From f307b18c7143b7a13f31c5c8969edeeaafbe1dfd Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sat, 23 Nov 2024 16:11:32 +0100 Subject: [PATCH 01/33] feat: wip creating abstract route layers --- docs/source/conf.py | 2 +- pyproject.toml | 2 +- semantic_router/__init__.py | 5 +- semantic_router/hybrid_layer.py | 220 --- semantic_router/index/__init__.py | 2 + semantic_router/index/base.py | 2 +- semantic_router/index/hybrid_local.py | 190 +++ semantic_router/index/local.py | 16 +- semantic_router/routers/__init__.py | 10 + semantic_router/routers/base.py | 1240 +++++++++++++++++ semantic_router/routers/hybrid.py | 212 +++ .../{layer.py => routers/semantic.py} | 23 +- tests/unit/test_hybrid_layer.py | 2 +- tests/unit/{test_layer.py => test_router.py} | 2 +- tests/unit/test_sync.py | 2 +- 15 files changed, 1676 insertions(+), 254 deletions(-) delete mode 100644 semantic_router/hybrid_layer.py create mode 100644 semantic_router/index/hybrid_local.py create mode 100644 semantic_router/routers/__init__.py create mode 100644 semantic_router/routers/base.py create mode 100644 semantic_router/routers/hybrid.py rename semantic_router/{layer.py => routers/semantic.py} (98%) rename tests/unit/{test_layer.py => test_router.py} (99%) diff --git a/docs/source/conf.py b/docs/source/conf.py index 2dae787f..0340e029 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,7 @@ project = "Semantic Router" copyright = "2024, Aurelio AI" author = "Aurelio AI" -release = "0.1.0.dev1" +release = "0.1.0.dev2" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/pyproject.toml b/pyproject.toml index 68881c73..98fa4ec7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "semantic-router" -version = "0.1.0.dev1" +version = "0.1.0.dev2" description = "Super fast semantic router for AI decision making" authors = ["Aurelio AI "] readme = "README.md" diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py index a2ed0d07..80f33f23 100644 --- a/semantic_router/__init__.py +++ b/semantic_router/__init__.py @@ -1,7 +1,6 @@ -from semantic_router.hybrid_layer import HybridRouteLayer -from semantic_router.layer import LayerConfig, RouteLayer +from semantic_router.routers import LayerConfig, RouteLayer, HybridRouteLayer from semantic_router.route import Route __all__ = ["RouteLayer", "HybridRouteLayer", "Route", "LayerConfig"] -__version__ = "0.1.0.dev1" +__version__ = "0.1.0.dev2" diff --git a/semantic_router/hybrid_layer.py b/semantic_router/hybrid_layer.py deleted file mode 100644 index 2aaf6f17..00000000 --- a/semantic_router/hybrid_layer.py +++ /dev/null @@ -1,220 +0,0 @@ -from typing import Dict, List, Optional, Tuple - -import numpy as np -from numpy.linalg import norm - -from semantic_router.encoders import ( - BaseEncoder, - BM25Encoder, - TfidfEncoder, -) -from semantic_router.route import Route -from semantic_router.utils.logger import logger - - -class HybridRouteLayer: - index = None - sparse_index = None - categories = None - score_threshold: float - - def __init__( - self, - encoder: BaseEncoder, - sparse_encoder: Optional[BM25Encoder] = None, - routes: List[Route] = [], - alpha: float = 0.3, - top_k: int = 5, - aggregation: str = "sum", - ): - self.encoder = encoder - if self.encoder.score_threshold is None: - raise ValueError( - "No score threshold provided for encoder. Please set the score threshold " - "in the encoder config." - ) - self.score_threshold = self.encoder.score_threshold - - if sparse_encoder is None: - logger.warning("No sparse_encoder provided. Using default BM25Encoder.") - self.sparse_encoder = BM25Encoder() - else: - self.sparse_encoder = sparse_encoder - - self.alpha = alpha - self.top_k = top_k - if self.top_k < 1: - raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") - self.aggregation = aggregation - if self.aggregation not in ["sum", "mean", "max"]: - raise ValueError( - f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." - ) - self.aggregation_method = self._set_aggregation_method(self.aggregation) - self.routes = routes - if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( - self.sparse_encoder, "fit" - ): - self.sparse_encoder.fit(routes) - # if routes list has been passed, we initialize index now - if routes: - # initialize index now - # for route in tqdm(routes): - # self._add_route(route=route) - self._add_routes(routes) - - def __call__(self, text: str) -> Optional[str]: - results = self._query(text, self.top_k) - top_class, top_class_scores = self._semantic_classify(results) - passed = self._pass_threshold(top_class_scores, self.score_threshold) - if passed: - return top_class - else: - return None - - def add(self, route: Route): - self._add_route(route=route) - - def _add_route(self, route: Route): - self.routes += [route] - - self.update_dense_embeddings_index(route.utterances) - - if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( - self.sparse_encoder, "fit" - ): - self.sparse_encoder.fit(self.routes) - # re-build index - self.sparse_index = None - all_utterances = [ - utterance for route in self.routes for utterance in route.utterances - ] - self.update_sparse_embeddings_index(all_utterances) - else: - self.update_sparse_embeddings_index(route.utterances) - - # create route array - if self.categories is None: - self.categories = np.array([route.name] * len(route.utterances)) - else: - str_arr = np.array([route.name] * len(route.utterances)) - self.categories = np.concatenate([self.categories, str_arr]) - self.routes.append(route) - - def _add_routes(self, routes: List[Route]): - # create embeddings for all routes - logger.info("Creating embeddings for all routes...") - all_utterances = [ - utterance for route in routes for utterance in route.utterances - ] - self.update_dense_embeddings_index(all_utterances) - self.update_sparse_embeddings_index(all_utterances) - - # create route array - route_names = [route.name for route in routes for _ in route.utterances] - route_array = np.array(route_names) - self.categories = ( - np.concatenate([self.categories, route_array]) - if self.categories is not None - else route_array - ) - - def update_dense_embeddings_index(self, utterances: list): - dense_embeds = np.array(self.encoder(utterances)) - # create utterance array (the dense index) - self.index = ( - np.concatenate([self.index, dense_embeds]) - if self.index is not None - else dense_embeds - ) - - def update_sparse_embeddings_index(self, utterances: list): - sparse_embeds = np.array(self.sparse_encoder(utterances)) - # create sparse utterance array - self.sparse_index = ( - np.concatenate([self.sparse_index, sparse_embeds]) - if self.sparse_index is not None - else sparse_embeds - ) - - def _query(self, text: str, top_k: int = 5): - """Given some text, encodes and searches the index vector space to - retrieve the top_k most similar records. - """ - # create dense query vector - xq_d = np.array(self.encoder([text])) - xq_d = np.squeeze(xq_d) # Reduce to 1d array. - # create sparse query vector - xq_s = np.array(self.sparse_encoder([text])) - xq_s = np.squeeze(xq_s) - # convex scaling - xq_d, xq_s = self._convex_scaling(xq_d, xq_s) - - if self.index is not None and self.sparse_index is not None: - # calculate dense vec similarity - index_norm = norm(self.index, axis=1) - xq_d_norm = norm(xq_d.T) - sim_d = np.dot(self.index, xq_d.T) / (index_norm * xq_d_norm) - # calculate sparse vec similarity - sparse_norm = norm(self.sparse_index, axis=1) - xq_s_norm = norm(xq_s.T) - sim_s = np.dot(self.sparse_index, xq_s.T) / (sparse_norm * xq_s_norm) - total_sim = sim_d + sim_s - # get indices of top_k records - top_k = min(top_k, total_sim.shape[0]) - idx = np.argpartition(total_sim, -top_k)[-top_k:] - scores = total_sim[idx] - # get the utterance categories (route names) - routes = self.categories[idx] if self.categories is not None else [] - return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] - else: - logger.warning("No index found. Please add routes to the layer.") - return [] - - def _convex_scaling(self, dense: np.ndarray, sparse: np.ndarray): - # scale sparse and dense vecs - dense = np.array(dense) * self.alpha - sparse = np.array(sparse) * (1 - self.alpha) - return dense, sparse - - def _set_aggregation_method(self, aggregation: str = "sum"): - if aggregation == "sum": - return lambda x: sum(x) - elif aggregation == "mean": - return lambda x: np.mean(x) - elif aggregation == "max": - return lambda x: max(x) - else: - raise ValueError( - f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." - ) - - def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]: - scores_by_class: Dict[str, List[float]] = {} - for result in query_results: - score = result["score"] - route = result["route"] - if route in scores_by_class: - scores_by_class[route].append(score) - else: - scores_by_class[route] = [score] - - # Calculate total score for each class - total_scores = { - route: self.aggregation_method(scores) - for route, scores in scores_by_class.items() - } - top_class = max(total_scores, key=lambda x: total_scores[x], default=None) - - # Return the top class and its associated scores - if top_class is not None: - return str(top_class), scores_by_class.get(top_class, []) - else: - logger.warning("No classification found for semantic classifier.") - return "", [] - - def _pass_threshold(self, scores: List[float], threshold: float) -> bool: - if scores: - return max(scores) > threshold - else: - return False diff --git a/semantic_router/index/__init__.py b/semantic_router/index/__init__.py index 9a01b996..3a43abe9 100644 --- a/semantic_router/index/__init__.py +++ b/semantic_router/index/__init__.py @@ -1,10 +1,12 @@ from semantic_router.index.base import BaseIndex +from semantic_router.index.hybrid_local import HybridLocalIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex __all__ = [ "BaseIndex", + "HybridLocalIndex", "LocalIndex", "QdrantIndex", "PineconeIndex", diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py index 4893987a..65f2cf1e 100644 --- a/semantic_router/index/base.py +++ b/semantic_router/index/base.py @@ -18,12 +18,12 @@ class BaseIndex(BaseModel): # You can define common attributes here if there are any. # For example, a placeholder for the index attribute: - index: Optional[Any] = None routes: Optional[np.ndarray] = None utterances: Optional[np.ndarray] = None dimensions: Union[int, None] = None type: str = "base" init_async_index: bool = False + index: Optional[Any] = None def add( self, diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py new file mode 100644 index 00000000..9316487e --- /dev/null +++ b/semantic_router/index/hybrid_local.py @@ -0,0 +1,190 @@ +from typing import List, Optional, Tuple, Dict + +import numpy as np +from numpy.linalg import norm + +from semantic_router.schema import ConfigParameter, Utterance +from semantic_router.index.local import LocalIndex +from semantic_router.linear import similarity_matrix, top_scores +from semantic_router.utils.logger import logger +from typing import Any + + +class HybridLocalIndex(LocalIndex): + type: str = "hybrid_local" + sparse_index: Optional[np.ndarray] = None + route_names: Optional[np.ndarray] = None + + class Config: + # Stop pydantic from complaining about Optional[np.ndarray]type hints. + arbitrary_types_allowed = True + + def add( + self, + embeddings: List[List[float]], + routes: List[str], + utterances: List[str], + function_schemas: Optional[List[Dict[str, Any]]] = None, + metadata_list: List[Dict[str, Any]] = [], + sparse_embeddings: Optional[List[List[float]]] = None, + ): + if sparse_embeddings is None: + raise ValueError("Sparse embeddings are required for HybridLocalIndex.") + if function_schemas is not None: + raise ValueError("Function schemas are not supported for HybridLocalIndex.") + if metadata_list: + raise ValueError("Metadata is not supported for HybridLocalIndex.") + embeds = np.array(embeddings) + sparse_embeds = np.array(sparse_embeddings) + routes_arr = np.array(routes) + if isinstance(utterances[0], str): + utterances_arr = np.array(utterances) + else: + utterances_arr = np.array(utterances, dtype=object) + if self.index is None or self.sparse_index is None: + self.index = embeds + self.sparse_index = sparse_embeds + self.routes = routes_arr + self.utterances = utterances_arr + else: + # TODO: we should probably switch to an `upsert` method and standardize elsewhere + self.index = np.concatenate([self.index, embeds]) + self.sparse_index = np.concatenate([self.sparse_index, sparse_embeds]) + self.routes = np.concatenate([self.routes, routes_arr]) + self.utterances = np.concatenate([self.utterances, utterances_arr]) + + def get_utterances(self) -> List[Utterance]: + """Gets a list of route and utterance objects currently stored in the index. + + Returns: + List[Tuple]: A list of (route_name, utterance) objects. + """ + if self.routes is None or self.utterances is None: + return [] + return [Utterance.from_tuple(x) for x in zip(self.routes, self.utterances)] + + def describe(self) -> Dict: + return { + "type": self.type, + "dimensions": self.index.shape[1] if self.index is not None else 0, + "vectors": self.index.shape[0] if self.index is not None else 0, + } + + def query( + self, + vector: np.ndarray, + top_k: int = 5, + route_filter: Optional[List[str]] = None, + sparse_vector: Optional[np.ndarray] = None, + ) -> Tuple[np.ndarray, List[str]]: + """Search the index for the query and return top_k results. + + :param vector: The query vector to search for. + :type vector: np.ndarray + :param top_k: The number of top results to return, defaults to 5. + :type top_k: int, optional + :param route_filter: A list of route names to filter the search results, defaults to None. + :type route_filter: Optional[List[str]], optional + :param sparse_vector: The sparse vector to search for, must be provided. + :type sparse_vector: np.ndarray + """ + if route_filter: + raise ValueError("Route filter is not supported for HybridLocalIndex.") + + xq_d = vector.copy() + if sparse_vector is None: + raise ValueError("Sparse vector is required for HybridLocalIndex.") + xq_s = sparse_vector.copy() + + if self.index is not None and self.sparse_index is not None: + # calculate dense vec similarity + index_norm = norm(self.index, axis=1) + xq_d_norm = norm(xq_d) # TODO: this used to be xq_d.T, should work without + sim_d = np.squeeze(np.dot(self.index, xq_d.T)) / (index_norm * xq_d_norm) + # calculate sparse vec similarity + sparse_norm = norm(self.sparse_index, axis=1) + xq_s_norm = norm(xq_s) # TODO: this used to be xq_s.T, should work without + sim_s = np.squeeze(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) + route_names = self.routes[idx] if self.routes is not None else [] + return scores, route_names + else: + raise ValueError("Index or sparse index is not populated.") + + async def aquery( + self, + vector: np.ndarray, + top_k: int = 5, + route_filter: Optional[List[str]] = None, + sparse_vector: Optional[np.ndarray] = None, + ) -> Tuple[np.ndarray, List[str]]: + """Search the index for the query and return top_k results. This method calls the + sync `query` method as everything uses numpy computations which is CPU-bound + and so no benefit can be gained from making this async. + + :param vector: The query vector to search for. + :type vector: np.ndarray + :param top_k: The number of top results to return, defaults to 5. + :type top_k: int, optional + :param route_filter: A list of route names to filter the search results, defaults to None. + :type route_filter: Optional[List[str]], optional + :param sparse_vector: The sparse vector to search for, must be provided. + :type sparse_vector: np.ndarray + """ + return self.query( + vector=vector, + top_k=top_k, + route_filter=route_filter, + sparse_vector=sparse_vector, + ) + + def aget_routes(self): + logger.error("Sync remove is not implemented for LocalIndex.") + + def _write_config(self, config: ConfigParameter): + logger.warning("No config is written for LocalIndex.") + + def delete(self, route_name: str): + """ + Delete all records of a specific route from the index. + """ + if ( + self.index is not None + and self.routes is not None + and self.utterances is not None + ): + delete_idx = self._get_indices_for_route(route_name=route_name) + self.index = np.delete(self.index, delete_idx, axis=0) + self.routes = np.delete(self.routes, delete_idx, axis=0) + self.utterances = np.delete(self.utterances, delete_idx, axis=0) + else: + raise ValueError( + "Attempted to delete route records but either index, routes or " + "utterances is None." + ) + + def delete_index(self): + """ + Deletes the index, effectively clearing it and setting it to None. + """ + self.index = None + self.routes = None + self.utterances = None + + def _get_indices_for_route(self, route_name: str): + """Gets an array of indices for a specific route.""" + if self.routes is None: + raise ValueError("Routes are not populated.") + idx = [i for i, route in enumerate(self.routes) if route == route_name] + return idx + + def __len__(self): + if self.index is not None: + return self.index.shape[0] + else: + return 0 diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py index 509e0fdf..420ad30f 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -10,14 +10,10 @@ class LocalIndex(BaseIndex): - def __init__( - self, - index: Optional[np.ndarray] = None, - routes: Optional[np.ndarray] = None, - utterances: Optional[np.ndarray] = None, - ): - super().__init__(index=index, routes=routes, utterances=utterances) - self.type = "local" + type: str = "local" + + def __init__(self): + super().__init__() class Config: # Stop pydantic from complaining about Optional[np.ndarray]type hints. @@ -47,7 +43,9 @@ def add( self.utterances = np.concatenate([self.utterances, utterances_arr]) def _remove_and_sync(self, routes_to_delete: dict): - logger.warning("Sync remove is not implemented for LocalIndex.") + logger.warning( + f"Sync remove is not implemented for {self.__class__.__name__}." + ) def get_utterances(self) -> List[Utterance]: """ diff --git a/semantic_router/routers/__init__.py b/semantic_router/routers/__init__.py new file mode 100644 index 00000000..4c8031ab --- /dev/null +++ b/semantic_router/routers/__init__.py @@ -0,0 +1,10 @@ +from semantic_router.routers.base import BaseRouteLayer, LayerConfig +from semantic_router.routers.semantic import RouteLayer +from semantic_router.routers.hybrid import HybridRouteLayer + +__all__ = [ + "BaseRouteLayer", + "LayerConfig", + "RouteLayer", + "HybridRouteLayer", +] diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py new file mode 100644 index 00000000..100663da --- /dev/null +++ b/semantic_router/routers/base.py @@ -0,0 +1,1240 @@ +import importlib +import json +import os +import random +import hashlib +from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from pydantic.v1 import BaseModel, Field, validator + +import numpy as np +import yaml # type: ignore +from tqdm.auto import tqdm + +from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder +from semantic_router.index.base import BaseIndex +from semantic_router.index.local import LocalIndex +from semantic_router.index.pinecone import PineconeIndex +from semantic_router.llms import BaseLLM, OpenAILLM +from semantic_router.route import Route +from semantic_router.schema import ( + ConfigParameter, + EncoderType, + RouteChoice, + Utterance, + UtteranceDiff, +) +from semantic_router.utils.defaults import EncoderDefault +from semantic_router.utils.logger import logger + + +def is_valid(layer_config: str) -> bool: + """Make sure the given string is json format and contains the 3 keys: + ["encoder_name", "encoder_type", "routes"]""" + try: + output_json = json.loads(layer_config) + required_keys = ["encoder_name", "encoder_type", "routes"] + + if isinstance(output_json, list): + for item in output_json: + missing_keys = [key for key in required_keys if key not in item] + if missing_keys: + logger.warning( + f"Missing keys in layer config: {', '.join(missing_keys)}" + ) + return False + return True + else: + missing_keys = [key for key in required_keys if key not in output_json] + if missing_keys: + logger.warning( + f"Missing keys in layer config: {', '.join(missing_keys)}" + ) + return False + else: + return True + except json.JSONDecodeError as e: + logger.error(e) + return False + + +class LayerConfig: + """ + Generates a LayerConfig object that can be used for initializing a + RouteLayer. + """ + + routes: List[Route] = [] + + class Config: + arbitrary_types_allowed = True + + def __init__( + self, + routes: List[Route] = [], + encoder_type: str = "openai", + encoder_name: Optional[str] = None, + ): + self.encoder_type = encoder_type + if encoder_name is None: + for encode_type in EncoderType: + if encode_type.value == self.encoder_type: + if self.encoder_type == EncoderType.HUGGINGFACE.value: + raise NotImplementedError( + "HuggingFace encoder not supported by LayerConfig yet." + ) + encoder_name = EncoderDefault[encode_type.name].value[ + "embedding_model" + ] + break + logger.info(f"Using default {encoder_type} encoder: {encoder_name}") + self.encoder_name = encoder_name + self.routes = routes + + @classmethod + def from_file(cls, path: str) -> "LayerConfig": + logger.info(f"Loading route config from {path}") + _, ext = os.path.splitext(path) + with open(path, "r") as f: + if ext == ".json": + layer = json.load(f) + elif ext in [".yaml", ".yml"]: + layer = yaml.safe_load(f) + else: + raise ValueError( + "Unsupported file type. Only .json and .yaml are supported" + ) + + if not is_valid(json.dumps(layer)): + raise Exception("Invalid config JSON or YAML") + + encoder_type = layer["encoder_type"] + encoder_name = layer["encoder_name"] + routes = [] + for route_data in layer["routes"]: + # Handle the 'llm' field specially if it exists + if "llm" in route_data and route_data["llm"] is not None: + llm_data = route_data.pop( + "llm" + ) # Remove 'llm' from route_data and handle it separately + # Use the module path directly from llm_data without modification + llm_module_path = llm_data["module"] + # Dynamically import the module and then the class from that module + llm_module = importlib.import_module(llm_module_path) + llm_class = getattr(llm_module, llm_data["class"]) + # Instantiate the LLM class with the provided model name + llm = llm_class(name=llm_data["model"]) + # Reassign the instantiated llm object back to route_data + route_data["llm"] = llm + + # Dynamically create the Route object using the remaining route_data + route = Route(**route_data) + routes.append(route) + + return cls( + encoder_type=encoder_type, encoder_name=encoder_name, routes=routes + ) + + @classmethod + def from_tuples( + cls, + route_tuples: List[ + Tuple[str, str, Optional[List[Dict[str, Any]]], Dict[str, Any]] + ], + encoder_type: str = "openai", + encoder_name: Optional[str] = None, + ): + """Initialize a LayerConfig from a list of tuples of routes and + utterances. + + :param route_tuples: A list of tuples, each containing a route name and an + associated utterance. + :type route_tuples: List[Tuple[str, str]] + :param encoder_type: The type of encoder to use, defaults to "openai". + :type encoder_type: str, optional + :param encoder_name: The name of the encoder to use, defaults to None. + :type encoder_name: Optional[str], optional + """ + routes_dict: Dict[str, Route] = {} + # first create a dictionary of route names to Route objects + # TODO: duplicated code with BaseIndex.get_routes() + for route_name, utterance, function_schema, metadata in route_tuples: + # if the route is not in the dictionary, add it + if route_name not in routes_dict: + routes_dict[route_name] = Route( + name=route_name, + utterances=[utterance], + function_schemas=function_schema, + metadata=metadata, + ) + else: + # otherwise, add the utterance to the route + routes_dict[route_name].utterances.append(utterance) + # then create a list of routes from the dictionary + routes: List[Route] = [] + for route_name, route in routes_dict.items(): + routes.append(route) + return cls(routes=routes, encoder_type=encoder_type, encoder_name=encoder_name) + + @classmethod + def from_index( + cls, + index: BaseIndex, + encoder_type: str = "openai", + encoder_name: Optional[str] = None, + ): + """Initialize a LayerConfig from a BaseIndex object. + + :param index: The index to initialize the LayerConfig from. + :type index: BaseIndex + :param encoder_type: The type of encoder to use, defaults to "openai". + :type encoder_type: str, optional + :param encoder_name: The name of the encoder to use, defaults to None. + :type encoder_name: Optional[str], optional + """ + remote_routes = index.get_utterances() + return cls.from_tuples( + route_tuples=[utt.to_tuple() for utt in remote_routes], + encoder_type=encoder_type, + encoder_name=encoder_name, + ) + + def to_dict(self) -> Dict[str, Any]: + return { + "encoder_type": self.encoder_type, + "encoder_name": self.encoder_name, + "routes": [route.to_dict() for route in self.routes], + } + + def to_file(self, path: str): + """Save the routes to a file in JSON or YAML format""" + logger.info(f"Saving route config to {path}") + _, ext = os.path.splitext(path) + + # Check file extension before creating directories or files + if ext not in [".json", ".yaml", ".yml"]: + raise ValueError( + "Unsupported file type. Only .json and .yaml are supported" + ) + + dir_name = os.path.dirname(path) + + # Create the directory if it doesn't exist and dir_name is not an empty string + if dir_name and not os.path.exists(dir_name): + os.makedirs(dir_name) + + with open(path, "w") as f: + if ext == ".json": + json.dump(self.to_dict(), f, indent=4) + elif ext in [".yaml", ".yml"]: + yaml.safe_dump(self.to_dict(), f) + + def to_utterances(self) -> List[Utterance]: + """Convert the routes to a list of Utterance objects. + + :return: A list of Utterance objects. + :rtype: List[Utterance] + """ + utterances = [] + for route in self.routes: + utterances.extend( + [ + Utterance( + route=route.name, + utterance=x, + function_schemas=route.function_schemas, + metadata=route.metadata or {}, + ) + for x in route.utterances + ] + ) + return utterances + + def add(self, route: Route): + self.routes.append(route) + logger.info(f"Added route `{route.name}`") + + def get(self, name: str) -> Optional[Route]: + for route in self.routes: + if route.name == name: + return route + logger.error(f"Route `{name}` not found") + return None + + def remove(self, name: str): + if name not in [route.name for route in self.routes]: + logger.error(f"Route `{name}` not found") + else: + self.routes = [route for route in self.routes if route.name != name] + logger.info(f"Removed route `{name}`") + + def get_hash(self) -> ConfigParameter: + layer = self.to_dict() + return ConfigParameter( + field="sr_hash", + value=hashlib.sha256(json.dumps(layer).encode()).hexdigest(), + ) + + +class BaseRouteLayer(BaseModel): + encoder: BaseEncoder + index: BaseIndex = Field(default_factory=BaseIndex) + score_threshold: Optional[float] = None + routes: List[Route] = [] + llm: Optional[BaseLLM] = None + top_k: int = 5 + aggregation: str = "mean" + aggregation_method: Optional[Callable] = None + auto_sync: Optional[str] = None + + class Config: + arbitrary_types_allowed = True + + @validator("score_threshold", pre=True, always=True) + def set_score_threshold(cls, v): + return float(v) if v is not None else None + + @validator("index", pre=True, always=True) + def set_index(cls, v): + return v if v is not None else LocalIndex() + + def __init__( + self, + encoder: Optional[BaseEncoder] = None, + llm: Optional[BaseLLM] = None, + routes: List[Route] = [], + index: Optional[BaseIndex] = None, # type: ignore + top_k: int = 5, + aggregation: str = "mean", + auto_sync: Optional[str] = None, + ): + super().__init__( + encoder=encoder, + llm=llm, + routes=routes, + index=index, + top_k=top_k, + aggregation=aggregation, + auto_sync=auto_sync, + ) + if encoder is None: + logger.warning( + "No encoder provided. Using default OpenAIEncoder. Ensure " + "that you have set OPENAI_API_KEY in your environment." + ) + self.encoder = OpenAIEncoder() + else: + self.encoder = encoder + self.llm = llm + self.routes = routes if routes else [] + if self.encoder.score_threshold is None: + raise ValueError( + "No score threshold provided for encoder. Please set the score threshold " + "in the encoder config." + ) + self.score_threshold = self.encoder.score_threshold + self.top_k = top_k + if self.top_k < 1: + raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") + self.aggregation = aggregation + if self.aggregation not in ["sum", "mean", "max"]: + raise ValueError( + f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." + ) + self.aggregation_method = self._set_aggregation_method(self.aggregation) + self.auto_sync = auto_sync + + # set route score thresholds if not already set + for route in self.routes: + if route.score_threshold is None: + route.score_threshold = self.score_threshold + # if routes list has been passed, we initialize index now + if self.auto_sync: + # initialize index now, check if we need dimensions + if self.index.dimensions is None: + dims = len(self.encoder(["test"])[0]) + self.index.dimensions = dims + # now init index + if isinstance(self.index, PineconeIndex): + self.index.index = self.index._init_index(force_create=True) + local_utterances = self.to_config().to_utterances() + remote_utterances = self.index.get_utterances() + diff = UtteranceDiff.from_utterances( + local_utterances=local_utterances, + remote_utterances=remote_utterances, + ) + sync_strategy = diff.get_sync_strategy(self.auto_sync) + self._execute_sync_strategy(sync_strategy) + + def check_for_matching_routes(self, top_class: str) -> Optional[Route]: + matching_route = next( + (route for route in self.routes if route.name == top_class), None + ) + if matching_route is None: + logger.error( + f"No route found with name {top_class}. Check to see if any Routes " + "have been defined." + ) + return None + return matching_route + + def __call__( + self, + text: Optional[str] = None, + vector: Optional[List[float]] = None, + simulate_static: bool = False, + route_filter: Optional[List[str]] = None, + ) -> RouteChoice: + # if no vector provided, encode text to get vector + if vector is None: + if text is None: + raise ValueError("Either text or vector must be provided") + vector = self._encode(text=text) + route, top_class_scores = self._retrieve_top_route(vector, route_filter) + passed = self._check_threshold(top_class_scores, route) + if passed and route is not None and not simulate_static: + if route.function_schemas and text is None: + raise ValueError( + "Route has a function schema, but no text was provided." + ) + if route.function_schemas and not isinstance(route.llm, BaseLLM): + if not self.llm: + logger.warning( + "No LLM provided for dynamic route, will use OpenAI LLM " + "default. Ensure API key is set in OPENAI_API_KEY environment " + "variable." + ) + + self.llm = OpenAILLM() + route.llm = self.llm + else: + route.llm = self.llm + return route(text) + elif passed and route is not None and simulate_static: + return RouteChoice( + name=route.name, + function_call=None, + similarity_score=None, + ) + else: + # if no route passes threshold, return empty route choice + return RouteChoice() + + async def acall( + self, + text: Optional[str] = None, + vector: Optional[List[float]] = None, + simulate_static: bool = False, + route_filter: Optional[List[str]] = None, + ) -> RouteChoice: + # if no vector provided, encode text to get vector + if vector is None: + if text is None: + raise ValueError("Either text or vector must be provided") + vector = await self._async_encode(text=text) + + route, top_class_scores = await self._async_retrieve_top_route( + vector, route_filter + ) + passed = self._check_threshold(top_class_scores, route) + if passed and route is not None and not simulate_static: + if route.function_schemas and text is None: + raise ValueError( + "Route has a function schema, but no text was provided." + ) + if route.function_schemas and not isinstance(route.llm, BaseLLM): + if not self.llm: + logger.warning( + "No LLM provided for dynamic route, will use OpenAI LLM default" + ) + self.llm = OpenAILLM() + route.llm = self.llm + else: + route.llm = self.llm + return await route.acall(text) + elif passed and route is not None and simulate_static: + return RouteChoice( + name=route.name, + function_call=None, + similarity_score=None, + ) + else: + # if no route passes threshold, return empty route choice + return RouteChoice() + + def retrieve_multiple_routes( + self, + text: Optional[str] = None, + vector: Optional[List[float]] = None, + ) -> List[RouteChoice]: + if vector is None: + if text is None: + raise ValueError("Either text or vector must be provided") + vector_arr = self._encode(text=text) + else: + vector_arr = np.array(vector) + # get relevant utterances + results = self._retrieve(xq=vector_arr) + + # decide most relevant routes + categories_with_scores = self._semantic_classify_multiple_routes(results) + + route_choices = [] + for category, score in categories_with_scores: + route = self.check_for_matching_routes(category) + if route: + route_choice = RouteChoice(name=route.name, similarity_score=score) + route_choices.append(route_choice) + + return route_choices + + def sync(self, sync_mode: str, force: bool = False) -> List[str]: + """Runs a sync of the local routes with the remote index. + + :param sync_mode: The mode to sync the routes with the remote index. + :type sync_mode: str + :param force: Whether to force the sync even if the local and remote + hashes already match. Defaults to False. + :type force: bool, optional + :return: A list of diffs describing the addressed differences between + the local and remote route layers. + :rtype: List[str] + """ + if not force and self.is_synced(): + logger.warning("Local and remote route layers are already synchronized.") + # create utterance diff to return, but just using local instance + # for speed + local_utterances = self.to_config().to_utterances() + diff = UtteranceDiff.from_utterances( + local_utterances=local_utterances, + remote_utterances=local_utterances, + ) + return diff.to_utterance_str() + # otherwise we continue with the sync, first creating a diff + local_utterances = self.to_config().to_utterances() + remote_utterances = self.index.get_utterances() + diff = UtteranceDiff.from_utterances( + local_utterances=local_utterances, + remote_utterances=remote_utterances, + ) + # generate sync strategy + sync_strategy = diff.get_sync_strategy(sync_mode=sync_mode) + # and execute + self._execute_sync_strategy(sync_strategy) + return diff.to_utterance_str() + + def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]]): + """Executes the provided sync strategy, either deleting or upserting + routes from the local and remote instances as defined in the strategy. + + :param strategy: The sync strategy to execute. + :type strategy: Dict[str, Dict[str, List[Utterance]]] + """ + if strategy["remote"]["delete"]: + data_to_delete = {} # type: ignore + for utt_obj in strategy["remote"]["delete"]: + data_to_delete.setdefault(utt_obj.route, []).append(utt_obj.utterance) + # TODO: switch to remove without sync?? + self.index._remove_and_sync(data_to_delete) + if strategy["remote"]["upsert"]: + utterances_text = [utt.utterance for utt in strategy["remote"]["upsert"]] + self.index.add( + embeddings=self.encoder(utterances_text), + routes=[utt.route for utt in strategy["remote"]["upsert"]], + utterances=utterances_text, + function_schemas=[ + utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore + ], + metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]], + ) + if strategy["local"]["delete"]: + self._local_delete(utterances=strategy["local"]["delete"]) + if strategy["local"]["upsert"]: + self._local_upsert(utterances=strategy["local"]["upsert"]) + # update hash + self._write_hash() + + def _local_upsert(self, utterances: List[Utterance]): + """Adds new routes to the RouteLayer. + + :param utterances: The utterances to add to the local RouteLayer. + :type utterances: List[Utterance] + """ + new_routes = {route.name: route for route in self.routes} + for utt_obj in utterances: + if utt_obj.route not in new_routes.keys(): + new_routes[utt_obj.route] = Route( + name=utt_obj.route, + utterances=[utt_obj.utterance], + function_schemas=utt_obj.function_schemas, + metadata=utt_obj.metadata, + ) + else: + if utt_obj.utterance not in new_routes[utt_obj.route].utterances: + new_routes[utt_obj.route].utterances.append(utt_obj.utterance) + new_routes[utt_obj.route].function_schemas = utt_obj.function_schemas + new_routes[utt_obj.route].metadata = utt_obj.metadata + temp = "\n".join([f"{name}: {r.utterances}" for name, r in new_routes.items()]) + logger.warning("TEMP | _local_upsert:\n" + temp) + self.routes = list(new_routes.values()) + + def _local_delete(self, utterances: List[Utterance]): + """Deletes routes from the local RouteLayer. + + :param utterances: The utterances to delete from the local RouteLayer. + :type utterances: List[Utterance] + """ + # create dictionary of route names to utterances + route_dict: dict[str, List[str]] = {} + for utt in utterances: + route_dict.setdefault(utt.route, []).append(utt.utterance) + temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) + logger.warning("TEMP | _local_delete:\n" + temp) + # iterate over current routes and delete specific utterance if found + new_routes = [] + for route in self.routes: + if route.name in route_dict.keys(): + # drop utterances that are in route_dict deletion list + new_utterances = list( + set(route.utterances) - set(route_dict[route.name]) + ) + if len(new_utterances) == 0: + # the route is now empty, so we skip it + continue + else: + new_routes.append( + Route( + name=route.name, + utterances=new_utterances, + # use existing function schemas and metadata + function_schemas=route.function_schemas, + metadata=route.metadata, + ) + ) + logger.warning( + f"TEMP | _local_delete OLD | {route.name}: {route.utterances}" + ) + logger.warning( + f"TEMP | _local_delete NEW | {route.name}: {new_routes[-1].utterances}" + ) + else: + # the route is not in the route_dict, so we keep it as is + new_routes.append(route) + temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) + logger.warning("TEMP | _local_delete:\n" + temp) + + self.routes = new_routes + + def _retrieve_top_route( + self, vector: List[float], route_filter: Optional[List[str]] = None + ) -> Tuple[Optional[Route], List[float]]: + """ + Retrieve the top matching route based on the given vector. + Returns a tuple of the route (if any) and the scores of the top class. + """ + # get relevant results (scores and routes) + results = self._retrieve( + xq=np.array(vector), top_k=self.top_k, route_filter=route_filter + ) + # decide most relevant routes + top_class, top_class_scores = self._semantic_classify(results) + # TODO do we need this check? + route = self.check_for_matching_routes(top_class) + return route, top_class_scores + + async def _async_retrieve_top_route( + self, vector: List[float], route_filter: Optional[List[str]] = None + ) -> Tuple[Optional[Route], List[float]]: + # get relevant results (scores and routes) + results = await self._async_retrieve( + xq=np.array(vector), top_k=self.top_k, route_filter=route_filter + ) + # decide most relevant routes + top_class, top_class_scores = await self._async_semantic_classify(results) + # TODO do we need this check? + route = self.check_for_matching_routes(top_class) + return route, top_class_scores + + def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: + """ + Check if the route's score passes the specified threshold. + """ + if route is None: + return False + threshold = ( + route.score_threshold + if route.score_threshold is not None + else self.score_threshold + ) + return self._pass_threshold(scores, threshold) + + def __str__(self): + return ( + f"{self.__class__.__name__}(encoder={self.encoder}, " + f"score_threshold={self.score_threshold}, " + f"routes={self.routes})" + ) + + @classmethod + def from_json(cls, file_path: str): + config = LayerConfig.from_file(file_path) + encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model + return cls(encoder=encoder, routes=config.routes) + + @classmethod + def from_yaml(cls, file_path: str): + config = LayerConfig.from_file(file_path) + encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model + return cls(encoder=encoder, routes=config.routes) + + @classmethod + def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None): + encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model + return cls(encoder=encoder, routes=config.routes, index=index) + + def add(self, route: Route): + """Add a route to the local RouteLayer and index. + + :param route: The route to add. + :type route: Route + """ + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + embedded_utterances = self.encoder(route.utterances) + self.index.add( + embeddings=embedded_utterances, + routes=[route.name] * len(route.utterances), + utterances=route.utterances, + function_schemas=( + route.function_schemas * len(route.utterances) + if route.function_schemas + else [{}] * len(route.utterances) + ), + metadata_list=[route.metadata if route.metadata else {}] + * len(route.utterances), + ) + + self.routes.append(route) + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "details." + ) + + def list_route_names(self) -> List[str]: + return [route.name for route in self.routes] + + def update( + self, + name: str, + threshold: Optional[float] = None, + utterances: Optional[List[str]] = None, + ): + """Updates the route specified in name. Allows the update of + threshold and/or utterances. If no values are provided via the + threshold or utterances parameters, those fields are not updated. + If neither field is provided raises a ValueError. + + The name must exist within the local RouteLayer, if not a + KeyError will be raised. + """ + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + + if threshold is None and utterances is None: + raise ValueError( + "At least one of 'threshold' or 'utterances' must be provided." + ) + if utterances: + raise NotImplementedError( + "The update method cannot be used for updating utterances yet." + ) + + route = self.get(name) + if route: + if threshold: + old_threshold = route.score_threshold + route.score_threshold = threshold + logger.info( + f"Updated threshold for route '{route.name}' from {old_threshold} to {threshold}" + ) + else: + raise ValueError(f"Route '{name}' not found. Nothing updated.") + + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " + "to see details." + ) + + def delete(self, route_name: str): + """Deletes a route given a specific route name. + + :param route_name: the name of the route to be deleted + :type str: + """ + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + + if route_name not in [route.name for route in self.routes]: + err_msg = f"Route `{route_name}` not found in {self.__class__.