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder -from semantic_router.layer import DecisionLayer # Replace with the actual module name +from semantic_router.layer import ( + DecisionLayer, + HybridDecisionLayer, +) # Replace with the actual module name from semantic_router.schema import Decision @@ -45,16 +48,16 @@ def decisions(): class TestDecisionLayer: def test_initialization(self, openai_encoder, decisions): decision_layer = DecisionLayer(encoder=openai_encoder, decisions=decisions) - assert decision_layer.similarity_threshold == 0.82 + assert decision_layer.score_threshold == 0.82 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 = DecisionLayer(encoder=cohere_encoder) - assert decision_layer_cohere.similarity_threshold == 0.3 + assert decision_layer_cohere.score_threshold == 0.3 decision_layer_openai = DecisionLayer(encoder=openai_encoder) - assert decision_layer_openai.similarity_threshold == 0.82 + assert decision_layer_openai.score_threshold == 0.82 def test_add_decision(self, openai_encoder): decision_layer = DecisionLayer(encoder=openai_encoder) @@ -107,9 +110,87 @@ def test_pass_threshold(self, openai_encoder): assert not decision_layer._pass_threshold([], 0.5) assert decision_layer._pass_threshold([0.6, 0.7], 0.5) - def test_failover_similarity_threshold(self, base_encoder): + def test_failover_score_threshold(self, base_encoder): decision_layer = DecisionLayer(encoder=base_encoder) - assert decision_layer.similarity_threshold == 0.82 + 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 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 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 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( + [ + {"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 = HybridDecisionLayer(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 = HybridDecisionLayer(encoder=base_encoder) + assert decision_layer.score_threshold == 0.82 # Add more tests for edge cases and error handling as needed.