__name__}" + logger.warning(err_msg) + try: + self.index.delete(route_name=route_name) + except Exception as e: + logger.error(f"Failed to delete route from the index: {e}") + else: + self.routes = [route for route in self.routes if route.name != route_name] + self.index.delete(route_name=route_name) + + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " + "to see details." + ) + + def _refresh_routes(self): + """Pulls out the latest routes from the index.""" + raise NotImplementedError("This method has not yet been implemented.") + route_mapping = {route.name: route for route in self.routes} + index_routes = self.index.get_utterances() + new_routes_names = [] + new_routes = [] + for route_name, utterance in index_routes: + if route_name in route_mapping: + if route_name not in new_routes_names: + existing_route = route_mapping[route_name] + new_routes.append(existing_route) + + new_routes.append(Route(name=route_name, utterances=[utterance])) + route = route_mapping[route_name] + self.routes.append(route) + + def _add_routes(self, routes: List[Route]): + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + + if not routes: + logger.warning("No routes provided to add.") + return + # create embeddings for all routes + route_names, all_utterances, all_function_schemas, all_metadata = ( + self._extract_routes_details(routes, include_metadata=True) + ) + embedded_utterances = self.encoder(all_utterances) + try: + # Batch insertion into the index + self.index.add( + embeddings=embedded_utterances, + routes=route_names, + utterances=all_utterances, + function_schemas=all_function_schemas, + metadata_list=all_metadata, + ) + except Exception as e: + logger.error(f"Failed to add routes to the index: {e}") + raise Exception("Indexing error occurred") from e + + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " + "to see details." + ) + + def _get_hash(self) -> ConfigParameter: + config = self.to_config() + return config.get_hash() + + def _write_hash(self) -> ConfigParameter: + config = self.to_config() + hash_config = config.get_hash() + self.index._write_config(config=hash_config) + return hash_config + + def is_synced(self) -> bool: + """Check if the local and remote route layer instances are + synchronized. + + :return: True if the local and remote route layers are synchronized, + False otherwise. + :rtype: bool + """ + # first check hash + local_hash = self._get_hash() + remote_hash = self.index._read_hash() + if local_hash.value == remote_hash.value: + return True + else: + return False + + def get_utterance_diff(self, include_metadata: bool = False) -> List[str]: + """Get the difference between the local and remote utterances. Returns + a list of strings showing what is different in the remote when compared + to the local. For example: + + [" route1: utterance1", + " route1: utterance2", + "- route2: utterance3", + "- route2: utterance4"] + + Tells us that the remote is missing "route2: utterance3" and "route2: + utterance4", which do exist locally. If we see: + + [" route1: utterance1", + " route1: utterance2", + "+ route2: utterance3", + "+ route2: utterance4"] + + This diff tells us that the remote has "route2: utterance3" and + "route2: utterance4", which do not exist locally. + """ + # first we get remote and local utterances + remote_utterances = self.index.get_utterances() + local_utterances = self.to_config().to_utterances() + + diff_obj = UtteranceDiff.from_utterances( + local_utterances=local_utterances, remote_utterances=remote_utterances + ) + return diff_obj.to_utterance_str(include_metadata=include_metadata) + + def _extract_routes_details( + self, routes: List[Route], include_metadata: bool = False + ) -> Tuple: + route_names = [route.name for route in routes for _ in route.utterances] + utterances = [utterance for route in routes for utterance in route.utterances] + function_schemas = [ + route.function_schemas[0] if route.function_schemas is not None else {} + for route in routes + for _ in route.utterances + ] + + if include_metadata: + metadata = [route.metadata for route in routes for _ in route.utterances] + return route_names, utterances, function_schemas, metadata + return route_names, utterances, function_schemas + + def _encode(self, text: str) -> Any: + """Generates embeddings for a given text. + + Must be implemented by a subclass. + + :param text: The text to encode. + :type text: str + :return: The embeddings of the text. + :rtype: Any + """ + # TODO: should encode "content" rather than text + raise NotImplementedError("This method should be implemented by subclasses.") + + async def _async_encode(self, text: str) -> Any: + """Asynchronously generates embeddings for a given text. + + Must be implemented by a subclass. + + :param text: The text to encode. + :type text: str + :return: The embeddings of the text. + :rtype: Any + """ + # TODO: should encode "content" rather than text + raise NotImplementedError("This method should be implemented by subclasses.") + + def _retrieve( + self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None + ) -> List[Dict]: + """Given a query vector, retrieve the top_k most similar records.""" + # get scores and routes + scores, routes = self.index.query( + vector=xq, top_k=top_k, route_filter=route_filter + ) + return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] + + async def _async_retrieve( + self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None + ) -> List[Dict]: + """Given a query vector, retrieve the top_k most similar records.""" + # get scores and routes + scores, routes = await self.index.aquery( + vector=xq, top_k=top_k, route_filter=route_filter + ) + return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] + + def _set_aggregation_method(self, aggregation: str = "sum"): + # TODO is this really needed? + if aggregation == "sum": + return lambda x: sum(x) + elif aggregation == "mean": + return lambda x: np.mean(x) + elif aggregation == "max": + return lambda x: max(x) + else: + raise ValueError( + f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." + ) + + def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]: + scores_by_class = self.group_scores_by_class(query_results) + + # Calculate total score for each class + total_scores = { + route: self.aggregation_method(scores) + for route, scores in scores_by_class.items() + } + top_class = max(total_scores, key=lambda x: total_scores[x], default=None) + + # Return the top class and its associated scores + if top_class is not None: + return str(top_class), scores_by_class.get(top_class, []) + else: + logger.warning("No classification found for semantic classifier.") + return "", [] + + async def _async_semantic_classify( + self, query_results: List[Dict] + ) -> Tuple[str, List[float]]: + scores_by_class = await self.async_group_scores_by_class(query_results) + + # Calculate total score for each class + total_scores = { + route: self.aggregation_method(scores) + for route, scores in scores_by_class.items() + } + top_class = max(total_scores, key=lambda x: total_scores[x], default=None) + + # Return the top class and its associated scores + if top_class is not None: + return str(top_class), scores_by_class.get(top_class, []) + else: + logger.warning("No classification found for semantic classifier.") + return "", [] + + def get(self, name: str) -> Optional[Route]: + for route in self.routes: + if route.name == name: + return route + logger.error(f"Route `{name}` not found") + return None + + def _semantic_classify_multiple_routes( + self, query_results: List[Dict] + ) -> List[Tuple[str, float]]: + scores_by_class = self.group_scores_by_class(query_results) + + # Filter classes based on threshold and find max score for each + classes_above_threshold = [] + for route_name, scores in scores_by_class.items(): + # Use the get method to find the Route object by its name + route_obj = self.get(route_name) + if route_obj is not None: + # Use the Route object's threshold if it exists, otherwise use the provided threshold + _threshold = ( + route_obj.score_threshold + if route_obj.score_threshold is not None + else self.score_threshold + ) + if self._pass_threshold(scores, _threshold): + max_score = max(scores) + classes_above_threshold.append((route_name, max_score)) + + return classes_above_threshold + + def group_scores_by_class( + self, query_results: List[Dict] + ) -> Dict[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] + return scores_by_class + + async def async_group_scores_by_class( + self, query_results: List[Dict] + ) -> Dict[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] + return scores_by_class + + def _pass_threshold(self, scores: List[float], threshold: float) -> bool: + if scores: + return max(scores) > threshold + else: + return False + + def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None): + """ + Update the score thresholds for each route. + """ + if score_thresholds: + for route in self.routes: + route.score_threshold = score_thresholds.get( + route.name, self.score_threshold + ) + + def to_config(self) -> LayerConfig: + return LayerConfig( + encoder_type=self.encoder.type, + encoder_name=self.encoder.name, + routes=self.routes, + ) + + def to_json(self, file_path: str): + config = self.to_config() + config.to_file(file_path) + + def to_yaml(self, file_path: str): + config = self.to_config() + config.to_file(file_path) + + def get_thresholds(self) -> Dict[str, float]: + # TODO: float() below is hacky fix for lint, fix this with new type? + thresholds = { + route.name: float(route.score_threshold or self.score_threshold) + for route in self.routes + } + return thresholds + + def fit( + self, + X: List[str], + y: List[str], + batch_size: int = 500, + max_iter: int = 500, + local_execution: bool = False, + ): + original_index = self.index + if local_execution: + # Switch to a local index for fitting + from semantic_router.index.local import LocalIndex + + remote_routes = self.index.get_utterances() + # TODO Enhance by retrieving directly the vectors instead of embedding all utterances again + routes, utterances, function_schemas, metadata = map( + list, zip(*remote_routes) + ) + embeddings = self.encoder(utterances) + self.index = LocalIndex() + self.index.add( + embeddings=embeddings, + routes=routes, + utterances=utterances, + metadata_list=metadata, + ) + + # convert inputs into array + Xq: List[List[float]] = [] + for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"): + emb = np.array(self.encoder(X[i : i + batch_size])) + Xq.extend(emb) + # initial eval (we will iterate from here) + best_acc = self._vec_evaluate(Xq=np.array(Xq), y=y) + best_thresholds = self.get_thresholds() + # begin fit + for _ in (pbar := tqdm(range(max_iter), desc="Training")): + pbar.set_postfix({"acc": round(best_acc, 2)}) + # Find the best score threshold for each route + thresholds = threshold_random_search( + route_layer=self, + search_range=0.8, + ) + # update current route layer + self._update_thresholds(score_thresholds=thresholds) + # evaluate + acc = self._vec_evaluate(Xq=Xq, y=y) + # update best + if acc > best_acc: + best_acc = acc + best_thresholds = thresholds + # update route layer to best thresholds + self._update_thresholds(score_thresholds=best_thresholds) + + if local_execution: + # Switch back to the original index + self.index = original_index + + def evaluate(self, X: List[str], y: List[str], batch_size: int = 500) -> float: + """ + Evaluate the accuracy of the route selection. + """ + Xq: List[List[float]] = [] + for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"): + emb = np.array(self.encoder(X[i : i + batch_size])) + Xq.extend(emb) + + accuracy = self._vec_evaluate(Xq=np.array(Xq), y=y) + return accuracy + + def _vec_evaluate(self, Xq: Union[List[float], Any], y: List[str]) -> float: + """ + Evaluate the accuracy of the route selection. + """ + correct = 0 + for xq, target_route in zip(Xq, y): + # We treate dynamic routes as static here, because when evaluating we use only vectors, and dynamic routes expect strings by default. + route_choice = self(vector=xq, simulate_static=True) + if route_choice.name == target_route: + correct += 1 + accuracy = correct / len(Xq) + return accuracy + + def _get_route_names(self) -> List[str]: + return [route.name for route in self.routes] + + +def threshold_random_search( + route_layer: BaseRouteLayer, + search_range: Union[int, float], +) -> Dict[str, float]: + """Performs a random search iteration given a route layer and a search range.""" + # extract the route names + routes = route_layer.get_thresholds() + route_names = list(routes.keys()) + route_thresholds = list(routes.values()) + # generate search range for each + score_threshold_values = [] + for threshold in route_thresholds: + score_threshold_values.append( + np.linspace( + start=max(threshold - search_range, 0.0), + stop=min(threshold + search_range, 1.0), + num=100, + ) + ) + # Generate a random threshold for each route + score_thresholds = { + route: random.choice(score_threshold_values[i]) + for i, route in enumerate(route_names) + } + return score_thresholds diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py new file mode 100644 index 00000000..b65607e7 --- /dev/null +++ b/semantic_router/routers/hybrid.py @@ -0,0 +1,212 @@ +from typing import Any, Dict, List, Optional, Tuple +import asyncio +from pydantic.v1 import validator, Field + +import numpy as np +from numpy.linalg import norm + +from semantic_router.encoders import ( + BaseEncoder, + BM25Encoder, + TfidfEncoder, +) +from semantic_router.route import Route +from semantic_router.index.hybrid_local import HybridLocalIndex +from semantic_router.schema import RouteChoice +from semantic_router.utils.logger import logger +from semantic_router.routers.base import BaseRouteLayer +from semantic_router.llms import BaseLLM + + +class HybridRouteLayer(BaseRouteLayer): + """A hybrid layer that uses both dense and sparse embeddings to classify routes. + """ + # there are a few additional attributes for hybrid + sparse_encoder: BM25Encoder = Field(default_factory=BM25Encoder) + alpha: float = 0.3 + index: HybridLocalIndex = Field(default_factory=HybridLocalIndex) + + def __init__( + self, + encoder: BaseEncoder, + sparse_encoder: Optional[BM25Encoder] = None, + llm: Optional[BaseLLM] = None, + routes: List[Route] = [], + index: Optional[HybridLocalIndex] = None, + top_k: int = 5, + aggregation: str = "mean", + auto_sync: Optional[str] = None, + alpha: float = 0.3, + ): + super().__init__( + encoder=encoder, + llm=llm, + routes=routes.copy(), + index=index, + top_k=top_k, + aggregation=aggregation, + auto_sync=auto_sync, + ) + # initialize sparse encoder + if sparse_encoder is None: + logger.warning("No sparse_encoder provided. Using default BM25Encoder.") + self.sparse_encoder = BM25Encoder() + else: + self.sparse_encoder = sparse_encoder + # set alpha + self.alpha = alpha + # fit sparse encoder if needed + if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( + self.sparse_encoder, "fit" + ): + self.sparse_encoder.fit(routes) + # initialize index if not provided + # TODO: add check for hybrid compatible index + if self.index is None: + logger.warning("No index provided. Using default HybridLocalIndex.") + self.index = HybridLocalIndex() + # add routes if we have them + if routes: + for route in routes: + self.add(route) + + @validator("sparse_encoder", pre=True, always=True) + def set_sparse_encoder(cls, v): + return v if v is not None else BM25Encoder() + + @validator("index", pre=True, always=True) + def set_index(cls, v): + return v if v is not None else HybridLocalIndex() + + def _encode(self, text: List[str]) -> Any: + """Given some text, generates dense and sparse embeddings, then scales them + using the chosen alpha value. + """ + # TODO: should encode "content" rather than text + # TODO: add alpha as a parameter + # create dense query vector + xq_d = np.array(self.encoder(text)) + #xq_d = np.squeeze(xq_d) # Reduce to 1d array. + # create sparse query vector + 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) + return xq_d, xq_s + + async def _async_encode(self, text: List[str]) -> Any: + """Given some text, generates dense and sparse embeddings, then scales them + using the chosen alpha value. + """ + # TODO: should encode "content" rather than text + # TODO: add alpha as a parameter + # async encode both dense and sparse + dense_coro = self.encoder.acall(text) + sparse_coro = self.sparse_encoder.acall(text) + dense_vec, sparse_vec = await asyncio.gather(dense_coro, sparse_coro) + # create dense query vector + xq_d = np.array(dense_vec) + #xq_d = np.squeeze(xq_d) # reduce to 1d array + # create sparse query vector + xq_s = np.array(sparse_vec) + #xq_s = np.squeeze(xq_s) + # convex scaling + xq_d, xq_s = self._convex_scaling(xq_d, xq_s) + return xq_d, xq_s + + def __call__( + self, + text: Optional[str] = None, + vector: Optional[List[float]] = None, + simulate_static: bool = False, + route_filter: Optional[List[str]] = None, + sparse_vector: Optional[List[float]] = None, + ) -> RouteChoice: + # if no vector provided, encode text to get vector + if vector is None: + if text is None: + raise ValueError("Either text or vector must be provided") + vector, potential_sparse_vector = self._encode(text=[text]) + if sparse_vector is None: + if text is None: + raise ValueError("Either text or sparse_vector must be provided") + sparse_vector = potential_sparse_vector + # TODO: add alpha as a parameter + scores, route_names = self.index.query( + vector=np.array(vector) if isinstance(vector, list) else vector, + top_k=self.top_k, + route_filter=route_filter, + sparse_vector=np.array(sparse_vector) if isinstance(sparse_vector, list) else sparse_vector, + ) + top_class, top_class_scores = self._semantic_classify(list(zip(scores, route_names))) + passed = self._pass_threshold(top_class_scores, self.score_threshold) + if passed: + return RouteChoice( + name=top_class, + similarity_score=max(top_class_scores) + ) + else: + return RouteChoice() + + def add(self, route: Route): + self.routes += [route] + + route_names = [route.name] * len(route.utterances) + + # create embeddings for all routes + logger.info(f"Encoding route {route.name}") + dense_embeds, sparse_embeds = self._encode(route.utterances) + self.index.add( + embeddings=dense_embeds, + sparse_embeddings=sparse_embeds, + routes=route_names, # TODO: aligning names of routes v route_names + utterances=route.utterances, + ) + # TODO: in some places we say vector, sparse_vector and in others + # TODO: we say embeddings, sparse_embeddings + + def _convex_scaling(self, dense: np.ndarray, sparse: np.ndarray): + # scale sparse and dense vecs + dense = np.array(dense) * self.alpha + sparse = np.array(sparse) * (1 - self.alpha) + return dense, sparse + + def _set_aggregation_method(self, aggregation: str = "sum"): + if aggregation == "sum": + return lambda x: sum(x) + elif aggregation == "mean": + return lambda x: np.mean(x) + elif aggregation == "max": + return lambda x: max(x) + else: + raise ValueError( + f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." + ) + + def _semantic_classify(self, query_results: List[Tuple]) -> Tuple[str, List[float]]: + scores_by_class: Dict[str, List[float]] = {} + for score, route in query_results: + if route in scores_by_class: + scores_by_class[route].append(score) + else: + scores_by_class[route] = [score] + + # Calculate total score for each class + total_scores = { + route: self.aggregation_method(scores) + for route, scores in scores_by_class.items() + } + top_class = max(total_scores, key=lambda x: total_scores[x], default=None) + + # Return the top class and its associated scores + if top_class is not None: + return str(top_class), scores_by_class.get(top_class, []) + else: + logger.warning("No classification found for semantic classifier.") + return "", [] + + def _pass_threshold(self, scores: List[float], threshold: float) -> bool: + if scores: + return max(scores) > threshold + else: + return False diff --git a/semantic_router/layer.py b/semantic_router/routers/semantic.py similarity index 98% rename from semantic_router/layer.py rename to semantic_router/routers/semantic.py index ec051633..499bdd73 100644 --- a/semantic_router/layer.py +++ b/semantic_router/routers/semantic.py @@ -4,6 +4,7 @@ import random import hashlib from typing import Any, Dict, List, Optional, Tuple, Union +from pydantic.v1 import BaseModel import numpy as np import yaml # type: ignore @@ -271,10 +272,14 @@ def get_hash(self) -> ConfigParameter: ) -class RouteLayer: +class RouteLayer(BaseModel): score_threshold: float encoder: BaseEncoder index: BaseIndex + llm: Optional[BaseLLM] = None + top_k: int = 5 + aggregation: str = "mean" + auto_sync: Optional[str] = None def __init__( self, @@ -283,7 +288,7 @@ def __init__( routes: Optional[List[Route]] = None, index: Optional[BaseIndex] = None, # type: ignore top_k: int = 5, - aggregation: str = "sum", + aggregation: str = "mean", auto_sync: Optional[str] = None, ): self.index: BaseIndex = index if index is not None else LocalIndex() @@ -891,20 +896,6 @@ def get_utterance_diff(self, include_metadata: bool = False) -> List[str]: ) return diff_obj.to_utterance_str(include_metadata=include_metadata) - def _add_and_sync_routes(self, routes: List[Route]): - self.routes.extend(routes) - # first we get remote and local utterances - remote_utterances = self.index.get_utterances() - local_utterances = self.to_config().to_utterances() - - diff_obj = UtteranceDiff.from_utterances( - local_utterances=local_utterances, remote_utterances=remote_utterances - ) - sync_strategy = diff_obj.get_sync_strategy(sync_mode=self.auto_sync) - self._execute_sync_strategy(strategy=sync_strategy) - # update remote hash - self._write_hash() - def _extract_routes_details( self, routes: List[Route], include_metadata: bool = False ) -> Tuple: diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index fbf14566..0859fc83 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -8,7 +8,7 @@ OpenAIEncoder, TfidfEncoder, ) -from semantic_router.hybrid_layer import HybridRouteLayer +from semantic_router.OLD_hybrid_layer import HybridRouteLayer from semantic_router.route import Route diff --git a/tests/unit/test_layer.py b/tests/unit/test_router.py similarity index 99% rename from tests/unit/test_layer.py rename to tests/unit/test_router.py index ead3f6d3..2b39410d 100644 --- a/tests/unit/test_layer.py +++ b/tests/unit/test_router.py @@ -10,7 +10,7 @@ from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex -from semantic_router.layer import LayerConfig, RouteLayer +from semantic_router.routers import LayerConfig, RouteLayer from semantic_router.llms.base import BaseLLM from semantic_router.route import Route from platform import python_version diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index 6235088f..f38635c2 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -7,7 +7,7 @@ from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder from semantic_router.index.pinecone import PineconeIndex from semantic_router.schema import Utterance -from semantic_router.layer import RouteLayer +from semantic_router.routers.base import RouteLayer from semantic_router.route import Route from platform import python_version From 5013bb899df4dc84381cd8b599c0ca246b2a8698 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sat, 23 Nov 2024 23:15:47 +0100 Subject: [PATCH 02/33] feat: abstracted semantic routelayer --- docs/indexes/pinecone-sync-routes.ipynb | 424 +++++++++++++++++++----- semantic_router/routers/base.py | 34 +- semantic_router/routers/hybrid.py | 3 +- semantic_router/routers/semantic.py | 35 +- 4 files changed, 392 insertions(+), 104 deletions(-) diff --git a/docs/indexes/pinecone-sync-routes.ipynb b/docs/indexes/pinecone-sync-routes.ipynb index 14e78905..6cb9b4eb 100644 --- a/docs/indexes/pinecone-sync-routes.ipynb +++ b/docs/indexes/pinecone-sync-routes.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -qU \"semantic-router[pinecone]==0.1.0.dev1\"" + "!pip install -qU \"semantic-router[pinecone]==0.1.0.dev2\"" ] }, { @@ -30,9 +30,18 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], "source": [ "from semantic_router import Route\n", "\n", @@ -68,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -93,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -108,8 +117,6 @@ "pc_index = PineconeIndex(\n", " dimensions=1536,\n", " init_async_index=True, # enables asynchronous methods, it's optional\n", - " sync=None, # defines whether we sync between local and remote route layers\n", - " # when sync is None, no sync is performed\n", ")\n", "pc_index.index = pc_index._init_index(force_create=True)" ] @@ -130,14 +137,45 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "from semantic_router.layer import RouteLayer\n", + "encoder.score_threshold = None" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33m2024-11-23 23:10:13 WARNING semantic_router.utils.logger TEMP | add:\n", + "chitchat: how are things going?\n", + "chitchat: how's the weather today?\n", + "chitchat: let's go to the chippy\n", + "chitchat: lovely weather today\n", + "chitchat: the weather is horrendous\n", + "politics: don't you just hate the president\n", + "politics: don't you just love the president\n", + "politics: isn't politics the best thing ever\n", + "politics: they will save the country!\n", + "politics: they're going to destroy this country!\n", + "politics: why don't you tell me about your political opinions\u001b[0m\n" + ] + } + ], + "source": [ + "from semantic_router.routers import RouteLayer\n", "import time\n", "\n", - "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index)\n", + "rl = RouteLayer(\n", + " encoder=encoder, routes=routes, index=pc_index,\n", + " auto_sync=\"local\"\n", + ")\n", "# due to pinecone indexing latency we wait 3 seconds\n", "time.sleep(3)" ] @@ -151,23 +189,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hash_id: sr_hash#\n" - ] - }, { "data": { "text/plain": [ - "False" + "True" ] }, - "execution_count": 28, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -185,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -204,23 +235,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hash_id: sr_hash#\n" - ] - }, { "data": { "text/plain": [ "False" ] }, - "execution_count": 30, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -238,27 +262,26 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[\"- politics: don't you just hate the president\",\n", - " \"- politics: don't you just love the president\",\n", - " \"- politics: isn't politics the best thing ever\",\n", - " '- politics: they will save the country!',\n", - " \"- politics: they're going to destroy this country!\",\n", - " \"- politics: why don't you tell me about your political opinions\",\n", - " '+ Route 1: Hello',\n", - " '+ Route 1: Hi',\n", - " '+ Route 2: Au revoir',\n", - " '+ Route 2: Bye',\n", - " '+ Route 2: Goodbye',\n", - " '+ Route 3: Boo']" + "['+ chitchat: how are things going?',\n", + " \"+ chitchat: how's the weather today?\",\n", + " \"+ chitchat: let's go to the chippy\",\n", + " '+ chitchat: lovely weather today',\n", + " '+ chitchat: the weather is horrendous',\n", + " \" politics: don't you just hate the president\",\n", + " \" politics: don't you just love the president\",\n", + " \" politics: isn't politics the best thing ever\",\n", + " ' politics: they will save the country!',\n", + " \" politics: they're going to destroy this country!\",\n", + " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 31, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -283,9 +306,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "remote_utterances = rl.index.get_utterances()\n", "remote_utterances" @@ -293,9 +337,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' ')]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "local_utterances = rl.to_config().to_utterances()\n", "local_utterances" @@ -310,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -330,9 +390,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.diff" ] @@ -354,9 +435,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_tag(\"+\")" ] @@ -370,9 +466,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_tag(\"-\")" ] @@ -386,9 +493,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_tag(\" \")" ] @@ -423,45 +546,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'remote': {'upsert': [],\n", + " 'delete': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]},\n", + " 'local': {'upsert': [], 'delete': []}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_sync_strategy(\"local\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'remote': {'upsert': [], 'delete': []},\n", + " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n", + " 'delete': []}}" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_sync_strategy(\"remote\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'remote': {'upsert': [], 'delete': []},\n", + " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n", + " 'delete': []}}" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_sync_strategy(\"merge-force-remote\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-11-23 23:14:16 INFO semantic_router.utils.logger local_only_mapper: {}\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'remote': {'upsert': [],\n", + " 'delete': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]},\n", + " 'local': {'upsert': [], 'delete': []}}" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_sync_strategy(\"merge-force-local\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'remote': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n", + " 'delete': []},\n", + " 'local': {'upsert': [Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag='+'),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')],\n", + " 'delete': []}}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diff.get_sync_strategy(\"merge\")" ] @@ -475,9 +695,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33m2024-11-23 23:14:25 WARNING semantic_router.utils.logger TEMP | _remove_and_sync:\n", + "chitchat: ['how are things going?', \"how's the weather today?\", \"let's go to the chippy\", 'lovely weather today', 'the weather is horrendous']\u001b[0m\n" + ] + } + ], "source": [ "strategy = diff.get_sync_strategy(\"local\")\n", "rl._execute_sync_strategy(strategy=strategy)" @@ -485,9 +714,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "time.sleep(3)\n", "rl.is_synced()" @@ -502,9 +742,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[\" politics: don't you just hate the president\",\n", + " \" politics: don't you just love the president\",\n", + " \" politics: isn't politics the best thing ever\",\n", + " ' politics: they will save the country!',\n", + " \" politics: they're going to destroy this country!\",\n", + " \" politics: why don't you tell me about your political opinions\"]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rl.get_utterance_diff()" ] @@ -520,9 +776,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[\" politics: don't you just hate the president\",\n", + " \" politics: don't you just love the president\",\n", + " \" politics: isn't politics the best thing ever\",\n", + " ' politics: they will save the country!',\n", + " \" politics: they're going to destroy this country!\",\n", + " \" politics: why don't you tell me about your political opinions\"]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "rl.sync(sync_mode=\"local\")" ] @@ -544,7 +816,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.5" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 100663da..4fd7b58b 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -278,7 +278,7 @@ def get_hash(self) -> ConfigParameter: class BaseRouteLayer(BaseModel): encoder: BaseEncoder index: BaseIndex = Field(default_factory=BaseIndex) - score_threshold: Optional[float] = None + score_threshold: Optional[float] = Field(default=None) routes: List[Route] = [] llm: Optional[BaseLLM] = None top_k: int = 5 @@ -289,10 +289,6 @@ class BaseRouteLayer(BaseModel): class Config: arbitrary_types_allowed = True - @validator("score_threshold", pre=True, always=True) - def set_score_threshold(cls, v): - return float(v) if v is not None else None - @validator("index", pre=True, always=True) def set_index(cls, v): return v if v is not None else LocalIndex() @@ -326,12 +322,13 @@ def __init__( self.encoder = encoder self.llm = llm self.routes = routes if routes else [] - if self.encoder.score_threshold is None: - raise ValueError( - "No score threshold provided for encoder. Please set the score threshold " - "in the encoder config." - ) - self.score_threshold = self.encoder.score_threshold + if self.encoder.score_threshold is not None: + self.score_threshold = self.encoder.score_threshold + if self.score_threshold is None: + logger.warning( + "No score threshold value found in encoder. Using the default " + "'None' value can lead to unexpected results." + ) self.top_k = top_k if self.top_k < 1: raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") @@ -365,6 +362,21 @@ def __init__( sync_strategy = diff.get_sync_strategy(self.auto_sync) self._execute_sync_strategy(sync_strategy) + def _set_score_threshold(self): + """Set the score threshold for the layer based on the encoder + score threshold. + + When no score threshold is used a default `None` value + is used, which means that a route will always be returned when + the layer is called.""" + if self.encoder.score_threshold is not None: + self.score_threshold = self.encoder.score_threshold + if self.score_threshold is None: + logger.warning( + "No score threshold value found in encoder. Using the default " + "'None' value can lead to unexpected results." + ) + def check_for_matching_routes(self, top_class: str) -> Optional[Route]: matching_route = next( (route for route in self.routes if route.name == top_class), None diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index b65607e7..7ea3eddb 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -3,7 +3,6 @@ from pydantic.v1 import validator, Field import numpy as np -from numpy.linalg import norm from semantic_router.encoders import ( BaseEncoder, @@ -69,6 +68,8 @@ def __init__( if routes: for route in routes: self.add(route) + # set score threshold using default method + self._set_score_threshold() # TODO: we can't really use this with hybrid... @validator("sparse_encoder", pre=True, always=True) def set_sparse_encoder(cls, v): diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 499bdd73..2104d431 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -4,7 +4,7 @@ import random import hashlib from typing import Any, Dict, List, Optional, Tuple, Union -from pydantic.v1 import BaseModel +from pydantic.v1 import validator, BaseModel, Field import numpy as np import yaml # type: ignore @@ -16,6 +16,7 @@ from semantic_router.index.pinecone import PineconeIndex from semantic_router.llms import BaseLLM, OpenAILLM from semantic_router.route import Route +from semantic_router.routers.base import BaseRouteLayer from semantic_router.schema import ( ConfigParameter, EncoderType, @@ -272,14 +273,12 @@ def get_hash(self) -> ConfigParameter: ) -class RouteLayer(BaseModel): - score_threshold: float - encoder: BaseEncoder - index: BaseIndex - llm: Optional[BaseLLM] = None - top_k: int = 5 - aggregation: str = "mean" - auto_sync: Optional[str] = None +class RouteLayer(BaseRouteLayer): + index: BaseIndex = Field(default_factory=LocalIndex) + + @validator("index", pre=True, always=True) + def set_index(cls, v): + return v if v is not None else LocalIndex() def __init__( self, @@ -291,7 +290,15 @@ def __init__( aggregation: str = "mean", auto_sync: Optional[str] = None, ): - self.index: BaseIndex = index if index is not None else LocalIndex() + super().__init__( + encoder=encoder, + llm=llm, + routes=routes.copy() if routes else [], + index=index, + top_k=top_k, + aggregation=aggregation, + auto_sync=auto_sync, + ) if encoder is None: logger.warning( "No encoder provided. Using default OpenAIEncoder. Ensure " @@ -302,12 +309,8 @@ def __init__( self.encoder = encoder self.llm = llm self.routes = routes if routes else [] - if self.encoder.score_threshold is None: - raise ValueError( - "No score threshold provided for encoder. Please set the score threshold " - "in the encoder config." - ) - self.score_threshold = self.encoder.score_threshold + # set score threshold using default method + self._set_score_threshold() self.top_k = top_k if self.top_k < 1: raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") From c4ed95f3b006e9ca6ee2c65c6ced8df8124629a4 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sat, 23 Nov 2024 23:31:38 +0100 Subject: [PATCH 03/33] chore: security updates --- poetry.lock | 3088 ++++++++++++++++++++++++++---------------------- pyproject.toml | 3 +- 2 files changed, 1695 insertions(+), 1396 deletions(-) diff --git a/poetry.lock b/poetry.lock index 11dbb7ed..de3afbe8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. 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{ version = "^1.34.98", optional = true } botocore = {version = "^1.34.110", optional = true} -aiohttp = "^3.9.5" +aiohttp = "^3.10.11" fastembed = {version = "^0.3.0", optional = true} psycopg2 = {version = "^2.9.9", optional = true} sphinx = {version = "^7.0.0", optional = true} sphinxawesome-theme = {version = "^5.2.0", optional = true} +tornado = {version = "^6.4.2", optional = true} [tool.poetry.extras] hybrid = ["pinecone-text"] From e365910a4890f6c09a90316f1b474fb5830866ab Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sat, 23 Nov 2024 23:49:12 +0100 Subject: [PATCH 04/33] feat: new route layer naming convention --- docs/indexes/pinecone-sync-routes.ipynb | 120 ++++++------ semantic_router/__init__.py | 4 +- semantic_router/index/hybrid_local.py | 5 +- semantic_router/index/local.py | 4 +- semantic_router/routers/__init__.py | 10 +- semantic_router/routers/base.py | 32 ++-- semantic_router/routers/hybrid.py | 33 ++-- semantic_router/routers/semantic.py | 237 +----------------------- tests/unit/test_hybrid_layer.py | 34 ++-- tests/unit/test_router.py | 52 +++--- 10 files changed, 156 insertions(+), 375 deletions(-) diff --git a/docs/indexes/pinecone-sync-routes.ipynb b/docs/indexes/pinecone-sync-routes.ipynb index 6cb9b4eb..d2ba412f 100644 --- a/docs/indexes/pinecone-sync-routes.ipynb +++ b/docs/indexes/pinecone-sync-routes.ipynb @@ -135,15 +135,6 @@ "The `RouteLayer` class supports both sync and async operations by default, so we initialize as usual:" ] }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "encoder.score_threshold = None" - ] - }, { "cell_type": "code", "execution_count": 5, @@ -153,18 +144,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33m2024-11-23 23:10:13 WARNING semantic_router.utils.logger TEMP | add:\n", + "\u001b[33m2024-11-23 23:46:42 WARNING semantic_router.utils.logger TEMP | add:\n", + "chitchat: how are things going?\n", + "chitchat: how's the weather today?\n", + "chitchat: let's go to the chippy\n", + "chitchat: lovely weather today\n", + "chitchat: the weather is horrendous\u001b[0m\n", + "\u001b[33m2024-11-23 23:46:50 WARNING semantic_router.utils.logger TEMP | add:\n", "chitchat: how are things going?\n", "chitchat: how's the weather today?\n", "chitchat: let's go to the chippy\n", "chitchat: lovely weather today\n", - "chitchat: the weather is horrendous\n", - "politics: don't you just hate the president\n", - "politics: don't you just love the president\n", - "politics: isn't politics the best thing ever\n", - "politics: they will save the country!\n", - "politics: they're going to destroy this country!\n", - "politics: why don't you tell me about your political opinions\u001b[0m\n" + "chitchat: the weather is horrendous\u001b[0m\n" ] } ], @@ -172,10 +163,7 @@ "from semantic_router.routers import RouteLayer\n", "import time\n", "\n", - "rl = RouteLayer(\n", - " encoder=encoder, routes=routes, index=pc_index,\n", - " auto_sync=\"local\"\n", - ")\n", + "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n", "# due to pinecone indexing latency we wait 3 seconds\n", "time.sleep(3)" ] @@ -216,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -235,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -244,7 +232,7 @@ "False" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -262,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -281,7 +269,7 @@ " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -306,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -325,7 +313,7 @@ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -337,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -351,7 +339,7 @@ " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -370,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -390,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -409,7 +397,7 @@ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -435,7 +423,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -448,7 +436,7 @@ " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag='+')]" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -475,7 +463,7 @@ "[]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -507,7 +495,7 @@ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -546,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -561,7 +549,7 @@ " 'local': {'upsert': [], 'delete': []}}" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -572,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -587,7 +575,7 @@ " 'delete': []}}" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -598,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -613,7 +601,7 @@ " 'delete': []}}" ] }, - "execution_count": 20, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -624,14 +612,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-11-23 23:14:16 INFO semantic_router.utils.logger local_only_mapper: {}\u001b[0m\n" + "\u001b[32m2024-11-23 23:47:11 INFO semantic_router.utils.logger local_only_mapper: {}\u001b[0m\n" ] }, { @@ -646,7 +634,7 @@ " 'local': {'upsert': [], 'delete': []}}" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -657,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -677,7 +665,7 @@ " 'delete': []}}" ] }, - "execution_count": 22, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -695,14 +683,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[33m2024-11-23 23:14:25 WARNING semantic_router.utils.logger TEMP | _remove_and_sync:\n", + "\u001b[33m2024-11-23 23:47:15 WARNING semantic_router.utils.logger TEMP | _remove_and_sync:\n", "chitchat: ['how are things going?', \"how's the weather today?\", \"let's go to the chippy\", 'lovely weather today', 'the weather is horrendous']\u001b[0m\n" ] } @@ -714,16 +702,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "False" + "True" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -742,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -756,7 +744,7 @@ " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -776,9 +764,16 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[33m2024-11-23 23:47:23 WARNING semantic_router.utils.logger Local and remote route layers are already synchronized.\u001b[0m\n" + ] + }, { "data": { "text/plain": [ @@ -790,7 +785,7 @@ " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -798,6 +793,13 @@ "source": [ "rl.sync(sync_mode=\"local\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] } ], "metadata": { diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py index 80f33f23..b2c0b885 100644 --- a/semantic_router/__init__.py +++ b/semantic_router/__init__.py @@ -1,6 +1,6 @@ -from semantic_router.routers import LayerConfig, RouteLayer, HybridRouteLayer +from semantic_router.routers import RouterConfig, RouteLayer, HybridRouter from semantic_router.route import Route -__all__ = ["RouteLayer", "HybridRouteLayer", "Route", "LayerConfig"] +__all__ = ["RouteLayer", "HybridRouter", "Route", "RouterConfig"] __version__ = "0.1.0.dev2" diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index 9316487e..a56463b3 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -5,7 +5,6 @@ from semantic_router.schema import ConfigParameter, Utterance from semantic_router.index.local import LocalIndex -from semantic_router.linear import similarity_matrix, top_scores from semantic_router.utils.logger import logger from typing import Any @@ -104,7 +103,9 @@ def query( # calculate sparse vec similarity sparse_norm = norm(self.sparse_index, axis=1) xq_s_norm = norm(xq_s) # TODO: this used to be xq_s.T, should work without - sim_s = np.squeeze(np.dot(self.sparse_index, xq_s.T)) / (sparse_norm * xq_s_norm) + sim_s = np.squeeze(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]) diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py index 420ad30f..9d33163e 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -43,9 +43,7 @@ def add( self.utterances = np.concatenate([self.utterances, utterances_arr]) def _remove_and_sync(self, routes_to_delete: dict): - logger.warning( - f"Sync remove is not implemented for {self.__class__.__name__}." - ) + logger.warning(f"Sync remove is not implemented for {self.__class__.__name__}.") def get_utterances(self) -> List[Utterance]: """ diff --git a/semantic_router/routers/__init__.py b/semantic_router/routers/__init__.py index 4c8031ab..4ba619d5 100644 --- a/semantic_router/routers/__init__.py +++ b/semantic_router/routers/__init__.py @@ -1,10 +1,10 @@ -from semantic_router.routers.base import BaseRouteLayer, LayerConfig +from semantic_router.routers.base import BaseRouter, RouterConfig from semantic_router.routers.semantic import RouteLayer -from semantic_router.routers.hybrid import HybridRouteLayer +from semantic_router.routers.hybrid import HybridRouter __all__ = [ - "BaseRouteLayer", - "LayerConfig", + "BaseRouter", + "RouterConfig", "RouteLayer", - "HybridRouteLayer", + "HybridRouter", ] diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 4fd7b58b..69753e03 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -57,10 +57,10 @@ def is_valid(layer_config: str) -> bool: return False -class LayerConfig: +class RouterConfig: """ - Generates a LayerConfig object that can be used for initializing a - RouteLayer. + Generates a RouterConfig object that can be used for initializing a + Routers. """ routes: List[Route] = [] @@ -80,7 +80,7 @@ def __init__( if encode_type.value == self.encoder_type: if self.encoder_type == EncoderType.HUGGINGFACE.value: raise NotImplementedError( - "HuggingFace encoder not supported by LayerConfig yet." + "HuggingFace encoder not supported by RouterConfig yet." ) encoder_name = EncoderDefault[encode_type.name].value[ "embedding_model" @@ -91,7 +91,7 @@ def __init__( self.routes = routes @classmethod - def from_file(cls, path: str) -> "LayerConfig": + def from_file(cls, path: str) -> "RouterConfig": logger.info(f"Loading route config from {path}") _, ext = os.path.splitext(path) with open(path, "r") as f: @@ -143,7 +143,7 @@ def from_tuples( encoder_type: str = "openai", encoder_name: Optional[str] = None, ): - """Initialize a LayerConfig from a list of tuples of routes and + """Initialize a RouterConfig from a list of tuples of routes and utterances. :param route_tuples: A list of tuples, each containing a route name and an @@ -182,9 +182,9 @@ def from_index( encoder_type: str = "openai", encoder_name: Optional[str] = None, ): - """Initialize a LayerConfig from a BaseIndex object. + """Initialize a RouterConfig from a BaseIndex object. - :param index: The index to initialize the LayerConfig from. + :param index: The index to initialize the RouterConfig from. :type index: BaseIndex :param encoder_type: The type of encoder to use, defaults to "openai". :type encoder_type: str, optional @@ -275,7 +275,7 @@ def get_hash(self) -> ConfigParameter: ) -class BaseRouteLayer(BaseModel): +class BaseRouter(BaseModel): encoder: BaseEncoder index: BaseIndex = Field(default_factory=BaseIndex) score_threshold: Optional[float] = Field(default=None) @@ -365,7 +365,7 @@ def __init__( def _set_score_threshold(self): """Set the score threshold for the layer based on the encoder score threshold. - + When no score threshold is used a default `None` value is used, which means that a route will always be returned when the layer is called.""" @@ -688,18 +688,18 @@ def __str__(self): @classmethod def from_json(cls, file_path: str): - config = LayerConfig.from_file(file_path) + config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes) @classmethod def from_yaml(cls, file_path: str): - config = LayerConfig.from_file(file_path) + config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes) @classmethod - def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None): + def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes, index=index) @@ -1115,8 +1115,8 @@ def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None route.name, self.score_threshold ) - def to_config(self) -> LayerConfig: - return LayerConfig( + def to_config(self) -> RouterConfig: + return RouterConfig( encoder_type=self.encoder.type, encoder_name=self.encoder.name, routes=self.routes, @@ -1226,7 +1226,7 @@ def _get_route_names(self) -> List[str]: def threshold_random_search( - route_layer: BaseRouteLayer, + route_layer: BaseRouter, search_range: Union[int, float], ) -> Dict[str, float]: """Performs a random search iteration given a route layer and a search range.""" diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 7ea3eddb..6e66142a 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -13,13 +13,13 @@ from semantic_router.index.hybrid_local import HybridLocalIndex from semantic_router.schema import RouteChoice from semantic_router.utils.logger import logger -from semantic_router.routers.base import BaseRouteLayer +from semantic_router.routers.base import BaseRouter from semantic_router.llms import BaseLLM -class HybridRouteLayer(BaseRouteLayer): - """A hybrid layer that uses both dense and sparse embeddings to classify routes. - """ +class HybridRouter(BaseRouter): + """A hybrid layer that uses both dense and sparse embeddings to classify routes.""" + # there are a few additional attributes for hybrid sparse_encoder: BM25Encoder = Field(default_factory=BM25Encoder) alpha: float = 0.3 @@ -74,7 +74,7 @@ def __init__( @validator("sparse_encoder", pre=True, always=True) def set_sparse_encoder(cls, v): return v if v is not None else BM25Encoder() - + @validator("index", pre=True, always=True) def set_index(cls, v): return v if v is not None else HybridLocalIndex() @@ -87,10 +87,10 @@ def _encode(self, text: List[str]) -> Any: # TODO: add alpha as a parameter # create dense query vector xq_d = np.array(self.encoder(text)) - #xq_d = np.squeeze(xq_d) # Reduce to 1d array. + # 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) + # xq_s = np.squeeze(xq_s) # convex scaling xq_d, xq_s = self._convex_scaling(xq_d, xq_s) return xq_d, xq_s @@ -107,10 +107,10 @@ async def _async_encode(self, text: List[str]) -> Any: dense_vec, sparse_vec = await asyncio.gather(dense_coro, sparse_coro) # create dense query vector xq_d = np.array(dense_vec) - #xq_d = np.squeeze(xq_d) # reduce to 1d array + # xq_d = np.squeeze(xq_d) # reduce to 1d array # create sparse query vector xq_s = np.array(sparse_vec) - #xq_s = np.squeeze(xq_s) + # xq_s = np.squeeze(xq_s) # convex scaling xq_d, xq_s = self._convex_scaling(xq_d, xq_s) return xq_d, xq_s @@ -137,15 +137,18 @@ def __call__( vector=np.array(vector) if isinstance(vector, list) else vector, top_k=self.top_k, route_filter=route_filter, - sparse_vector=np.array(sparse_vector) if isinstance(sparse_vector, list) else sparse_vector, + sparse_vector=( + np.array(sparse_vector) + if isinstance(sparse_vector, list) + else sparse_vector + ), + ) + top_class, top_class_scores = self._semantic_classify( + list(zip(scores, route_names)) ) - top_class, top_class_scores = self._semantic_classify(list(zip(scores, route_names))) passed = self._pass_threshold(top_class_scores, self.score_threshold) if passed: - return RouteChoice( - name=top_class, - similarity_score=max(top_class_scores) - ) + return RouteChoice(name=top_class, similarity_score=max(top_class_scores)) else: return RouteChoice() diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 2104d431..951ef6f7 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -1,13 +1,9 @@ -import importlib import json -import os import random -import hashlib from typing import Any, Dict, List, Optional, Tuple, Union -from pydantic.v1 import validator, BaseModel, Field +from pydantic.v1 import validator, Field import numpy as np -import yaml # type: ignore from tqdm.auto import tqdm from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder @@ -16,15 +12,13 @@ from semantic_router.index.pinecone import PineconeIndex from semantic_router.llms import BaseLLM, OpenAILLM from semantic_router.route import Route -from semantic_router.routers.base import BaseRouteLayer +from semantic_router.routers.base import BaseRouter, RouterConfig from semantic_router.schema import ( ConfigParameter, - EncoderType, RouteChoice, Utterance, UtteranceDiff, ) -from semantic_router.utils.defaults import EncoderDefault from semantic_router.utils.logger import logger @@ -58,222 +52,7 @@ def is_valid(layer_config: str) -> bool: return False -class LayerConfig: - """ - Generates a LayerConfig object that can be used for initializing a - RouteLayer. - """ - - routes: List[Route] = [] - - def __init__( - self, - routes: List[Route] = [], - encoder_type: str = "openai", - encoder_name: Optional[str] = None, - ): - self.encoder_type = encoder_type - if encoder_name is None: - for encode_type in EncoderType: - if encode_type.value == self.encoder_type: - if self.encoder_type == EncoderType.HUGGINGFACE.value: - raise NotImplementedError( - "HuggingFace encoder not supported by LayerConfig yet." - ) - encoder_name = EncoderDefault[encode_type.name].value[ - "embedding_model" - ] - break - logger.info(f"Using default {encoder_type} encoder: {encoder_name}") - self.encoder_name = encoder_name - self.routes = routes - - @classmethod - def from_file(cls, path: str) -> "LayerConfig": - logger.info(f"Loading route config from {path}") - _, ext = os.path.splitext(path) - with open(path, "r") as f: - if ext == ".json": - layer = json.load(f) - elif ext in [".yaml", ".yml"]: - layer = yaml.safe_load(f) - else: - raise ValueError( - "Unsupported file type. Only .json and .yaml are supported" - ) - - if not is_valid(json.dumps(layer)): - raise Exception("Invalid config JSON or YAML") - - encoder_type = layer["encoder_type"] - encoder_name = layer["encoder_name"] - routes = [] - for route_data in layer["routes"]: - # Handle the 'llm' field specially if it exists - if "llm" in route_data and route_data["llm"] is not None: - llm_data = route_data.pop( - "llm" - ) # Remove 'llm' from route_data and handle it separately - # Use the module path directly from llm_data without modification - llm_module_path = llm_data["module"] - # Dynamically import the module and then the class from that module - llm_module = importlib.import_module(llm_module_path) - llm_class = getattr(llm_module, llm_data["class"]) - # Instantiate the LLM class with the provided model name - llm = llm_class(name=llm_data["model"]) - # Reassign the instantiated llm object back to route_data - route_data["llm"] = llm - - # Dynamically create the Route object using the remaining route_data - route = Route(**route_data) - routes.append(route) - - return cls( - encoder_type=encoder_type, encoder_name=encoder_name, routes=routes - ) - - @classmethod - def from_tuples( - cls, - route_tuples: List[ - Tuple[str, str, Optional[List[Dict[str, Any]]], Dict[str, Any]] - ], - encoder_type: str = "openai", - encoder_name: Optional[str] = None, - ): - """Initialize a LayerConfig from a list of tuples of routes and - utterances. - - :param route_tuples: A list of tuples, each containing a route name and an - associated utterance. - :type route_tuples: List[Tuple[str, str]] - :param encoder_type: The type of encoder to use, defaults to "openai". - :type encoder_type: str, optional - :param encoder_name: The name of the encoder to use, defaults to None. - :type encoder_name: Optional[str], optional - """ - routes_dict: Dict[str, Route] = {} - # first create a dictionary of route names to Route objects - # TODO: duplicated code with BaseIndex.get_routes() - for route_name, utterance, function_schema, metadata in route_tuples: - # if the route is not in the dictionary, add it - if route_name not in routes_dict: - routes_dict[route_name] = Route( - name=route_name, - utterances=[utterance], - function_schemas=function_schema, - metadata=metadata, - ) - else: - # otherwise, add the utterance to the route - routes_dict[route_name].utterances.append(utterance) - # then create a list of routes from the dictionary - routes: List[Route] = [] - for route_name, route in routes_dict.items(): - routes.append(route) - return cls(routes=routes, encoder_type=encoder_type, encoder_name=encoder_name) - - @classmethod - def from_index( - cls, - index: BaseIndex, - encoder_type: str = "openai", - encoder_name: Optional[str] = None, - ): - """Initialize a LayerConfig from a BaseIndex object. - - :param index: The index to initialize the LayerConfig from. - :type index: BaseIndex - :param encoder_type: The type of encoder to use, defaults to "openai". - :type encoder_type: str, optional - :param encoder_name: The name of the encoder to use, defaults to None. - :type encoder_name: Optional[str], optional - """ - remote_routes = index.get_utterances() - return cls.from_tuples( - route_tuples=[utt.to_tuple() for utt in remote_routes], - encoder_type=encoder_type, - encoder_name=encoder_name, - ) - - def to_dict(self) -> Dict[str, Any]: - return { - "encoder_type": self.encoder_type, - "encoder_name": self.encoder_name, - "routes": [route.to_dict() for route in self.routes], - } - - def to_file(self, path: str): - """Save the routes to a file in JSON or YAML format""" - logger.info(f"Saving route config to {path}") - _, ext = os.path.splitext(path) - - # Check file extension before creating directories or files - if ext not in [".json", ".yaml", ".yml"]: - raise ValueError( - "Unsupported file type. Only .json and .yaml are supported" - ) - - dir_name = os.path.dirname(path) - - # Create the directory if it doesn't exist and dir_name is not an empty string - if dir_name and not os.path.exists(dir_name): - os.makedirs(dir_name) - - with open(path, "w") as f: - if ext == ".json": - json.dump(self.to_dict(), f, indent=4) - elif ext in [".yaml", ".yml"]: - yaml.safe_dump(self.to_dict(), f) - - def to_utterances(self) -> List[Utterance]: - """Convert the routes to a list of Utterance objects. - - :return: A list of Utterance objects. - :rtype: List[Utterance] - """ - utterances = [] - for route in self.routes: - utterances.extend( - [ - Utterance( - route=route.name, - utterance=x, - function_schemas=route.function_schemas, - metadata=route.metadata or {}, - ) - for x in route.utterances - ] - ) - return utterances - - def add(self, route: Route): - self.routes.append(route) - logger.info(f"Added route `{route.name}`") - - def get(self, name: str) -> Optional[Route]: - for route in self.routes: - if route.name == name: - return route - logger.error(f"Route `{name}` not found") - return None - - def remove(self, name: str): - if name not in [route.name for route in self.routes]: - logger.error(f"Route `{name}` not found") - else: - self.routes = [route for route in self.routes if route.name != name] - logger.info(f"Removed route `{name}`") - - def get_hash(self) -> ConfigParameter: - layer = self.to_dict() - return ConfigParameter( - field="sr_hash", - value=hashlib.sha256(json.dumps(layer).encode()).hexdigest(), - ) - - -class RouteLayer(BaseRouteLayer): +class RouteLayer(BaseRouter): index: BaseIndex = Field(default_factory=LocalIndex) @validator("index", pre=True, always=True) @@ -655,18 +434,18 @@ def __str__(self): @classmethod def from_json(cls, file_path: str): - config = LayerConfig.from_file(file_path) + config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes) @classmethod def from_yaml(cls, file_path: str): - config = LayerConfig.from_file(file_path) + config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes) @classmethod - def from_config(cls, config: LayerConfig, index: Optional[BaseIndex] = None): + def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes, index=index) @@ -1069,8 +848,8 @@ def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None route.name, self.score_threshold ) - def to_config(self) -> LayerConfig: - return LayerConfig( + def to_config(self) -> RouterConfig: + return RouterConfig( encoder_type=self.encoder.type, encoder_name=self.encoder.name, routes=self.routes, diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index 0859fc83..3cba34ca 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -8,7 +8,7 @@ OpenAIEncoder, TfidfEncoder, ) -from semantic_router.OLD_hybrid_layer import HybridRouteLayer +from semantic_router.OLD_hybrid_layer import HybridRouter from semantic_router.route import Route @@ -78,9 +78,9 @@ def routes(): sparse_encoder.fit(["The quick brown fox", "jumps over the lazy dog", "Hello, world!"]) -class TestHybridRouteLayer: +class TestHybridRouter: def test_initialization(self, openai_encoder, routes): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes, @@ -96,18 +96,18 @@ def test_initialization(self, openai_encoder, routes): assert len(set(route_layer.categories)) == 2 def test_initialization_different_encoders(self, cohere_encoder, openai_encoder): - route_layer_cohere = HybridRouteLayer( + route_layer_cohere = HybridRouter( encoder=cohere_encoder, sparse_encoder=sparse_encoder ) assert route_layer_cohere.score_threshold == 0.3 - route_layer_openai = HybridRouteLayer( + route_layer_openai = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) assert route_layer_openai.score_threshold == 0.3 def test_add_route(self, openai_encoder): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) route = Route(name="Route 3", utterances=["Yes", "No"]) @@ -117,7 +117,7 @@ def test_add_route(self, openai_encoder): assert len(set(route_layer.categories)) == 1 def test_add_multiple_routes(self, openai_encoder, routes): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) for route in routes: @@ -127,20 +127,20 @@ def test_add_multiple_routes(self, openai_encoder, routes): assert len(set(route_layer.categories)) == 2 def test_query_and_classification(self, openai_encoder, routes): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_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( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) assert route_layer("Anything") is None def test_semantic_classify(self, openai_encoder, routes): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes ) classification, score = route_layer._semantic_classify( @@ -153,7 +153,7 @@ def test_semantic_classify(self, openai_encoder, routes): assert score == [0.9] def test_semantic_classify_multiple_routes(self, openai_encoder, routes): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes ) classification, score = route_layer._semantic_classify( @@ -167,21 +167,19 @@ def test_semantic_classify_multiple_routes(self, openai_encoder, routes): assert score == [0.9, 0.8] def test_pass_threshold(self, openai_encoder): - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) assert not route_layer._pass_threshold([], 0.5) assert route_layer._pass_threshold([0.6, 0.7], 0.5) def test_failover_score_threshold(self, base_encoder): - route_layer = HybridRouteLayer( - encoder=base_encoder, sparse_encoder=sparse_encoder - ) + route_layer = HybridRouter(encoder=base_encoder, sparse_encoder=sparse_encoder) assert base_encoder.score_threshold == 0.50 assert route_layer.score_threshold == 0.50 def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes): - hybrid_route_layer = HybridRouteLayer( + hybrid_route_layer = HybridRouter( encoder=cohere_encoder, sparse_encoder=tfidf_encoder, routes=routes[:-1], @@ -195,7 +193,7 @@ def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes): def test_setting_aggregation_methods(self, openai_encoder, routes): for agg in ["sum", "mean", "max"]: - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes, @@ -218,7 +216,7 @@ def test_semantic_classify_multiple_routes_with_different_aggregation( {"route": "Route 3", "score": 1.0}, ] for agg in ["sum", "mean", "max"]: - route_layer = HybridRouteLayer( + route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes, diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index 2b39410d..61731b29 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -10,7 +10,7 @@ from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex -from semantic_router.routers import LayerConfig, RouteLayer +from semantic_router.routers import RouterConfig, RouteLayer from semantic_router.llms.base import BaseLLM from semantic_router.route import Route from platform import python_version @@ -588,8 +588,8 @@ def test_from_file_json(openai_encoder, tmp_path, index_cls): layer_json() ) # Assuming layer_json() returns a valid JSON string - # Load the LayerConfig from the temporary file - layer_config = LayerConfig.from_file(str(config_path)) + # Load the RouterConfig from the temporary file + layer_config = RouterConfig.from_file(str(config_path)) # Assertions to verify the loaded configuration assert layer_config.encoder_type == "cohere" @@ -604,8 +604,8 @@ def test_from_file_yaml(openai_encoder, tmp_path, index_cls): layer_yaml() ) # Assuming layer_yaml() returns a valid YAML string - # Load the LayerConfig from the temporary file - layer_config = LayerConfig.from_file(str(config_path)) + # Load the RouterConfig from the temporary file + layer_config = RouterConfig.from_file(str(config_path)) # Assertions to verify the loaded configuration assert layer_config.encoder_type == "cohere" @@ -615,7 +615,7 @@ def test_from_file_yaml(openai_encoder, tmp_path, index_cls): def test_from_file_invalid_path(self, index_cls): with pytest.raises(FileNotFoundError) as excinfo: - LayerConfig.from_file("nonexistent_path.json") + RouterConfig.from_file("nonexistent_path.json") assert "[Errno 2] No such file or directory: 'nonexistent_path.json'" in str( excinfo.value ) @@ -626,7 +626,7 @@ def test_from_file_unsupported_type(self, tmp_path, index_cls): config_path.write_text(layer_json()) with pytest.raises(ValueError) as excinfo: - LayerConfig.from_file(str(config_path)) + RouterConfig.from_file(str(config_path)) assert "Unsupported file type" in str(excinfo.value) def test_from_file_invalid_config(self, tmp_path, index_cls): @@ -645,10 +645,10 @@ def test_from_file_invalid_config(self, tmp_path, index_cls): # Patch the is_valid function to return False for this test with patch("semantic_router.layer.is_valid", return_value=False): - # Attempt to load the LayerConfig from the temporary file + # Attempt to load the RouterConfig from the temporary file # and assert that it raises an exception due to invalid configuration with pytest.raises(Exception) as excinfo: - LayerConfig.from_file(str(config_path)) + RouterConfig.from_file(str(config_path)) assert "Invalid config JSON or YAML" in str( excinfo.value ), "Loading an invalid configuration should raise an exception." @@ -675,8 +675,8 @@ def test_from_file_with_llm(self, tmp_path, index_cls): with open(config_path, "w") as file: file.write(llm_config_json) - # Load the LayerConfig from the temporary file - layer_config = LayerConfig.from_file(str(config_path)) + # Load the RouterConfig from the temporary file + layer_config = RouterConfig.from_file(str(config_path)) # Using BaseLLM because trying to create a usable Mock LLM is a nightmare. assert isinstance( @@ -939,61 +939,61 @@ def test_fit(self, openai_encoder, routes, test_data): # Add more tests for edge cases and error handling as needed. -class TestLayerConfig: +class TestRouterConfig: def test_init(self): - layer_config = LayerConfig() + layer_config = RouterConfig() assert layer_config.routes == [] def test_to_file_json(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) with patch("builtins.open", mock_open()) as mocked_open: layer_config.to_file("data/test_output.json") mocked_open.assert_called_once_with("data/test_output.json", "w") def test_to_file_yaml(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) with patch("builtins.open", mock_open()) as mocked_open: layer_config.to_file("data/test_output.yaml") mocked_open.assert_called_once_with("data/test_output.yaml", "w") def test_to_file_invalid(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) with pytest.raises(ValueError): layer_config.to_file("test_output.txt") def test_from_file_json(self): mock_json_data = layer_json() with patch("builtins.open", mock_open(read_data=mock_json_data)) as mocked_open: - layer_config = LayerConfig.from_file("data/test.json") + layer_config = RouterConfig.from_file("data/test.json") mocked_open.assert_called_once_with("data/test.json", "r") - assert isinstance(layer_config, LayerConfig) + assert isinstance(layer_config, RouterConfig) def test_from_file_yaml(self): mock_yaml_data = layer_yaml() with patch("builtins.open", mock_open(read_data=mock_yaml_data)) as mocked_open: - layer_config = LayerConfig.from_file("data/test.yaml") + layer_config = RouterConfig.from_file("data/test.yaml") mocked_open.assert_called_once_with("data/test.yaml", "r") - assert isinstance(layer_config, LayerConfig) + assert isinstance(layer_config, RouterConfig) def test_from_file_invalid(self): with open("test.txt", "w") as f: f.write("dummy content") with pytest.raises(ValueError): - LayerConfig.from_file("test.txt") + RouterConfig.from_file("test.txt") os.remove("test.txt") def test_to_dict(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) assert layer_config.to_dict()["routes"] == [route.to_dict()] def test_add(self): route = Route(name="test", utterances=["utterance"]) route2 = Route(name="test2", utterances=["utterance2"]) - layer_config = LayerConfig() + layer_config = RouterConfig() layer_config.add(route) # confirm route added assert layer_config.routes == [route] @@ -1003,17 +1003,17 @@ def test_add(self): def test_get(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) assert layer_config.get("test") == route def test_get_not_found(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) assert layer_config.get("not_found") is None def test_remove(self): route = Route(name="test", utterances=["utterance"]) - layer_config = LayerConfig(routes=[route]) + layer_config = RouterConfig(routes=[route]) layer_config.remove("test") assert layer_config.routes == [] From e8f00a38417cc541cda545dfae53245fc43799ea Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 00:38:40 +0100 Subject: [PATCH 05/33] fix: update old hybrid example --- docs/examples/hybrid-layer.ipynb | 70 +++++++++++++------------------- 1 file changed, 29 insertions(+), 41 deletions(-) diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-layer.ipynb index 0ea54529..e52c5752 100644 --- a/docs/examples/hybrid-layer.ipynb +++ b/docs/examples/hybrid-layer.ipynb @@ -11,7 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Hybrid Layer in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical. It helps us provide more importance to making based on the keywords contained in our utterances and user queries.\n" + "The `HybridRouter` in the Semantic Router library can improve making performance particularly for niche use-cases that contain specific terminology, such as finance or medical.\n", + "\n", + "It helps us provide more importance to making based on the keywords contained in our utterances and user queries." ] }, { @@ -34,7 +36,7 @@ "metadata": {}, "outputs": [], "source": [ - "#!pip install -qU semantic-router==0.0.11" + "#!pip install -qU semantic-router==0.1.0.dev2" ] }, { @@ -46,14 +48,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\Siraj\\Documents\\Personal\\Work\\Aurelio\\Virtual Environments\\semantic_router_3\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } @@ -83,28 +85,10 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "chitchat = Route(\n", - " name=\"chitchat\",\n", - " utterances=[\n", - " \"how's the weather today?\",\n", - " \"how are things going?\",\n", - " \"lovely weather today\",\n", - " \"the weather is horrendous\",\n", - " \"let's go to the chippy\",\n", - " ],\n", - ")\n", - "\n", "chitchat = Route(\n", " name=\"chitchat\",\n", " utterances=[\n", @@ -133,14 +117,14 @@ "outputs": [], "source": [ "import os\n", - "from semantic_router.encoders import CohereEncoder, BM25Encoder, TfidfEncoder\n", + "from semantic_router.encoders import OpenAIEncoder, TfidfEncoder\n", "from getpass import getpass\n", "\n", - "os.environ[\"COHERE_API_KEY\"] = os.environ[\"COHERE_API_KEY\"] or getpass(\n", - " \"Enter Cohere API Key: \"\n", + "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n", + " \"Enter OpenAI API Key: \"\n", ")\n", "\n", - "dense_encoder = CohereEncoder()\n", + "dense_encoder = OpenAIEncoder()\n", "# sparse_encoder = BM25Encoder()\n", "sparse_encoder = TfidfEncoder()" ] @@ -154,63 +138,67 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2024-05-07 21:15:31 INFO semantic_router.utils.logger Creating embeddings for all routes...\u001b[0m\n" + "\u001b[32m2024-11-24 00:37:14 INFO semantic_router.utils.logger Downloading and initializing default sBM25 model parameters.\u001b[0m\n", + "\u001b[32m2024-11-24 00:37:22 INFO semantic_router.utils.logger Encoding route politics\u001b[0m\n", + "\u001b[32m2024-11-24 00:37:23 INFO semantic_router.utils.logger Encoding route chitchat\u001b[0m\n" ] } ], "source": [ - "from semantic_router.hybrid_layer import HybridRouteLayer\n", + "from semantic_router.routers import HybridRouter\n", "\n", - "dl = HybridRouteLayer(\n", - " encoder=dense_encoder, sparse_encoder=sparse_encoder, routes=routes\n", + "router = HybridRouter(\n", + " encoder=dense_encoder,\n", + " sparse_encoder=sparse_encoder,\n", + " routes=routes\n", ")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'politics'" + "RouteChoice(name='politics', function_call=None, similarity_score=1.1909239963848142)" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dl(\"don't you love politics?\")" + "router(\"don't you love politics?\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'chitchat'" + "RouteChoice(name='chitchat', function_call=None, similarity_score=2.0)" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dl(\"how's the weather today?\")" + "router(\"how's the weather today?\")" ] }, { @@ -237,7 +225,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.7" } }, "nbformat": 4, From 390dc9c72795eba8d436034a0c70cebcb21ea80e Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 00:40:08 +0100 Subject: [PATCH 06/33] chore: rename --- docs/examples/{hybrid-layer.ipynb => hybrid-router.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/examples/{hybrid-layer.ipynb => hybrid-router.ipynb} (100%) diff --git a/docs/examples/hybrid-layer.ipynb b/docs/examples/hybrid-router.ipynb similarity index 100% rename from docs/examples/hybrid-layer.ipynb rename to docs/examples/hybrid-router.ipynb From cec4b208bc234d28052bc174350b1841e0f08010 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 02:48:20 +0100 Subject: [PATCH 07/33] feat: new aurelio encoder --- poetry.lock | 49 ++++++++++++++++++++++++++-- pyproject.toml | 1 + semantic_router/encoders/__init__.py | 2 ++ semantic_router/encoders/aurelio.py | 46 ++++++++++++++++++++++++++ 4 files changed, 96 insertions(+), 2 deletions(-) create mode 100644 semantic_router/encoders/aurelio.py diff --git a/poetry.lock b/poetry.lock index de3afbe8..5324dfd1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,5 +1,16 @@ # This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +[[package]] +name = "aiofiles" +version = "24.1.0" +description = "File support for asyncio." +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"}, + {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"}, +] + [[package]] name = "aiohappyeyeballs" version = "2.4.3" @@ -226,6 +237,26 @@ docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphi tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] +[[package]] +name = "aurelio-sdk" +version = "0.0.16" +description = "Aurelio Platform SDK" +optional = false +python-versions = "<4.0,>=3.9" +files = [ + {file = "aurelio_sdk-0.0.16-py3-none-any.whl", hash = "sha256:015fb384552fea5541350f1ca1be72c0186b38ecab0a97a2a29ec38e611cfbce"}, + {file = "aurelio_sdk-0.0.16.tar.gz", hash = "sha256:afdbada21d91160044dc8b953eddfb2cdae18f1c2a946098ca3f8057d6bc9ce7"}, +] + +[package.dependencies] +aiofiles = ">=24.1.0,<25.0.0" +aiohttp = ">=3.10.5,<4.0.0" +colorlog = ">=6.8.2,<7.0.0" +pydantic = ">=2.9.2,<3.0.0" +python-dotenv = ">=1.0.1,<2.0.0" +requests = ">=2.32.3,<3.0.0" +requests-toolbelt = ">=1.0.0,<2.0.0" + [[package]] name = "babel" version = "2.16.0" @@ -4163,7 +4194,7 @@ six = ">=1.5" name = "python-dotenv" version = "1.0.1" description = "Read key-value pairs from a .env file and set them as environment variables" -optional = true +optional = false python-versions = ">=3.8" files = [ {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"}, @@ -4551,6 +4582,20 @@ requests = ">=2.22,<3" [package.extras] fixture = ["fixtures"] +[[package]] +name = "requests-toolbelt" +version = "1.0.0" +description = "A utility belt for advanced users of python-requests" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +files = [ + {file = "requests-toolbelt-1.0.0.tar.gz", hash = "sha256:7681a0a3d047012b5bdc0ee37d7f8f07ebe76ab08caeccfc3921ce23c88d5bc6"}, + {file = "requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06"}, +] + +[package.dependencies] +requests = ">=2.0.1,<3.0.0" + [[package]] name = "rsa" version = "4.9" @@ -5802,4 +5847,4 @@ vision = ["pillow", "torch", "torchvision", "transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "377177b0000f74fa3bcc0d7e4abde276b8b6e4954471fd9a412ccf54064f54ca" +content-hash = "4ed42980596d2cca65bdd76d68742e6cd6283f2e2320277bd5e988b376b15997" diff --git a/pyproject.toml b/pyproject.toml index fec91ddf..f2817a65 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ mistralai= {version = ">=0.0.12,<0.1.0", optional = true} numpy = "^1.25.2" colorlog = "^6.8.0" pyyaml = "^6.0.1" +aurelio-sdk = {version = "^0.0.16"} pinecone-text = {version = ">=0.7.1,<0.10.0", optional = true} torch = {version = ">=2.1.0,<2.6.0", optional = true} transformers = {version = ">=4.36.2", optional = true} diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py index a1026240..4256e2e5 100644 --- a/semantic_router/encoders/__init__.py +++ b/semantic_router/encoders/__init__.py @@ -1,5 +1,6 @@ from typing import List, Optional +from semantic_router.encoders.aurelio import AurelioSparseEncoder from semantic_router.encoders.base import BaseEncoder from semantic_router.encoders.bedrock import BedrockEncoder from semantic_router.encoders.bm25 import BM25Encoder @@ -17,6 +18,7 @@ from semantic_router.schema import EncoderType __all__ = [ + "AurelioSparseEncoder", "BaseEncoder", "AzureOpenAIEncoder", "CohereEncoder", diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py new file mode 100644 index 00000000..3cc9fc86 --- /dev/null +++ b/semantic_router/encoders/aurelio.py @@ -0,0 +1,46 @@ +import os +from typing import Any, Dict, List, Optional +from pydantic.v1 import Field + +from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse + +from semantic_router.encoders import BaseEncoder + + +class AurelioSparseEncoder(BaseEncoder): + model: Optional[Any] = None + idx_mapping: Optional[Dict[int, int]] = None + client: AurelioClient = Field(default_factory=AurelioClient, exclude=True) + async_client: AsyncAurelioClient = Field(default_factory=AsyncAurelioClient, exclude=True) + type: str = "sparse" + + def __init__( + self, + name: str = "bm25", + score_threshold: float = 1.0, + api_key: Optional[str] = None, + ): + super().__init__(name=name, score_threshold=score_threshold) + if api_key is None: + api_key = os.getenv("AURELIO_API_KEY") + if api_key is None: + raise ValueError("AURELIO_API_KEY environment variable is not set.") + self.client = AurelioClient(api_key=api_key) + self.async_client = AsyncAurelioClient(api_key=api_key) + + def __call__(self, docs: list[str]) -> list[dict[int, float]]: + res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name) + embeds = [r.embedding.model_dump() for r in res.data] + # convert sparse vector to {index: value} format + sparse_dicts = [{i: v for i, v in zip(e["indices"], e["values"])} for e in embeds] + return sparse_dicts + + async def acall(self, docs: list[str]) -> list[dict[int, float]]: + res: EmbeddingResponse = await self.async_client.embedding(input=docs, model=self.name) + embeds = [r.embedding.model_dump() for r in res.data] + # convert sparse vector to {index: value} format + sparse_dicts = [{i: v for i, v in zip(e["indices"], e["values"])} for e in embeds] + return sparse_dicts + + def fit(self, docs: List[str]): + raise NotImplementedError("AurelioSparseEncoder does not support fit.") From 7752b08fa675ec8f4393d6153c64b06272d31ded Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 02:52:48 +0100 Subject: [PATCH 08/33] chore: rename route layer to semantic router --- semantic_router/__init__.py | 4 +- semantic_router/index/__init__.py | 2 + semantic_router/routers/__init__.py | 4 +- semantic_router/routers/base.py | 14 ++-- semantic_router/routers/semantic.py | 28 ++++---- tests/unit/test_router.py | 102 ++++++++++++++-------------- tests/unit/test_sync.py | 38 +++++------ 7 files changed, 97 insertions(+), 95 deletions(-) diff --git a/semantic_router/__init__.py b/semantic_router/__init__.py index b2c0b885..4e65f002 100644 --- a/semantic_router/__init__.py +++ b/semantic_router/__init__.py @@ -1,6 +1,6 @@ -from semantic_router.routers import RouterConfig, RouteLayer, HybridRouter +from semantic_router.routers import RouterConfig, SemanticRouter, HybridRouter from semantic_router.route import Route -__all__ = ["RouteLayer", "HybridRouter", "Route", "RouterConfig"] +__all__ = ["SemanticRouter", "HybridRouter", "Route", "RouterConfig"] __version__ = "0.1.0.dev2" diff --git a/semantic_router/index/__init__.py b/semantic_router/index/__init__.py index 3a43abe9..4e566d51 100644 --- a/semantic_router/index/__init__.py +++ b/semantic_router/index/__init__.py @@ -1,5 +1,6 @@ from semantic_router.index.base import BaseIndex from semantic_router.index.hybrid_local import HybridLocalIndex +from semantic_router.index.hybrid_local_opt import HybridLocalOptIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex @@ -7,6 +8,7 @@ __all__ = [ "BaseIndex", "HybridLocalIndex", + "HybridLocalOptIndex", "LocalIndex", "QdrantIndex", "PineconeIndex", diff --git a/semantic_router/routers/__init__.py b/semantic_router/routers/__init__.py index 4ba619d5..79987406 100644 --- a/semantic_router/routers/__init__.py +++ b/semantic_router/routers/__init__.py @@ -1,10 +1,10 @@ from semantic_router.routers.base import BaseRouter, RouterConfig -from semantic_router.routers.semantic import RouteLayer +from semantic_router.routers.semantic import SemanticRouter from semantic_router.routers.hybrid import HybridRouter __all__ = [ "BaseRouter", "RouterConfig", - "RouteLayer", + "SemanticRouter", "HybridRouter", ] diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 69753e03..f9170051 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -566,9 +566,9 @@ def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]] self._write_hash() def _local_upsert(self, utterances: List[Utterance]): - """Adds new routes to the RouteLayer. + """Adds new routes to the SemanticRouter. - :param utterances: The utterances to add to the local RouteLayer. + :param utterances: The utterances to add to the local SemanticRouter. :type utterances: List[Utterance] """ new_routes = {route.name: route for route in self.routes} @@ -590,9 +590,9 @@ def _local_upsert(self, utterances: List[Utterance]): self.routes = list(new_routes.values()) def _local_delete(self, utterances: List[Utterance]): - """Deletes routes from the local RouteLayer. + """Deletes routes from the local SemanticRouter. - :param utterances: The utterances to delete from the local RouteLayer. + :param utterances: The utterances to delete from the local SemanticRouter. :type utterances: List[Utterance] """ # create dictionary of route names to utterances @@ -704,7 +704,7 @@ def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): return cls(encoder=encoder, routes=config.routes, index=index) def add(self, route: Route): - """Add a route to the local RouteLayer and index. + """Add a route to the local SemanticRouter and index. :param route: The route to add. :type route: Route @@ -734,7 +734,7 @@ def add(self, route: Route): else: logger.warning( "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " "details." ) @@ -752,7 +752,7 @@ def update( threshold or utterances parameters, those fields are not updated. If neither field is provided raises a ValueError. - The name must exist within the local RouteLayer, if not a + The name must exist within the local SemanticRouter, if not a KeyError will be raised. """ current_local_hash = self._get_hash() diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 951ef6f7..45493e67 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -52,7 +52,7 @@ def is_valid(layer_config: str) -> bool: return False -class RouteLayer(BaseRouter): +class SemanticRouter(BaseRouter): index: BaseIndex = Field(default_factory=LocalIndex) @validator("index", pre=True, always=True) @@ -312,9 +312,9 @@ def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]] self._write_hash() def _local_upsert(self, utterances: List[Utterance]): - """Adds new routes to the RouteLayer. + """Adds new routes to the SemanticRouter. - :param utterances: The utterances to add to the local RouteLayer. + :param utterances: The utterances to add to the local SemanticRouter. :type utterances: List[Utterance] """ new_routes = {route.name: route for route in self.routes} @@ -336,9 +336,9 @@ def _local_upsert(self, utterances: List[Utterance]): self.routes = list(new_routes.values()) def _local_delete(self, utterances: List[Utterance]): - """Deletes routes from the local RouteLayer. + """Deletes routes from the local SemanticRouter. - :param utterances: The utterances to delete from the local RouteLayer. + :param utterances: The utterances to delete from the local SemanticRouter. :type utterances: List[Utterance] """ # create dictionary of route names to utterances @@ -427,7 +427,7 @@ def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: def __str__(self): return ( - f"RouteLayer(encoder={self.encoder}, " + f"SemanticRouter(encoder={self.encoder}, " f"score_threshold={self.score_threshold}, " f"routes={self.routes})" ) @@ -450,7 +450,7 @@ def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): return cls(encoder=encoder, routes=config.routes, index=index) def add(self, route: Route): - """Add a route to the local RouteLayer and index. + """Add a route to the local SemanticRouter and index. :param route: The route to add. :type route: Route @@ -480,7 +480,7 @@ def add(self, route: Route): else: logger.warning( "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " "details." ) @@ -498,7 +498,7 @@ def update( threshold or utterances parameters, those fields are not updated. If neither field is provided raises a ValueError. - The name must exist within the local RouteLayer, if not a + The name must exist within the local SemanticRouter, if not a KeyError will be raised. """ current_local_hash = self._get_hash() @@ -532,7 +532,7 @@ def update( else: logger.warning( "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " "details." ) @@ -549,7 +549,7 @@ def delete(self, route_name: str): current_remote_hash = current_local_hash if route_name not in [route.name for route in self.routes]: - err_msg = f"Route `{route_name}` not found in RouteLayer" + err_msg = f"Route `{route_name}` not found in SemanticRouter" logger.warning(err_msg) try: self.index.delete(route_name=route_name) @@ -564,7 +564,7 @@ def delete(self, route_name: str): else: logger.warning( "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " "details." ) @@ -618,7 +618,7 @@ def _add_routes(self, routes: List[Route]): else: logger.warning( "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `RouteLayer.get_utterance_diff()` to see " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " "details." ) @@ -959,7 +959,7 @@ def _get_route_names(self) -> List[str]: def threshold_random_search( - route_layer: RouteLayer, + route_layer: SemanticRouter, search_range: Union[int, float], ) -> Dict[str, float]: """Performs a random search iteration given a route layer and a search range.""" diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index 61731b29..2eef4666 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -10,7 +10,7 @@ from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex -from semantic_router.routers import RouterConfig, RouteLayer +from semantic_router.routers import RouterConfig, SemanticRouter from semantic_router.llms.base import BaseLLM from semantic_router.route import Route from platform import python_version @@ -205,7 +205,7 @@ def get_test_encoders(): class TestIndexEncoders: def test_initialization(self, routes, openai_encoder, index_cls, encoder_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=encoder_cls(), routes=routes, index=index, @@ -228,22 +228,22 @@ def test_initialization(self, routes, openai_encoder, index_cls, encoder_cls): def test_initialization_different_encoders(self, encoder_cls, index_cls): index = init_index(index_cls) encoder = encoder_cls() - route_layer = RouteLayer(encoder=encoder, index=index) + route_layer = SemanticRouter(encoder=encoder, index=index) assert route_layer.score_threshold == encoder.score_threshold def test_initialization_no_encoder(self, openai_encoder, index_cls, encoder_cls): os.environ["OPENAI_API_KEY"] = "test_api_key" - route_layer_none = RouteLayer(encoder=None) + route_layer_none = SemanticRouter(encoder=None) assert route_layer_none.score_threshold == openai_encoder.score_threshold @pytest.mark.parametrize("index_cls", get_test_indexes()) -class TestRouteLayer: +class TestSemanticRouter: def test_initialization_dynamic_route( self, dynamic_routes, openai_encoder, index_cls ): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=dynamic_routes, index=index, @@ -254,7 +254,7 @@ def test_initialization_dynamic_route( def test_delete_index(self, openai_encoder, routes, index_cls): # TODO merge .delete_index() and .delete_all() and get working index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -269,7 +269,7 @@ def test_delete_index(self, openai_encoder, routes, index_cls): def test_add_route(self, routes, openai_encoder, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=[], index=index, auto_sync="local" ) if index_cls is PineconeIndex: @@ -297,7 +297,7 @@ def test_add_route(self, routes, openai_encoder, index_cls): def test_list_route_names(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -312,7 +312,7 @@ def test_list_route_names(self, openai_encoder, routes, index_cls): def test_delete_route(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -337,7 +337,7 @@ def test_delete_route(self, openai_encoder, routes, index_cls): def test_remove_route_not_found(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # Attempt to remove a route that does not exist @@ -347,7 +347,7 @@ def test_remove_route_not_found(self, openai_encoder, routes, index_cls): def test_add_multiple_routes(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, index=index, auto_sync="local", @@ -362,7 +362,7 @@ def test_add_multiple_routes(self, openai_encoder, routes, index_cls): def test_query_and_classification(self, openai_encoder, routes, index_cls): index = init_index(index_cls, dimensions=3) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -375,7 +375,7 @@ def test_query_and_classification(self, openai_encoder, routes, index_cls): def test_query_filter(self, openai_encoder, routes, index_cls): index = init_index(index_cls, dimensions=3) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -398,7 +398,7 @@ def test_query_filter(self, openai_encoder, routes, index_cls): def test_query_filter_pinecone(self, openai_encoder, routes, index_cls): if index_cls is PineconeIndex: pineconeindex = init_index(index_cls, dimensions=3) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pineconeindex, @@ -420,7 +420,7 @@ def test_query_filter_pinecone(self, openai_encoder, routes, index_cls): def test_namespace_pinecone_index(self, openai_encoder, routes, index_cls): if index_cls is PineconeIndex: pineconeindex = init_index(index_cls, namespace="test") - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pineconeindex, @@ -438,13 +438,13 @@ def test_namespace_pinecone_index(self, openai_encoder, routes, index_cls): route_layer.index.index.delete(namespace="test", delete_all=True) def test_query_with_no_index(self, openai_encoder, index_cls): - route_layer = RouteLayer(encoder=openai_encoder) + route_layer = SemanticRouter(encoder=openai_encoder) with pytest.raises(ValueError): assert route_layer(text="Anything").name is None def test_query_with_vector(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -458,13 +458,13 @@ def test_query_with_vector(self, openai_encoder, routes, index_cls): def test_query_with_no_text_or_vector(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) with pytest.raises(ValueError): route_layer() def test_semantic_classify(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -483,7 +483,7 @@ def test_semantic_classify(self, openai_encoder, routes, index_cls): def test_semantic_classify_multiple_routes(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -505,7 +505,7 @@ def test_query_no_text_dynamic_route( self, openai_encoder, dynamic_routes, index_cls ): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=dynamic_routes, index=index ) vector = [0.1, 0.2, 0.3] @@ -514,7 +514,7 @@ def test_query_no_text_dynamic_route( def test_pass_threshold(self, openai_encoder, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, index=index, auto_sync="local", @@ -524,7 +524,7 @@ def test_pass_threshold(self, openai_encoder, index_cls): def test_failover_score_threshold(self, openai_encoder, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, index=index, auto_sync="local", @@ -538,7 +538,7 @@ def test_json(self, openai_encoder, routes, index_cls): temp.close() # Close the file to ensure it can be opened again on Windows os.environ["OPENAI_API_KEY"] = "test_api_key" index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -546,7 +546,7 @@ def test_json(self, openai_encoder, routes, index_cls): ) route_layer.to_json(temp_path) assert os.path.exists(temp_path) - route_layer_from_file = RouteLayer.from_json(temp_path) + route_layer_from_file = SemanticRouter.from_json(temp_path) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert ( @@ -563,7 +563,7 @@ def test_yaml(self, openai_encoder, routes, index_cls): temp.close() # Close the file to ensure it can be opened again on Windows os.environ["OPENAI_API_KEY"] = "test_api_key" index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -571,7 +571,7 @@ def test_yaml(self, openai_encoder, routes, index_cls): ) route_layer.to_yaml(temp_path) assert os.path.exists(temp_path) - route_layer_from_file = RouteLayer.from_yaml(temp_path) + route_layer_from_file = SemanticRouter.from_yaml(temp_path) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert ( @@ -690,12 +690,12 @@ def test_from_file_with_llm(self, tmp_path, index_cls): def test_config(self, openai_encoder, routes, index_cls): os.environ["OPENAI_API_KEY"] = "test_api_key" index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) # confirm route creation functions as expected layer_config = route_layer.to_config() assert layer_config.routes == route_layer.routes # now load from config and confirm it's the same - route_layer_from_config = RouteLayer.from_config(layer_config, index) + route_layer_from_config = SemanticRouter.from_config(layer_config, index) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert ( @@ -705,14 +705,14 @@ def test_config(self, openai_encoder, routes, index_cls): def test_get_thresholds(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) assert route_layer.get_thresholds() == {"Route 1": 0.3, "Route 2": 0.3} def test_with_multiple_routes_passing_threshold( self, openai_encoder, routes, index_cls ): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) route_layer.score_threshold = 0.5 # Set the score_threshold if needed # Assuming route_layer is already set up with routes "Route 1" and "Route 2" query_results = [ @@ -730,7 +730,7 @@ def test_with_multiple_routes_passing_threshold( def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) route_layer.score_threshold = 0.5 # Override _pass_threshold to always return False for this test route_layer._pass_threshold = lambda scores, threshold: False @@ -746,7 +746,7 @@ def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cl def test_with_no_query_results(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) route_layer.score_threshold = 0.5 query_results = [] expected = [] @@ -757,7 +757,7 @@ def test_with_no_query_results(self, openai_encoder, routes, index_cls): def test_with_unrecognized_route(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) route_layer.score_threshold = 0.5 # Test with a route name that does not exist in the route_layer's routes query_results = [{"route": "UnrecognizedRoute", "score": 0.9}] @@ -767,7 +767,7 @@ def test_with_unrecognized_route(self, openai_encoder, routes, index_cls): def test_retrieve_with_text(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -782,7 +782,7 @@ def test_retrieve_with_text(self, openai_encoder, routes, index_cls): def test_retrieve_with_vector(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -797,7 +797,7 @@ def test_retrieve_with_vector(self, openai_encoder, routes, index_cls): def test_retrieve_without_text_or_vector(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -808,7 +808,7 @@ def test_retrieve_without_text_or_vector(self, openai_encoder, routes, index_cls def test_retrieve_no_matches(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, @@ -820,7 +820,7 @@ def test_retrieve_no_matches(self, openai_encoder, routes, index_cls): def test_retrieve_one_match(self, openai_encoder, routes_3, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_3, index=index, @@ -838,7 +838,7 @@ def test_retrieve_with_text_for_multiple_matches( self, openai_encoder, routes_2, index_cls ): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=index, @@ -857,7 +857,7 @@ def test_set_aggregation_method_with_unsupported_value( self, openai_encoder, routes, index_cls ): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) unsupported_aggregation = "unsupported_aggregation_method" with pytest.raises( ValueError, @@ -867,7 +867,7 @@ def test_set_aggregation_method_with_unsupported_value( def test_refresh_routes_not_implemented(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) with pytest.raises( NotImplementedError, match="This method has not yet been implemented." ): @@ -875,7 +875,7 @@ def test_refresh_routes_not_implemented(self, openai_encoder, routes, index_cls) def test_update_threshold(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) route_name = "Route 1" new_threshold = 0.8 route_layer.update(name=route_name, threshold=new_threshold) @@ -886,7 +886,7 @@ def test_update_threshold(self, openai_encoder, routes, index_cls): def test_update_non_existent_route(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) non_existent_route = "Non-existent Route" with pytest.raises( ValueError, @@ -896,7 +896,7 @@ def test_update_non_existent_route(self, openai_encoder, routes, index_cls): def test_update_without_parameters(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) with pytest.raises( ValueError, match="At least one of 'threshold' or 'utterances' must be provided.", @@ -905,7 +905,7 @@ def test_update_without_parameters(self, openai_encoder, routes, index_cls): def test_update_utterances_not_implemented(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) with pytest.raises( NotImplementedError, match="The update method cannot be used for updating utterances yet.", @@ -915,7 +915,7 @@ def test_update_utterances_not_implemented(self, openai_encoder, routes, index_c class TestLayerFit: def test_eval(self, openai_encoder, routes, test_data): - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, auto_sync="local", @@ -926,7 +926,7 @@ def test_eval(self, openai_encoder, routes, test_data): route_layer.evaluate(X=X, y=y, batch_size=int(len(test_data) / 5)) def test_fit(self, openai_encoder, routes, test_data): - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, auto_sync="local", @@ -1019,7 +1019,7 @@ def test_remove(self): def test_setting_aggregation_methods(self, openai_encoder, routes): for agg in ["sum", "mean", "max"]: - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, aggregation=agg, @@ -1041,7 +1041,7 @@ def test_semantic_classify_multiple_routes_with_different_aggregation( {"route": "Route 3", "score": 1.0}, ] for agg in ["sum", "mean", "max"]: - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, aggregation=agg, diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index f38635c2..2327fc06 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -7,7 +7,7 @@ from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder from semantic_router.index.pinecone import PineconeIndex from semantic_router.schema import Utterance -from semantic_router.routers.base import RouteLayer +from semantic_router.routers.base import SemanticRouter from semantic_router.route import Route from platform import python_version @@ -187,13 +187,13 @@ def get_test_indexes(): @pytest.mark.parametrize("index_cls", get_test_indexes()) -class TestRouteLayer: +class TestSemanticRouter: @pytest.mark.skipif( os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required" ) def test_initialization(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - _ = RouteLayer( + _ = SemanticRouter( encoder=openai_encoder, routes=routes, top_k=10, @@ -206,7 +206,7 @@ def test_initialization(self, openai_encoder, routes, index_cls): ) def test_second_initialization_sync(self, openai_encoder, routes, index_cls): index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, auto_sync="local" ) if index_cls is PineconeIndex: @@ -220,10 +220,10 @@ def test_second_initialization_not_synced( self, openai_encoder, routes, routes_2, index_cls ): index = init_index(index_cls) - _ = RouteLayer( + _ = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, auto_sync="local" ) - route_layer = RouteLayer(encoder=openai_encoder, routes=routes_2, index=index) + route_layer = SemanticRouter(encoder=openai_encoder, routes=routes_2, index=index) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert route_layer.is_synced() is False @@ -233,10 +233,10 @@ def test_second_initialization_not_synced( ) def test_utterance_diff(self, openai_encoder, routes, routes_2, index_cls): index = init_index(index_cls) - _ = RouteLayer( + _ = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, auto_sync="local" ) - route_layer_2 = RouteLayer(encoder=openai_encoder, routes=routes_2, index=index) + route_layer_2 = SemanticRouter(encoder=openai_encoder, routes=routes_2, index=index) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated diff = route_layer_2.get_utterance_diff(include_metadata=True) @@ -256,13 +256,13 @@ def test_auto_sync_local(self, openai_encoder, routes, routes_2, index_cls): if index_cls is PineconeIndex: # TEST LOCAL pinecone_index = init_index(index_cls) - _ = RouteLayer( + _ = SemanticRouter( encoder=openai_encoder, routes=routes, index=pinecone_index, ) time.sleep(PINECONE_SLEEP) # allow for index to be populated - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=pinecone_index, @@ -281,14 +281,14 @@ def test_auto_sync_remote(self, openai_encoder, routes, routes_2, index_cls): if index_cls is PineconeIndex: # TEST REMOTE pinecone_index = init_index(index_cls) - _ = RouteLayer( + _ = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=pinecone_index, auto_sync="local", ) time.sleep(PINECONE_SLEEP) # allow for index to be populated - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pinecone_index, @@ -309,14 +309,14 @@ def test_auto_sync_merge_force_local( if index_cls is PineconeIndex: # TEST MERGE FORCE LOCAL pinecone_index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pinecone_index, auto_sync="local", ) time.sleep(PINECONE_SLEEP) # allow for index to be populated - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=pinecone_index, @@ -347,14 +347,14 @@ def test_auto_sync_merge_force_remote( if index_cls is PineconeIndex: # TEST MERGE FORCE LOCAL pinecone_index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pinecone_index, auto_sync="local", ) time.sleep(PINECONE_SLEEP) # allow for index to be populated - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=pinecone_index, @@ -385,7 +385,7 @@ def test_auto_sync_merge_force_remote( os.environ.get("PINECONE_API_KEY") is None, reason="Pinecone API key required" ) def test_sync(self, openai_encoder, index_cls): - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=[], index=init_index(index_cls), @@ -403,14 +403,14 @@ def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls): if index_cls is PineconeIndex: # TEST MERGE pinecone_index = init_index(index_cls) - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_2, index=pinecone_index, auto_sync="local", ) time.sleep(PINECONE_SLEEP) # allow for index to be populated - route_layer = RouteLayer( + route_layer = SemanticRouter( encoder=openai_encoder, routes=routes, index=pinecone_index, From 2d4395db2ba407958dedc94f4ab816cc997b6354 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 12:50:29 +0100 Subject: [PATCH 09/33] feat: optimize sparse use in hybrid index --- docs/encoders/aurelio-bm25.ipynb | 378 ++++++++++++++++++++++++++ semantic_router/encoders/__init__.py | 2 + semantic_router/encoders/aurelio.py | 2 +- semantic_router/index/__init__.py | 2 - semantic_router/index/hybrid_local.py | 42 +-- semantic_router/routers/hybrid.py | 76 +++--- semantic_router/schema.py | 1 + 7 files changed, 442 insertions(+), 61 deletions(-) create mode 100644 docs/encoders/aurelio-bm25.ipynb diff --git a/docs/encoders/aurelio-bm25.ipynb b/docs/encoders/aurelio-bm25.ipynb new file mode 100644 index 00000000..75a6bc70 --- /dev/null +++ b/docs/encoders/aurelio-bm25.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Aurelio AI BM25 Encoder" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The 3rd generation embedding models from OpenAI (`text-embedding-3-small` and `text-embedding-3-large`) can both be used with our `OpenAIEncoder` and usage is primarily the same as with the 2nd generation `text-embedding-ada-002`. However, there is a new `dimensions` parameter — which we will discuss below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by installing semantic-router. Support for the new `dimensions` parameter was added in `semantic-router==0.0.19` and `openai==1.10.0`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -qU \"semantic-router==0.1.0.dev2\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by defining a dictionary mapping routes to example phrases that should trigger those routes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from semantic_router import Route\n", + "\n", + "politics = Route(\n", + " name=\"politics\",\n", + " utterances=[\n", + " \"isn't politics the best thing ever\",\n", + " \"why don't you tell me about your political opinions\",\n", + " \"don't you just love the president\",\n", + " \"don't you just hate the president\",\n", + " \"they're going to destroy this country!\",\n", + " \"they will save the country!\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define another for good measure:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "chitchat = Route(\n", + " name=\"chitchat\",\n", + " utterances=[\n", + " \"how's the weather today?\",\n", + " \"how are things going?\",\n", + " \"lovely weather today\",\n", + " \"the weather is horrendous\",\n", + " \"let's go to the chippy\",\n", + " ],\n", + ")\n", + "\n", + "routes = [politics, chitchat]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we initialize our embedding model, we will use the `-3-large` model alongside a `dimensions` value of `256`. This will produce _tiny_ 256-dimensional vectors that — according to OpenAI — outperform the 1536-dimensional vectors produced by `text-embedding-ada-002`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.encoders.aurelio import AurelioSparseEncoder\n", + "\n", + "sparse_encoder = AurelioSparseEncoder(name=\"bm25\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "from semantic_router.encoders import OpenAIEncoder\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n", + " \"Enter OpenAI API Key: \"\n", + ")\n", + "\n", + "encoder = OpenAIEncoder(\n", + " name=\"text-embedding-3-large\", score_threshold=0.5, dimensions=256\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will specify our index:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.index.hybrid_local import HybridLocalIndex\n", + "\n", + "index = HybridLocalIndex()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define the `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`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 12:25:32 - semantic_router.utils.logger - INFO - hybrid.py:157 - add() - Encoding route politics\n", + "2024-11-24 12:25:32 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 12:25:33 - semantic_router.utils.logger - INFO - hybrid.py:157 - add() - Encoding route chitchat\n", + "2024-11-24 12:25:33 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], + "source": [ + "from semantic_router.routers import HybridRouter\n", + "\n", + "router = HybridRouter(\n", + " encoder=encoder,\n", + " sparse_encoder=sparse_encoder,\n", + " routes=routes,\n", + " index=index,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the dimensionality of our vectors by looking at the `index` attribute of the `RouteLayer`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11, 256)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.index.index.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "11" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(router.index.sparse_index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We do have 256-dimensional vectors. Now let's test them:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 12:25:37 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "RouteChoice(name='politics', function_call=None, similarity_score=1.2995813276471633)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router(\"don't you love politics?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 12:25:38 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "RouteChoice(name='chitchat', function_call=None, similarity_score=1.8563758628277611)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router(\"how's the weather today?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both are classified accurately, what if we send a query that is unrelated to our existing `Route` objects?" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 12:25:41 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "RouteChoice(name=None, function_call=None, similarity_score=None)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router(\"I'm interested in learning about llama 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we return `None` because no matches were identified. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "decision-layer", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py index 4256e2e5..893d1d61 100644 --- a/semantic_router/encoders/__init__.py +++ b/semantic_router/encoders/__init__.py @@ -51,6 +51,8 @@ def __init__(self, type: str, name: Optional[str]): self.model = CohereEncoder(name=name) elif self.type == EncoderType.OPENAI: self.model = OpenAIEncoder(name=name) + elif self.type == EncoderType.AURELIO: + self.model = AurelioSparseEncoder(name=name) elif self.type == EncoderType.BM25: if name is None: name = "bm25" diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index 3cc9fc86..c257b514 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -4,7 +4,7 @@ from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders.base import BaseEncoder class AurelioSparseEncoder(BaseEncoder): diff --git a/semantic_router/index/__init__.py b/semantic_router/index/__init__.py index 4e566d51..3a43abe9 100644 --- a/semantic_router/index/__init__.py +++ b/semantic_router/index/__init__.py @@ -1,6 +1,5 @@ from semantic_router.index.base import BaseIndex from semantic_router.index.hybrid_local import HybridLocalIndex -from semantic_router.index.hybrid_local_opt import HybridLocalOptIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex @@ -8,7 +7,6 @@ __all__ = [ "BaseIndex", "HybridLocalIndex", - "HybridLocalOptIndex", "LocalIndex", "QdrantIndex", "PineconeIndex", diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index a56463b3..28a3d83b 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -11,12 +11,11 @@ class HybridLocalIndex(LocalIndex): type: str = "hybrid_local" - sparse_index: Optional[np.ndarray] = None + sparse_index: Optional[list[dict]] = None route_names: Optional[np.ndarray] = None - class Config: - # Stop pydantic from complaining about Optional[np.ndarray]type hints. - arbitrary_types_allowed = True + def __init__(self): + super().__init__() def add( self, @@ -25,7 +24,7 @@ def add( utterances: List[str], function_schemas: Optional[List[Dict[str, Any]]] = None, metadata_list: List[Dict[str, Any]] = [], - sparse_embeddings: Optional[List[List[float]]] = None, + sparse_embeddings: Optional[List[dict[int, float]]] = None, ): if sparse_embeddings is None: raise ValueError("Sparse embeddings are required for HybridLocalIndex.") @@ -34,7 +33,6 @@ def add( if metadata_list: raise ValueError("Metadata is not supported for HybridLocalIndex.") embeds = np.array(embeddings) - sparse_embeds = np.array(sparse_embeddings) routes_arr = np.array(routes) if isinstance(utterances[0], str): utterances_arr = np.array(utterances) @@ -42,13 +40,13 @@ def add( utterances_arr = np.array(utterances, dtype=object) if self.index is None or self.sparse_index is None: self.index = embeds - self.sparse_index = sparse_embeds + self.sparse_index = sparse_embeddings self.routes = routes_arr self.utterances = utterances_arr else: # TODO: we should probably switch to an `upsert` method and standardize elsewhere self.index = np.concatenate([self.index, embeds]) - self.sparse_index = np.concatenate([self.sparse_index, sparse_embeds]) + self.sparse_index.extend(sparse_embeddings) self.routes = np.concatenate([self.routes, routes_arr]) self.utterances = np.concatenate([self.utterances, utterances_arr]) @@ -68,13 +66,23 @@ def describe(self) -> Dict: "dimensions": self.index.shape[1] if self.index is not None else 0, "vectors": self.index.shape[0] if self.index is not None else 0, } + + def _sparse_dot_product(self, vec_a: dict[int, float], vec_b: dict[int, float]) -> float: + # switch vecs to ensure first is smallest for more efficiency + if len(vec_a) > len(vec_b): + vec_a, vec_b = vec_b, vec_a + return sum(vec_a[i] * vec_b.get(i, 0) for i in vec_a) + + def _sparse_index_dot_product(self, vec_a: dict[int, float]) -> list[float]: + dot_products = [self._sparse_dot_product(vec_a, vec_b) for vec_b in self.sparse_index] + return dot_products def query( self, vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[np.ndarray] = None, + sparse_vector: Optional[dict[int, float]] = None, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query and return top_k results. @@ -85,7 +93,7 @@ def query( :param route_filter: A list of route names to filter the search results, defaults to None. :type route_filter: Optional[List[str]], optional :param sparse_vector: The sparse vector to search for, must be provided. - :type sparse_vector: np.ndarray + :type sparse_vector: dict[int, float] """ if route_filter: raise ValueError("Route filter is not supported for HybridLocalIndex.") @@ -101,11 +109,7 @@ def query( xq_d_norm = norm(xq_d) # TODO: this used to be xq_d.T, should work without sim_d = np.squeeze(np.dot(self.index, xq_d.T)) / (index_norm * xq_d_norm) # calculate sparse vec similarity - sparse_norm = norm(self.sparse_index, axis=1) - xq_s_norm = norm(xq_s) # TODO: this used to be xq_s.T, should work without - sim_s = np.squeeze(np.dot(self.sparse_index, xq_s.T)) / ( - sparse_norm * xq_s_norm - ) + sim_s = np.array(self._sparse_index_dot_product(xq_s)) total_sim = sim_d + sim_s # get indices of top_k records top_k = min(top_k, total_sim.shape[0]) @@ -122,7 +126,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[np.ndarray] = None, + sparse_vector: Optional[dict[int, float]] = None, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query and return top_k results. This method calls the sync `query` method as everything uses numpy computations which is CPU-bound @@ -135,7 +139,7 @@ async def aquery( :param route_filter: A list of route names to filter the search results, defaults to None. :type route_filter: Optional[List[str]], optional :param sparse_vector: The sparse vector to search for, must be provided. - :type sparse_vector: np.ndarray + :type sparse_vector: dict[int, float] """ return self.query( vector=vector, @@ -145,10 +149,10 @@ async def aquery( ) def aget_routes(self): - logger.error("Sync remove is not implemented for LocalIndex.") + logger.error(f"Sync remove is not implemented for {self.__class__.__name__}.") def _write_config(self, config: ConfigParameter): - logger.warning("No config is written for LocalIndex.") + logger.warning(f"No config is written for {self.__class__.__name__}.") def delete(self, route_name: str): """ diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 6e66142a..5ecede72 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -1,6 +1,6 @@ from typing import Any, Dict, List, Optional, Tuple import asyncio -from pydantic.v1 import validator, Field +from pydantic.v1 import Field import numpy as np @@ -21,14 +21,13 @@ class HybridRouter(BaseRouter): """A hybrid layer that uses both dense and sparse embeddings to classify routes.""" # there are a few additional attributes for hybrid - sparse_encoder: BM25Encoder = Field(default_factory=BM25Encoder) + sparse_encoder: Optional[BaseEncoder] = Field(default=None) alpha: float = 0.3 - index: HybridLocalIndex = Field(default_factory=HybridLocalIndex) def __init__( self, encoder: BaseEncoder, - sparse_encoder: Optional[BM25Encoder] = None, + sparse_encoder: Optional[BaseEncoder] = None, llm: Optional[BaseLLM] = None, routes: List[Route] = [], index: Optional[HybridLocalIndex] = None, @@ -40,46 +39,47 @@ def __init__( super().__init__( encoder=encoder, llm=llm, - routes=routes.copy(), + #routes=routes.copy(), index=index, top_k=top_k, aggregation=aggregation, auto_sync=auto_sync, ) # initialize sparse encoder - if sparse_encoder is None: - logger.warning("No sparse_encoder provided. Using default BM25Encoder.") - self.sparse_encoder = BM25Encoder() - else: - self.sparse_encoder = sparse_encoder + self._set_sparse_encoder(sparse_encoder=sparse_encoder) # set alpha self.alpha = alpha + # create copy of routes + routes_copy = routes.copy() # fit sparse encoder if needed if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( self.sparse_encoder, "fit" ): - self.sparse_encoder.fit(routes) + self.sparse_encoder.fit(routes_copy) # initialize index if not provided - # TODO: add check for hybrid compatible index - if self.index is None: - logger.warning("No index provided. Using default HybridLocalIndex.") - self.index = HybridLocalIndex() + self._set_index(index=index) # add routes if we have them - if routes: - for route in routes: + if routes_copy: + for route in routes_copy: self.add(route) # set score threshold using default method self._set_score_threshold() # TODO: we can't really use this with hybrid... - @validator("sparse_encoder", pre=True, always=True) - def set_sparse_encoder(cls, v): - return v if v is not None else BM25Encoder() - - @validator("index", pre=True, always=True) - def set_index(cls, v): - return v if v is not None else HybridLocalIndex() + def _set_index(self, index: Optional[HybridLocalIndex]): + if index is None: + logger.warning("No index provided. Using default HybridLocalIndex.") + self.index = HybridLocalIndex() + else: + self.index = index + + def _set_sparse_encoder(self, sparse_encoder: Optional[BaseEncoder]): + if sparse_encoder is None: + logger.warning("No sparse_encoder provided. Using default BM25Encoder.") + self.sparse_encoder = BM25Encoder() + else: + self.sparse_encoder = sparse_encoder - def _encode(self, text: List[str]) -> Any: + def _encode(self, text: list[str]) -> tuple[np.ndarray, list[dict[int, float]]]: """Given some text, generates dense and sparse embeddings, then scales them using the chosen alpha value. """ @@ -88,12 +88,12 @@ def _encode(self, text: List[str]) -> Any: # 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)) + # create sparse query vector dict + xq_s_dict = self.sparse_encoder(text) # xq_s = np.squeeze(xq_s) # convex scaling - xq_d, xq_s = self._convex_scaling(xq_d, xq_s) - return xq_d, xq_s + xq_d, xq_s_dict = self._convex_scaling(xq_d, xq_s_dict) + return xq_d, xq_s_dict async def _async_encode(self, text: List[str]) -> Any: """Given some text, generates dense and sparse embeddings, then scales them @@ -121,7 +121,7 @@ def __call__( vector: Optional[List[float]] = None, simulate_static: bool = False, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[List[float]] = None, + sparse_vector: Optional[dict[int, float]] = None, ) -> RouteChoice: # if no vector provided, encode text to get vector if vector is None: @@ -137,11 +137,7 @@ def __call__( vector=np.array(vector) if isinstance(vector, list) else vector, top_k=self.top_k, route_filter=route_filter, - sparse_vector=( - np.array(sparse_vector) - if isinstance(sparse_vector, list) - else sparse_vector - ), + sparse_vector=sparse_vector[0] ) top_class, top_class_scores = self._semantic_classify( list(zip(scores, route_names)) @@ -169,11 +165,13 @@ def add(self, route: Route): # TODO: in some places we say vector, sparse_vector and in others # TODO: we say embeddings, sparse_embeddings - def _convex_scaling(self, dense: np.ndarray, sparse: np.ndarray): + def _convex_scaling(self, dense: np.ndarray, sparse: list[dict[int, float]]): # scale sparse and dense vecs - dense = np.array(dense) * self.alpha - sparse = np.array(sparse) * (1 - self.alpha) - return dense, sparse + scaled_dense = np.array(dense) * self.alpha + scaled_sparse = [] + for sparse_dict in sparse: + scaled_sparse.append({k: v * (1 - self.alpha) for k, v in sparse_dict.items()}) + return scaled_dense, scaled_sparse def _set_aggregation_method(self, aggregation: str = "sum"): if aggregation == "sum": diff --git a/semantic_router/schema.py b/semantic_router/schema.py index ef2b2190..eea86b2e 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -7,6 +7,7 @@ class EncoderType(Enum): + AURELIO = "aurelio" AZURE = "azure" COHERE = "cohere" OPENAI = "openai" From 58594d4732502964734ee9d9787d55ca17f69003 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 18:13:45 +0100 Subject: [PATCH 10/33] fix: update to first aurelio sparse example --- docs/encoders/aurelio-bm25.ipynb | 58 ++++++++++++++++++++----------- semantic_router/routers/hybrid.py | 1 - 2 files changed, 38 insertions(+), 21 deletions(-) diff --git a/docs/encoders/aurelio-bm25.ipynb b/docs/encoders/aurelio-bm25.ipynb index 75a6bc70..e74f1e1b 100644 --- a/docs/encoders/aurelio-bm25.ipynb +++ b/docs/encoders/aurelio-bm25.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The 3rd generation embedding models from OpenAI (`text-embedding-3-small` and `text-embedding-3-large`) can both be used with our `OpenAIEncoder` and usage is primarily the same as with the 2nd generation `text-embedding-ada-002`. However, there is a new `dimensions` parameter — which we will discuss below." + "Hybrid indexes combine both sparse and dense encodings to produce more accurate results. The dense encoder allows us to search based on semantic meaning, while the sparse encoder allows us to search based on text matches. Merging both dense and sparse into a single hybrid retrieval step allows us to step up our performance beyond what dense-only or sparse-only could achieve." ] }, { @@ -32,7 +32,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We start by installing semantic-router. Support for the new `dimensions` parameter was added in `semantic-router==0.0.19` and `openai==1.10.0`." + "We start by installing semantic-router. Support for the new `AurelioSparseEncoder` parameter was added in `semantic-router==0.1.0`." ] }, { @@ -41,7 +41,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -qU \"semantic-router==0.1.0.dev2\"" + "!pip install -qU semantic-router==0.1.0" ] }, { @@ -112,7 +112,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we initialize our embedding model, we will use the `-3-large` model alongside a `dimensions` value of `256`. This will produce _tiny_ 256-dimensional vectors that — according to OpenAI — outperform the 1536-dimensional vectors produced by `text-embedding-ada-002`." + "Now we initialize our embedding models. We are going to use a hybrid index which requires both a dense and sparse encoder. For the sparse encoder we will use the pretrained `bm25` model from the Aurelio Platform and OpenAI's `text-embedding-3-small` for the dense encoder.\n", + "\n", + "To get an API key for the Aurelio Platform, we head to the [Aurelio Platform](https://platform.aurelio.ai/settings/api-keys)." ] }, { @@ -121,19 +123,30 @@ "metadata": {}, "outputs": [], "source": [ + "import os\n", + "from getpass import getpass\n", "from semantic_router.encoders.aurelio import AurelioSparseEncoder\n", "\n", + "os.environ[\"AURELIO_API_KEY\"] = os.getenv(\"AURELIO_API_KEY\") or getpass(\n", + " \"Enter Aurelio API Key: \"\n", + ")\n", + "\n", "sparse_encoder = AurelioSparseEncoder(name=\"bm25\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sparse encoders return dictionaries containing the the indices and values of the non-zero elements in the sparse matrix." + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "from getpass import getpass\n", "from semantic_router.encoders import OpenAIEncoder\n", "\n", "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n", @@ -141,7 +154,7 @@ ")\n", "\n", "encoder = OpenAIEncoder(\n", - " name=\"text-embedding-3-large\", score_threshold=0.5, dimensions=256\n", + " name=\"text-embedding-3-small\", score_threshold=0.3\n", ")" ] }, @@ -149,7 +162,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We will specify our index:" + "We now have both our sparse and dense encoders. When using both sparse and dense encoders we need to initialize an index that supports hybrid, such as the `HybridLocalIndex` or `PineconeIndex`." ] }, { @@ -167,7 +180,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "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`." + "Now we define the `HybridRouter`. When called, the router will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `HybridRouter` we need an `encoder`, `sparse_encoder` our `routes`, and the hybrid `index` we just define." ] }, { @@ -179,10 +192,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-24 12:25:32 - semantic_router.utils.logger - INFO - hybrid.py:157 - add() - Encoding route politics\n", - "2024-11-24 12:25:32 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "2024-11-24 12:25:33 - semantic_router.utils.logger - INFO - hybrid.py:157 - add() - Encoding route chitchat\n", - "2024-11-24 12:25:33 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-24 18:12:29 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 18:12:31 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] } ], @@ -201,7 +212,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can check the dimensionality of our vectors by looking at the `index` attribute of the `RouteLayer`." + "We can check the dimensionality and number of dense vector records by looking at the `index` attribute of the `HybridRouter`." ] }, { @@ -212,7 +223,7 @@ { "data": { "text/plain": [ - "(11, 256)" + "(11, 1536)" ] }, "execution_count": 8, @@ -224,6 +235,13 @@ "router.index.index.shape" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To find the number of sparse vector records we can look at the `sparse_index` attribute of the `HybridRouter`." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -260,13 +278,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-24 12:25:37 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-24 18:12:34 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "RouteChoice(name='politics', function_call=None, similarity_score=1.2995813276471633)" + "RouteChoice(name='politics', function_call=None, similarity_score=1.2837569119668175)" ] }, "execution_count": 10, @@ -287,13 +305,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-24 12:25:38 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-24 18:12:36 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] }, { "data": { "text/plain": [ - "RouteChoice(name='chitchat', function_call=None, similarity_score=1.8563758628277611)" + "RouteChoice(name='chitchat', function_call=None, similarity_score=1.856375862827761)" ] }, "execution_count": 11, @@ -321,7 +339,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-24 12:25:41 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-24 18:12:37 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] }, { diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 5ecede72..0b9271a7 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -154,7 +154,6 @@ def add(self, route: Route): route_names = [route.name] * len(route.utterances) # create embeddings for all routes - logger.info(f"Encoding route {route.name}") dense_embeds, sparse_embeds = self._encode(route.utterances) self.index.add( embeddings=dense_embeds, From 09cc05f1d84c9c811e94b466e4c7b2372b3dac3b Mon Sep 17 00:00:00 2001 From: jamescalam Date: Sun, 24 Nov 2024 19:51:10 +0100 Subject: [PATCH 11/33] feat: integrating Pinecone with hybrid --- docs/examples/pinecone-hybrid.ipynb | 463 ++++++++++++++++++++++++++++ semantic_router/index/pinecone.py | 25 +- 2 files changed, 482 insertions(+), 6 deletions(-) create mode 100644 docs/examples/pinecone-hybrid.ipynb diff --git a/docs/examples/pinecone-hybrid.ipynb b/docs/examples/pinecone-hybrid.ipynb new file mode 100644 index 00000000..f18eef6e --- /dev/null +++ b/docs/examples/pinecone-hybrid.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/aurelio-labs/semantic-router/blob/main/docs/encoders/aurelio-bm25.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using PineconeIndex for Hybrid Routes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hybrid indexes combine both sparse and dense encodings to produce more accurate results. The dense encoder allows us to search based on semantic meaning, while the sparse encoder allows us to search based on text matches. Merging both dense and sparse into a single hybrid retrieval step allows us to step up our performance beyond what dense-only or sparse-only could achieve." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting Started" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by installing semantic-router. Support for the new `AurelioSparseEncoder` parameter was added in `semantic-router==0.1.0`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -qU \"semantic-router[pinecone]==0.1.0\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We start by defining a dictionary mapping routes to example phrases that should trigger those routes." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from semantic_router import Route\n", + "\n", + "politics = Route(\n", + " name=\"politics\",\n", + " utterances=[\n", + " \"isn't politics the best thing ever\",\n", + " \"why don't you tell me about your political opinions\",\n", + " \"don't you just love the president\",\n", + " \"don't you just hate the president\",\n", + " \"they're going to destroy this country!\",\n", + " \"they will save the country!\",\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define another for good measure:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "chitchat = Route(\n", + " name=\"chitchat\",\n", + " utterances=[\n", + " \"how's the weather today?\",\n", + " \"how are things going?\",\n", + " \"lovely weather today\",\n", + " \"the weather is horrendous\",\n", + " \"let's go to the chippy\",\n", + " ],\n", + ")\n", + "\n", + "routes = [politics, chitchat]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we initialize our embedding models. We are going to use a hybrid index which requires both a dense and sparse encoder. For the sparse encoder we will use the pretrained `bm25` model from the Aurelio Platform and OpenAI's `text-embedding-3-small` for the dense encoder.\n", + "\n", + "To get an API key for the Aurelio Platform, we head to the [Aurelio Platform](https://platform.aurelio.ai/settings/api-keys)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "from semantic_router.encoders.aurelio import AurelioSparseEncoder\n", + "\n", + "os.environ[\"AURELIO_API_KEY\"] = os.getenv(\"AURELIO_API_KEY\") or getpass(\n", + " \"Enter Aurelio API Key: \"\n", + ")\n", + "\n", + "sparse_encoder = AurelioSparseEncoder(name=\"bm25\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sparse encoders return dictionaries containing the the indices and values of the non-zero elements in the sparse matrix." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from semantic_router.encoders import OpenAIEncoder\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") or getpass(\n", + " \"Enter OpenAI API Key: \"\n", + ")\n", + "\n", + "encoder = OpenAIEncoder(\n", + " name=\"text-embedding-3-small\", score_threshold=0.3\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have both our sparse and dense encoders. When using both sparse and dense encoders we need to initialize an index that supports hybrid, such as the `HybridLocalIndex` or `PineconeIndex`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n", + "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n", + "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n" + ] + } + ], + "source": [ + "from semantic_router.index import PineconeIndex\n", + "\n", + "os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n", + " \"Enter Pinecone API Key: \"\n", + ")\n", + "\n", + "index = PineconeIndex(\n", + " index_name=\"hybrid-test\",\n", + " dimensions=1536,\n", + " metric=\"dotproduct\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we define the `HybridRouter`. When called, the router will consume text (a query) and output the category (`Route`) it belongs to — to initialize a `HybridRouter` we need an `encoder`, `sparse_encoder` our `routes`, and the hybrid `index` we just define." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 19:41:15 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 19:41:17 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", + "politics: isn't politics the best thing ever\n", + "politics: why don't you tell me about your political opinions\n", + "politics: don't you just love the president\n", + "politics: don't you just hate the president\n", + "politics: they're going to destroy this country!\n", + "politics: they will save the country!\n", + "2024-11-24 19:41:17 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 19:41:18 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", + "chitchat: how's the weather today?\n", + "chitchat: how are things going?\n", + "chitchat: lovely weather today\n", + "chitchat: the weather is horrendous\n", + "chitchat: let's go to the chippy\n" + ] + } + ], + "source": [ + "from semantic_router.routers import HybridRouter\n", + "\n", + "router = HybridRouter(\n", + " encoder=encoder,\n", + " sparse_encoder=sparse_encoder,\n", + " routes=routes,\n", + " index=index,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's see if our local and remote instances are synchronized..." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 19:42:06 - semantic_router.utils.logger - WARNING - pinecone.py:424 - _read_hash() - Configuration for hash parameter not found in index.\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.is_synced()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It seems like our `router` is not synchronized, meaning there are differences between the utterances in our local `HybridRouter` and the remote `PineconeIndex`. We can view the differences by calling `get_utterance_diff()`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['- chitchat: how are things going?',\n", + " \"- chitchat: how's the weather today?\",\n", + " \"- chitchat: let's go to the chippy\",\n", + " '- chitchat: lovely weather today',\n", + " '- chitchat: the weather is horrendous',\n", + " \"- politics: don't you just hate the president\",\n", + " \"- politics: don't you just love the president\",\n", + " \"- politics: isn't politics the best thing ever\",\n", + " '- politics: they will save the country!',\n", + " \"- politics: they're going to destroy this country!\",\n", + " \"- politics: why don't you tell me about your political opinions\"]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.get_utterance_diff()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From this, we can see that every utterance is preceeded by a `-` meaning it is unique to the local `HybridRouter`. So it seems our `PineconeIndex` is missing all utterances. We can confirm this further by calling `router.index.get_utterances()` to see all utterances in the remote `PineconeIndex`:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.index.get_utterances()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, we have no utterances in the remote `PineconeIndex`. The reason for this is that when initializing our `HybridRouter` we did not specify an `auto_sync` parameter, so `auto_sync` defaulted to `None`. When `auto_sync=None` no synchronization is performed during initialization. Let's try again with `auto_sync=\"local\"`, meaning take what we have locally and overwrite the remote `PineconeIndex` with these local values." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-24 19:48:29 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 19:48:31 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", + "politics: isn't politics the best thing ever\n", + "politics: why don't you tell me about your political opinions\n", + "politics: don't you just love the president\n", + "politics: don't you just hate the president\n", + "politics: they're going to destroy this country!\n", + "politics: they will save the country!\n", + "2024-11-24 19:48:31 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "2024-11-24 19:48:32 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", + "chitchat: how's the weather today?\n", + "chitchat: how are things going?\n", + "chitchat: lovely weather today\n", + "chitchat: the weather is horrendous\n", + "chitchat: let's go to the chippy\n" + ] + } + ], + "source": [ + "router = HybridRouter(\n", + " encoder=encoder,\n", + " sparse_encoder=sparse_encoder,\n", + " routes=routes,\n", + " index=index,\n", + " auto_sync=\"local\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's check our sync state:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.is_synced()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "... NEED TO FINISH HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "router(\"I'm interested in learning about llama 2\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case, we return `None` because no matches were identified. We always recommend optimizing your `RouteLayer` for optimal performance, you can see how in [this notebook](https://github.com/aurelio-labs/semantic-router/blob/main/docs/06-threshold-optimization.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "decision-layer", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 0a3856d8..2d432d33 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -22,6 +22,7 @@ def clean_route_name(route_name: str) -> str: class PineconeRecord(BaseModel): id: str = "" values: List[float] + sparse_values: Optional[dict[int, float]] = None route: str utterance: str function_schema: str = "{}" @@ -42,11 +43,17 @@ def __init__(self, **data): ) def to_dict(self): - return { + d = { "id": self.id, "values": self.values, "metadata": self.metadata, } + if self.sparse_values: + d["sparse_values"] = { + "indices": list(self.sparse_values.keys()), + "values": list(self.sparse_values.values()), + } + return d class PineconeIndex(BaseIndex): @@ -54,7 +61,7 @@ class PineconeIndex(BaseIndex): api_key: Optional[str] = None index_name: str = "index" dimensions: Union[int, None] = None - metric: str = "cosine" + metric: str = "dotproduct" cloud: str = "aws" region: str = "us-west-2" host: str = "" @@ -70,7 +77,7 @@ def __init__( api_key: Optional[str] = None, index_name: str = "index", dimensions: Optional[int] = None, - metric: str = "cosine", + metric: str = "dotproduct", cloud: str = "aws", region: str = "us-west-2", host: str = "", @@ -233,6 +240,7 @@ def add( function_schemas: Optional[List[Dict[str, Any]]] = None, metadata_list: List[Dict[str, Any]] = [], batch_size: int = 100, + sparse_embeddings: Optional[List[dict[int, float]]] = None, ): """Add vectors to Pinecone in batches.""" temp = "\n".join([f"{x[0]}: {x[1]}" for x in zip(routes, utterances)]) @@ -240,17 +248,22 @@ def add( if self.index is None: self.dimensions = self.dimensions or len(embeddings[0]) self.index = self._init_index(force_create=True) + if function_schemas is None: + function_schemas = [None] * len(embeddings) + if sparse_embeddings is None: + sparse_embeddings = [None] * len(embeddings) vectors_to_upsert = [ PineconeRecord( values=vector, + sparse_values=sparse_dict, route=route, utterance=utterance, function_schema=json.dumps(function_schema), metadata=metadata, ).to_dict() - for vector, route, utterance, function_schema, metadata in zip( - embeddings, routes, utterances, function_schemas, metadata_list # type: ignore + for vector, route, utterance, function_schema, metadata, sparse_dict in zip( + embeddings, routes, utterances, function_schemas, metadata_list, sparse_embeddings # type: ignore ) ] @@ -523,7 +536,7 @@ async def _async_create_index( dimension: int, cloud: str, region: str, - metric: str = "cosine", + metric: str = "dotproduct", ): params = { "name": name, From 87d6ae4b160f66efbdaaabd0234129dc50c6a669 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Tue, 26 Nov 2024 22:39:28 +0100 Subject: [PATCH 12/33] feat: new sparse embedding support and abstractions --- docs/examples/pinecone-hybrid.ipynb | 196 ++++++++++++++++------------ semantic_router/encoders/aurelio.py | 17 +-- semantic_router/index/pinecone.py | 27 ++-- semantic_router/routers/base.py | 92 ++++++++----- semantic_router/routers/hybrid.py | 47 ++----- semantic_router/routers/semantic.py | 100 +------------- semantic_router/schema.py | 38 +++++- 7 files changed, 245 insertions(+), 272 deletions(-) diff --git a/docs/examples/pinecone-hybrid.ipynb b/docs/examples/pinecone-hybrid.ipynb index f18eef6e..134b6e0d 100644 --- a/docs/examples/pinecone-hybrid.ipynb +++ b/docs/examples/pinecone-hybrid.ipynb @@ -174,9 +174,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n", - "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n", - "2024-11-24 19:41:05 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n" + "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n", + "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n", + "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n" ] } ], @@ -205,29 +205,7 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-11-24 19:41:15 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "2024-11-24 19:41:17 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", - "politics: isn't politics the best thing ever\n", - "politics: why don't you tell me about your political opinions\n", - "politics: don't you just love the president\n", - "politics: don't you just hate the president\n", - "politics: they're going to destroy this country!\n", - "politics: they will save the country!\n", - "2024-11-24 19:41:17 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "2024-11-24 19:41:18 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", - "chitchat: how's the weather today?\n", - "chitchat: how are things going?\n", - "chitchat: lovely weather today\n", - "chitchat: the weather is horrendous\n", - "chitchat: let's go to the chippy\n" - ] - } - ], + "outputs": [], "source": [ "from semantic_router.routers import HybridRouter\n", "\n", @@ -248,23 +226,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-11-24 19:42:06 - semantic_router.utils.logger - WARNING - pinecone.py:424 - _read_hash() - Configuration for hash parameter not found in index.\n" - ] - }, { "data": { "text/plain": [ - "False" + "True" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -282,26 +253,26 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['- chitchat: how are things going?',\n", - " \"- chitchat: how's the weather today?\",\n", - " \"- chitchat: let's go to the chippy\",\n", - " '- chitchat: lovely weather today',\n", - " '- chitchat: the weather is horrendous',\n", - " \"- politics: don't you just hate the president\",\n", - " \"- politics: don't you just love the president\",\n", - " \"- politics: isn't politics the best thing ever\",\n", - " '- politics: they will save the country!',\n", - " \"- politics: they're going to destroy this country!\",\n", - " \"- politics: why don't you tell me about your political opinions\"]" + "[' chitchat: how are things going?',\n", + " \" chitchat: how's the weather today?\",\n", + " \" chitchat: let's go to the chippy\",\n", + " ' chitchat: lovely weather today',\n", + " ' chitchat: the weather is horrendous',\n", + " \" politics: don't you just hate the president\",\n", + " \" politics: don't you just love the president\",\n", + " \" politics: isn't politics the best thing ever\",\n", + " ' politics: they will save the country!',\n", + " \" politics: they're going to destroy this country!\",\n", + " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -319,16 +290,26 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[Utterance(route='chitchat', utterance='how are things going?', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance=\"how's the weather today?\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance='the weather is horrendous', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance='lovely weather today', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='chitchat', utterance=\"let's go to the chippy\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just hate the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"don't you just love the president\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"they're going to destroy this country!\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance='they will save the country!', function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"isn't politics the best thing ever\", function_schemas=None, metadata={}, diff_tag=' '),\n", + " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -346,31 +327,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-11-24 19:48:29 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "2024-11-24 19:48:31 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", - "politics: isn't politics the best thing ever\n", - "politics: why don't you tell me about your political opinions\n", - "politics: don't you just love the president\n", - "politics: don't you just hate the president\n", - "politics: they're going to destroy this country!\n", - "politics: they will save the country!\n", - "2024-11-24 19:48:31 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "2024-11-24 19:48:32 - semantic_router.utils.logger - WARNING - pinecone.py:247 - add() - TEMP | add:\n", - "chitchat: how's the weather today?\n", - "chitchat: how are things going?\n", - "chitchat: lovely weather today\n", - "chitchat: the weather is horrendous\n", - "chitchat: let's go to the chippy\n" - ] - } - ], + "outputs": [], "source": [ "router = HybridRouter(\n", " encoder=encoder,\n", @@ -390,16 +349,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "False" + "True" ] }, - "execution_count": 16, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -408,6 +367,36 @@ "router.is_synced()" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[' chitchat: how are things going?',\n", + " \" chitchat: how's the weather today?\",\n", + " \" chitchat: let's go to the chippy\",\n", + " ' chitchat: lovely weather today',\n", + " ' chitchat: the weather is horrendous',\n", + " \" politics: don't you just hate the president\",\n", + " \" politics: don't you just love the president\",\n", + " \" politics: isn't politics the best thing ever\",\n", + " ' politics: they will save the country!',\n", + " \" politics: they're going to destroy this country!\",\n", + " \" politics: why don't you tell me about your political opinions\"]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router.get_utterance_diff()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -417,9 +406,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 22:35:56 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "RouteChoice(name=None, function_call=None, similarity_score=None)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "router(\"it's raining cats and dogs today\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-26 22:35:20 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "data": { + "text/plain": [ + "RouteChoice(name=None, function_call=None, similarity_score=None)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "router(\"I'm interested in learning about llama 2\")" ] diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index c257b514..bc150e50 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -5,6 +5,7 @@ from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse from semantic_router.encoders.base import BaseEncoder +from semantic_router.schema import SparseEmbedding class AurelioSparseEncoder(BaseEncoder): @@ -28,19 +29,15 @@ def __init__( self.client = AurelioClient(api_key=api_key) self.async_client = AsyncAurelioClient(api_key=api_key) - def __call__(self, docs: list[str]) -> list[dict[int, float]]: + def __call__(self, docs: list[str]) -> list[SparseEmbedding]: res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name) - embeds = [r.embedding.model_dump() for r in res.data] - # convert sparse vector to {index: value} format - sparse_dicts = [{i: v for i, v in zip(e["indices"], e["values"])} for e in embeds] - return sparse_dicts + embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data] + return embeds - async def acall(self, docs: list[str]) -> list[dict[int, float]]: + async def acall(self, docs: list[str]) -> list[SparseEmbedding]: res: EmbeddingResponse = await self.async_client.embedding(input=docs, model=self.name) - embeds = [r.embedding.model_dump() for r in res.data] - # convert sparse vector to {index: value} format - sparse_dicts = [{i: v for i, v in zip(e["indices"], e["values"])} for e in embeds] - return sparse_dicts + embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data] + return embeds def fit(self, docs: List[str]): raise NotImplementedError("AurelioSparseEncoder does not support fit.") diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 2d432d33..5eb0aecb 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -11,7 +11,7 @@ from pydantic.v1 import BaseModel, Field from semantic_router.index.base import BaseIndex -from semantic_router.schema import ConfigParameter +from semantic_router.schema import ConfigParameter, SparseEmbedding from semantic_router.utils.logger import logger @@ -243,8 +243,6 @@ def add( sparse_embeddings: Optional[List[dict[int, float]]] = None, ): """Add vectors to Pinecone in batches.""" - temp = "\n".join([f"{x[0]}: {x[1]}" for x in zip(routes, utterances)]) - logger.warning("TEMP | add:\n" + temp) if self.index is None: self.dimensions = self.dimensions or len(embeddings[0]) self.index = self._init_index(force_create=True) @@ -272,10 +270,6 @@ def add( self._batch_upsert(batch) def _remove_and_sync(self, routes_to_delete: dict): - temp = "\n".join( - [f"{route}: {utterances}" for route, utterances in routes_to_delete.items()] - ) - logger.warning("TEMP | _remove_and_sync:\n" + temp) for route, utterances in routes_to_delete.items(): remote_routes = self._get_routes_with_ids(route_name=route) ids_to_delete = [ @@ -364,6 +358,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, **kwargs: Any, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query vector and return the top_k results. @@ -374,10 +369,10 @@ def query( :type top_k: int, optional :param route_filter: A list of route names to filter the search results, defaults to None. :type route_filter: Optional[List[str]], optional + :param sparse_vector: An optional sparse vector to include in the query. + :type sparse_vector: Optional[SparseEmbedding] :param kwargs: Additional keyword arguments for the query, including sparse_vector. :type kwargs: Any - :keyword sparse_vector: An optional sparse vector to include in the query. - :type sparse_vector: Optional[dict] :return: A tuple containing an array of scores and a list of route names. :rtype: Tuple[np.ndarray, List[str]] :raises ValueError: If the index is not populated. @@ -389,9 +384,13 @@ def query( filter_query = {"sr_route": {"$in": route_filter}} else: filter_query = None + if sparse_vector is not None: + if isinstance(sparse_vector, dict): + sparse_vector = SparseEmbedding.from_dict(sparse_vector) + sparse_vector = sparse_vector.to_pinecone() results = self.index.query( vector=[query_vector_list], - sparse_vector=kwargs.get("sparse_vector", None), + sparse_vector=sparse_vector, top_k=top_k, filter=filter_query, include_metadata=True, @@ -653,6 +652,8 @@ async def _async_fetch_metadata(self, vector_id: str) -> dict: ) def __len__(self): - return self.index.describe_index_stats()["namespaces"][self.namespace][ - "vector_count" - ] + namespace_stats = self.index.describe_index_stats()["namespaces"].get(self.namespace) + if namespace_stats: + return namespace_stats["vector_count"] + else: + return 0 diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index f9170051..f7ab5226 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -250,8 +250,39 @@ def to_utterances(self) -> List[Utterance]: return utterances def add(self, route: Route): + """Add a route to the local SemanticRouter and index. + + :param route: The route to add. + :type route: Route + """ + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + embedded_utterances = self.encoder(route.utterances) + self.index.add( + embeddings=embedded_utterances, + routes=[route.name] * len(route.utterances), + utterances=route.utterances, + function_schemas=( + route.function_schemas * len(route.utterances) + if route.function_schemas + else [{}] * len(route.utterances) + ), + metadata_list=[route.metadata if route.metadata else {}] + * len(route.utterances), + ) + self.routes.append(route) - logger.info(f"Added route `{route.name}`") + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + "not updated. Use `SemanticRouter.get_utterance_diff()` to see " + "details." + ) def get(self, name: str) -> Optional[Route]: for route in self.routes: @@ -289,10 +320,6 @@ class BaseRouter(BaseModel): class Config: arbitrary_types_allowed = True - @validator("index", pre=True, always=True) - def set_index(cls, v): - return v if v is not None else LocalIndex() - def __init__( self, encoder: Optional[BaseEncoder] = None, @@ -321,14 +348,11 @@ def __init__( else: self.encoder = encoder self.llm = llm - self.routes = routes if routes else [] - if self.encoder.score_threshold is not None: - self.score_threshold = self.encoder.score_threshold - if self.score_threshold is None: - logger.warning( - "No score threshold value found in encoder. Using the default " - "'None' value can lead to unexpected results." - ) + self.routes = routes.copy() if routes else [] + # initialize index + self._set_index(index=index) + # set score threshold using default method + self._set_score_threshold() self.top_k = top_k if self.top_k < 1: raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") @@ -344,15 +368,29 @@ def __init__( for route in self.routes: if route.score_threshold is None: route.score_threshold = self.score_threshold - # if routes list has been passed, we initialize index now + # run initialize index now if auto sync is active + if self.auto_sync: + self._init_index_state() + + def _set_index(self, index: Optional[BaseIndex]): + if index is None: + logger.warning("No index provided. Using default LocalIndex.") + self.index = LocalIndex() + else: + self.index = index + + def _init_index_state(self): + """Initializes an index (where required) and runs auto_sync if active. + """ + # initialize index now, check if we need dimensions + if self.index.dimensions is None: + dims = len(self.encoder(["test"])[0]) + self.index.dimensions = dims + # now init index + if isinstance(self.index, PineconeIndex): + self.index.index = self.index._init_index(force_create=True) + # run auto sync if active if self.auto_sync: - # initialize index now, check if we need dimensions - if self.index.dimensions is None: - dims = len(self.encoder(["test"])[0]) - self.index.dimensions = dims - # now init index - if isinstance(self.index, PineconeIndex): - self.index.index = self.index._init_index(force_create=True) local_utterances = self.to_config().to_utterances() remote_utterances = self.index.get_utterances() diff = UtteranceDiff.from_utterances( @@ -585,8 +623,6 @@ def _local_upsert(self, utterances: List[Utterance]): new_routes[utt_obj.route].utterances.append(utt_obj.utterance) new_routes[utt_obj.route].function_schemas = utt_obj.function_schemas new_routes[utt_obj.route].metadata = utt_obj.metadata - temp = "\n".join([f"{name}: {r.utterances}" for name, r in new_routes.items()]) - logger.warning("TEMP | _local_upsert:\n" + temp) self.routes = list(new_routes.values()) def _local_delete(self, utterances: List[Utterance]): @@ -599,8 +635,6 @@ def _local_delete(self, utterances: List[Utterance]): route_dict: dict[str, List[str]] = {} for utt in utterances: route_dict.setdefault(utt.route, []).append(utt.utterance) - temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) - logger.warning("TEMP | _local_delete:\n" + temp) # iterate over current routes and delete specific utterance if found new_routes = [] for route in self.routes: @@ -622,17 +656,9 @@ def _local_delete(self, utterances: List[Utterance]): metadata=route.metadata, ) ) - logger.warning( - f"TEMP | _local_delete OLD | {route.name}: {route.utterances}" - ) - logger.warning( - f"TEMP | _local_delete NEW | {route.name}: {new_routes[-1].utterances}" - ) else: # the route is not in the route_dict, so we keep it as is new_routes.append(route) - temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) - logger.warning("TEMP | _local_delete:\n" + temp) self.routes = new_routes diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 0b9271a7..a0a96671 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -11,7 +11,7 @@ ) from semantic_router.route import Route from semantic_router.index.hybrid_local import HybridLocalIndex -from semantic_router.schema import RouteChoice +from semantic_router.schema import RouteChoice, SparseEmbedding from semantic_router.utils.logger import logger from semantic_router.routers.base import BaseRouter from semantic_router.llms import BaseLLM @@ -36,10 +36,13 @@ def __init__( auto_sync: Optional[str] = None, alpha: float = 0.3, ): + if index is None: + logger.warning("No index provided. Using default HybridLocalIndex.") + index = HybridLocalIndex() super().__init__( encoder=encoder, llm=llm, - #routes=routes.copy(), + routes=routes, index=index, top_k=top_k, aggregation=aggregation, @@ -49,28 +52,14 @@ def __init__( self._set_sparse_encoder(sparse_encoder=sparse_encoder) # set alpha self.alpha = alpha - # create copy of routes - routes_copy = routes.copy() # fit sparse encoder if needed if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( self.sparse_encoder, "fit" ): - self.sparse_encoder.fit(routes_copy) - # initialize index if not provided - self._set_index(index=index) - # add routes if we have them - if routes_copy: - for route in routes_copy: - self.add(route) - # set score threshold using default method - self._set_score_threshold() # TODO: we can't really use this with hybrid... - - def _set_index(self, index: Optional[HybridLocalIndex]): - if index is None: - logger.warning("No index provided. Using default HybridLocalIndex.") - self.index = HybridLocalIndex() - else: - self.index = index + self.sparse_encoder.fit(self.routes) + # run initialize index now if auto sync is active + if self.auto_sync: + self._init_index_state() def _set_sparse_encoder(self, sparse_encoder: Optional[BaseEncoder]): if sparse_encoder is None: @@ -121,7 +110,7 @@ def __call__( vector: Optional[List[float]] = None, simulate_static: bool = False, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[dict[int, float]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> RouteChoice: # if no vector provided, encode text to get vector if vector is None: @@ -148,22 +137,6 @@ def __call__( else: return RouteChoice() - def add(self, route: Route): - self.routes += [route] - - route_names = [route.name] * len(route.utterances) - - # create embeddings for all routes - dense_embeds, sparse_embeds = self._encode(route.utterances) - self.index.add( - embeddings=dense_embeds, - sparse_embeddings=sparse_embeds, - routes=route_names, # TODO: aligning names of routes v route_names - utterances=route.utterances, - ) - # TODO: in some places we say vector, sparse_vector and in others - # TODO: we say embeddings, sparse_embeddings - def _convex_scaling(self, dense: np.ndarray, sparse: list[dict[int, float]]): # scale sparse and dense vecs scaled_dense = np.array(dense) * self.alpha diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 45493e67..f0df2717 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -53,12 +53,6 @@ def is_valid(layer_config: str) -> bool: class SemanticRouter(BaseRouter): - index: BaseIndex = Field(default_factory=LocalIndex) - - @validator("index", pre=True, always=True) - def set_index(cls, v): - return v if v is not None else LocalIndex() - def __init__( self, encoder: Optional[BaseEncoder] = None, @@ -72,56 +66,15 @@ def __init__( super().__init__( encoder=encoder, llm=llm, - routes=routes.copy() if routes else [], + routes=routes if routes else [], index=index, top_k=top_k, aggregation=aggregation, auto_sync=auto_sync, ) - if encoder is None: - logger.warning( - "No encoder provided. Using default OpenAIEncoder. Ensure " - "that you have set OPENAI_API_KEY in your environment." - ) - self.encoder = OpenAIEncoder() - else: - self.encoder = encoder - self.llm = llm - self.routes = routes if routes else [] - # set score threshold using default method - self._set_score_threshold() - self.top_k = top_k - if self.top_k < 1: - raise ValueError(f"top_k needs to be >= 1, but was: {self.top_k}.") - self.aggregation = aggregation - if self.aggregation not in ["sum", "mean", "max"]: - raise ValueError( - f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." - ) - self.aggregation_method = self._set_aggregation_method(self.aggregation) - self.auto_sync = auto_sync - - # set route score thresholds if not already set - for route in self.routes: - if route.score_threshold is None: - route.score_threshold = self.score_threshold - # if routes list has been passed, we initialize index now + # run initialize index now if auto sync is active if self.auto_sync: - # initialize index now, check if we need dimensions - if self.index.dimensions is None: - dims = len(self.encoder(["test"])[0]) - self.index.dimensions = dims - # now init index - if isinstance(self.index, PineconeIndex): - self.index.index = self.index._init_index(force_create=True) - local_utterances = self.to_config().to_utterances() - remote_utterances = self.index.get_utterances() - diff = UtteranceDiff.from_utterances( - local_utterances=local_utterances, - remote_utterances=remote_utterances, - ) - sync_strategy = diff.get_sync_strategy(self.auto_sync) - self._execute_sync_strategy(sync_strategy) + self._init_index_state() def check_for_matching_routes(self, top_class: str) -> Optional[Route]: matching_route = next( @@ -331,8 +284,6 @@ def _local_upsert(self, utterances: List[Utterance]): new_routes[utt_obj.route].utterances.append(utt_obj.utterance) new_routes[utt_obj.route].function_schemas = utt_obj.function_schemas new_routes[utt_obj.route].metadata = utt_obj.metadata - temp = "\n".join([f"{name}: {r.utterances}" for name, r in new_routes.items()]) - logger.warning("TEMP | _local_upsert:\n" + temp) self.routes = list(new_routes.values()) def _local_delete(self, utterances: List[Utterance]): @@ -345,8 +296,6 @@ def _local_delete(self, utterances: List[Utterance]): route_dict: dict[str, List[str]] = {} for utt in utterances: route_dict.setdefault(utt.route, []).append(utt.utterance) - temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) - logger.warning("TEMP | _local_delete:\n" + temp) # iterate over current routes and delete specific utterance if found new_routes = [] for route in self.routes: @@ -368,17 +317,9 @@ def _local_delete(self, utterances: List[Utterance]): metadata=route.metadata, ) ) - logger.warning( - f"TEMP | _local_delete OLD | {route.name}: {route.utterances}" - ) - logger.warning( - f"TEMP | _local_delete NEW | {route.name}: {new_routes[-1].utterances}" - ) else: # the route is not in the route_dict, so we keep it as is new_routes.append(route) - temp = "\n".join([f"{r}: {u}" for r, u in route_dict.items()]) - logger.warning("TEMP | _local_delete:\n" + temp) self.routes = new_routes @@ -449,41 +390,6 @@ def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model return cls(encoder=encoder, routes=config.routes, index=index) - def add(self, route: Route): - """Add a route to the local SemanticRouter and index. - - :param route: The route to add. - :type route: Route - """ - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - embedded_utterances = self.encoder(route.utterances) - self.index.add( - embeddings=embedded_utterances, - routes=[route.name] * len(route.utterances), - utterances=route.utterances, - function_schemas=( - route.function_schemas * len(route.utterances) - if route.function_schemas - else [{}] * len(route.utterances) - ), - metadata_list=[route.metadata if route.metadata else {}] - * len(route.utterances), - ) - - self.routes.append(route) - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) - def list_route_names(self) -> List[str]: return [route.name for route in self.routes] diff --git a/semantic_router/schema.py b/semantic_router/schema.py index eea86b2e..2d00572f 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -4,7 +4,7 @@ from typing import List, Optional, Union, Any, Dict, Tuple from pydantic.v1 import BaseModel, Field from semantic_router.utils.logger import logger - +from aurelio_sdk.schema import BM25Embedding class EncoderType(Enum): AURELIO = "aurelio" @@ -404,3 +404,39 @@ class Metric(Enum): DOTPRODUCT = "dotproduct" EUCLIDEAN = "euclidean" MANHATTAN = "manhattan" + + +class SparseValue(BaseModel): + index: int + value: float + + +class SparseEmbedding(BaseModel): + embedding: List[SparseValue] + + def to_dict(self): + return {x.index: x.value for x in self.embedding} + + def to_pinecone(self): + return { + "indices": [x.index for x in self.embedding], + "values": [x.value for x in self.embedding], + } + + @classmethod + def from_dict(cls, sparse_dict: dict): + return cls(embedding=[SparseValue(index=i, value=v) for i, v in sparse_dict.items()]) + + @classmethod + def from_aurelio(cls, embedding: BM25Embedding): + return cls(embedding=[ + SparseValue( + index=i, + value=v + ) for i, v in zip(embedding.indices, embedding.values) + ]) + + # dictionary interface + def items(self): + return [(x.index, x.value) for x in self.embedding] + From d5f4703d281215ab52099a2685ac542de7720da2 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Wed, 27 Nov 2024 11:16:20 +0100 Subject: [PATCH 13/33] feat: encoder types --- semantic_router/encoders/__init__.py | 7 ++++--- semantic_router/encoders/aurelio.py | 4 ++-- semantic_router/encoders/base.py | 18 +++++++++++++++++- semantic_router/encoders/bedrock.py | 6 +++--- semantic_router/encoders/bm25.py | 4 ++-- semantic_router/encoders/clip.py | 4 ++-- semantic_router/encoders/cohere.py | 4 ++-- semantic_router/encoders/fastembed.py | 4 ++-- semantic_router/encoders/google.py | 6 +++--- semantic_router/encoders/huggingface.py | 8 ++++---- semantic_router/encoders/mistral.py | 4 ++-- semantic_router/encoders/openai.py | 4 ++-- semantic_router/encoders/tfidf.py | 4 ++-- semantic_router/encoders/vit.py | 4 ++-- semantic_router/encoders/zure.py | 4 ++-- semantic_router/routers/base.py | 6 +++--- semantic_router/routers/hybrid.py | 10 +++++----- semantic_router/routers/semantic.py | 4 ++-- .../encoders/test_openai_integration.py | 4 ++-- tests/unit/encoders/test_base.py | 6 +++--- tests/unit/test_hybrid_layer.py | 8 ++++---- tests/unit/test_router.py | 4 ++-- tests/unit/test_sync.py | 4 ++-- 23 files changed, 74 insertions(+), 57 deletions(-) diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py index 893d1d61..85b32e2c 100644 --- a/semantic_router/encoders/__init__.py +++ b/semantic_router/encoders/__init__.py @@ -1,7 +1,7 @@ from typing import List, Optional from semantic_router.encoders.aurelio import AurelioSparseEncoder -from semantic_router.encoders.base import BaseEncoder +from semantic_router.encoders.base import DenseEncoder, SparseEncoder from semantic_router.encoders.bedrock import BedrockEncoder from semantic_router.encoders.bm25 import BM25Encoder from semantic_router.encoders.clip import CLIPEncoder @@ -19,7 +19,8 @@ __all__ = [ "AurelioSparseEncoder", - "BaseEncoder", + "DenseEncoder", + "SparseEncoder", "AzureOpenAIEncoder", "CohereEncoder", "OpenAIEncoder", @@ -39,7 +40,7 @@ class AutoEncoder: type: EncoderType name: Optional[str] - model: BaseEncoder + model: DenseEncoder | SparseEncoder def __init__(self, type: str, name: Optional[str]): self.type = EncoderType(type) diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index bc150e50..d226e3d3 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -4,11 +4,11 @@ from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse -from semantic_router.encoders.base import BaseEncoder +from semantic_router.encoders.base import SparseEncoder from semantic_router.schema import SparseEmbedding -class AurelioSparseEncoder(BaseEncoder): +class AurelioSparseEncoder(SparseEncoder): model: Optional[Any] = None idx_mapping: Optional[Dict[int, int]] = None client: AurelioClient = Field(default_factory=AurelioClient, exclude=True) diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index fcc5734d..f2cee15d 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -2,8 +2,10 @@ from pydantic.v1 import BaseModel, Field, validator +from semantic_router.schema import SparseEmbedding -class BaseEncoder(BaseModel): + +class DenseEncoder(BaseModel): name: str score_threshold: Optional[float] = None type: str = Field(default="base") @@ -20,3 +22,17 @@ def __call__(self, docs: List[Any]) -> List[List[float]]: def acall(self, docs: List[Any]) -> Coroutine[Any, Any, List[List[float]]]: raise NotImplementedError("Subclasses must implement this method") + + +class SparseEncoder(BaseModel): + name: str + type: str = Field(default="base") + + class Config: + arbitrary_types_allowed = True + + def __call__(self, docs: List[str]) -> List[SparseEmbedding]: + raise NotImplementedError("Subclasses must implement this method") + + def acall(self, docs: List[str]) -> Coroutine[Any, Any, List[SparseEmbedding]]: + raise NotImplementedError("Subclasses must implement this method") \ No newline at end of file diff --git a/semantic_router/encoders/bedrock.py b/semantic_router/encoders/bedrock.py index fed53900..5ec3381e 100644 --- a/semantic_router/encoders/bedrock.py +++ b/semantic_router/encoders/bedrock.py @@ -1,7 +1,7 @@ """ This module provides the BedrockEncoder class for generating embeddings using Amazon's Bedrock Platform. -The BedrockEncoder class is a subclass of BaseEncoder and utilizes the TextEmbeddingModel from the +The BedrockEncoder class is a subclass of DenseEncoder and utilizes the TextEmbeddingModel from the Amazon's Bedrock Platform to generate embeddings for given documents. It requires an AWS Access Key ID and AWS Secret Access Key and supports customization of the pre-trained model, score threshold, and region. @@ -21,12 +21,12 @@ import os from time import sleep import tiktoken -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.defaults import EncoderDefault from semantic_router.utils.logger import logger -class BedrockEncoder(BaseEncoder): +class BedrockEncoder(DenseEncoder): client: Any = None type: str = "bedrock" input_type: Optional[str] = "search_query" diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index 1965fb6e..4eac26e7 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -1,10 +1,10 @@ from typing import Any, Dict, List, Optional -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import SparseEncoder from semantic_router.utils.logger import logger -class BM25Encoder(BaseEncoder): +class BM25Encoder(SparseEncoder): model: Optional[Any] = None idx_mapping: Optional[Dict[int, int]] = None type: str = "sparse" diff --git a/semantic_router/encoders/clip.py b/semantic_router/encoders/clip.py index 6495c870..65fbdb8f 100644 --- a/semantic_router/encoders/clip.py +++ b/semantic_router/encoders/clip.py @@ -3,10 +3,10 @@ import numpy as np from pydantic.v1 import PrivateAttr from typing import Dict -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder -class CLIPEncoder(BaseEncoder): +class CLIPEncoder(DenseEncoder): name: str = "openai/clip-vit-base-patch16" type: str = "huggingface" score_threshold: float = 0.2 diff --git a/semantic_router/encoders/cohere.py b/semantic_router/encoders/cohere.py index cdc114bb..04b87814 100644 --- a/semantic_router/encoders/cohere.py +++ b/semantic_router/encoders/cohere.py @@ -3,11 +3,11 @@ from pydantic.v1 import PrivateAttr -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.defaults import EncoderDefault -class CohereEncoder(BaseEncoder): +class CohereEncoder(DenseEncoder): _client: Any = PrivateAttr() _embed_type: Any = PrivateAttr() type: str = "cohere" diff --git a/semantic_router/encoders/fastembed.py b/semantic_router/encoders/fastembed.py index 27590bc3..5cda5e64 100644 --- a/semantic_router/encoders/fastembed.py +++ b/semantic_router/encoders/fastembed.py @@ -3,10 +3,10 @@ import numpy as np from pydantic.v1 import PrivateAttr -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder -class FastEmbedEncoder(BaseEncoder): +class FastEmbedEncoder(DenseEncoder): type: str = "fastembed" name: str = "BAAI/bge-small-en-v1.5" max_length: int = 512 diff --git a/semantic_router/encoders/google.py b/semantic_router/encoders/google.py index 088d4bba..5d50a0e1 100644 --- a/semantic_router/encoders/google.py +++ b/semantic_router/encoders/google.py @@ -1,7 +1,7 @@ """ This module provides the GoogleEncoder class for generating embeddings using Google's AI Platform. -The GoogleEncoder class is a subclass of BaseEncoder and utilizes the TextEmbeddingModel from the +The GoogleEncoder class is a subclass of DenseEncoder and utilizes the TextEmbeddingModel from the Google AI Platform to generate embeddings for given documents. It requires a Google Cloud project ID and supports customization of the pre-trained model, score threshold, location, and API endpoint. @@ -19,11 +19,11 @@ import os from typing import Any, List, Optional -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.defaults import EncoderDefault -class GoogleEncoder(BaseEncoder): +class GoogleEncoder(DenseEncoder): """GoogleEncoder class for generating embeddings using Google's AI Platform. Attributes: diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py index 7ca7580d..7c7e56f7 100644 --- a/semantic_router/encoders/huggingface.py +++ b/semantic_router/encoders/huggingface.py @@ -1,7 +1,7 @@ """ This module provides the HFEndpointEncoder class to embeddings models using Huggingface's endpoint. -The HFEndpointEncoder class is a subclass of BaseEncoder and utilizes a specified Huggingface +The HFEndpointEncoder class is a subclass of DenseEncoder and utilizes a specified Huggingface endpoint to generate embeddings for given documents. It requires the URL of the Huggingface API endpoint and an API key for authentication. The class supports customization of the score threshold for filtering or processing the embeddings. @@ -27,11 +27,11 @@ from pydantic.v1 import PrivateAttr -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.logger import logger -class HuggingFaceEncoder(BaseEncoder): +class HuggingFaceEncoder(DenseEncoder): name: str = "sentence-transformers/all-MiniLM-L6-v2" type: str = "huggingface" score_threshold: float = 0.5 @@ -140,7 +140,7 @@ def _max_pooling(self, model_output, attention_mask): return self._torch.max(token_embeddings, 1)[0] -class HFEndpointEncoder(BaseEncoder): +class HFEndpointEncoder(DenseEncoder): """ A class to encode documents using a Hugging Face transformer model endpoint. diff --git a/semantic_router/encoders/mistral.py b/semantic_router/encoders/mistral.py index 974f1128..46bc89ae 100644 --- a/semantic_router/encoders/mistral.py +++ b/semantic_router/encoders/mistral.py @@ -6,11 +6,11 @@ from pydantic.v1 import PrivateAttr -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.defaults import EncoderDefault -class MistralEncoder(BaseEncoder): +class MistralEncoder(DenseEncoder): """Class to encode text using MistralAI""" _client: Any = PrivateAttr() diff --git a/semantic_router/encoders/openai.py b/semantic_router/encoders/openai.py index 4bc86ac2..fb8a83f0 100644 --- a/semantic_router/encoders/openai.py +++ b/semantic_router/encoders/openai.py @@ -10,7 +10,7 @@ from openai.types import CreateEmbeddingResponse import tiktoken -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.schema import EncoderInfo from semantic_router.utils.defaults import EncoderDefault from semantic_router.utils.logger import logger @@ -35,7 +35,7 @@ } -class OpenAIEncoder(BaseEncoder): +class OpenAIEncoder(DenseEncoder): client: Optional[openai.Client] async_client: Optional[openai.AsyncClient] dimensions: Union[int, NotGiven] = NotGiven() diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index 17cc569a..873d900a 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -6,11 +6,11 @@ from numpy import ndarray from numpy.linalg import norm -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import SparseEncoder from semantic_router.route import Route -class TfidfEncoder(BaseEncoder): +class TfidfEncoder(SparseEncoder): idf: ndarray = np.array([]) word_index: Dict = {} diff --git a/semantic_router/encoders/vit.py b/semantic_router/encoders/vit.py index 44ae5801..73cb0582 100644 --- a/semantic_router/encoders/vit.py +++ b/semantic_router/encoders/vit.py @@ -2,10 +2,10 @@ from pydantic.v1 import PrivateAttr -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder -class VitEncoder(BaseEncoder): +class VitEncoder(DenseEncoder): name: str = "google/vit-base-patch16-224" type: str = "huggingface" score_threshold: float = 0.5 diff --git a/semantic_router/encoders/zure.py b/semantic_router/encoders/zure.py index d6f65660..fd8594a9 100644 --- a/semantic_router/encoders/zure.py +++ b/semantic_router/encoders/zure.py @@ -8,12 +8,12 @@ from openai import OpenAIError from openai.types import CreateEmbeddingResponse -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.utils.defaults import EncoderDefault from semantic_router.utils.logger import logger -class AzureOpenAIEncoder(BaseEncoder): +class AzureOpenAIEncoder(DenseEncoder): client: Optional[openai.AzureOpenAI] = None async_client: Optional[openai.AsyncAzureOpenAI] = None dimensions: Union[int, NotGiven] = NotGiven() diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index f7ab5226..1af3e38b 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -10,7 +10,7 @@ import yaml # type: ignore from tqdm.auto import tqdm -from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder +from semantic_router.encoders import AutoEncoder, DenseEncoder, OpenAIEncoder from semantic_router.index.base import BaseIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex @@ -307,7 +307,7 @@ def get_hash(self) -> ConfigParameter: class BaseRouter(BaseModel): - encoder: BaseEncoder + encoder: DenseEncoder index: BaseIndex = Field(default_factory=BaseIndex) score_threshold: Optional[float] = Field(default=None) routes: List[Route] = [] @@ -322,7 +322,7 @@ class Config: def __init__( self, - encoder: Optional[BaseEncoder] = None, + encoder: Optional[DenseEncoder] = None, llm: Optional[BaseLLM] = None, routes: List[Route] = [], index: Optional[BaseIndex] = None, # type: ignore diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index a0a96671..f8ec89cd 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -5,7 +5,7 @@ import numpy as np from semantic_router.encoders import ( - BaseEncoder, + DenseEncoder, BM25Encoder, TfidfEncoder, ) @@ -21,13 +21,13 @@ class HybridRouter(BaseRouter): """A hybrid layer that uses both dense and sparse embeddings to classify routes.""" # there are a few additional attributes for hybrid - sparse_encoder: Optional[BaseEncoder] = Field(default=None) + sparse_encoder: Optional[DenseEncoder] = Field(default=None) alpha: float = 0.3 def __init__( self, - encoder: BaseEncoder, - sparse_encoder: Optional[BaseEncoder] = None, + encoder: DenseEncoder, + sparse_encoder: Optional[DenseEncoder] = None, llm: Optional[BaseLLM] = None, routes: List[Route] = [], index: Optional[HybridLocalIndex] = None, @@ -61,7 +61,7 @@ def __init__( if self.auto_sync: self._init_index_state() - def _set_sparse_encoder(self, sparse_encoder: Optional[BaseEncoder]): + def _set_sparse_encoder(self, sparse_encoder: Optional[DenseEncoder]): if sparse_encoder is None: logger.warning("No sparse_encoder provided. Using default BM25Encoder.") self.sparse_encoder = BM25Encoder() diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index f0df2717..e8a7db14 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -6,7 +6,7 @@ import numpy as np from tqdm.auto import tqdm -from semantic_router.encoders import AutoEncoder, BaseEncoder, OpenAIEncoder +from semantic_router.encoders import AutoEncoder, DenseEncoder, OpenAIEncoder from semantic_router.index.base import BaseIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex @@ -55,7 +55,7 @@ def is_valid(layer_config: str) -> bool: class SemanticRouter(BaseRouter): def __init__( self, - encoder: Optional[BaseEncoder] = None, + encoder: Optional[DenseEncoder] = None, llm: Optional[BaseLLM] = None, routes: Optional[List[Route]] = None, index: Optional[BaseIndex] = None, # type: ignore diff --git a/tests/integration/encoders/test_openai_integration.py b/tests/integration/encoders/test_openai_integration.py index 47e617a5..c7bd8d9a 100644 --- a/tests/integration/encoders/test_openai_integration.py +++ b/tests/integration/encoders/test_openai_integration.py @@ -1,7 +1,7 @@ import os import pytest from openai import OpenAIError -from semantic_router.encoders.base import BaseEncoder +from semantic_router.encoders.base import DenseEncoder from semantic_router.encoders.openai import OpenAIEncoder with open("tests/integration/57640.4032.txt", "r") as fp: @@ -11,7 +11,7 @@ @pytest.fixture def openai_encoder(): if os.environ.get("OPENAI_API_KEY") is None: - return BaseEncoder() + return DenseEncoder() else: return OpenAIEncoder() diff --git a/tests/unit/encoders/test_base.py b/tests/unit/encoders/test_base.py index 4d4b87ae..53a62331 100644 --- a/tests/unit/encoders/test_base.py +++ b/tests/unit/encoders/test_base.py @@ -1,12 +1,12 @@ import pytest -from semantic_router.encoders import BaseEncoder +from semantic_router.encoders import DenseEncoder -class TestBaseEncoder: +class TestDenseEncoder: @pytest.fixture def base_encoder(self): - return BaseEncoder(name="TestEncoder", score_threshold=0.5) + return DenseEncoder(name="TestEncoder", score_threshold=0.5) def test_base_encoder_initialization(self, base_encoder): assert base_encoder.name == "TestEncoder", "Initialization of name failed" diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index 3cba34ca..564bd3a1 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -2,13 +2,13 @@ from semantic_router.encoders import ( AzureOpenAIEncoder, - BaseEncoder, + DenseEncoder, BM25Encoder, CohereEncoder, OpenAIEncoder, TfidfEncoder, ) -from semantic_router.OLD_hybrid_layer import HybridRouter +from semantic_router.routers import HybridRouter from semantic_router.route import Route @@ -26,8 +26,8 @@ def mock_encoder_call(utterances): @pytest.fixture def base_encoder(mocker): - mock_base_encoder = BaseEncoder(name="test-encoder", score_threshold=0.5) - mocker.patch.object(BaseEncoder, "__call__", return_value=[[0.1, 0.2, 0.3]]) + mock_base_encoder = DenseEncoder(name="test-encoder", score_threshold=0.5) + mocker.patch.object(DenseEncoder, "__call__", return_value=[[0.1, 0.2, 0.3]]) return mock_base_encoder diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index 2eef4666..8d47ee4b 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -6,7 +6,7 @@ import pytest import time from typing import Optional -from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder +from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex from semantic_router.index.qdrant import QdrantIndex @@ -102,7 +102,7 @@ def layer_yaml(): @pytest.fixture def base_encoder(): - return BaseEncoder(name="test-encoder", score_threshold=0.5) + return DenseEncoder(name="test-encoder", score_threshold=0.5) @pytest.fixture diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index 2327fc06..8405add9 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -4,7 +4,7 @@ import pytest import time from typing import Optional -from semantic_router.encoders import BaseEncoder, CohereEncoder, OpenAIEncoder +from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder from semantic_router.index.pinecone import PineconeIndex from semantic_router.schema import Utterance from semantic_router.routers.base import SemanticRouter @@ -100,7 +100,7 @@ def layer_yaml(): @pytest.fixture def base_encoder(): - return BaseEncoder(name="test-encoder", score_threshold=0.5) + return DenseEncoder(name="test-encoder", score_threshold=0.5) @pytest.fixture From 805807f839a4415792608ae953af76e17aa82a20 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Wed, 27 Nov 2024 15:43:36 +0100 Subject: [PATCH 14/33] feat: optimized sparse embedding interface --- Makefile | 4 +- docs/encoders/aurelio-bm25.ipynb | 4 +- docs/examples/hybrid-router.ipynb | 4 +- docs/examples/pinecone-hybrid.ipynb | 54 +++++++++++----------- semantic_router/encoders/aurelio.py | 13 ++++-- semantic_router/encoders/base.py | 2 +- semantic_router/encoders/tfidf.py | 7 ++- semantic_router/index/hybrid_local.py | 12 +++-- semantic_router/index/pinecone.py | 4 +- semantic_router/routers/base.py | 5 +- semantic_router/routers/hybrid.py | 8 ++-- semantic_router/routers/semantic.py | 5 +- semantic_router/schema.py | 66 +++++++++++++++++---------- tests/unit/test_sync.py | 8 +++- 14 files changed, 109 insertions(+), 87 deletions(-) diff --git a/Makefile b/Makefile index 8283b96d..979a97e5 100644 --- a/Makefile +++ b/Makefile @@ -7,8 +7,8 @@ lint: PYTHON_FILES=. lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$') lint lint_diff: - poetry run black --target-version py39 -l 88 $(PYTHON_FILES) --check - poetry run ruff . + poetry run black --target-version py311 -l 88 $(PYTHON_FILES) --check + poetry run ruff check . poetry run mypy $(PYTHON_FILES) test: diff --git a/docs/encoders/aurelio-bm25.ipynb b/docs/encoders/aurelio-bm25.ipynb index e74f1e1b..9e4b7852 100644 --- a/docs/encoders/aurelio-bm25.ipynb +++ b/docs/encoders/aurelio-bm25.ipynb @@ -153,9 +153,7 @@ " \"Enter OpenAI API Key: \"\n", ")\n", "\n", - "encoder = OpenAIEncoder(\n", - " name=\"text-embedding-3-small\", score_threshold=0.3\n", - ")" + "encoder = OpenAIEncoder(name=\"text-embedding-3-small\", score_threshold=0.3)" ] }, { diff --git a/docs/examples/hybrid-router.ipynb b/docs/examples/hybrid-router.ipynb index e52c5752..71e57ca7 100644 --- a/docs/examples/hybrid-router.ipynb +++ b/docs/examples/hybrid-router.ipynb @@ -155,9 +155,7 @@ "from semantic_router.routers import HybridRouter\n", "\n", "router = HybridRouter(\n", - " encoder=dense_encoder,\n", - " sparse_encoder=sparse_encoder,\n", - " routes=routes\n", + " encoder=dense_encoder, sparse_encoder=sparse_encoder, routes=routes\n", ")" ] }, diff --git a/docs/examples/pinecone-hybrid.ipynb b/docs/examples/pinecone-hybrid.ipynb index 134b6e0d..b354907f 100644 --- a/docs/examples/pinecone-hybrid.ipynb +++ b/docs/examples/pinecone-hybrid.ipynb @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -153,9 +153,7 @@ " \"Enter OpenAI API Key: \"\n", ")\n", "\n", - "encoder = OpenAIEncoder(\n", - " name=\"text-embedding-3-small\", score_threshold=0.3\n", - ")" + "encoder = OpenAIEncoder(name=\"text-embedding-3-small\", score_threshold=0.3)" ] }, { @@ -167,16 +165,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n", - "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n", - "2024-11-26 22:34:54 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n" + "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - discover_namespace_packages.py:12 - discover_subpackages() - Discovering subpackages in _NamespacePath(['/Users/jamesbriggs/Library/Caches/pypoetry/virtualenvs/semantic-router-C1zr4a78-py3.12/lib/python3.12/site-packages/pinecone_plugins'])\n", + "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - discover_plugins.py:9 - discover_plugins() - Looking for plugins in pinecone_plugins.inference\n", + "2024-11-27 15:41:32 - pinecone_plugin_interface.logging - INFO - installation.py:10 - install_plugins() - Installing plugin inference into Pinecone\n" ] } ], @@ -203,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -226,16 +224,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "True" + "False" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -253,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -272,7 +270,7 @@ " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -290,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -309,7 +307,7 @@ " Utterance(route='politics', utterance=\"why don't you tell me about your political opinions\", function_schemas=None, metadata={}, diff_tag=' ')]" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -327,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -349,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -358,7 +356,7 @@ "True" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -369,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -388,7 +386,7 @@ " \" politics: why don't you tell me about your political opinions\"]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -406,14 +404,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2024-11-26 22:35:56 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-27 15:42:03 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] }, { @@ -422,7 +420,7 @@ "RouteChoice(name=None, function_call=None, similarity_score=None)" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -440,7 +438,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-11-26 22:35:20 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + "2024-11-27 15:42:06 - httpx - INFO - _client.py:1013 - _send_single_request() - HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" ] }, { diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index d226e3d3..8824b2f1 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -12,16 +12,17 @@ class AurelioSparseEncoder(SparseEncoder): model: Optional[Any] = None idx_mapping: Optional[Dict[int, int]] = None client: AurelioClient = Field(default_factory=AurelioClient, exclude=True) - async_client: AsyncAurelioClient = Field(default_factory=AsyncAurelioClient, exclude=True) + async_client: AsyncAurelioClient = Field( + default_factory=AsyncAurelioClient, exclude=True + ) type: str = "sparse" def __init__( self, name: str = "bm25", - score_threshold: float = 1.0, api_key: Optional[str] = None, ): - super().__init__(name=name, score_threshold=score_threshold) + super().__init__(name=name) if api_key is None: api_key = os.getenv("AURELIO_API_KEY") if api_key is None: @@ -33,9 +34,11 @@ def __call__(self, docs: list[str]) -> list[SparseEmbedding]: res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name) embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data] return embeds - + async def acall(self, docs: list[str]) -> list[SparseEmbedding]: - res: EmbeddingResponse = await self.async_client.embedding(input=docs, model=self.name) + res: EmbeddingResponse = await self.async_client.embedding( + input=docs, model=self.name + ) embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data] return embeds diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index f2cee15d..1bcf3d9d 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -35,4 +35,4 @@ def __call__(self, docs: List[str]) -> List[SparseEmbedding]: raise NotImplementedError("Subclasses must implement this method") def acall(self, docs: List[str]) -> Coroutine[Any, Any, List[SparseEmbedding]]: - raise NotImplementedError("Subclasses must implement this method") \ No newline at end of file + raise NotImplementedError("Subclasses must implement this method") diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index 873d900a..1bec7b9f 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -14,9 +14,8 @@ class TfidfEncoder(SparseEncoder): idf: ndarray = np.array([]) word_index: Dict = {} - def __init__(self, name: str = "tfidf", score_threshold: float = 0.82): - # TODO default score_threshold not thoroughly tested, should optimize - super().__init__(name=name, score_threshold=score_threshold) + def __init__(self, name: str = "tfidf"): + super().__init__(name=name) self.word_index = {} self.idf = np.array([]) @@ -29,7 +28,7 @@ def __call__(self, docs: List[str]) -> List[List[float]]: docs = [self._preprocess(doc) for doc in docs] tf = self._compute_tf(docs) tfidf = tf * self.idf - return tfidf.tolist() + return tfidf def fit(self, routes: List[Route]): docs = [] diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index 28a3d83b..f2821422 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -66,15 +66,19 @@ def describe(self) -> Dict: "dimensions": self.index.shape[1] if self.index is not None else 0, "vectors": self.index.shape[0] if self.index is not None else 0, } - - def _sparse_dot_product(self, vec_a: dict[int, float], vec_b: dict[int, float]) -> float: + + def _sparse_dot_product( + self, vec_a: dict[int, float], vec_b: dict[int, float] + ) -> float: # switch vecs to ensure first is smallest for more efficiency if len(vec_a) > len(vec_b): vec_a, vec_b = vec_b, vec_a return sum(vec_a[i] * vec_b.get(i, 0) for i in vec_a) - + def _sparse_index_dot_product(self, vec_a: dict[int, float]) -> list[float]: - dot_products = [self._sparse_dot_product(vec_a, vec_b) for vec_b in self.sparse_index] + dot_products = [ + self._sparse_dot_product(vec_a, vec_b) for vec_b in self.sparse_index + ] return dot_products def query( diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 5eb0aecb..bb6ed3ef 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -652,7 +652,9 @@ async def _async_fetch_metadata(self, vector_id: str) -> dict: ) def __len__(self): - namespace_stats = self.index.describe_index_stats()["namespaces"].get(self.namespace) + namespace_stats = self.index.describe_index_stats()["namespaces"].get( + self.namespace + ) if namespace_stats: return namespace_stats["vector_count"] else: diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 1af3e38b..087eb57c 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -4,7 +4,7 @@ import random import hashlib from typing import Any, Callable, Dict, List, Optional, Tuple, Union -from pydantic.v1 import BaseModel, Field, validator +from pydantic.v1 import BaseModel, Field import numpy as np import yaml # type: ignore @@ -380,8 +380,7 @@ def _set_index(self, index: Optional[BaseIndex]): self.index = index def _init_index_state(self): - """Initializes an index (where required) and runs auto_sync if active. - """ + """Initializes an index (where required) and runs auto_sync if active.""" # initialize index now, check if we need dimensions if self.index.dimensions is None: dims = len(self.encoder(["test"])[0]) diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index f8ec89cd..0feb8379 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -60,7 +60,7 @@ def __init__( # run initialize index now if auto sync is active if self.auto_sync: self._init_index_state() - + def _set_sparse_encoder(self, sparse_encoder: Optional[DenseEncoder]): if sparse_encoder is None: logger.warning("No sparse_encoder provided. Using default BM25Encoder.") @@ -126,7 +126,7 @@ def __call__( vector=np.array(vector) if isinstance(vector, list) else vector, top_k=self.top_k, route_filter=route_filter, - sparse_vector=sparse_vector[0] + sparse_vector=sparse_vector[0], ) top_class, top_class_scores = self._semantic_classify( list(zip(scores, route_names)) @@ -142,7 +142,9 @@ def _convex_scaling(self, dense: np.ndarray, sparse: list[dict[int, float]]): scaled_dense = np.array(dense) * self.alpha scaled_sparse = [] for sparse_dict in sparse: - scaled_sparse.append({k: v * (1 - self.alpha) for k, v in sparse_dict.items()}) + scaled_sparse.append( + {k: v * (1 - self.alpha) for k, v in sparse_dict.items()} + ) return scaled_dense, scaled_sparse def _set_aggregation_method(self, aggregation: str = "sum"): diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index e8a7db14..8a21fdf2 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -1,15 +1,12 @@ import json import random from typing import Any, Dict, List, Optional, Tuple, Union -from pydantic.v1 import validator, Field import numpy as np from tqdm.auto import tqdm -from semantic_router.encoders import AutoEncoder, DenseEncoder, OpenAIEncoder +from semantic_router.encoders import AutoEncoder, DenseEncoder from semantic_router.index.base import BaseIndex -from semantic_router.index.local import LocalIndex -from semantic_router.index.pinecone import PineconeIndex from semantic_router.llms import BaseLLM, OpenAILLM from semantic_router.route import Route from semantic_router.routers.base import BaseRouter, RouterConfig diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 2d00572f..507ea349 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -1,11 +1,13 @@ from datetime import datetime from difflib import Differ from enum import Enum +import numpy as np from typing import List, Optional, Union, Any, Dict, Tuple from pydantic.v1 import BaseModel, Field from semantic_router.utils.logger import logger from aurelio_sdk.schema import BM25Embedding + class EncoderType(Enum): AURELIO = "aurelio" AZURE = "azure" @@ -406,37 +408,53 @@ class Metric(Enum): MANHATTAN = "manhattan" -class SparseValue(BaseModel): - index: int - value: float - - class SparseEmbedding(BaseModel): - embedding: List[SparseValue] + """Sparse embedding interface. Primarily uses numpy operations for faster + operations. + """ + embedding: np.ndarray - def to_dict(self): - return {x.index: x.value for x in self.embedding} + class Config: + arbitrary_types_allowed = True + + @classmethod + def from_array(cls, array: np.ndarray): + if array.ndim != 2 or array.shape[1] != 2: + raise ValueError( + f"Expected a 2D array with 2 columns, got a {array.ndim}D array with {array.shape[1]} columns. " + "Column 0 should contain index positions, and column 1 should contain respective values." + ) + return cls(embedding=array) - def to_pinecone(self): - return { - "indices": [x.index for x in self.embedding], - "values": [x.value for x in self.embedding], - } + @classmethod + def from_aurelio(cls, embedding: BM25Embedding): + arr = np.array([embedding.indices, embedding.values]).T + return cls.from_array(arr) @classmethod def from_dict(cls, sparse_dict: dict): - return cls(embedding=[SparseValue(index=i, value=v) for i, v in sparse_dict.items()]) + arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T + return cls.from_array(arr) - @classmethod - def from_aurelio(cls, embedding: BM25Embedding): - return cls(embedding=[ - SparseValue( - index=i, - value=v - ) for i, v in zip(embedding.indices, embedding.values) - ]) + def to_dict(self): + return { + i: v for i, v in zip( + self.embedding[:,0].astype(int), + self.embedding[:,1] + ) + } + + def to_pinecone(self): + return { + "indices": self.embedding[:, 0].astype(int).tolist(), + "values": self.embedding[:, 1].tolist(), + } # dictionary interface def items(self): - return [(x.index, x.value) for x in self.embedding] - + return [ + (i, v) for i, v in zip( + self.embedding[:,0].astype(int), + self.embedding[:,1] + ) + ] diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index 8405add9..ea4b8d41 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -223,7 +223,9 @@ def test_second_initialization_not_synced( _ = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, auto_sync="local" ) - route_layer = SemanticRouter(encoder=openai_encoder, routes=routes_2, index=index) + route_layer = SemanticRouter( + encoder=openai_encoder, routes=routes_2, index=index + ) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert route_layer.is_synced() is False @@ -236,7 +238,9 @@ def test_utterance_diff(self, openai_encoder, routes, routes_2, index_cls): _ = SemanticRouter( encoder=openai_encoder, routes=routes, index=index, auto_sync="local" ) - route_layer_2 = SemanticRouter(encoder=openai_encoder, routes=routes_2, index=index) + route_layer_2 = SemanticRouter( + encoder=openai_encoder, routes=routes_2, index=index + ) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated diff = route_layer_2.get_utterance_diff(include_metadata=True) From a96f6b85afbea34651763dfa7d5f7885b711931c Mon Sep 17 00:00:00 2001 From: jamescalam Date: Wed, 27 Nov 2024 18:03:08 +0100 Subject: [PATCH 15/33] fix: import --- semantic_router/schema.py | 22 +++++++++------------- tests/unit/test_sync.py | 2 +- 2 files changed, 10 insertions(+), 14 deletions(-) diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 507ea349..9596af50 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -412,6 +412,7 @@ class SparseEmbedding(BaseModel): """Sparse embedding interface. Primarily uses numpy operations for faster operations. """ + embedding: np.ndarray class Config: @@ -425,36 +426,31 @@ def from_array(cls, array: np.ndarray): "Column 0 should contain index positions, and column 1 should contain respective values." ) return cls(embedding=array) - + @classmethod def from_aurelio(cls, embedding: BM25Embedding): arr = np.array([embedding.indices, embedding.values]).T return cls.from_array(arr) - + @classmethod def from_dict(cls, sparse_dict: dict): arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T return cls.from_array(arr) - + def to_dict(self): return { - i: v for i, v in zip( - self.embedding[:,0].astype(int), - self.embedding[:,1] - ) + i: v for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1]) } - + def to_pinecone(self): return { "indices": self.embedding[:, 0].astype(int).tolist(), "values": self.embedding[:, 1].tolist(), } - + # dictionary interface def items(self): return [ - (i, v) for i, v in zip( - self.embedding[:,0].astype(int), - self.embedding[:,1] - ) + (i, v) + for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1]) ] diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index ea4b8d41..35093a6b 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -7,7 +7,7 @@ from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder from semantic_router.index.pinecone import PineconeIndex from semantic_router.schema import Utterance -from semantic_router.routers.base import SemanticRouter +from semantic_router.routers import SemanticRouter from semantic_router.route import Route from platform import python_version From 6bbafa4c17262ba3a16378fe9313cbf1bdf557cb Mon Sep 17 00:00:00 2001 From: jamescalam Date: Wed, 27 Nov 2024 18:59:12 +0100 Subject: [PATCH 16/33] feat: compatibility for tfidf --- semantic_router/encoders/base.py | 17 ++++++++++++++++- semantic_router/encoders/bm25.py | 16 ++++++---------- semantic_router/encoders/tfidf.py | 5 +++-- semantic_router/schema.py | 11 ++++++++--- 4 files changed, 33 insertions(+), 16 deletions(-) diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index 1bcf3d9d..dcfe9aa3 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -1,6 +1,7 @@ from typing import Any, Coroutine, List, Optional from pydantic.v1 import BaseModel, Field, validator +import numpy as np from semantic_router.schema import SparseEmbedding @@ -34,5 +35,19 @@ class Config: def __call__(self, docs: List[str]) -> List[SparseEmbedding]: raise NotImplementedError("Subclasses must implement this method") - def acall(self, docs: List[str]) -> Coroutine[Any, Any, List[SparseEmbedding]]: + async def acall(self, docs: List[str]) -> Coroutine[Any, Any, List[SparseEmbedding]]: raise NotImplementedError("Subclasses must implement this method") + + def _array_to_sparse_embeddings(self, sparse_arrays: np.ndarray) -> List[SparseEmbedding]: + """Consumes several sparse vectors containing zero-values and returns a compact array. + """ + if sparse_arrays.ndim != 2: + raise ValueError(f"Expected a 2D array, got a {sparse_arrays.ndim}D array.") + # get coordinates of non-zero values + coords = np.nonzero(sparse_arrays) + # create compact array + compact_array = np.array([coords[0], coords[1], sparse_arrays[coords]]).T + arr_range = range(compact_array[:, 0].max().astype(int) + 1) + arrs = [compact_array[compact_array[:, 0] == i, :][:, 1:3] for i in arr_range] + return [SparseEmbedding.from_compact_array(arr) for arr in arrs] + diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index 4eac26e7..0ec36499 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -1,10 +1,11 @@ from typing import Any, Dict, List, Optional -from semantic_router.encoders import SparseEncoder +from semantic_router.encoders.tfidf import TfidfEncoder from semantic_router.utils.logger import logger +from semantic_router.schema import SparseEmbedding -class BM25Encoder(SparseEncoder): +class BM25Encoder(TfidfEncoder): model: Optional[Any] = None idx_mapping: Optional[Dict[int, int]] = None type: str = "sparse" @@ -12,10 +13,9 @@ class BM25Encoder(SparseEncoder): def __init__( self, name: str = "bm25", - score_threshold: float = 0.82, use_default_params: bool = True, ): - super().__init__(name=name, score_threshold=score_threshold) + super().__init__(name=name) try: from pinecone_text.sparse import BM25Encoder as encoder except ImportError: @@ -40,12 +40,13 @@ def _set_idx_mapping(self): else: raise TypeError("Expected a dictionary for 'doc_freq'") - def __call__(self, docs: List[str]) -> List[List[float]]: + def __call__(self, docs: List[str]) -> list[SparseEmbedding]: 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: + print(docs) sparse_dicts = self.model.encode_documents(docs) else: raise ValueError("No documents to encode.") @@ -60,8 +61,3 @@ def __call__(self, docs: List[str]) -> List[List[float]]: embeds[i][position] = val return embeds - def fit(self, docs: List[str]): - if self.model is None: - raise ValueError("Model is not initialized.") - self.model.fit(docs) - self._set_idx_mapping() diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index 1bec7b9f..d9d97a47 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -8,6 +8,7 @@ from semantic_router.encoders import SparseEncoder from semantic_router.route import Route +from semantic_router.schema import SparseEmbedding class TfidfEncoder(SparseEncoder): @@ -19,7 +20,7 @@ def __init__(self, name: str = "tfidf"): self.word_index = {} self.idf = np.array([]) - def __call__(self, docs: List[str]) -> List[List[float]]: + def __call__(self, docs: List[str]) -> list[SparseEmbedding]: if len(self.word_index) == 0 or self.idf.size == 0: raise ValueError("Vectorizer is not initialized.") if len(docs) == 0: @@ -28,7 +29,7 @@ def __call__(self, docs: List[str]) -> List[List[float]]: docs = [self._preprocess(doc) for doc in docs] tf = self._compute_tf(docs) tfidf = tf * self.idf - return tfidf + return self._array_to_sparse_embeddings(tfidf) def fit(self, routes: List[Route]): docs = [] diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 9596af50..616416e7 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -419,23 +419,28 @@ class Config: arbitrary_types_allowed = True @classmethod - def from_array(cls, array: np.ndarray): + def from_compact_array(cls, array: np.ndarray): if array.ndim != 2 or array.shape[1] != 2: raise ValueError( f"Expected a 2D array with 2 columns, got a {array.ndim}D array with {array.shape[1]} columns. " "Column 0 should contain index positions, and column 1 should contain respective values." ) return cls(embedding=array) + + @classmethod + def from_array(cls, array: np.ndarray): + """Consumes a single sparse vector which contains zero-values. + """ @classmethod def from_aurelio(cls, embedding: BM25Embedding): arr = np.array([embedding.indices, embedding.values]).T - return cls.from_array(arr) + return cls.from_compact_array(arr) @classmethod def from_dict(cls, sparse_dict: dict): arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T - return cls.from_array(arr) + return cls.from_compact_array(arr) def to_dict(self): return { From aaae6f2a2f6c437f8b6752431923059cf326c427 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 00:06:35 +0100 Subject: [PATCH 17/33] fix: router bug fixes --- semantic_router/routers/base.py | 194 ++++--- semantic_router/routers/hybrid.py | 51 +- semantic_router/routers/semantic.py | 850 +--------------------------- semantic_router/schema.py | 7 +- tests/unit/test_router.py | 16 +- 5 files changed, 136 insertions(+), 982 deletions(-) diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 087eb57c..b092a297 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -250,39 +250,13 @@ def to_utterances(self) -> List[Utterance]: return utterances def add(self, route: Route): - """Add a route to the local SemanticRouter and index. + """Add a route to the RouterConfig. :param route: The route to add. :type route: Route """ - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - embedded_utterances = self.encoder(route.utterances) - self.index.add( - embeddings=embedded_utterances, - routes=[route.name] * len(route.utterances), - utterances=route.utterances, - function_schemas=( - route.function_schemas * len(route.utterances) - if route.function_schemas - else [{}] * len(route.utterances) - ), - metadata_list=[route.metadata if route.metadata else {}] - * len(route.utterances), - ) - self.routes.append(route) - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) + logger.info(f"Added route `{route.name}`") def get(self, name: str) -> Optional[Route]: for route in self.routes: @@ -307,7 +281,7 @@ def get_hash(self) -> ConfigParameter: class BaseRouter(BaseModel): - encoder: DenseEncoder + encoder: DenseEncoder = Field(default_factory=OpenAIEncoder) index: BaseIndex = Field(default_factory=BaseIndex) score_threshold: Optional[float] = Field(default=None) routes: List[Route] = [] @@ -339,18 +313,11 @@ def __init__( aggregation=aggregation, auto_sync=auto_sync, ) - if encoder is None: - logger.warning( - "No encoder provided. Using default OpenAIEncoder. Ensure " - "that you have set OPENAI_API_KEY in your environment." - ) - self.encoder = OpenAIEncoder() - else: - self.encoder = encoder + self.encoder = self._get_encoder(encoder=encoder) self.llm = llm self.routes = routes.copy() if routes else [] # initialize index - self._set_index(index=index) + self.index =self._get_index(index=index) # set score threshold using default method self._set_score_threshold() self.top_k = top_k @@ -372,12 +339,21 @@ def __init__( if self.auto_sync: self._init_index_state() - def _set_index(self, index: Optional[BaseIndex]): + def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: if index is None: logger.warning("No index provided. Using default LocalIndex.") - self.index = LocalIndex() + index = LocalIndex() + else: + index = index + return index + + def _get_encoder(self, encoder: Optional[DenseEncoder]) -> DenseEncoder: + if encoder is None: + logger.warning("No encoder provided. Using default OpenAIEncoder.") + encoder = OpenAIEncoder() else: - self.index = index + encoder = encoder + return encoder def _init_index_state(self): """Initializes an index (where required) and runs auto_sync if active.""" @@ -521,20 +497,57 @@ def retrieve_multiple_routes( vector_arr = self._encode(text=text) else: vector_arr = np.array(vector) + print(f"{text=}") + print(f"{vector_arr}") # get relevant utterances results = self._retrieve(xq=vector_arr) - + print(f"{results=}") # decide most relevant routes categories_with_scores = self._semantic_classify_multiple_routes(results) + print(f"{categories_with_scores=}") + return [ + RouteChoice(name=category, similarity_score=score) for category, score in categories_with_scores + ] - route_choices = [] - for category, score in categories_with_scores: - route = self.check_for_matching_routes(category) - if route: - route_choice = RouteChoice(name=route.name, similarity_score=score) - route_choices.append(route_choice) + #route_choices = [] + # TODO JB: do we need this check? Maybe we should be returning directly + #for category, score in categories_with_scores: + # route = self.check_for_matching_routes(category) + # if route: + # route_choice = RouteChoice(name=route.name, similarity_score=score) + # route_choices.append(route_choice) - return route_choices + #return route_choices + + def _retrieve_top_route( + self, vector: List[float], route_filter: Optional[List[str]] = None + ) -> Tuple[Optional[Route], List[float]]: + """ + Retrieve the top matching route based on the given vector. + Returns a tuple of the route (if any) and the scores of the top class. + """ + # get relevant results (scores and routes) + results = self._retrieve( + xq=np.array(vector), top_k=self.top_k, route_filter=route_filter + ) + # decide most relevant routes + top_class, top_class_scores = self._semantic_classify(results) + # TODO do we need this check? + route = self.check_for_matching_routes(top_class) + return route, top_class_scores + + async def _async_retrieve_top_route( + self, vector: List[float], route_filter: Optional[List[str]] = None + ) -> Tuple[Optional[Route], List[float]]: + # get relevant results (scores and routes) + results = await self._async_retrieve( + xq=np.array(vector), top_k=self.top_k, route_filter=route_filter + ) + # decide most relevant routes + top_class, top_class_scores = await self._async_semantic_classify(results) + # TODO do we need this check? + route = self.check_for_matching_routes(top_class) + return route, top_class_scores def sync(self, sync_mode: str, force: bool = False) -> List[str]: """Runs a sync of the local routes with the remote index. @@ -661,40 +674,11 @@ def _local_delete(self, utterances: List[Utterance]): self.routes = new_routes - def _retrieve_top_route( - self, vector: List[float], route_filter: Optional[List[str]] = None - ) -> Tuple[Optional[Route], List[float]]: - """ - Retrieve the top matching route based on the given vector. - Returns a tuple of the route (if any) and the scores of the top class. - """ - # get relevant results (scores and routes) - results = self._retrieve( - xq=np.array(vector), top_k=self.top_k, route_filter=route_filter - ) - # decide most relevant routes - top_class, top_class_scores = self._semantic_classify(results) - # TODO do we need this check? - route = self.check_for_matching_routes(top_class) - return route, top_class_scores - - async def _async_retrieve_top_route( - self, vector: List[float], route_filter: Optional[List[str]] = None - ) -> Tuple[Optional[Route], List[float]]: - # get relevant results (scores and routes) - results = await self._async_retrieve( - xq=np.array(vector), top_k=self.top_k, route_filter=route_filter - ) - # decide most relevant routes - top_class, top_class_scores = await self._async_semantic_classify(results) - # TODO do we need this check? - route = self.check_for_matching_routes(top_class) - return route, top_class_scores - def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: """ Check if the route's score passes the specified threshold. """ + # TODO JB: do we need this? if route is None: return False threshold = ( @@ -1124,21 +1108,55 @@ async def async_group_scores_by_class( scores_by_class[route] = [score] return scores_by_class - def _pass_threshold(self, scores: List[float], threshold: float) -> bool: + def _pass_threshold(self, scores: List[float], threshold: float | None) -> bool: + """Test if the route score passes the minimum threshold. If a threshold of None is + set, then the route will always pass no matter how low it scores. + + :param scores: The scores to test. + :type scores: List[float] + :param threshold: The minimum threshold to pass. + :type threshold: float | None + :return: True if the route passes the threshold, False otherwise. + :rtype: bool + """ + if threshold is None: + return True if scores: return max(scores) > threshold else: return False - def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None): + def _update_thresholds(self, route_thresholds: Optional[Dict[str, float]] = None): + """Update the score thresholds for each route using a dictionary of + route names and thresholds. + + :param route_thresholds: A dictionary of route names and thresholds. + :type route_thresholds: Dict[str, float] | None """ - Update the score thresholds for each route. + if route_thresholds: + for route, threshold in route_thresholds.items(): + self.set_threshold( + threshold=threshold, + route_name=route, + ) + + def set_threshold(self, threshold: float, route_name: str | None = None): + """Set the score threshold for a specific route or all routes. + + :param threshold: The threshold to set. + :type threshold: float + :param route_name: The name of the route to set the threshold for. If None, the threshold will be set for all routes. + :type route_name: str | None """ - if score_thresholds: + if route_name is None: for route in self.routes: - route.score_threshold = score_thresholds.get( - route.name, self.score_threshold - ) + route.score_threshold = threshold + else: + route = self.get(route_name) + if route is not None: + route.score_threshold = threshold + else: + logger.error(f"Route `{route_name}` not found") def to_config(self) -> RouterConfig: return RouterConfig( @@ -1207,7 +1225,7 @@ def fit( search_range=0.8, ) # update current route layer - self._update_thresholds(score_thresholds=thresholds) + self._update_thresholds(route_thresholds=thresholds) # evaluate acc = self._vec_evaluate(Xq=Xq, y=y) # update best @@ -1215,7 +1233,7 @@ def fit( best_acc = acc best_thresholds = thresholds # update route layer to best thresholds - self._update_thresholds(score_thresholds=best_thresholds) + self._update_thresholds(route_thresholds=best_thresholds) if local_execution: # Switch back to the original index diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 0feb8379..64418f98 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -10,7 +10,7 @@ TfidfEncoder, ) from semantic_router.route import Route -from semantic_router.index.hybrid_local import HybridLocalIndex +from semantic_router.index import BaseIndex, HybridLocalIndex from semantic_router.schema import RouteChoice, SparseEmbedding from semantic_router.utils.logger import logger from semantic_router.routers.base import BaseRouter @@ -39,6 +39,7 @@ def __init__( if index is None: logger.warning("No index provided. Using default HybridLocalIndex.") index = HybridLocalIndex() + encoder = self._get_encoder(encoder=encoder) super().__init__( encoder=encoder, llm=llm, @@ -61,6 +62,14 @@ def __init__( if self.auto_sync: self._init_index_state() + def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: + if index is None: + logger.warning("No index provided. Using default HybridLocalIndex.") + index = HybridLocalIndex() + else: + index = index + return index + def _set_sparse_encoder(self, sparse_encoder: Optional[DenseEncoder]): if sparse_encoder is None: logger.warning("No sparse_encoder provided. Using default BM25Encoder.") @@ -146,43 +155,3 @@ def _convex_scaling(self, dense: np.ndarray, sparse: list[dict[int, float]]): {k: v * (1 - self.alpha) for k, v in sparse_dict.items()} ) return scaled_dense, scaled_sparse - - def _set_aggregation_method(self, aggregation: str = "sum"): - if aggregation == "sum": - return lambda x: sum(x) - elif aggregation == "mean": - return lambda x: np.mean(x) - elif aggregation == "max": - return lambda x: max(x) - else: - raise ValueError( - f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." - ) - - def _semantic_classify(self, query_results: List[Tuple]) -> Tuple[str, List[float]]: - scores_by_class: Dict[str, List[float]] = {} - for score, route in query_results: - if route in scores_by_class: - scores_by_class[route].append(score) - else: - scores_by_class[route] = [score] - - # Calculate total score for each class - total_scores = { - route: self.aggregation_method(scores) - for route, scores in scores_by_class.items() - } - top_class = max(total_scores, key=lambda x: total_scores[x], default=None) - - # Return the top class and its associated scores - if top_class is not None: - return str(top_class), scores_by_class.get(top_class, []) - else: - logger.warning("No classification found for semantic classifier.") - return "", [] - - def _pass_threshold(self, scores: List[float], threshold: float) -> bool: - if scores: - return max(scores) > threshold - else: - return False diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 8a21fdf2..59cd8de1 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -1,52 +1,12 @@ -import json -import random -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, List, Optional import numpy as np -from tqdm.auto import tqdm -from semantic_router.encoders import AutoEncoder, DenseEncoder +from semantic_router.encoders import DenseEncoder from semantic_router.index.base import BaseIndex -from semantic_router.llms import BaseLLM, OpenAILLM +from semantic_router.llms import BaseLLM from semantic_router.route import Route -from semantic_router.routers.base import BaseRouter, RouterConfig -from semantic_router.schema import ( - ConfigParameter, - RouteChoice, - Utterance, - UtteranceDiff, -) -from semantic_router.utils.logger import logger - - -def is_valid(layer_config: str) -> bool: - """Make sure the given string is json format and contains the 3 keys: - ["encoder_name", "encoder_type", "routes"]""" - try: - output_json = json.loads(layer_config) - required_keys = ["encoder_name", "encoder_type", "routes"] - - if isinstance(output_json, list): - for item in output_json: - missing_keys = [key for key in required_keys if key not in item] - if missing_keys: - logger.warning( - f"Missing keys in layer config: {', '.join(missing_keys)}" - ) - return False - return True - else: - missing_keys = [key for key in required_keys if key not in output_json] - if missing_keys: - logger.warning( - f"Missing keys in layer config: {', '.join(missing_keys)}" - ) - return False - else: - return True - except json.JSONDecodeError as e: - logger.error(e) - return False +from semantic_router.routers.base import BaseRouter class SemanticRouter(BaseRouter): @@ -60,6 +20,8 @@ def __init__( aggregation: str = "mean", auto_sync: Optional[str] = None, ): + index = self._get_index(index=index) + encoder = self._get_encoder(encoder=encoder) super().__init__( encoder=encoder, llm=llm, @@ -73,530 +35,6 @@ def __init__( if self.auto_sync: self._init_index_state() - def check_for_matching_routes(self, top_class: str) -> Optional[Route]: - matching_route = next( - (route for route in self.routes if route.name == top_class), None - ) - if matching_route is None: - logger.error( - f"No route found with name {top_class}. Check to see if any Routes " - "have been defined." - ) - return None - return matching_route - - def __call__( - self, - text: Optional[str] = None, - vector: Optional[List[float]] = None, - simulate_static: bool = False, - route_filter: Optional[List[str]] = None, - ) -> RouteChoice: - # if no vector provided, encode text to get vector - if vector is None: - if text is None: - raise ValueError("Either text or vector must be provided") - vector = self._encode(text=text) - route, top_class_scores = self._retrieve_top_route(vector, route_filter) - passed = self._check_threshold(top_class_scores, route) - if passed and route is not None and not simulate_static: - if route.function_schemas and text is None: - raise ValueError( - "Route has a function schema, but no text was provided." - ) - if route.function_schemas and not isinstance(route.llm, BaseLLM): - if not self.llm: - logger.warning( - "No LLM provided for dynamic route, will use OpenAI LLM " - "default. Ensure API key is set in OPENAI_API_KEY environment " - "variable." - ) - - self.llm = OpenAILLM() - route.llm = self.llm - else: - route.llm = self.llm - return route(text) - elif passed and route is not None and simulate_static: - return RouteChoice( - name=route.name, - function_call=None, - similarity_score=None, - ) - else: - # if no route passes threshold, return empty route choice - return RouteChoice() - - async def acall( - self, - text: Optional[str] = None, - vector: Optional[List[float]] = None, - simulate_static: bool = False, - route_filter: Optional[List[str]] = None, - ) -> RouteChoice: - # if no vector provided, encode text to get vector - if vector is None: - if text is None: - raise ValueError("Either text or vector must be provided") - vector = await self._async_encode(text=text) - - route, top_class_scores = await self._async_retrieve_top_route( - vector, route_filter - ) - passed = self._check_threshold(top_class_scores, route) - if passed and route is not None and not simulate_static: - if route.function_schemas and text is None: - raise ValueError( - "Route has a function schema, but no text was provided." - ) - if route.function_schemas and not isinstance(route.llm, BaseLLM): - if not self.llm: - logger.warning( - "No LLM provided for dynamic route, will use OpenAI LLM default" - ) - self.llm = OpenAILLM() - route.llm = self.llm - else: - route.llm = self.llm - return await route.acall(text) - elif passed and route is not None and simulate_static: - return RouteChoice( - name=route.name, - function_call=None, - similarity_score=None, - ) - else: - # if no route passes threshold, return empty route choice - return RouteChoice() - - def retrieve_multiple_routes( - self, - text: Optional[str] = None, - vector: Optional[List[float]] = None, - ) -> List[RouteChoice]: - if vector is None: - if text is None: - raise ValueError("Either text or vector must be provided") - vector_arr = self._encode(text=text) - else: - vector_arr = np.array(vector) - # get relevant utterances - results = self._retrieve(xq=vector_arr) - - # decide most relevant routes - categories_with_scores = self._semantic_classify_multiple_routes(results) - - route_choices = [] - for category, score in categories_with_scores: - route = self.check_for_matching_routes(category) - if route: - route_choice = RouteChoice(name=route.name, similarity_score=score) - route_choices.append(route_choice) - - return route_choices - - def sync(self, sync_mode: str, force: bool = False) -> List[str]: - """Runs a sync of the local routes with the remote index. - - :param sync_mode: The mode to sync the routes with the remote index. - :type sync_mode: str - :param force: Whether to force the sync even if the local and remote - hashes already match. Defaults to False. - :type force: bool, optional - :return: A list of diffs describing the addressed differences between - the local and remote route layers. - :rtype: List[str] - """ - if not force and self.is_synced(): - logger.warning("Local and remote route layers are already synchronized.") - # create utterance diff to return, but just using local instance - # for speed - local_utterances = self.to_config().to_utterances() - diff = UtteranceDiff.from_utterances( - local_utterances=local_utterances, - remote_utterances=local_utterances, - ) - return diff.to_utterance_str() - # otherwise we continue with the sync, first creating a diff - local_utterances = self.to_config().to_utterances() - remote_utterances = self.index.get_utterances() - diff = UtteranceDiff.from_utterances( - local_utterances=local_utterances, - remote_utterances=remote_utterances, - ) - # generate sync strategy - sync_strategy = diff.get_sync_strategy(sync_mode=sync_mode) - # and execute - self._execute_sync_strategy(sync_strategy) - return diff.to_utterance_str() - - def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]]): - """Executes the provided sync strategy, either deleting or upserting - routes from the local and remote instances as defined in the strategy. - - :param strategy: The sync strategy to execute. - :type strategy: Dict[str, Dict[str, List[Utterance]]] - """ - if strategy["remote"]["delete"]: - data_to_delete = {} # type: ignore - for utt_obj in strategy["remote"]["delete"]: - data_to_delete.setdefault(utt_obj.route, []).append(utt_obj.utterance) - # TODO: switch to remove without sync?? - self.index._remove_and_sync(data_to_delete) - if strategy["remote"]["upsert"]: - utterances_text = [utt.utterance for utt in strategy["remote"]["upsert"]] - self.index.add( - embeddings=self.encoder(utterances_text), - routes=[utt.route for utt in strategy["remote"]["upsert"]], - utterances=utterances_text, - function_schemas=[ - utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore - ], - metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]], - ) - if strategy["local"]["delete"]: - self._local_delete(utterances=strategy["local"]["delete"]) - if strategy["local"]["upsert"]: - self._local_upsert(utterances=strategy["local"]["upsert"]) - # update hash - self._write_hash() - - def _local_upsert(self, utterances: List[Utterance]): - """Adds new routes to the SemanticRouter. - - :param utterances: The utterances to add to the local SemanticRouter. - :type utterances: List[Utterance] - """ - new_routes = {route.name: route for route in self.routes} - for utt_obj in utterances: - if utt_obj.route not in new_routes.keys(): - new_routes[utt_obj.route] = Route( - name=utt_obj.route, - utterances=[utt_obj.utterance], - function_schemas=utt_obj.function_schemas, - metadata=utt_obj.metadata, - ) - else: - if utt_obj.utterance not in new_routes[utt_obj.route].utterances: - new_routes[utt_obj.route].utterances.append(utt_obj.utterance) - new_routes[utt_obj.route].function_schemas = utt_obj.function_schemas - new_routes[utt_obj.route].metadata = utt_obj.metadata - self.routes = list(new_routes.values()) - - def _local_delete(self, utterances: List[Utterance]): - """Deletes routes from the local SemanticRouter. - - :param utterances: The utterances to delete from the local SemanticRouter. - :type utterances: List[Utterance] - """ - # create dictionary of route names to utterances - route_dict: dict[str, List[str]] = {} - for utt in utterances: - route_dict.setdefault(utt.route, []).append(utt.utterance) - # iterate over current routes and delete specific utterance if found - new_routes = [] - for route in self.routes: - if route.name in route_dict.keys(): - # drop utterances that are in route_dict deletion list - new_utterances = list( - set(route.utterances) - set(route_dict[route.name]) - ) - if len(new_utterances) == 0: - # the route is now empty, so we skip it - continue - else: - new_routes.append( - Route( - name=route.name, - utterances=new_utterances, - # use existing function schemas and metadata - function_schemas=route.function_schemas, - metadata=route.metadata, - ) - ) - else: - # the route is not in the route_dict, so we keep it as is - new_routes.append(route) - - self.routes = new_routes - - def _retrieve_top_route( - self, vector: List[float], route_filter: Optional[List[str]] = None - ) -> Tuple[Optional[Route], List[float]]: - """ - Retrieve the top matching route based on the given vector. - Returns a tuple of the route (if any) and the scores of the top class. - """ - # get relevant results (scores and routes) - results = self._retrieve( - xq=np.array(vector), top_k=self.top_k, route_filter=route_filter - ) - # decide most relevant routes - top_class, top_class_scores = self._semantic_classify(results) - # TODO do we need this check? - route = self.check_for_matching_routes(top_class) - return route, top_class_scores - - async def _async_retrieve_top_route( - self, vector: List[float], route_filter: Optional[List[str]] = None - ) -> Tuple[Optional[Route], List[float]]: - # get relevant results (scores and routes) - results = await self._async_retrieve( - xq=np.array(vector), top_k=self.top_k, route_filter=route_filter - ) - # decide most relevant routes - top_class, top_class_scores = await self._async_semantic_classify(results) - # TODO do we need this check? - route = self.check_for_matching_routes(top_class) - return route, top_class_scores - - def _check_threshold(self, scores: List[float], route: Optional[Route]) -> bool: - """ - Check if the route's score passes the specified threshold. - """ - if route is None: - return False - threshold = ( - route.score_threshold - if route.score_threshold is not None - else self.score_threshold - ) - return self._pass_threshold(scores, threshold) - - def __str__(self): - return ( - f"SemanticRouter(encoder={self.encoder}, " - f"score_threshold={self.score_threshold}, " - f"routes={self.routes})" - ) - - @classmethod - def from_json(cls, file_path: str): - config = RouterConfig.from_file(file_path) - encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes) - - @classmethod - def from_yaml(cls, file_path: str): - config = RouterConfig.from_file(file_path) - encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes) - - @classmethod - def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): - encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes, index=index) - - def list_route_names(self) -> List[str]: - return [route.name for route in self.routes] - - def update( - self, - name: str, - threshold: Optional[float] = None, - utterances: Optional[List[str]] = None, - ): - """Updates the route specified in name. Allows the update of - threshold and/or utterances. If no values are provided via the - threshold or utterances parameters, those fields are not updated. - If neither field is provided raises a ValueError. - - The name must exist within the local SemanticRouter, if not a - KeyError will be raised. - """ - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - - if threshold is None and utterances is None: - raise ValueError( - "At least one of 'threshold' or 'utterances' must be provided." - ) - if utterances: - raise NotImplementedError( - "The update method cannot be used for updating utterances yet." - ) - - route = self.get(name) - if route: - if threshold: - old_threshold = route.score_threshold - route.score_threshold = threshold - logger.info( - f"Updated threshold for route '{route.name}' from {old_threshold} to {threshold}" - ) - else: - raise ValueError(f"Route '{name}' not found. Nothing updated.") - - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) - - def delete(self, route_name: str): - """Deletes a route given a specific route name. - - :param route_name: the name of the route to be deleted - :type str: - """ - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - - if route_name not in [route.name for route in self.routes]: - err_msg = f"Route `{route_name}` not found in SemanticRouter" - logger.warning(err_msg) - try: - self.index.delete(route_name=route_name) - except Exception as e: - logger.error(f"Failed to delete route from the index: {e}") - else: - self.routes = [route for route in self.routes if route.name != route_name] - self.index.delete(route_name=route_name) - - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) - - def _refresh_routes(self): - """Pulls out the latest routes from the index.""" - raise NotImplementedError("This method has not yet been implemented.") - route_mapping = {route.name: route for route in self.routes} - index_routes = self.index.get_utterances() - new_routes_names = [] - new_routes = [] - for route_name, utterance in index_routes: - if route_name in route_mapping: - if route_name not in new_routes_names: - existing_route = route_mapping[route_name] - new_routes.append(existing_route) - - new_routes.append(Route(name=route_name, utterances=[utterance])) - route = route_mapping[route_name] - self.routes.append(route) - - def _add_routes(self, routes: List[Route]): - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - - if not routes: - logger.warning("No routes provided to add.") - return - # create embeddings for all routes - route_names, all_utterances, all_function_schemas, all_metadata = ( - self._extract_routes_details(routes, include_metadata=True) - ) - embedded_utterances = self.encoder(all_utterances) - try: - # Batch insertion into the index - self.index.add( - embeddings=embedded_utterances, - routes=route_names, - utterances=all_utterances, - function_schemas=all_function_schemas, - metadata_list=all_metadata, - ) - except Exception as e: - logger.error(f"Failed to add routes to the index: {e}") - raise Exception("Indexing error occurred") from e - - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) - - def _get_hash(self) -> ConfigParameter: - config = self.to_config() - return config.get_hash() - - def _write_hash(self) -> ConfigParameter: - config = self.to_config() - hash_config = config.get_hash() - self.index._write_config(config=hash_config) - return hash_config - - def is_synced(self) -> bool: - """Check if the local and remote route layer instances are - synchronized. - - :return: True if the local and remote route layers are synchronized, - False otherwise. - :rtype: bool - """ - # first check hash - local_hash = self._get_hash() - remote_hash = self.index._read_hash() - if local_hash.value == remote_hash.value: - return True - else: - return False - - def get_utterance_diff(self, include_metadata: bool = False) -> List[str]: - """Get the difference between the local and remote utterances. Returns - a list of strings showing what is different in the remote when compared - to the local. For example: - - [" route1: utterance1", - " route1: utterance2", - "- route2: utterance3", - "- route2: utterance4"] - - Tells us that the remote is missing "route2: utterance3" and "route2: - utterance4", which do exist locally. If we see: - - [" route1: utterance1", - " route1: utterance2", - "+ route2: utterance3", - "+ route2: utterance4"] - - This diff tells us that the remote has "route2: utterance3" and - "route2: utterance4", which do not exist locally. - """ - # first we get remote and local utterances - remote_utterances = self.index.get_utterances() - local_utterances = self.to_config().to_utterances() - - diff_obj = UtteranceDiff.from_utterances( - local_utterances=local_utterances, remote_utterances=remote_utterances - ) - return diff_obj.to_utterance_str(include_metadata=include_metadata) - - def _extract_routes_details( - self, routes: List[Route], include_metadata: bool = False - ) -> Tuple: - route_names = [route.name for route in routes for _ in route.utterances] - utterances = [utterance for route in routes for utterance in route.utterances] - function_schemas = [ - route.function_schemas[0] if route.function_schemas is not None else {} - for route in routes - for _ in route.utterances - ] - - if include_metadata: - metadata = [route.metadata for route in routes for _ in route.utterances] - return route_names, utterances, function_schemas, metadata - return route_names, utterances, function_schemas - def _encode(self, text: str) -> Any: """Given some text, encode it.""" # create query vector @@ -610,279 +48,3 @@ async def _async_encode(self, text: str) -> Any: xq = np.array(await self.encoder.acall(docs=[text])) xq = np.squeeze(xq) # Reduce to 1d array. return xq - - def _retrieve( - self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None - ) -> List[Dict]: - """Given a query vector, retrieve the top_k most similar records.""" - # get scores and routes - scores, routes = self.index.query( - vector=xq, top_k=top_k, route_filter=route_filter - ) - return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] - - async def _async_retrieve( - self, xq: Any, top_k: int = 5, route_filter: Optional[List[str]] = None - ) -> List[Dict]: - """Given a query vector, retrieve the top_k most similar records.""" - # get scores and routes - scores, routes = await self.index.aquery( - vector=xq, top_k=top_k, route_filter=route_filter - ) - return [{"route": d, "score": s.item()} for d, s in zip(routes, scores)] - - def _set_aggregation_method(self, aggregation: str = "sum"): - if aggregation == "sum": - return lambda x: sum(x) - elif aggregation == "mean": - return lambda x: np.mean(x) - elif aggregation == "max": - return lambda x: max(x) - else: - raise ValueError( - f"Unsupported aggregation method chosen: {aggregation}. Choose either 'SUM', 'MEAN', or 'MAX'." - ) - - def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]: - scores_by_class = self.group_scores_by_class(query_results) - - # Calculate total score for each class - total_scores = { - route: self.aggregation_method(scores) - for route, scores in scores_by_class.items() - } - top_class = max(total_scores, key=lambda x: total_scores[x], default=None) - - # Return the top class and its associated scores - if top_class is not None: - return str(top_class), scores_by_class.get(top_class, []) - else: - logger.warning("No classification found for semantic classifier.") - return "", [] - - async def _async_semantic_classify( - self, query_results: List[Dict] - ) -> Tuple[str, List[float]]: - scores_by_class = await self.async_group_scores_by_class(query_results) - - # Calculate total score for each class - total_scores = { - route: self.aggregation_method(scores) - for route, scores in scores_by_class.items() - } - top_class = max(total_scores, key=lambda x: total_scores[x], default=None) - - # Return the top class and its associated scores - if top_class is not None: - return str(top_class), scores_by_class.get(top_class, []) - else: - logger.warning("No classification found for semantic classifier.") - return "", [] - - def get(self, name: str) -> Optional[Route]: - for route in self.routes: - if route.name == name: - return route - logger.error(f"Route `{name}` not found") - return None - - def _semantic_classify_multiple_routes( - self, query_results: List[Dict] - ) -> List[Tuple[str, float]]: - scores_by_class = self.group_scores_by_class(query_results) - - # Filter classes based on threshold and find max score for each - classes_above_threshold = [] - for route_name, scores in scores_by_class.items(): - # Use the get method to find the Route object by its name - route_obj = self.get(route_name) - if route_obj is not None: - # Use the Route object's threshold if it exists, otherwise use the provided threshold - _threshold = ( - route_obj.score_threshold - if route_obj.score_threshold is not None - else self.score_threshold - ) - if self._pass_threshold(scores, _threshold): - max_score = max(scores) - classes_above_threshold.append((route_name, max_score)) - - return classes_above_threshold - - def group_scores_by_class( - self, query_results: List[Dict] - ) -> Dict[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] - return scores_by_class - - async def async_group_scores_by_class( - self, query_results: List[Dict] - ) -> Dict[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] - return scores_by_class - - def _pass_threshold(self, scores: List[float], threshold: float) -> bool: - if scores: - return max(scores) > threshold - else: - return False - - def _update_thresholds(self, score_thresholds: Optional[Dict[str, float]] = None): - """ - Update the score thresholds for each route. - """ - if score_thresholds: - for route in self.routes: - route.score_threshold = score_thresholds.get( - route.name, self.score_threshold - ) - - def to_config(self) -> RouterConfig: - return RouterConfig( - encoder_type=self.encoder.type, - encoder_name=self.encoder.name, - routes=self.routes, - ) - - def to_json(self, file_path: str): - config = self.to_config() - config.to_file(file_path) - - def to_yaml(self, file_path: str): - config = self.to_config() - config.to_file(file_path) - - def get_thresholds(self) -> Dict[str, float]: - # TODO: float() below is hacky fix for lint, fix this with new type? - thresholds = { - route.name: float(route.score_threshold or self.score_threshold) - for route in self.routes - } - return thresholds - - def fit( - self, - X: List[str], - y: List[str], - batch_size: int = 500, - max_iter: int = 500, - local_execution: bool = False, - ): - original_index = self.index - if local_execution: - # Switch to a local index for fitting - from semantic_router.index.local import LocalIndex - - remote_routes = self.index.get_utterances() - # TODO Enhance by retrieving directly the vectors instead of embedding all utterances again - routes, utterances, function_schemas, metadata = map( - list, zip(*remote_routes) - ) - embeddings = self.encoder(utterances) - self.index = LocalIndex() - self.index.add( - embeddings=embeddings, - routes=routes, - utterances=utterances, - metadata_list=metadata, - ) - - # convert inputs into array - Xq: List[List[float]] = [] - for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"): - emb = np.array(self.encoder(X[i : i + batch_size])) - Xq.extend(emb) - # initial eval (we will iterate from here) - best_acc = self._vec_evaluate(Xq=np.array(Xq), y=y) - best_thresholds = self.get_thresholds() - # begin fit - for _ in (pbar := tqdm(range(max_iter), desc="Training")): - pbar.set_postfix({"acc": round(best_acc, 2)}) - # Find the best score threshold for each route - thresholds = threshold_random_search( - route_layer=self, - search_range=0.8, - ) - # update current route layer - self._update_thresholds(score_thresholds=thresholds) - # evaluate - acc = self._vec_evaluate(Xq=Xq, y=y) - # update best - if acc > best_acc: - best_acc = acc - best_thresholds = thresholds - # update route layer to best thresholds - self._update_thresholds(score_thresholds=best_thresholds) - - if local_execution: - # Switch back to the original index - self.index = original_index - - def evaluate(self, X: List[str], y: List[str], batch_size: int = 500) -> float: - """ - Evaluate the accuracy of the route selection. - """ - Xq: List[List[float]] = [] - for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"): - emb = np.array(self.encoder(X[i : i + batch_size])) - Xq.extend(emb) - - accuracy = self._vec_evaluate(Xq=np.array(Xq), y=y) - return accuracy - - def _vec_evaluate(self, Xq: Union[List[float], Any], y: List[str]) -> float: - """ - Evaluate the accuracy of the route selection. - """ - correct = 0 - for xq, target_route in zip(Xq, y): - # We treate dynamic routes as static here, because when evaluating we use only vectors, and dynamic routes expect strings by default. - route_choice = self(vector=xq, simulate_static=True) - if route_choice.name == target_route: - correct += 1 - accuracy = correct / len(Xq) - return accuracy - - def _get_route_names(self) -> List[str]: - return [route.name for route in self.routes] - - -def threshold_random_search( - route_layer: SemanticRouter, - search_range: Union[int, float], -) -> Dict[str, float]: - """Performs a random search iteration given a route layer and a search range.""" - # extract the route names - routes = route_layer.get_thresholds() - route_names = list(routes.keys()) - route_thresholds = list(routes.values()) - # generate search range for each - score_threshold_values = [] - for threshold in route_thresholds: - score_threshold_values.append( - np.linspace( - start=max(threshold - search_range, 0.0), - stop=min(threshold + search_range, 1.0), - num=100, - ) - ) - # Generate a random threshold for each route - score_thresholds = { - route: random.choice(score_threshold_values[i]) - for i, route in enumerate(route_names) - } - return score_thresholds diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 616416e7..b54a452f 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -428,9 +428,12 @@ def from_compact_array(cls, array: np.ndarray): return cls(embedding=array) @classmethod - def from_array(cls, array: np.ndarray): - """Consumes a single sparse vector which contains zero-values. + def from_vector(cls, vector: np.ndarray): + """Consumes an array of sparse vectors containing zero-values. """ + if vector.ndim != 1: + raise ValueError(f"Expected a 1D array, got a {vector.ndim}D array.") + return cls.from_compact_array(np.array([np.arange(len(vector)), vector]).T) @classmethod def from_aurelio(cls, embedding: BM25Embedding): diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index 8d47ee4b..62b49fcd 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -644,7 +644,7 @@ def test_from_file_invalid_config(self, tmp_path, index_cls): file.write(invalid_config_json) # Patch the is_valid function to return False for this test - with patch("semantic_router.layer.is_valid", return_value=False): + with patch("semantic_router.routers.base.is_valid", return_value=False): # Attempt to load the RouterConfig from the temporary file # and assert that it raises an exception due to invalid configuration with pytest.raises(Exception) as excinfo: @@ -720,8 +720,6 @@ def test_with_multiple_routes_passing_threshold( {"route": "Route 2", "score": 0.7}, {"route": "Route 1", "score": 0.8}, ] - # Override _pass_threshold to always return True for this test - route_layer._pass_threshold = lambda scores, threshold: True expected = [("Route 1", 0.8), ("Route 2", 0.7)] results = route_layer._semantic_classify_multiple_routes(query_results) assert sorted(results) == sorted( @@ -731,9 +729,8 @@ def test_with_multiple_routes_passing_threshold( def test_with_no_routes_passing_threshold(self, openai_encoder, routes, index_cls): index = init_index(index_cls) route_layer = SemanticRouter(encoder=openai_encoder, routes=routes, index=index) - route_layer.score_threshold = 0.5 - # Override _pass_threshold to always return False for this test - route_layer._pass_threshold = lambda scores, threshold: False + # set threshold to 1.0 so that no routes pass + route_layer.score_threshold = 1.0 query_results = [ {"route": "Route 1", "score": 0.3}, {"route": "Route 2", "score": 0.2}, @@ -815,11 +812,13 @@ def test_retrieve_no_matches(self, openai_encoder, routes, index_cls): auto_sync="local", ) text = "Asparagus" + if index_cls is PineconeIndex: + time.sleep(PINECONE_SLEEP) results = route_layer.retrieve_multiple_routes(text=text) assert len(results) == 0, f"Expected no results, but got {len(results)}" def test_retrieve_one_match(self, openai_encoder, routes_3, index_cls): - index = init_index(index_cls) + index = init_index(index_cls, dimensions=3) route_layer = SemanticRouter( encoder=openai_encoder, routes=routes_3, @@ -827,6 +826,8 @@ def test_retrieve_one_match(self, openai_encoder, routes_3, index_cls): auto_sync="local", ) text = "Hello" + # set low threshold + route_layer.set_threshold(threshold=0.1, route_name="Route 1") if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) results = route_layer.retrieve_multiple_routes(text=text) @@ -845,6 +846,7 @@ def test_retrieve_with_text_for_multiple_matches( auto_sync="local", ) text = "Hello" + route_layer.set_threshold(threshold=0.01, route_name=None) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) results = route_layer.retrieve_multiple_routes(text=text) From cb6cb68ce08cf9248467c6541725ccf1664b7763 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 00:22:45 +0100 Subject: [PATCH 18/33] chore: lint --- semantic_router/encoders/base.py | 14 ++++++++------ semantic_router/encoders/bm25.py | 1 - semantic_router/routers/base.py | 15 ++++++++------- semantic_router/routers/hybrid.py | 2 +- semantic_router/schema.py | 5 ++--- 5 files changed, 19 insertions(+), 18 deletions(-) diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index dcfe9aa3..ed0eb523 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -35,12 +35,15 @@ class Config: def __call__(self, docs: List[str]) -> List[SparseEmbedding]: raise NotImplementedError("Subclasses must implement this method") - async def acall(self, docs: List[str]) -> Coroutine[Any, Any, List[SparseEmbedding]]: + async def acall( + self, docs: List[str] + ) -> Coroutine[Any, Any, List[SparseEmbedding]]: raise NotImplementedError("Subclasses must implement this method") - - def _array_to_sparse_embeddings(self, sparse_arrays: np.ndarray) -> List[SparseEmbedding]: - """Consumes several sparse vectors containing zero-values and returns a compact array. - """ + + def _array_to_sparse_embeddings( + self, sparse_arrays: np.ndarray + ) -> List[SparseEmbedding]: + """Consumes several sparse vectors containing zero-values and returns a compact array.""" if sparse_arrays.ndim != 2: raise ValueError(f"Expected a 2D array, got a {sparse_arrays.ndim}D array.") # get coordinates of non-zero values @@ -50,4 +53,3 @@ def _array_to_sparse_embeddings(self, sparse_arrays: np.ndarray) -> List[SparseE arr_range = range(compact_array[:, 0].max().astype(int) + 1) arrs = [compact_array[compact_array[:, 0] == i, :][:, 1:3] for i in arr_range] return [SparseEmbedding.from_compact_array(arr) for arr in arrs] - diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index 0ec36499..e2bb24c1 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -60,4 +60,3 @@ def __call__(self, docs: List[str]) -> list[SparseEmbedding]: position = self.idx_mapping[idx] embeds[i][position] = val return embeds - diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index b092a297..21ff9a32 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -317,7 +317,7 @@ def __init__( self.llm = llm self.routes = routes.copy() if routes else [] # initialize index - self.index =self._get_index(index=index) + self.index = self._get_index(index=index) # set score threshold using default method self._set_score_threshold() self.top_k = top_k @@ -346,7 +346,7 @@ def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: else: index = index return index - + def _get_encoder(self, encoder: Optional[DenseEncoder]) -> DenseEncoder: if encoder is None: logger.warning("No encoder provided. Using default OpenAIEncoder.") @@ -506,19 +506,20 @@ def retrieve_multiple_routes( categories_with_scores = self._semantic_classify_multiple_routes(results) print(f"{categories_with_scores=}") return [ - RouteChoice(name=category, similarity_score=score) for category, score in categories_with_scores + RouteChoice(name=category, similarity_score=score) + for category, score in categories_with_scores ] - #route_choices = [] + # route_choices = [] # TODO JB: do we need this check? Maybe we should be returning directly - #for category, score in categories_with_scores: + # for category, score in categories_with_scores: # route = self.check_for_matching_routes(category) # if route: # route_choice = RouteChoice(name=route.name, similarity_score=score) # route_choices.append(route_choice) - #return route_choices - + # return route_choices + def _retrieve_top_route( self, vector: List[float], route_filter: Optional[List[str]] = None ) -> Tuple[Optional[Route], List[float]]: diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 64418f98..be3880ab 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, List, Optional import asyncio from pydantic.v1 import Field diff --git a/semantic_router/schema.py b/semantic_router/schema.py index b54a452f..7fcc8371 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -426,11 +426,10 @@ def from_compact_array(cls, array: np.ndarray): "Column 0 should contain index positions, and column 1 should contain respective values." ) return cls(embedding=array) - + @classmethod def from_vector(cls, vector: np.ndarray): - """Consumes an array of sparse vectors containing zero-values. - """ + """Consumes an array of sparse vectors containing zero-values.""" if vector.ndim != 1: raise ValueError(f"Expected a 1D array, got a {vector.ndim}D array.") return cls.from_compact_array(np.array([np.arange(len(vector)), vector]).T) From b8d44aaf1d48177019b4ae9795599ba627a38534 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 00:40:18 +0100 Subject: [PATCH 19/33] chore: lint --- semantic_router/encoders/aurelio.py | 4 +++- semantic_router/encoders/base.py | 4 +--- semantic_router/encoders/bm25.py | 4 +++- semantic_router/encoders/tfidf.py | 4 +++- semantic_router/index/pinecone.py | 12 +++++++++--- semantic_router/routers/base.py | 15 ++++++++++++--- 6 files changed, 31 insertions(+), 12 deletions(-) diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index 8824b2f1..779fe6b1 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -19,9 +19,11 @@ class AurelioSparseEncoder(SparseEncoder): def __init__( self, - name: str = "bm25", + name: str | None = None, api_key: Optional[str] = None, ): + if name is None: + name = "bm25" super().__init__(name=name) if api_key is None: api_key = os.getenv("AURELIO_API_KEY") diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py index ed0eb523..0a25f210 100644 --- a/semantic_router/encoders/base.py +++ b/semantic_router/encoders/base.py @@ -35,9 +35,7 @@ class Config: def __call__(self, docs: List[str]) -> List[SparseEmbedding]: raise NotImplementedError("Subclasses must implement this method") - async def acall( - self, docs: List[str] - ) -> Coroutine[Any, Any, List[SparseEmbedding]]: + async def acall(self, docs: List[str]) -> list[SparseEmbedding]: raise NotImplementedError("Subclasses must implement this method") def _array_to_sparse_embeddings( diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index e2bb24c1..3357ded8 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -12,9 +12,11 @@ class BM25Encoder(TfidfEncoder): def __init__( self, - name: str = "bm25", + name: str | None = None, use_default_params: bool = True, ): + if name is None: + name = "bm25" super().__init__(name=name) try: from pinecone_text.sparse import BM25Encoder as encoder diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index d9d97a47..a7ac9136 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -15,7 +15,9 @@ class TfidfEncoder(SparseEncoder): idf: ndarray = np.array([]) word_index: Dict = {} - def __init__(self, name: str = "tfidf"): + def __init__(self, name: str | None = None): + if name is None: + name = "tfidf" super().__init__(name=name) self.word_index = {} self.idf = np.array([]) diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index bb6ed3ef..6086de1c 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -223,7 +223,7 @@ async def _init_async_index(self, force_create: bool = False): # if the index doesn't exist and we don't have the dimensions # we raise warning logger.warning("Index could not be initialized.") - self.host = index_stats["host"] if index_stats else None + self.host = index_stats["host"] if index_stats else "" def _batch_upsert(self, batch: List[Dict]): """Helper method for upserting a single batch of records.""" @@ -466,7 +466,7 @@ async def aquery( :rtype: Tuple[np.ndarray, List[str]] :raises ValueError: If the index is not populated. """ - if self.async_client is None or self.host is None: + if self.async_client is None or self.host == "": raise ValueError("Async client or host are not initialized.") query_vector_list = vector.tolist() if route_filter is not None: @@ -492,7 +492,7 @@ async def aget_routes(self) -> list[tuple]: :return: A list of (route_name, utterance) objects. :rtype: List[Tuple] """ - if self.async_client is None or self.host is None: + if self.async_client is None or self.host == "": raise ValueError("Async client or host are not initialized.") return await self._async_get_routes() @@ -519,6 +519,8 @@ async def _async_query( "top_k": top_k, "include_metadata": include_metadata, } + if self.host == "": + raise ValueError("self.host is not initialized.") async with self.async_client.post( f"https://{self.host}/query", json=params, @@ -569,6 +571,8 @@ async def _async_get_all( """ if self.index is None: raise ValueError("Index is None, could not retrieve vector IDs.") + if self.host == "": + raise ValueError("self.host is not initialized.") all_vector_ids = [] next_page_token = None @@ -623,6 +627,8 @@ async def _async_fetch_metadata(self, vector_id: str) -> dict: :return: A dictionary containing the metadata for the vector. :rtype: dict """ + if self.host == "": + raise ValueError("self.host is not initialized.") url = f"https://{self.host}/vectors/fetch" params = { diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 21ff9a32..0172e5d8 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -700,18 +700,27 @@ def __str__(self): def from_json(cls, file_path: str): config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes) + if isinstance(encoder, DenseEncoder): + return cls(encoder=encoder, routes=config.routes) + else: + raise ValueError(f"{type(encoder)} not supported for loading from JSON.") @classmethod def from_yaml(cls, file_path: str): config = RouterConfig.from_file(file_path) encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes) + if isinstance(encoder, DenseEncoder): + return cls(encoder=encoder, routes=config.routes) + else: + raise ValueError(f"{type(encoder)} not supported for loading from YAML.") @classmethod def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): encoder = AutoEncoder(type=config.encoder_type, name=config.encoder_name).model - return cls(encoder=encoder, routes=config.routes, index=index) + if isinstance(encoder, DenseEncoder): + return cls(encoder=encoder, routes=config.routes, index=index) + else: + raise ValueError(f"{type(encoder)} not supported for loading from config.") def add(self, route: Route): """Add a route to the local SemanticRouter and index. From da7b26ccf669695d656a27628134c18d1a42bf0d Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 03:10:29 +0100 Subject: [PATCH 20/33] chore: lint --- semantic_router/index/pinecone.py | 4 ++- semantic_router/routers/base.py | 20 ++++++++----- semantic_router/routers/hybrid.py | 45 +++++++++++++++++------------ semantic_router/routers/semantic.py | 8 ++--- 4 files changed, 47 insertions(+), 30 deletions(-) diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 6086de1c..7b4dba53 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -387,7 +387,9 @@ def query( if sparse_vector is not None: if isinstance(sparse_vector, dict): sparse_vector = SparseEmbedding.from_dict(sparse_vector) - sparse_vector = sparse_vector.to_pinecone() + if isinstance(sparse_vector, SparseEmbedding): + # unnecessary if-statement but mypy didn't like this otherwise + sparse_vector = sparse_vector.to_pinecone() results = self.index.query( vector=[query_vector_list], sparse_vector=sparse_vector, diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 0172e5d8..5d59ae58 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -413,7 +413,7 @@ def __call__( if vector is None: if text is None: raise ValueError("Either text or vector must be provided") - vector = self._encode(text=text) + vector = self._encode(text=[text]) route, top_class_scores = self._retrieve_top_route(vector, route_filter) passed = self._check_threshold(top_class_scores, route) if passed and route is not None and not simulate_static: @@ -455,7 +455,7 @@ async def acall( if vector is None: if text is None: raise ValueError("Either text or vector must be provided") - vector = await self._async_encode(text=text) + vector = await self._async_encode(text=[text]) route, top_class_scores = await self._async_retrieve_top_route( vector, route_filter @@ -494,7 +494,7 @@ def retrieve_multiple_routes( if vector is None: if text is None: raise ValueError("Either text or vector must be provided") - vector_arr = self._encode(text=text) + vector_arr = self._encode(text=[text]) else: vector_arr = np.array(vector) print(f"{text=}") @@ -967,26 +967,26 @@ def _extract_routes_details( return route_names, utterances, function_schemas, metadata return route_names, utterances, function_schemas - def _encode(self, text: str) -> Any: + def _encode(self, text: list[str]) -> Any: """Generates embeddings for a given text. Must be implemented by a subclass. :param text: The text to encode. - :type text: str + :type text: list[str] :return: The embeddings of the text. :rtype: Any """ # TODO: should encode "content" rather than text raise NotImplementedError("This method should be implemented by subclasses.") - async def _async_encode(self, text: str) -> Any: + async def _async_encode(self, text: list[str]) -> Any: """Asynchronously generates embeddings for a given text. Must be implemented by a subclass. :param text: The text to encode. - :type text: str + :type text: list[str] :return: The embeddings of the text. :rtype: Any """ @@ -1029,6 +1029,9 @@ def _set_aggregation_method(self, aggregation: str = "sum"): def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]: scores_by_class = self.group_scores_by_class(query_results) + if self.aggregation_method is None: + raise ValueError("self.aggregation_method is not set.") + # Calculate total score for each class total_scores = { route: self.aggregation_method(scores) @@ -1048,6 +1051,9 @@ async def _async_semantic_classify( ) -> Tuple[str, List[float]]: scores_by_class = await self.async_group_scores_by_class(query_results) + if self.aggregation_method is None: + raise ValueError("self.aggregation_method is not set.") + # Calculate total score for each class total_scores = { route: self.aggregation_method(scores) diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index be3880ab..91ecf2ef 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -1,4 +1,4 @@ -from typing import Any, List, Optional +from typing import List, Optional import asyncio from pydantic.v1 import Field @@ -6,6 +6,7 @@ from semantic_router.encoders import ( DenseEncoder, + SparseEncoder, BM25Encoder, TfidfEncoder, ) @@ -21,13 +22,13 @@ class HybridRouter(BaseRouter): """A hybrid layer that uses both dense and sparse embeddings to classify routes.""" # there are a few additional attributes for hybrid - sparse_encoder: Optional[DenseEncoder] = Field(default=None) + sparse_encoder: Optional[SparseEncoder] = Field(default=None) alpha: float = 0.3 def __init__( self, encoder: DenseEncoder, - sparse_encoder: Optional[DenseEncoder] = None, + sparse_encoder: Optional[SparseEncoder] = None, llm: Optional[BaseLLM] = None, routes: List[Route] = [], index: Optional[HybridLocalIndex] = None, @@ -70,47 +71,49 @@ def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: index = index return index - def _set_sparse_encoder(self, sparse_encoder: Optional[DenseEncoder]): + def _set_sparse_encoder(self, sparse_encoder: Optional[SparseEncoder]): if sparse_encoder is None: logger.warning("No sparse_encoder provided. Using default BM25Encoder.") self.sparse_encoder = BM25Encoder() else: self.sparse_encoder = sparse_encoder - def _encode(self, text: list[str]) -> tuple[np.ndarray, list[dict[int, float]]]: + def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]: """Given some text, generates dense and sparse embeddings, then scales them using the chosen alpha value. """ + if self.sparse_encoder is None: + raise ValueError("self.sparse_encoder is not set.") # TODO: should encode "content" rather than text # TODO: add alpha as a parameter # create dense query vector xq_d = np.array(self.encoder(text)) # xq_d = np.squeeze(xq_d) # Reduce to 1d array. # create sparse query vector dict - xq_s_dict = self.sparse_encoder(text) + xq_s = self.sparse_encoder(text) # xq_s = np.squeeze(xq_s) # convex scaling - xq_d, xq_s_dict = self._convex_scaling(xq_d, xq_s_dict) - return xq_d, xq_s_dict + xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) + return xq_d, xq_s - async def _async_encode(self, text: List[str]) -> Any: + async def _async_encode( + self, text: List[str] + ) -> tuple[np.ndarray, list[SparseEmbedding]]: """Given some text, generates dense and sparse embeddings, then scales them using the chosen alpha value. """ + if self.sparse_encoder is None: + raise ValueError("self.sparse_encoder is not set.") # TODO: should encode "content" rather than text # TODO: add alpha as a parameter # async encode both dense and sparse dense_coro = self.encoder.acall(text) sparse_coro = self.sparse_encoder.acall(text) - dense_vec, sparse_vec = await asyncio.gather(dense_coro, sparse_coro) + dense_vec, xq_s = await asyncio.gather(dense_coro, sparse_coro) # create dense query vector xq_d = np.array(dense_vec) - # xq_d = np.squeeze(xq_d) # reduce to 1d array - # create sparse query vector - xq_s = np.array(sparse_vec) - # xq_s = np.squeeze(xq_s) # convex scaling - xq_d, xq_s = self._convex_scaling(xq_d, xq_s) + xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) return xq_d, xq_s def __call__( @@ -146,12 +149,18 @@ def __call__( else: return RouteChoice() - def _convex_scaling(self, dense: np.ndarray, sparse: list[dict[int, float]]): + def _convex_scaling( + self, dense: np.ndarray, sparse: list[SparseEmbedding] + ) -> tuple[np.ndarray, list[SparseEmbedding]]: + # TODO: better way to do this? + sparse_dicts = [sparse_vec.to_dict() for sparse_vec in sparse] # scale sparse and dense vecs scaled_dense = np.array(dense) * self.alpha scaled_sparse = [] - for sparse_dict in sparse: + for sparse_dict in sparse_dicts: scaled_sparse.append( - {k: v * (1 - self.alpha) for k, v in sparse_dict.items()} + SparseEmbedding.from_dict( + {k: v * (1 - self.alpha) for k, v in sparse_dict.items()} + ) ) return scaled_dense, scaled_sparse diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 59cd8de1..ff7f73f2 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -35,16 +35,16 @@ def __init__( if self.auto_sync: self._init_index_state() - def _encode(self, text: str) -> Any: + def _encode(self, text: list[str]) -> Any: """Given some text, encode it.""" # create query vector - xq = np.array(self.encoder([text])) + xq = np.array(self.encoder(text)) xq = np.squeeze(xq) # Reduce to 1d array. return xq - async def _async_encode(self, text: str) -> Any: + async def _async_encode(self, text: list[str]) -> Any: """Given some text, encode it.""" # create query vector - xq = np.array(await self.encoder.acall(docs=[text])) + xq = np.array(await self.encoder.acall(docs=text)) xq = np.squeeze(xq) # Reduce to 1d array. return xq From 6eb71c1c5b6a5309c7c178e4bd74ca361d6d3c9e Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 13:01:25 +0100 Subject: [PATCH 21/33] chore: mypy lint --- semantic_router/encoders/__init__.py | 4 ++-- semantic_router/encoders/bm25.py | 10 +++------ semantic_router/index/base.py | 4 +++- semantic_router/index/hybrid_local.py | 18 ++++++++++------ semantic_router/index/local.py | 4 +++- semantic_router/index/pinecone.py | 29 ++++++++++++++++++------- semantic_router/index/postgres.py | 3 ++- semantic_router/index/qdrant.py | 4 +++- semantic_router/routers/base.py | 31 +++++++++++++++++++++------ semantic_router/routers/hybrid.py | 20 ++++++++++++----- semantic_router/schema.py | 5 +++++ tests/unit/test_hybrid_layer.py | 10 ++++++++- 12 files changed, 102 insertions(+), 40 deletions(-) diff --git a/semantic_router/encoders/__init__.py b/semantic_router/encoders/__init__.py index 85b32e2c..07e468d8 100644 --- a/semantic_router/encoders/__init__.py +++ b/semantic_router/encoders/__init__.py @@ -15,7 +15,7 @@ from semantic_router.encoders.tfidf import TfidfEncoder from semantic_router.encoders.vit import VitEncoder from semantic_router.encoders.zure import AzureOpenAIEncoder -from semantic_router.schema import EncoderType +from semantic_router.schema import EncoderType, SparseEmbedding __all__ = [ "AurelioSparseEncoder", @@ -79,5 +79,5 @@ def __init__(self, type: str, name: Optional[str]): else: raise ValueError(f"Encoder type '{type}' not supported") - def __call__(self, texts: List[str]) -> List[List[float]]: + def __call__(self, texts: List[str]) -> List[List[float]] | List[SparseEmbedding]: return self.model(texts) diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index 3357ded8..ea0a89a6 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -53,12 +53,8 @@ def __call__(self, docs: List[str]) -> list[SparseEmbedding]: else: raise ValueError("No documents to encode.") - embeds = [[0.0] * len(self.idx_mapping)] * len(docs) + embeds = [] for i, output in enumerate(sparse_dicts): - indices = output["indices"] - values = output["values"] - for idx, val in zip(indices, values): - if idx in self.idx_mapping: - position = self.idx_mapping[idx] - embeds[i][position] = val + if isinstance(output, dict): + embeds.append(SparseEmbedding.from_pinecone_dict(output)) return embeds diff --git a/semantic_router/index/base.py b/semantic_router/index/base.py index 65f2cf1e..97fe3bd4 100644 --- a/semantic_router/index/base.py +++ b/semantic_router/index/base.py @@ -4,7 +4,7 @@ import numpy as np from pydantic.v1 import BaseModel -from semantic_router.schema import ConfigParameter, Utterance +from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance from semantic_router.route import Route from semantic_router.utils.logger import logger @@ -108,6 +108,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Search the index for the query_vector and return top_k results. @@ -120,6 +121,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Search the index for the query_vector and return top_k results. diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index f2821422..e2a75778 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -3,7 +3,7 @@ import numpy as np from numpy.linalg import norm -from semantic_router.schema import ConfigParameter, Utterance +from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance from semantic_router.index.local import LocalIndex from semantic_router.utils.logger import logger from typing import Any @@ -76,6 +76,8 @@ def _sparse_dot_product( return sum(vec_a[i] * vec_b.get(i, 0) for i in vec_a) def _sparse_index_dot_product(self, vec_a: dict[int, float]) -> list[float]: + if self.sparse_index is None: + raise ValueError("self.sparse_index is not populated.") dot_products = [ self._sparse_dot_product(vec_a, vec_b) for vec_b in self.sparse_index ] @@ -86,7 +88,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[dict[int, float]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query and return top_k results. @@ -103,9 +105,13 @@ def query( raise ValueError("Route filter is not supported for HybridLocalIndex.") xq_d = vector.copy() - if sparse_vector is None: - raise ValueError("Sparse vector is required for HybridLocalIndex.") - xq_s = sparse_vector.copy() + # align sparse vector type + if isinstance(sparse_vector, SparseEmbedding): + xq_s = sparse_vector.to_dict() + elif isinstance(sparse_vector, dict): + xq_s = sparse_vector + else: + raise ValueError("Sparse vector must be a SparseEmbedding or dict.") if self.index is not None and self.sparse_index is not None: # calculate dense vec similarity @@ -130,7 +136,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, - sparse_vector: Optional[dict[int, float]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query and return top_k results. This method calls the sync `query` method as everything uses numpy computations which is CPU-bound diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py index 9d33163e..83cc4f51 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -2,7 +2,7 @@ import numpy as np -from semantic_router.schema import ConfigParameter, Utterance +from semantic_router.schema import ConfigParameter, SparseEmbedding, Utterance from semantic_router.index.base import BaseIndex from semantic_router.linear import similarity_matrix, top_scores from semantic_router.utils.logger import logger @@ -68,6 +68,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Search the index for the query and return top_k results. @@ -97,6 +98,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Search the index for the query and return top_k results. diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 7b4dba53..de324f28 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -237,19 +237,19 @@ def add( embeddings: List[List[float]], routes: List[str], utterances: List[str], - function_schemas: Optional[List[Dict[str, Any]]] = None, + function_schemas: Optional[Optional[List[Dict[str, Any]]]] = None, metadata_list: List[Dict[str, Any]] = [], batch_size: int = 100, - sparse_embeddings: Optional[List[dict[int, float]]] = None, + sparse_embeddings: Optional[Optional[List[dict[int, float]]]] = None, ): """Add vectors to Pinecone in batches.""" if self.index is None: self.dimensions = self.dimensions or len(embeddings[0]) self.index = self._init_index(force_create=True) if function_schemas is None: - function_schemas = [None] * len(embeddings) + function_schemas = [{}] * len(embeddings) if sparse_embeddings is None: - sparse_embeddings = [None] * len(embeddings) + sparse_embeddings = [{}] * len(embeddings) vectors_to_upsert = [ PineconeRecord( @@ -261,7 +261,12 @@ def add( metadata=metadata, ).to_dict() for vector, route, utterance, function_schema, metadata, sparse_dict in zip( - embeddings, routes, utterances, function_schemas, metadata_list, sparse_embeddings # type: ignore + embeddings, + routes, + utterances, + function_schemas, + metadata_list, + sparse_embeddings, ) ] @@ -449,7 +454,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, - **kwargs: Any, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Asynchronously search the index for the query vector and return the top_k results. @@ -475,9 +480,17 @@ async def aquery( filter_query = {"sr_route": {"$in": route_filter}} else: filter_query = None + # set sparse_vector_obj + sparse_vector_obj: dict[str, Any] | None = None + if sparse_vector is not None: + if isinstance(sparse_vector, dict): + sparse_vector_obj = SparseEmbedding.from_dict(sparse_vector) + if isinstance(sparse_vector, SparseEmbedding): + # unnecessary if-statement but mypy didn't like this otherwise + sparse_vector_obj = sparse_vector.to_pinecone() results = await self._async_query( vector=query_vector_list, - sparse_vector=kwargs.get("sparse_vector", None), + sparse_vector=sparse_vector_obj, namespace=self.namespace or "", filter=filter_query, top_k=top_k, @@ -507,7 +520,7 @@ def delete_index(self): async def _async_query( self, vector: list[float], - sparse_vector: Optional[dict] = None, + sparse_vector: dict[str, Any] | None = None, namespace: str = "", filter: Optional[dict] = None, top_k: int = 5, diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py index 0d80d745..67de0ada 100644 --- a/semantic_router/index/postgres.py +++ b/semantic_router/index/postgres.py @@ -8,7 +8,7 @@ from pydantic import BaseModel from semantic_router.index.base import BaseIndex -from semantic_router.schema import ConfigParameter, Metric +from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding from semantic_router.utils.logger import logger @@ -340,6 +340,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: """ Searches the index for the query vector and returns the top_k results. diff --git a/semantic_router/index/qdrant.py b/semantic_router/index/qdrant.py index eb7dc688..0da5c25e 100644 --- a/semantic_router/index/qdrant.py +++ b/semantic_router/index/qdrant.py @@ -4,7 +4,7 @@ from pydantic.v1 import Field from semantic_router.index.base import BaseIndex -from semantic_router.schema import ConfigParameter, Metric, Utterance +from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding, Utterance from semantic_router.utils.logger import logger DEFAULT_COLLECTION_NAME = "semantic-router-index" @@ -259,6 +259,7 @@ def query( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: from qdrant_client import QdrantClient, models @@ -292,6 +293,7 @@ async def aquery( vector: np.ndarray, top_k: int = 5, route_filter: Optional[List[str]] = None, + sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> Tuple[np.ndarray, List[str]]: from qdrant_client import AsyncQdrantClient, models diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 5d59ae58..ad2971a0 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -1027,6 +1027,15 @@ def _set_aggregation_method(self, aggregation: str = "sum"): ) def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float]]: + """Classify the query results into a single class based on the highest total score. + If no classification is found, return an empty string and an empty list. + + :param query_results: The query results to classify. Expected format is a list of + dictionaries with "route" and "score" keys. + :type query_results: List[Dict] + :return: A tuple containing the top class and its associated scores. + :rtype: Tuple[str, List[float]] + """ scores_by_class = self.group_scores_by_class(query_results) if self.aggregation_method is None: @@ -1049,6 +1058,15 @@ def _semantic_classify(self, query_results: List[Dict]) -> Tuple[str, List[float async def _async_semantic_classify( self, query_results: List[Dict] ) -> Tuple[str, List[float]]: + """Classify the query results into a single class based on the highest total score. + If no classification is found, return an empty string and an empty list. + + :param query_results: The query results to classify. Expected format is a list of + dictionaries with "route" and "score" keys. + :type query_results: List[Dict] + :return: A tuple containing the top class and its associated scores. + :rtype: Tuple[str, List[float]] + """ scores_by_class = await self.async_group_scores_by_class(query_results) if self.aggregation_method is None: @@ -1125,8 +1143,8 @@ async def async_group_scores_by_class( return scores_by_class def _pass_threshold(self, scores: List[float], threshold: float | None) -> bool: - """Test if the route score passes the minimum threshold. If a threshold of None is - set, then the route will always pass no matter how low it scores. + """Test if the route score passes the minimum threshold. A threshold of None defaults + to 0.0, so the route will always pass no matter how low it scores. :param scores: The scores to test. :type scores: List[float] @@ -1168,9 +1186,9 @@ def set_threshold(self, threshold: float, route_name: str | None = None): for route in self.routes: route.score_threshold = threshold else: - route = self.get(route_name) - if route is not None: - route.score_threshold = threshold + route_get: Route | None = self.get(route_name) + if route_get is not None: + route_get.score_threshold = threshold else: logger.error(f"Route `{route_name}` not found") @@ -1190,9 +1208,8 @@ def to_yaml(self, file_path: str): config.to_file(file_path) def get_thresholds(self) -> Dict[str, float]: - # TODO: float() below is hacky fix for lint, fix this with new type? thresholds = { - route.name: float(route.score_threshold or self.score_threshold) + route.name: route.score_threshold or self.score_threshold or 0.0 for route in self.routes } return thresholds diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index 91ecf2ef..f07429c3 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -124,24 +124,34 @@ def __call__( route_filter: Optional[List[str]] = None, sparse_vector: dict[int, float] | SparseEmbedding | None = None, ) -> RouteChoice: + vector_arr: np.ndarray | None = None + potential_sparse_vector: List[SparseEmbedding] | None = None # if no vector provided, encode text to get vector if vector is None: if text is None: raise ValueError("Either text or vector must be provided") - vector, potential_sparse_vector = self._encode(text=[text]) + vector_arr, potential_sparse_vector = self._encode(text=[text]) if sparse_vector is None: if text is None: raise ValueError("Either text or sparse_vector must be provided") - sparse_vector = potential_sparse_vector + sparse_vector = ( + potential_sparse_vector[0] if potential_sparse_vector else None + ) + if sparse_vector is None: + raise ValueError("Sparse vector is required for HybridLocalIndex.") + vector_arr = vector_arr if vector_arr else np.array(vector) # TODO: add alpha as a parameter scores, route_names = self.index.query( - vector=np.array(vector) if isinstance(vector, list) else vector, + vector=vector_arr, top_k=self.top_k, route_filter=route_filter, - sparse_vector=sparse_vector[0], + sparse_vector=sparse_vector, ) top_class, top_class_scores = self._semantic_classify( - list(zip(scores, route_names)) + [ + {"score": score, "route": route} + for score, route in zip(scores, route_names) + ] ) passed = self._pass_threshold(top_class_scores, self.score_threshold) if passed: diff --git a/semantic_router/schema.py b/semantic_router/schema.py index 7fcc8371..c9e63943 100644 --- a/semantic_router/schema.py +++ b/semantic_router/schema.py @@ -444,6 +444,11 @@ def from_dict(cls, sparse_dict: dict): arr = np.array([list(sparse_dict.keys()), list(sparse_dict.values())]).T return cls.from_compact_array(arr) + @classmethod + def from_pinecone_dict(cls, sparse_dict: dict): + arr = np.array([sparse_dict["indices"], sparse_dict["values"]]).T + return cls.from_compact_array(arr) + def to_dict(self): return { i: v for i, v in zip(self.embedding[:, 0].astype(int), self.embedding[:, 1]) diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index 564bd3a1..f3fbe6da 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -75,7 +75,15 @@ def routes(): sparse_encoder = BM25Encoder(use_default_params=False) -sparse_encoder.fit(["The quick brown fox", "jumps over the lazy dog", "Hello, world!"]) +sparse_encoder.fit( + [ + Route( + name="Route 1", + utterances=["The quick brown fox", "jumps over the lazy dog"], + ), + Route(name="Route 2", utterances=["Hello, world!"]), + ] +) class TestHybridRouter: From 381e8f5e49966d86a3eff0f99ddd5943dca6614e Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 14:25:29 +0100 Subject: [PATCH 22/33] fix: 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f2817a65..9e922661 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -28,7 +28,6 @@ regex = ">=2023.12.25" torchvision = { version = ">=0.17.0,<0.18.0", optional = true} pillow = { version = ">=10.2.0,<11.0.0", optional = true} tiktoken = ">=0.6.0,<1.0.0" -matplotlib = { version = "^3.8.3", optional = true} qdrant-client = {version = "^1.11.1", optional = true} google-cloud-aiplatform = {version = "^1.45.0", optional = true} requests-mock = "^1.12.1" @@ -46,7 +45,6 @@ hybrid = ["pinecone-text"] local = ["torch", "transformers", "tokenizers", "huggingface-hub", "llama-cpp-python"] pinecone = ["pinecone"] vision = ["torch", "torchvision", "transformers", "pillow"] -processing = ["matplotlib"] mistralai = ["mistralai"] qdrant = ["qdrant-client"] google = ["google-cloud-aiplatform"] @@ -59,10 +57,11 @@ cohere = ["cohere"] [tool.poetry.group.dev.dependencies] ipykernel = "^6.25.0" ruff = "^0.1.5" -pytest = "^7.4.3" +pytest = "^8.2" pytest-mock = "^3.12.0" pytest-cov = "^4.1.0" pytest-xdist = "^3.5.0" +pytest-asyncio = "^0.24.0" mypy = "^1.7.1" types-pyyaml = "^6.0.12.12" types-requests = "^2.31.0" From 362e98ef89959f978c2653753e52722de09ddd29 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 16:11:41 +0100 Subject: [PATCH 23/33] fix: router test bug fixes --- semantic_router/index/local.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py index 83cc4f51..8f674c62 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -42,8 +42,23 @@ def add( self.routes = np.concatenate([self.routes, routes_arr]) self.utterances = np.concatenate([self.utterances, utterances_arr]) - def _remove_and_sync(self, routes_to_delete: dict): - logger.warning(f"Sync remove is not implemented for {self.__class__.__name__}.") + def _remove_and_sync(self, routes_to_delete: dict) -> np.ndarray: + if self.index is None or self.routes is None or self.utterances is None: + raise ValueError("Index, routes, or utterances are not populated.") + # TODO JB: implement routes and utterances as a numpy array + route_utterances = np.array([self.routes, self.utterances]).T + # initialize our mask with all true values (ie keep all) + mask = np.ones(len(route_utterances), dtype=bool) + for route, utterances in routes_to_delete.items(): + # TODO JB: we should be able to vectorize this? + for utterance in utterances: + mask &= ~((route_utterances[:, 0] == route) & (route_utterances[:, 1] == utterance)) + # apply the mask to index, routes, and utterances + self.index = self.index[mask] + self.routes = self.routes[mask] + self.utterances = self.utterances[mask] + # return what was removed + return route_utterances[~mask] def get_utterances(self) -> List[Utterance]: """ From 7a6e8234c80197f0db18e4c0ce927bdcd16f126b Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 22:24:21 +0100 Subject: [PATCH 24/33] fix: sync issues --- docs/indexes/pinecone-sync-routes.ipynb | 10 +-- poetry.lock | 82 +++++++++++++++++++++---- pyproject.toml | 2 +- semantic_router/encoders/bm25.py | 1 - semantic_router/index/__init__.py | 2 + semantic_router/index/pinecone.py | 1 - semantic_router/index/postgres.py | 17 +++-- semantic_router/routers/base.py | 4 -- semantic_router/routers/semantic.py | 3 - tests/unit/test_sync.py | 51 +++++++++++++-- 10 files changed, 137 insertions(+), 36 deletions(-) diff --git a/docs/indexes/pinecone-sync-routes.ipynb b/docs/indexes/pinecone-sync-routes.ipynb index d2ba412f..0c6dc4e0 100644 --- a/docs/indexes/pinecone-sync-routes.ipynb +++ b/docs/indexes/pinecone-sync-routes.ipynb @@ -125,14 +125,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## RouteLayer" + "## SemanticRouter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `RouteLayer` class supports both sync and async operations by default, so we initialize as usual:" + "The `SemanticRouter` class supports both sync and async operations by default, so we initialize as usual:" ] }, { @@ -160,10 +160,10 @@ } ], "source": [ - "from semantic_router.routers import RouteLayer\n", + "from semantic_router.routers import SemanticRouter\n", "import time\n", "\n", - "rl = RouteLayer(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n", + "rl = SemanticRouter(encoder=encoder, routes=routes, index=pc_index, auto_sync=\"local\")\n", "# due to pinecone indexing latency we wait 3 seconds\n", "time.sleep(3)" ] @@ -210,7 +210,7 @@ "source": [ "del rl\n", "\n", - "rl = RouteLayer(encoder=encoder, routes=[politics], index=pc_index)\n", + "rl = SemanticRouter(encoder=encoder, routes=[politics], index=pc_index)\n", "time.sleep(3)" ] }, diff --git a/poetry.lock b/poetry.lock index 17a234f6..19288adb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3377,21 +3377,79 @@ dev = ["black", "check-manifest", "coverage", "packaging", "pylint", "pyperf", " test = ["pytest", "pytest-xdist", "setuptools"] 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"4e5cc6b4f04b77600390de5aef4ec0afd983df5cae13639dce7e2d3f1101aae4" +content-hash = "d649e614d9c7122c642ed351abf8886cc008960f0fd7664483cfb5dde7865808" diff --git a/pyproject.toml b/pyproject.toml index 9e922661..e1eff0c7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,7 +35,7 @@ boto3 = { version = "^1.34.98", optional = true } botocore = {version = "^1.34.110", optional = true} aiohttp = "^3.10.11" fastembed = {version = "^0.3.0", optional = true} -psycopg2 = {version = "^2.9.9", optional = true} +psycopg2-binary = {version = "^2.9.9", optional = true} sphinx = {version = "^7.0.0", optional = true} sphinxawesome-theme = {version = "^5.2.0", optional = true} tornado = {version = "^6.4.2", optional = true} diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index ea0a89a6..b5365cee 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -48,7 +48,6 @@ def __call__(self, docs: List[str]) -> list[SparseEmbedding]: if len(docs) == 1: sparse_dicts = self.model.encode_queries(docs) elif len(docs) > 1: - print(docs) sparse_dicts = self.model.encode_documents(docs) else: raise ValueError("No documents to encode.") diff --git a/semantic_router/index/__init__.py b/semantic_router/index/__init__.py index 3a43abe9..14b60da3 100644 --- a/semantic_router/index/__init__.py +++ b/semantic_router/index/__init__.py @@ -2,6 +2,7 @@ from semantic_router.index.hybrid_local import HybridLocalIndex from semantic_router.index.local import LocalIndex from semantic_router.index.pinecone import PineconeIndex +from semantic_router.index.postgres import PostgresIndex from semantic_router.index.qdrant import QdrantIndex __all__ = [ @@ -10,4 +11,5 @@ "LocalIndex", "QdrantIndex", "PineconeIndex", + "PostgresIndex", ] diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index de324f28..25df1c47 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -364,7 +364,6 @@ def query( top_k: int = 5, route_filter: Optional[List[str]] = None, sparse_vector: dict[int, float] | SparseEmbedding | None = None, - **kwargs: Any, ) -> Tuple[np.ndarray, List[str]]: """Search the index for the query vector and return the top_k results. diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py index 67de0ada..4cdd94f3 100644 --- a/semantic_router/index/postgres.py +++ b/semantic_router/index/postgres.py @@ -1,16 +1,18 @@ import os import uuid from enum import Enum -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union import numpy as np -import psycopg2 -from pydantic import BaseModel +from pydantic import BaseModel, Field from semantic_router.index.base import BaseIndex from semantic_router.schema import ConfigParameter, Metric, SparseEmbedding from semantic_router.utils.logger import logger +if TYPE_CHECKING: + import psycopg2 + class MetricPgVecOperatorMap(Enum): """ @@ -104,8 +106,9 @@ class PostgresIndex(BaseIndex): dimensions: int = 1536 metric: Metric = Metric.COSINE namespace: Optional[str] = "" - conn: Optional[psycopg2.extensions.connection] = None + conn: Optional["psycopg2.extensions.connection"] = None type: str = "postgres" + pg2: Any = Field(default=None, exclude=True) def __init__( self, @@ -133,6 +136,12 @@ def __init__( :type namespace: Optional[str] """ super().__init__() + # try and import psycopg2 + try: + import psycopg2 + except ImportError: + raise ImportError("Please install psycopg2 to use PostgresIndex. " + "You can install it with: `pip install 'semantic-router[postgres]'`") if connection_string: self.connection_string = connection_string else: diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index ad2971a0..f21131c8 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -497,14 +497,10 @@ def retrieve_multiple_routes( vector_arr = self._encode(text=[text]) else: vector_arr = np.array(vector) - print(f"{text=}") - print(f"{vector_arr}") # get relevant utterances results = self._retrieve(xq=vector_arr) - print(f"{results=}") # decide most relevant routes categories_with_scores = self._semantic_classify_multiple_routes(results) - print(f"{categories_with_scores=}") return [ RouteChoice(name=category, similarity_score=score) for category, score in categories_with_scores diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index ff7f73f2..64ccbaf2 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -31,9 +31,6 @@ def __init__( aggregation=aggregation, auto_sync=auto_sync, ) - # run initialize index now if auto sync is active - if self.auto_sync: - self._init_index_state() def _encode(self, text: list[str]) -> Any: """Given some text, encode it.""" diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index 35093a6b..3a640f02 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -5,14 +5,20 @@ import time from typing import Optional from semantic_router.encoders import DenseEncoder, CohereEncoder, OpenAIEncoder -from semantic_router.index.pinecone import PineconeIndex +from semantic_router.index import ( + PineconeIndex, + HybridLocalIndex, + LocalIndex, + QdrantIndex, + PostgresIndex, +) from semantic_router.schema import Utterance from semantic_router.routers import SemanticRouter from semantic_router.route import Route from platform import python_version -PINECONE_SLEEP = 12 +PINECONE_SLEEP = 6 def mock_encoder_call(utterances): @@ -97,6 +103,34 @@ def layer_yaml(): - how are things going? """ +# not all indexes support metadata, so we map the feature here +INCLUDE_METADATA_MAP = { + PineconeIndex: True, + HybridLocalIndex: False, + LocalIndex: False, + QdrantIndex: False, + PostgresIndex: False, +} +def include_metadata(index_cls): + return INCLUDE_METADATA_MAP.get(index_cls, False) + +MERGE_FORCE_LOCAL_RESULT_WITH_METADATA = [ + Utterance(route="Route 1", utterance="Hello"), + Utterance(route="Route 1", utterance="Hi"), + Utterance(route="Route 2", utterance="Au revoir"), + Utterance(route="Route 2", utterance="Bye"), + Utterance(route="Route 2", utterance="Goodbye"), + Utterance(route="Route 2", utterance="Hi"), +] + +MERGE_FORCE_LOCAL_RESULT_WITHOUT_METADATA = [ + Utterance(route="Route 1", utterance="Hello"), + Utterance(route="Route 1", utterance="Hi"), + Utterance(route="Route 2", utterance="Au revoir"), + Utterance(route="Route 2", utterance="Bye"), + Utterance(route="Route 2", utterance="Goodbye"), + Utterance(route="Route 2", utterance="Hi"), +] @pytest.fixture def base_encoder(): @@ -332,7 +366,10 @@ def test_auto_sync_merge_force_local( # now confirm utterances are correct local_utterances = route_layer.index.get_utterances() # we sort to ensure order is the same - local_utterances.sort(key=lambda x: x.to_str(include_metadata=True)) + # TODO JB: there is a bug here where if we include_metadata=True it fails + local_utterances.sort( + key=lambda x: x.to_str(include_metadata=False) + ) assert local_utterances == [ Utterance(route="Route 1", utterance="Hello"), Utterance(route="Route 1", utterance="Hi"), @@ -370,7 +407,9 @@ def test_auto_sync_merge_force_remote( # now confirm utterances are correct local_utterances = route_layer.index.get_utterances() # we sort to ensure order is the same - local_utterances.sort(key=lambda x: x.to_str(include_metadata=True)) + local_utterances.sort( + key=lambda x: x.to_str(include_metadata=include_metadata(index_cls)) + ) assert local_utterances == [ Utterance( route="Route 1", utterance="Hello", metadata={"type": "default"} @@ -426,7 +465,9 @@ def test_auto_sync_merge(self, openai_encoder, routes, routes_2, index_cls): # now confirm utterances are correct local_utterances = route_layer.index.get_utterances() # we sort to ensure order is the same - local_utterances.sort(key=lambda x: x.to_str(include_metadata=True)) + local_utterances.sort( + key=lambda x: x.to_str(include_metadata=include_metadata(index_cls)) + ) assert local_utterances == [ Utterance( route="Route 1", utterance="Hello", metadata={"type": "default"} From 8aaab96bd516655235908c5f906164bac3dc9a46 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 22:27:29 +0100 Subject: [PATCH 25/33] chore: lint --- semantic_router/index/local.py | 5 ++++- semantic_router/index/postgres.py | 6 ++++-- tests/unit/test_sync.py | 9 ++++++--- 3 files changed, 14 insertions(+), 6 deletions(-) diff --git a/semantic_router/index/local.py b/semantic_router/index/local.py index 8f674c62..76d44d82 100644 --- a/semantic_router/index/local.py +++ b/semantic_router/index/local.py @@ -52,7 +52,10 @@ def _remove_and_sync(self, routes_to_delete: dict) -> np.ndarray: for route, utterances in routes_to_delete.items(): # TODO JB: we should be able to vectorize this? for utterance in utterances: - mask &= ~((route_utterances[:, 0] == route) & (route_utterances[:, 1] == utterance)) + mask &= ~( + (route_utterances[:, 0] == route) + & (route_utterances[:, 1] == utterance) + ) # apply the mask to index, routes, and utterances self.index = self.index[mask] self.routes = self.routes[mask] diff --git a/semantic_router/index/postgres.py b/semantic_router/index/postgres.py index 4cdd94f3..6d445f2b 100644 --- a/semantic_router/index/postgres.py +++ b/semantic_router/index/postgres.py @@ -140,8 +140,10 @@ def __init__( try: import psycopg2 except ImportError: - raise ImportError("Please install psycopg2 to use PostgresIndex. " - "You can install it with: `pip install 'semantic-router[postgres]'`") + raise ImportError( + "Please install psycopg2 to use PostgresIndex. " + "You can install it with: `pip install 'semantic-router[postgres]'`" + ) if connection_string: self.connection_string = connection_string else: diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py index 3a640f02..2f19236b 100644 --- a/tests/unit/test_sync.py +++ b/tests/unit/test_sync.py @@ -103,6 +103,7 @@ def layer_yaml(): - how are things going? """ + # not all indexes support metadata, so we map the feature here INCLUDE_METADATA_MAP = { PineconeIndex: True, @@ -111,9 +112,12 @@ def layer_yaml(): QdrantIndex: False, PostgresIndex: False, } + + def include_metadata(index_cls): return INCLUDE_METADATA_MAP.get(index_cls, False) + MERGE_FORCE_LOCAL_RESULT_WITH_METADATA = [ Utterance(route="Route 1", utterance="Hello"), Utterance(route="Route 1", utterance="Hi"), @@ -132,6 +136,7 @@ def include_metadata(index_cls): Utterance(route="Route 2", utterance="Hi"), ] + @pytest.fixture def base_encoder(): return DenseEncoder(name="test-encoder", score_threshold=0.5) @@ -367,9 +372,7 @@ def test_auto_sync_merge_force_local( local_utterances = route_layer.index.get_utterances() # we sort to ensure order is the same # TODO JB: there is a bug here where if we include_metadata=True it fails - local_utterances.sort( - key=lambda x: x.to_str(include_metadata=False) - ) + local_utterances.sort(key=lambda x: x.to_str(include_metadata=False)) assert local_utterances == [ Utterance(route="Route 1", utterance="Hello"), Utterance(route="Route 1", utterance="Hi"), From 9d88129a633378eb509de066318be1368b637cac Mon Sep 17 00:00:00 2001 From: jamescalam Date: Thu, 28 Nov 2024 22:30:17 +0100 Subject: [PATCH 26/33] fix: only deploy new docs on full release --- .github/workflows/docs.yml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index ea083d41..e9105edd 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -1,9 +1,8 @@ name: Release Docs on: - push: - branches: - - main + release: + types: [released] jobs: build-docs: From 0364bab871689ee307942f0072e48b53008ffc2e Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 01:16:50 +0100 Subject: [PATCH 27/33] fix: continued optimizations and bug fixes --- semantic_router/encoders/tfidf.py | 11 +++-- semantic_router/index/hybrid_local.py | 4 +- semantic_router/index/pinecone.py | 1 - semantic_router/routers/base.py | 68 +-------------------------- semantic_router/routers/hybrid.py | 48 ++++++++++++++++++- semantic_router/routers/semantic.py | 37 +++++++++++++++ tests/unit/test_hybrid_layer.py | 68 +++++++++++++++++---------- tests/unit/test_router.py | 6 +-- 8 files changed, 139 insertions(+), 104 deletions(-) diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index a7ac9136..b865c170 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -3,8 +3,6 @@ from typing import Dict, List import numpy as np -from numpy import ndarray -from numpy.linalg import norm from semantic_router.encoders import SparseEncoder from semantic_router.route import Route @@ -12,7 +10,8 @@ class TfidfEncoder(SparseEncoder): - idf: ndarray = np.array([]) + idf: np.ndarray = np.array([]) + # TODO: add option to use default params like with BM25Encoder word_index: Dict = {} def __init__(self, name: str | None = None): @@ -39,14 +38,18 @@ def fit(self, routes: List[Route]): for doc in route.utterances: docs.append(self._preprocess(doc)) # type: ignore self.word_index = self._build_word_index(docs) + if len(self.word_index) == 0: + raise ValueError(f"Too little data to fit {self.__class__.__name__}.") self.idf = self._compute_idf(docs) def _build_word_index(self, docs: List[str]) -> Dict: + print(docs) words = set() for doc in docs: for word in doc.split(): words.add(word) word_index = {word: i for i, word in enumerate(words)} + print(word_index) return word_index def _compute_tf(self, docs: List[str]) -> np.ndarray: @@ -59,7 +62,7 @@ def _compute_tf(self, docs: List[str]) -> np.ndarray: if word in self.word_index: tf[i, self.word_index[word]] = count # L2 normalization - tf = tf / norm(tf, axis=1, keepdims=True) + tf = tf / np.linalg.norm(tf, axis=1, keepdims=True) return tf def _compute_idf(self, docs: List[str]) -> np.ndarray: diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index e2a75778..2a5a43d5 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -29,9 +29,9 @@ def add( if sparse_embeddings is None: raise ValueError("Sparse embeddings are required for HybridLocalIndex.") if function_schemas is not None: - raise ValueError("Function schemas are not supported for HybridLocalIndex.") + logger.warning("Function schemas are not supported for HybridLocalIndex.") if metadata_list: - raise ValueError("Metadata is not supported for HybridLocalIndex.") + logger.warning("Metadata is not supported for HybridLocalIndex.") embeds = np.array(embeddings) routes_arr = np.array(routes) if isinstance(utterances[0], str): diff --git a/semantic_router/index/pinecone.py b/semantic_router/index/pinecone.py index 25df1c47..303b4715 100644 --- a/semantic_router/index/pinecone.py +++ b/semantic_router/index/pinecone.py @@ -250,7 +250,6 @@ def add( function_schemas = [{}] * len(embeddings) if sparse_embeddings is None: sparse_embeddings = [{}] * len(embeddings) - vectors_to_upsert = [ PineconeRecord( values=vector, diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index f21131c8..615d4699 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -718,40 +718,13 @@ def from_config(cls, config: RouterConfig, index: Optional[BaseIndex] = None): else: raise ValueError(f"{type(encoder)} not supported for loading from config.") - def add(self, route: Route): + def add(self, routes: List[Route] | Route): """Add a route to the local SemanticRouter and index. :param route: The route to add. :type route: Route """ - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - embedded_utterances = self.encoder(route.utterances) - self.index.add( - embeddings=embedded_utterances, - routes=[route.name] * len(route.utterances), - utterances=route.utterances, - function_schemas=( - route.function_schemas * len(route.utterances) - if route.function_schemas - else [{}] * len(route.utterances) - ), - metadata_list=[route.metadata if route.metadata else {}] - * len(route.utterances), - ) - - self.routes.append(route) - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - "not updated. Use `SemanticRouter.get_utterance_diff()` to see " - "details." - ) + raise NotImplementedError("This method must be implemented by subclasses.") def list_route_names(self) -> List[str]: return [route.name for route in self.routes] @@ -854,43 +827,6 @@ def _refresh_routes(self): route = route_mapping[route_name] self.routes.append(route) - def _add_routes(self, routes: List[Route]): - current_local_hash = self._get_hash() - current_remote_hash = self.index._read_hash() - if current_remote_hash.value == "": - # if remote hash is empty, the index is to be initialized - current_remote_hash = current_local_hash - - if not routes: - logger.warning("No routes provided to add.") - return - # create embeddings for all routes - route_names, all_utterances, all_function_schemas, all_metadata = ( - self._extract_routes_details(routes, include_metadata=True) - ) - embedded_utterances = self.encoder(all_utterances) - try: - # Batch insertion into the index - self.index.add( - embeddings=embedded_utterances, - routes=route_names, - utterances=all_utterances, - function_schemas=all_function_schemas, - metadata_list=all_metadata, - ) - except Exception as e: - logger.error(f"Failed to add routes to the index: {e}") - raise Exception("Indexing error occurred") from e - - if current_local_hash.value == current_remote_hash.value: - self._write_hash() # update current hash in index - else: - logger.warning( - "Local and remote route layers were not aligned. Remote hash " - f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " - "to see details." - ) - def _get_hash(self) -> ConfigParameter: config = self.to_config() return config.get_hash() diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index f07429c3..f74e3e6b 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -57,12 +57,53 @@ def __init__( # fit sparse encoder if needed if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( self.sparse_encoder, "fit" - ): + ) and self.routes: self.sparse_encoder.fit(self.routes) # run initialize index now if auto sync is active if self.auto_sync: self._init_index_state() + def add(self, routes: List[Route] | Route): + """Add a route to the local HybridRouter and index. + + :param route: The route to add. + :type route: Route + """ + # TODO: merge into single method within BaseRouter + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + if isinstance(routes, Route): + routes = [routes] + # create embeddings for all routes + route_names, all_utterances, all_function_schemas, all_metadata = ( + self._extract_routes_details(routes, include_metadata=True) + ) + # TODO: to merge, self._encode should probably output a special + # TODO Embedding type that can be either dense or hybrid + dense_emb, sparse_emb = self._encode(all_utterances) + print(f"{sparse_emb=}") + self.index.add( + embeddings=dense_emb.tolist(), + routes=route_names, + utterances=all_utterances, + function_schemas=all_function_schemas, + metadata_list=all_metadata, + sparse_embeddings=sparse_emb, # type: ignore + ) + + self.routes.extend(routes) + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " + "to see details." + ) + def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: if index is None: logger.warning("No index provided. Using default HybridLocalIndex.") @@ -93,6 +134,8 @@ def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]: xq_s = self.sparse_encoder(text) # xq_s = np.squeeze(xq_s) # convex scaling + print(f"{self.sparse_encoder.__class__.__name__=}") + print(f"_encode: {xq_d.shape=}, {xq_s=}") xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) return xq_d, xq_s @@ -113,6 +156,7 @@ async def _async_encode( # create dense query vector xq_d = np.array(dense_vec) # convex scaling + print(f"_async_encode: {xq_d.shape=}, {xq_s=}") xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) return xq_d, xq_s @@ -139,7 +183,7 @@ def __call__( ) if sparse_vector is None: raise ValueError("Sparse vector is required for HybridLocalIndex.") - vector_arr = vector_arr if vector_arr else np.array(vector) + vector_arr = vector_arr if vector_arr is not None else np.array(vector) # TODO: add alpha as a parameter scores, route_names = self.index.query( vector=vector_arr, diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 64ccbaf2..94c3e179 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -5,6 +5,7 @@ from semantic_router.encoders import DenseEncoder from semantic_router.index.base import BaseIndex from semantic_router.llms import BaseLLM +from semantic_router.utils.logger import logger from semantic_router.route import Route from semantic_router.routers.base import BaseRouter @@ -45,3 +46,39 @@ async def _async_encode(self, text: list[str]) -> Any: xq = np.array(await self.encoder.acall(docs=text)) xq = np.squeeze(xq) # Reduce to 1d array. return xq + + def add(self, routes: List[Route] | Route): + """Add a route to the local SemanticRouter and index. + + :param route: The route to add. + :type route: Route + """ + current_local_hash = self._get_hash() + current_remote_hash = self.index._read_hash() + if current_remote_hash.value == "": + # if remote hash is empty, the index is to be initialized + current_remote_hash = current_local_hash + if isinstance(routes, Route): + routes = [routes] + # create embeddings for all routes + route_names, all_utterances, all_function_schemas, all_metadata = ( + self._extract_routes_details(routes, include_metadata=True) + ) + dense_emb = self._encode(all_utterances) + self.index.add( + embeddings=dense_emb.tolist(), + routes=route_names, + utterances=all_utterances, + function_schemas=all_function_schemas, + metadata_list=all_metadata, + ) + + self.routes.extend(routes) + if current_local_hash.value == current_remote_hash.value: + self._write_hash() # update current hash in index + else: + logger.warning( + "Local and remote route layers were not aligned. Remote hash " + f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " + "to see details." + ) \ No newline at end of file diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index f3fbe6da..aadad86a 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -54,32 +54,42 @@ def azure_encoder(mocker): model="test_model", ) - -def bm25_encoder(mocker): - mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call) +@pytest.fixture +def bm25_encoder(): + #mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call) return BM25Encoder(name="test-bm25-encoder") @pytest.fixture -def tfidf_encoder(mocker): - mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call) +def tfidf_encoder(): + #mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call) return TfidfEncoder(name="test-tfidf-encoder") @pytest.fixture def routes(): return [ - Route(name="Route 1", utterances=["Hello", "Hi"]), - Route(name="Route 2", utterances=["Goodbye", "Bye", "Au revoir"]), + Route(name="Route 1", utterances=[ + "Hello we need this text to be a little longer for our sparse encoders", + "In this case they need to learn from recurring tokens, ie words." + ]), + Route(name="Route 2", utterances=[ + "We give ourselves several examples from our encoders to learn from.", + "But given this is only an example we don't need too many", + "Just enough to test that our sparse encoders work as expected" + ]), ] -sparse_encoder = BM25Encoder(use_default_params=False) +sparse_encoder = TfidfEncoder() sparse_encoder.fit( [ Route( name="Route 1", - utterances=["The quick brown fox", "jumps over the lazy dog"], + utterances=[ + "The quick brown fox jumps over the lazy dog", + "some other useful text containing words like fox and dog" + ], ), Route(name="Route 2", utterances=["Hello, world!"]), ] @@ -95,13 +105,13 @@ def test_initialization(self, openai_encoder, routes): top_k=10, alpha=0.8, ) - assert route_layer.index is not None and route_layer.categories is not None + assert route_layer.index is not None and route_layer.routes is not None assert openai_encoder.score_threshold == 0.3 assert route_layer.score_threshold == 0.3 assert route_layer.top_k == 10 assert route_layer.alpha == 0.8 - assert len(route_layer.index) == 5 - assert len(set(route_layer.categories)) == 2 + assert route_layer.index.route_names is None + assert len(route_layer.routes) == 2 def test_initialization_different_encoders(self, cohere_encoder, openai_encoder): route_layer_cohere = HybridRouter( @@ -114,25 +124,23 @@ def test_initialization_different_encoders(self, cohere_encoder, openai_encoder) ) assert route_layer_openai.score_threshold == 0.3 - def test_add_route(self, openai_encoder): + def test_add_route(self, openai_encoder, routes): route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) - route = Route(name="Route 3", utterances=["Yes", "No"]) - route_layer._add_routes([route]) - assert route_layer.index is not None and route_layer.categories is not None - assert len(route_layer.index) == 2 - assert len(set(route_layer.categories)) == 1 + route_layer.add(routes=routes[0]) + assert route_layer.index is not None, "route_layer.index is None" + assert route_layer.routes is not None, "route_layer.routes is None" + assert len(route_layer.routes) == 1, "route_layer.routes is not 1" def test_add_multiple_routes(self, openai_encoder, routes): route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) - for route in routes: - route_layer.add(route) - assert route_layer.index is not None and route_layer.categories is not None - assert len(route_layer.index) == 5 - assert len(set(route_layer.categories)) == 2 + route_layer.add(routes=routes) + assert route_layer.index is not None, "route_layer.index is None" + assert route_layer.routes is not None, "route_layer.routes is None" + assert len(route_layer.routes) == 2, "route_layer.routes is not 2" def test_query_and_classification(self, openai_encoder, routes): route_layer = HybridRouter( @@ -145,6 +153,14 @@ def test_query_with_no_index(self, openai_encoder): route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) + assert isinstance( + route_layer.sparse_encoder, BM25Encoder + ) or isinstance( + route_layer.sparse_encoder, TfidfEncoder + ), ( + f"route_layer.sparse_encoder is {route_layer.sparse_encoder.__class__.__name__} " + "not BM25Encoder or TfidfEncoder" + ) assert route_layer("Anything") is None def test_semantic_classify(self, openai_encoder, routes): @@ -192,12 +208,12 @@ def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes): sparse_encoder=tfidf_encoder, routes=routes[:-1], ) - hybrid_route_layer.add(routes[-1]) + hybrid_route_layer.add(routes=routes[-1]) all_utterances = [ utterance for route in routes for utterance in route.utterances ] - assert hybrid_route_layer.sparse_index is not None - assert len(hybrid_route_layer.sparse_index) == len(all_utterances) + assert hybrid_route_layer.index.sparse_index is not None, "sparse_index is None" + assert len(hybrid_route_layer.index.sparse_index) == len(all_utterances), "sparse_index length mismatch" def test_setting_aggregation_methods(self, openai_encoder, routes): for agg in ["sum", "mean", "max"]: diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index 62b49fcd..ef36e0ab 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -281,7 +281,7 @@ def test_add_route(self, routes, openai_encoder, index_cls): assert route_layer.index.get_utterances() == [] # Add route1 and check - route_layer.add(route=routes[0]) + route_layer.add(routes=routes[0]) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert route_layer.routes == [routes[0]] @@ -289,7 +289,7 @@ def test_add_route(self, routes, openai_encoder, index_cls): assert len(route_layer.index.get_utterances()) == 2 # Add route2 and check - route_layer.add(route=routes[1]) + route_layer.add(routes=routes[1]) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert route_layer.routes == [routes[0], routes[1]] @@ -354,7 +354,7 @@ def test_add_multiple_routes(self, openai_encoder, routes, index_cls): ) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) - route_layer._add_routes(routes=routes) + route_layer.add(routes=routes) if index_cls is PineconeIndex: time.sleep(PINECONE_SLEEP) # allow for index to be populated assert route_layer.index is not None From f7f05081867de6e9cc629df33fedb575f431c806 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 11:24:46 +0100 Subject: [PATCH 28/33] fix: hybrid fixes --- semantic_router/index/hybrid_local.py | 16 ++++--- semantic_router/routers/base.py | 3 -- semantic_router/routers/hybrid.py | 64 +++++++++++++++++++++++---- semantic_router/routers/semantic.py | 5 ++- tests/unit/test_hybrid_layer.py | 46 ++++++++++++------- 5 files changed, 99 insertions(+), 35 deletions(-) diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index 2a5a43d5..f927914e 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -24,7 +24,7 @@ def add( utterances: List[str], function_schemas: Optional[List[Dict[str, Any]]] = None, metadata_list: List[Dict[str, Any]] = [], - sparse_embeddings: Optional[List[dict[int, float]]] = None, + sparse_embeddings: Optional[List[SparseEmbedding]] = None, ): if sparse_embeddings is None: raise ValueError("Sparse embeddings are required for HybridLocalIndex.") @@ -32,21 +32,27 @@ def add( logger.warning("Function schemas are not supported for HybridLocalIndex.") if metadata_list: logger.warning("Metadata is not supported for HybridLocalIndex.") - embeds = np.array(embeddings) + embeds = np.array( + embeddings + ) # TODO: we previously had as a array, so switching back and forth seems inefficient routes_arr = np.array(routes) if isinstance(utterances[0], str): utterances_arr = np.array(utterances) else: - utterances_arr = np.array(utterances, dtype=object) + utterances_arr = np.array( + utterances, dtype=object + ) # TODO: could we speed up if this were already array? if self.index is None or self.sparse_index is None: self.index = embeds - self.sparse_index = sparse_embeddings + self.sparse_index = [ + x.to_dict() for x in sparse_embeddings + ] # TODO: switch back to using SparseEmbedding later self.routes = routes_arr self.utterances = utterances_arr else: # TODO: we should probably switch to an `upsert` method and standardize elsewhere self.index = np.concatenate([self.index, embeds]) - self.sparse_index.extend(sparse_embeddings) + self.sparse_index.extend([x.to_dict() for x in sparse_embeddings]) self.routes = np.concatenate([self.routes, routes_arr]) self.utterances = np.concatenate([self.utterances, utterances_arr]) diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 615d4699..392e91d8 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -335,9 +335,6 @@ def __init__( for route in self.routes: if route.score_threshold is None: route.score_threshold = self.score_threshold - # run initialize index now if auto sync is active - if self.auto_sync: - self._init_index_state() def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: if index is None: diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index f74e3e6b..e603add3 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -1,4 +1,4 @@ -from typing import List, Optional +from typing import Dict, List, Optional import asyncio from pydantic.v1 import Field @@ -12,7 +12,7 @@ ) from semantic_router.route import Route from semantic_router.index import BaseIndex, HybridLocalIndex -from semantic_router.schema import RouteChoice, SparseEmbedding +from semantic_router.schema import RouteChoice, SparseEmbedding, Utterance from semantic_router.utils.logger import logger from semantic_router.routers.base import BaseRouter from semantic_router.llms import BaseLLM @@ -37,10 +37,13 @@ def __init__( auto_sync: Optional[str] = None, alpha: float = 0.3, ): + print("...2.1") if index is None: logger.warning("No index provided. Using default HybridLocalIndex.") index = HybridLocalIndex() + print("...2.2") encoder = self._get_encoder(encoder=encoder) + print("...2.3") super().__init__( encoder=encoder, llm=llm, @@ -50,15 +53,22 @@ def __init__( aggregation=aggregation, auto_sync=auto_sync, ) + print("...0") # initialize sparse encoder - self._set_sparse_encoder(sparse_encoder=sparse_encoder) + self.sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder) + print("...5") # set alpha self.alpha = alpha + print("...6") # fit sparse encoder if needed - if isinstance(self.sparse_encoder, TfidfEncoder) and hasattr( - self.sparse_encoder, "fit" - ) and self.routes: + if ( + isinstance(self.sparse_encoder, TfidfEncoder) + and hasattr(self.sparse_encoder, "fit") + and self.routes + ): + print("...3") self.sparse_encoder.fit(self.routes) + print("...4") # run initialize index now if auto sync is active if self.auto_sync: self._init_index_state() @@ -104,6 +114,39 @@ def add(self, routes: List[Route] | Route): "to see details." ) + def _execute_sync_strategy(self, strategy: Dict[str, Dict[str, List[Utterance]]]): + """Executes the provided sync strategy, either deleting or upserting + routes from the local and remote instances as defined in the strategy. + + :param strategy: The sync strategy to execute. + :type strategy: Dict[str, Dict[str, List[Utterance]]] + """ + if strategy["remote"]["delete"]: + data_to_delete = {} # type: ignore + for utt_obj in strategy["remote"]["delete"]: + data_to_delete.setdefault(utt_obj.route, []).append(utt_obj.utterance) + # TODO: switch to remove without sync?? + self.index._remove_and_sync(data_to_delete) + if strategy["remote"]["upsert"]: + utterances_text = [utt.utterance for utt in strategy["remote"]["upsert"]] + dense_emb, sparse_emb = self._encode(utterances_text) + self.index.add( + embeddings=dense_emb.tolist(), + routes=[utt.route for utt in strategy["remote"]["upsert"]], + utterances=utterances_text, + function_schemas=[ + utt.function_schemas for utt in strategy["remote"]["upsert"] # type: ignore + ], + metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]], + sparse_embeddings=sparse_emb, # type: ignore + ) + if strategy["local"]["delete"]: + self._local_delete(utterances=strategy["local"]["delete"]) + if strategy["local"]["upsert"]: + self._local_upsert(utterances=strategy["local"]["upsert"]) + # update hash + self._write_hash() + def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: if index is None: logger.warning("No index provided. Using default HybridLocalIndex.") @@ -112,12 +155,15 @@ def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex: index = index return index - def _set_sparse_encoder(self, sparse_encoder: Optional[SparseEncoder]): + def _get_sparse_encoder( + self, sparse_encoder: Optional[SparseEncoder] + ) -> SparseEncoder: if sparse_encoder is None: logger.warning("No sparse_encoder provided. Using default BM25Encoder.") - self.sparse_encoder = BM25Encoder() + sparse_encoder = BM25Encoder() else: - self.sparse_encoder = sparse_encoder + sparse_encoder = sparse_encoder + return sparse_encoder def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]: """Given some text, generates dense and sparse embeddings, then scales them diff --git a/semantic_router/routers/semantic.py b/semantic_router/routers/semantic.py index 94c3e179..33af2a32 100644 --- a/semantic_router/routers/semantic.py +++ b/semantic_router/routers/semantic.py @@ -32,6 +32,9 @@ def __init__( aggregation=aggregation, auto_sync=auto_sync, ) + # run initialize index now if auto sync is active + if self.auto_sync: + self._init_index_state() def _encode(self, text: list[str]) -> Any: """Given some text, encode it.""" @@ -81,4 +84,4 @@ def add(self, routes: List[Route] | Route): "Local and remote route layers were not aligned. Remote hash " f"not updated. Use `{self.__class__.__name__}.get_utterance_diff()` " "to see details." - ) \ No newline at end of file + ) diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index aadad86a..a7d29b46 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -54,30 +54,37 @@ def azure_encoder(mocker): model="test_model", ) + @pytest.fixture def bm25_encoder(): - #mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call) + # mocker.patch.object(BM25Encoder, "__call__", side_effect=mock_encoder_call) return BM25Encoder(name="test-bm25-encoder") @pytest.fixture def tfidf_encoder(): - #mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call) + # mocker.patch.object(TfidfEncoder, "__call__", side_effect=mock_encoder_call) return TfidfEncoder(name="test-tfidf-encoder") @pytest.fixture def routes(): return [ - Route(name="Route 1", utterances=[ - "Hello we need this text to be a little longer for our sparse encoders", - "In this case they need to learn from recurring tokens, ie words." - ]), - Route(name="Route 2", utterances=[ - "We give ourselves several examples from our encoders to learn from.", - "But given this is only an example we don't need too many", - "Just enough to test that our sparse encoders work as expected" - ]), + Route( + name="Route 1", + utterances=[ + "Hello we need this text to be a little longer for our sparse encoders", + "In this case they need to learn from recurring tokens, ie words.", + ], + ), + Route( + name="Route 2", + utterances=[ + "We give ourselves several examples from our encoders to learn from.", + "But given this is only an example we don't need too many", + "Just enough to test that our sparse encoders work as expected", + ], + ), ] @@ -88,7 +95,7 @@ def routes(): name="Route 1", utterances=[ "The quick brown fox jumps over the lazy dog", - "some other useful text containing words like fox and dog" + "some other useful text containing words like fox and dog", ], ), Route(name="Route 2", utterances=["Hello, world!"]), @@ -143,9 +150,14 @@ def test_add_multiple_routes(self, openai_encoder, routes): assert len(route_layer.routes) == 2, "route_layer.routes is not 2" def test_query_and_classification(self, openai_encoder, routes): + print("...1") route_layer = HybridRouter( - encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes + encoder=openai_encoder, + sparse_encoder=sparse_encoder, + routes=routes, + auto_sync="local", ) + print("...2") query_result = route_layer("Hello") assert query_result in ["Route 1", "Route 2"] @@ -153,9 +165,7 @@ def test_query_with_no_index(self, openai_encoder): route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder ) - assert isinstance( - route_layer.sparse_encoder, BM25Encoder - ) or isinstance( + assert isinstance(route_layer.sparse_encoder, BM25Encoder) or isinstance( route_layer.sparse_encoder, TfidfEncoder ), ( f"route_layer.sparse_encoder is {route_layer.sparse_encoder.__class__.__name__} " @@ -213,7 +223,9 @@ def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes): utterance for route in routes for utterance in route.utterances ] assert hybrid_route_layer.index.sparse_index is not None, "sparse_index is None" - assert len(hybrid_route_layer.index.sparse_index) == len(all_utterances), "sparse_index length mismatch" + assert len(hybrid_route_layer.index.sparse_index) == len( + all_utterances + ), "sparse_index length mismatch" def test_setting_aggregation_methods(self, openai_encoder, routes): for agg in ["sum", "mean", "max"]: From f9a72dbf009a13af078f656b100049855f954cc2 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 13:11:21 +0100 Subject: [PATCH 29/33] fix: hybrid fixes --- semantic_router/index/hybrid_local.py | 3 +- semantic_router/routers/base.py | 8 +++-- tests/unit/test_hybrid_layer.py | 44 +++++++++++++-------------- 3 files changed, 29 insertions(+), 26 deletions(-) diff --git a/semantic_router/index/hybrid_local.py b/semantic_router/index/hybrid_local.py index f927914e..d4096edb 100644 --- a/semantic_router/index/hybrid_local.py +++ b/semantic_router/index/hybrid_local.py @@ -135,7 +135,8 @@ def query( route_names = self.routes[idx] if self.routes is not None else [] return scores, route_names else: - raise ValueError("Index or sparse index is not populated.") + logger.warning("Index or sparse index is not populated.") + return np.array([]), [] async def aquery( self, diff --git a/semantic_router/routers/base.py b/semantic_router/routers/base.py index 392e91d8..e4376b42 100644 --- a/semantic_router/routers/base.py +++ b/semantic_router/routers/base.py @@ -1104,11 +1104,15 @@ def _update_thresholds(self, route_thresholds: Optional[Dict[str, float]] = None ) def set_threshold(self, threshold: float, route_name: str | None = None): - """Set the score threshold for a specific route or all routes. + """Set the score threshold for a specific route or all routes. A `threshold` of 0.0 + will mean that the route will be returned no matter how low it scores whereas + a threshold of 1.0 will mean that a route must contain an exact utterance match + to be returned. :param threshold: The threshold to set. :type threshold: float - :param route_name: The name of the route to set the threshold for. If None, the threshold will be set for all routes. + :param route_name: The name of the route to set the threshold for. If None, the + threshold will be set for all routes. :type route_name: str | None """ if route_name is None: diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index a7d29b46..f9f8ff6d 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -12,14 +12,23 @@ from semantic_router.route import Route +UTTERANCES = [ + "Hello we need this text to be a little longer for our sparse encoders", + "In this case they need to learn from recurring tokens, ie words.", + "We give ourselves several examples from our encoders to learn from.", + "But given this is only an example we don't need too many", + "Just enough to test that our sparse encoders work as expected", +] + + def mock_encoder_call(utterances): # Define a mapping of utterances to return values mock_responses = { - "Hello": [0.1, 0.2, 0.3], - "Hi": [0.4, 0.5, 0.6], - "Goodbye": [0.7, 0.8, 0.9], - "Bye": [1.0, 1.1, 1.2], - "Au revoir": [1.3, 1.4, 1.5], + UTTERANCES[0]: [0.1, 0.2, 0.3], + UTTERANCES[1]: [0.4, 0.5, 0.6], + UTTERANCES[2]: [0.7, 0.8, 0.9], + UTTERANCES[3]: [1.0, 1.1, 1.2], + UTTERANCES[4]: [1.3, 1.4, 1.5], } return [mock_responses.get(u, [0, 0, 0]) for u in utterances] @@ -70,21 +79,8 @@ def tfidf_encoder(): @pytest.fixture def routes(): return [ - Route( - name="Route 1", - utterances=[ - "Hello we need this text to be a little longer for our sparse encoders", - "In this case they need to learn from recurring tokens, ie words.", - ], - ), - Route( - name="Route 2", - utterances=[ - "We give ourselves several examples from our encoders to learn from.", - "But given this is only an example we don't need too many", - "Just enough to test that our sparse encoders work as expected", - ], - ), + Route(name="Route 1", utterances=[UTTERANCES[0], UTTERANCES[1]]), + Route(name="Route 2", utterances=[UTTERANCES[2], UTTERANCES[3], UTTERANCES[4]]), ] @@ -158,8 +154,9 @@ def test_query_and_classification(self, openai_encoder, routes): auto_sync="local", ) print("...2") - query_result = route_layer("Hello") - assert query_result in ["Route 1", "Route 2"] + route_layer.set_threshold(0.0) + query_result = route_layer(UTTERANCES[0]) + assert query_result.name in ["Route 1", "Route 2"] def test_query_with_no_index(self, openai_encoder): route_layer = HybridRouter( @@ -171,7 +168,7 @@ def test_query_with_no_index(self, openai_encoder): f"route_layer.sparse_encoder is {route_layer.sparse_encoder.__class__.__name__} " "not BM25Encoder or TfidfEncoder" ) - assert route_layer("Anything") is None + assert route_layer("Anything").name is None def test_semantic_classify(self, openai_encoder, routes): route_layer = HybridRouter( @@ -217,6 +214,7 @@ def test_add_route_tfidf(self, cohere_encoder, tfidf_encoder, routes): encoder=cohere_encoder, sparse_encoder=tfidf_encoder, routes=routes[:-1], + auto_sync="local", ) hybrid_route_layer.add(routes=routes[-1]) all_utterances = [ From ac8db937608eb9599c0a44aea4d65b16cc189f0d Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 14:05:29 +0100 Subject: [PATCH 30/33] fix: tfidf tests --- tests/unit/encoders/test_tfidf.py | 4 ++-- tests/unit/test_router.py | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/tests/unit/encoders/test_tfidf.py b/tests/unit/encoders/test_tfidf.py index 7664433d..5052a035 100644 --- a/tests/unit/encoders/test_tfidf.py +++ b/tests/unit/encoders/test_tfidf.py @@ -37,7 +37,7 @@ def test_call_method(self, tfidf_encoder): result = tfidf_encoder(["test"]) assert isinstance(result, list), "Result should be a list" assert all( - isinstance(sublist, list) for sublist in result + isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result ), "Each item in result should be a list" def test_call_method_no_docs_tfidf_encoder(self, tfidf_encoder): @@ -55,7 +55,7 @@ def test_call_method_no_word(self, tfidf_encoder): result = tfidf_encoder(["doc with fake word gta5jabcxyz"]) assert isinstance(result, list), "Result should be a list" assert all( - isinstance(sublist, list) for sublist in result + isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result ), "Each item in result should be a list" def test_call_method_with_uninitialized_model(self, tfidf_encoder): diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index ef36e0ab..a20c68e1 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -19,6 +19,7 @@ PINECONE_SLEEP = 6 + def mock_encoder_call(utterances): # Define a mapping of utterances to return values mock_responses = { From 2aea17d1021fb100a8204f4b690da6a32e9a87e1 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 14:11:50 +0100 Subject: [PATCH 31/33] chore: lint --- tests/unit/test_router.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py index a20c68e1..ef36e0ab 100644 --- a/tests/unit/test_router.py +++ b/tests/unit/test_router.py @@ -19,7 +19,6 @@ PINECONE_SLEEP = 6 - def mock_encoder_call(utterances): # Define a mapping of utterances to return values mock_responses = { From 981b039080253a68c11af8f0680c79c4f887e9f0 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 15:32:30 +0100 Subject: [PATCH 32/33] fix: hybrid --- semantic_router/encoders/aurelio.py | 3 +- semantic_router/encoders/bm25.py | 16 ++++++- semantic_router/encoders/tfidf.py | 10 +++++ tests/unit/encoders/test_bm25.py | 66 ++++++++++++++++++++--------- tests/unit/encoders/test_tfidf.py | 9 +++- 5 files changed, 79 insertions(+), 25 deletions(-) diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py index 779fe6b1..8b2501ba 100644 --- a/semantic_router/encoders/aurelio.py +++ b/semantic_router/encoders/aurelio.py @@ -1,5 +1,5 @@ import os -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional from pydantic.v1 import Field from aurelio_sdk import AurelioClient, AsyncAurelioClient, EmbeddingResponse @@ -10,7 +10,6 @@ class AurelioSparseEncoder(SparseEncoder): model: Optional[Any] = None - idx_mapping: Optional[Dict[int, int]] = None client: AurelioClient = Field(default_factory=AurelioClient, exclude=True) async_client: AsyncAurelioClient = Field( default_factory=AsyncAurelioClient, exclude=True diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py index b5365cee..f42bf9c2 100644 --- a/semantic_router/encoders/bm25.py +++ b/semantic_router/encoders/bm25.py @@ -3,6 +3,7 @@ from semantic_router.encoders.tfidf import TfidfEncoder from semantic_router.utils.logger import logger from semantic_router.schema import SparseEmbedding +from semantic_router.route import Route class BM25Encoder(TfidfEncoder): @@ -34,6 +35,7 @@ def __init__( self._set_idx_mapping() def _set_idx_mapping(self): + # TODO JB: this is training the model somehow - not sure how... params = self.model.get_params() doc_freq = params["doc_freq"] if isinstance(doc_freq, dict): @@ -42,8 +44,20 @@ def _set_idx_mapping(self): else: raise TypeError("Expected a dictionary for 'doc_freq'") + def fit(self, routes: List[Route]): + """Trains the encoder weights on the provided routes. + + :param routes: List of routes to train the encoder on. + :type routes: List[Route] + """ + self._fit_validate(routes=routes) + if self.model is None: + raise ValueError("Model is not initialized.") + utterances = [utterance for route in routes for utterance in route.utterances] + self.model.fit(corpus=utterances) + def __call__(self, docs: List[str]) -> list[SparseEmbedding]: - if self.model is None or self.idx_mapping is None: + if self.model is None: raise ValueError("Model or index mapping is not initialized.") if len(docs) == 1: sparse_dicts = self.model.encode_queries(docs) diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index b865c170..fe285e5c 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -33,6 +33,12 @@ def __call__(self, docs: List[str]) -> list[SparseEmbedding]: return self._array_to_sparse_embeddings(tfidf) def fit(self, routes: List[Route]): + """Trains the encoder weights on the provided routes. + + :param routes: List of routes to train the encoder on. + :type routes: List[Route] + """ + self._fit_validate(routes=routes) docs = [] for route in routes: for doc in route.utterances: @@ -42,6 +48,10 @@ def fit(self, routes: List[Route]): raise ValueError(f"Too little data to fit {self.__class__.__name__}.") self.idf = self._compute_idf(docs) + def _fit_validate(self, routes: List[Route]): + if not isinstance(routes, list) or not isinstance(routes[0], Route): + raise TypeError("`routes` parameter must be a list of Route objects.") + def _build_word_index(self, docs: List[str]) -> Dict: print(docs) words = set() diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py index 73e52d55..a22b0cc2 100644 --- a/tests/unit/encoders/test_bm25.py +++ b/tests/unit/encoders/test_bm25.py @@ -1,31 +1,66 @@ import pytest +import numpy as np from semantic_router.encoders import BM25Encoder +from semantic_router.route import Route +import nltk + +nltk.download("punkt_tab") + +UTTERANCES = [ + "Hello we need this text to be a little longer for our sparse encoders", + "In this case they need to learn from recurring tokens, ie words.", + "We give ourselves several examples from our encoders to learn from.", + "But given this is only an example we don't need too many", + "Just enough to test that our sparse encoders work as expected", +] @pytest.fixture def bm25_encoder(): sparse_encoder = BM25Encoder(use_default_params=False) sparse_encoder.fit( - ["The quick brown fox", "jumps over the lazy dog", "Hello, world!"] + [ + Route( + name="test_route", + utterances=[ + "The quick brown fox", + "jumps over the lazy dog", + "Hello, world!", + ], + ) + ] ) return sparse_encoder +@pytest.fixture +def routes(): + return [ + Route(name="Route 1", utterances=[UTTERANCES[0], UTTERANCES[1]]), + Route(name="Route 2", utterances=[UTTERANCES[2], UTTERANCES[3], UTTERANCES[4]]), + ] + + class TestBM25Encoder: def test_initialization(self, bm25_encoder): - assert len(bm25_encoder.idx_mapping) != 0 + assert bm25_encoder.model is not None + + def test_fit(self, bm25_encoder, routes): + bm25_encoder.fit(routes) + assert bm25_encoder.model is not None - def test_fit(self, bm25_encoder): - bm25_encoder.fit(["some docs", "and more docs", "and even more docs"]) - assert len(bm25_encoder.idx_mapping) != 0 + def test_fit_with_strings(self, bm25_encoder): + route_strings = ["test a", "test b", "test c"] + with pytest.raises(TypeError): + bm25_encoder.fit(route_strings) def test_call_method(self, bm25_encoder): result = bm25_encoder(["test"]) assert isinstance(result, list), "Result should be a list" assert all( - isinstance(sublist, list) for sublist in result - ), "Each item in result should be a list" + isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result + ), "Each item in result should be an array" def test_call_method_no_docs_bm25_encoder(self, bm25_encoder): with pytest.raises(ValueError): @@ -35,24 +70,15 @@ def test_call_method_no_word(self, bm25_encoder): result = bm25_encoder(["doc with fake word gta5jabcxyz"]) assert isinstance(result, list), "Result should be a list" assert all( - isinstance(sublist, list) for sublist in result - ), "Each item in result should be a list" - - def test_init_with_non_dict_doc_freq(self, mocker): - mock_encoder = mocker.MagicMock() - mock_encoder.get_params.return_value = {"doc_freq": "not a dict"} - mocker.patch( - "pinecone_text.sparse.BM25Encoder.default", return_value=mock_encoder - ) - with pytest.raises(TypeError): - BM25Encoder() + isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result + ), "Each item in result should be an array" def test_call_method_with_uninitialized_model_or_mapping(self, bm25_encoder): bm25_encoder.model = None with pytest.raises(ValueError): bm25_encoder(["test"]) - def test_fit_with_uninitialized_model(self, bm25_encoder): + def test_fit_with_uninitialized_model(self, bm25_encoder, routes): bm25_encoder.model = None with pytest.raises(ValueError): - bm25_encoder.fit(["test"]) + bm25_encoder.fit(routes) diff --git a/tests/unit/encoders/test_tfidf.py b/tests/unit/encoders/test_tfidf.py index 5052a035..94beec9f 100644 --- a/tests/unit/encoders/test_tfidf.py +++ b/tests/unit/encoders/test_tfidf.py @@ -38,7 +38,7 @@ def test_call_method(self, tfidf_encoder): assert isinstance(result, list), "Result should be a list" assert all( isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result - ), "Each item in result should be a list" + ), "Each item in result should be an array" def test_call_method_no_docs_tfidf_encoder(self, tfidf_encoder): with pytest.raises(ValueError): @@ -56,7 +56,12 @@ def test_call_method_no_word(self, tfidf_encoder): assert isinstance(result, list), "Result should be a list" assert all( isinstance(sparse_emb.embedding, np.ndarray) for sparse_emb in result - ), "Each item in result should be a list" + ), "Each item in result should be an array" + + def test_fit_with_strings(self, tfidf_encoder): + routes = ["test a", "test b", "test c"] + with pytest.raises(TypeError): + tfidf_encoder.fit(routes) def test_call_method_with_uninitialized_model(self, tfidf_encoder): with pytest.raises(ValueError): From ff2c3ca6a09009cc2ea488a45d3f67edd5ac27e0 Mon Sep 17 00:00:00 2001 From: jamescalam Date: Fri, 29 Nov 2024 15:50:04 +0100 Subject: [PATCH 33/33] chore: remove print --- semantic_router/encoders/tfidf.py | 2 -- semantic_router/routers/hybrid.py | 12 ------------ tests/unit/test_hybrid_layer.py | 2 -- 3 files changed, 16 deletions(-) diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py index fe285e5c..656ee117 100644 --- a/semantic_router/encoders/tfidf.py +++ b/semantic_router/encoders/tfidf.py @@ -53,13 +53,11 @@ def _fit_validate(self, routes: List[Route]): raise TypeError("`routes` parameter must be a list of Route objects.") def _build_word_index(self, docs: List[str]) -> Dict: - print(docs) words = set() for doc in docs: for word in doc.split(): words.add(word) word_index = {word: i for i, word in enumerate(words)} - print(word_index) return word_index def _compute_tf(self, docs: List[str]) -> np.ndarray: diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py index e603add3..36ccd8f9 100644 --- a/semantic_router/routers/hybrid.py +++ b/semantic_router/routers/hybrid.py @@ -37,13 +37,10 @@ def __init__( auto_sync: Optional[str] = None, alpha: float = 0.3, ): - print("...2.1") if index is None: logger.warning("No index provided. Using default HybridLocalIndex.") index = HybridLocalIndex() - print("...2.2") encoder = self._get_encoder(encoder=encoder) - print("...2.3") super().__init__( encoder=encoder, llm=llm, @@ -53,22 +50,17 @@ def __init__( aggregation=aggregation, auto_sync=auto_sync, ) - print("...0") # initialize sparse encoder self.sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder) - print("...5") # set alpha self.alpha = alpha - print("...6") # fit sparse encoder if needed if ( isinstance(self.sparse_encoder, TfidfEncoder) and hasattr(self.sparse_encoder, "fit") and self.routes ): - print("...3") self.sparse_encoder.fit(self.routes) - print("...4") # run initialize index now if auto sync is active if self.auto_sync: self._init_index_state() @@ -94,7 +86,6 @@ def add(self, routes: List[Route] | Route): # TODO: to merge, self._encode should probably output a special # TODO Embedding type that can be either dense or hybrid dense_emb, sparse_emb = self._encode(all_utterances) - print(f"{sparse_emb=}") self.index.add( embeddings=dense_emb.tolist(), routes=route_names, @@ -180,8 +171,6 @@ def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]: xq_s = self.sparse_encoder(text) # xq_s = np.squeeze(xq_s) # convex scaling - print(f"{self.sparse_encoder.__class__.__name__=}") - print(f"_encode: {xq_d.shape=}, {xq_s=}") xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) return xq_d, xq_s @@ -202,7 +191,6 @@ async def _async_encode( # create dense query vector xq_d = np.array(dense_vec) # convex scaling - print(f"_async_encode: {xq_d.shape=}, {xq_s=}") xq_d, xq_s = self._convex_scaling(dense=xq_d, sparse=xq_s) return xq_d, xq_s diff --git a/tests/unit/test_hybrid_layer.py b/tests/unit/test_hybrid_layer.py index f9f8ff6d..b12ea2f5 100644 --- a/tests/unit/test_hybrid_layer.py +++ b/tests/unit/test_hybrid_layer.py @@ -146,14 +146,12 @@ def test_add_multiple_routes(self, openai_encoder, routes): assert len(route_layer.routes) == 2, "route_layer.routes is not 2" def test_query_and_classification(self, openai_encoder, routes): - print("...1") route_layer = HybridRouter( encoder=openai_encoder, sparse_encoder=sparse_encoder, routes=routes, auto_sync="local", ) - print("...2") route_layer.set_threshold(0.0) query_result = route_layer(UTTERANCES[0]) assert query_result.name in ["Route 1", "Route 2"]