diff --git a/coverage.xml b/coverage.xml
index bcccea3f3bcfa3f2adc89fcb5b32308b04522852..797bf7980f1f3c11d1bf1fe2e0326db2beeb5a85 100644
--- a/coverage.xml
+++ b/coverage.xml
@@ -1,148 +1,139 @@
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@@ -602,300 +415,267 @@
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</class>
- <class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="1" branch-rate="0">
+ <class name="logger.py" filename="utils/logger.py" complexity="0" line-rate="0.5263" branch-rate="0">
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diff --git a/pyproject.toml b/pyproject.toml
index 1e647a6205d510f89c51a887e52c9d4a9905b799..ccec0457191b2ad58dee5b03e8deb63be9ecebb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,6 @@ dependencies = [
]
[project.optional-dependencies]
-hybrid = ["pinecone-text>=0.7.1"]
local = [
"torch>=2.1.0,<2.6.0 ; python_version < '3.13'",
"transformers>=4.36.2 ; python_version < '3.13'",
@@ -51,7 +50,6 @@ docs = ["pydoc-markdown>=4.8.2 ; python_version < '3.12'"]
cohere = ["cohere>=5.9.4,<6.00"]
all = [
"semantic-router[local]",
- "semantic-router[hybrid]",
"semantic-router[pinecone]",
"semantic-router[vision]",
"semantic-router[mistralai]",
diff --git a/semantic_router/encoders/aurelio.py b/semantic_router/encoders/aurelio.py
index 5f39a4728aff3841a5f49491c580a18d4f3d1b55..2859289b016b88440466e2ee679fede4b183c8b6 100644
--- a/semantic_router/encoders/aurelio.py
+++ b/semantic_router/encoders/aurelio.py
@@ -45,13 +45,34 @@ class AurelioSparseEncoder(SparseEncoder):
def __call__(self, docs: list[str]) -> list[SparseEmbedding]:
"""Encode a list of documents using the Aurelio Platform embedding API. Documents
must be strings, sparse encoders do not support other types.
-
- :param docs: The documents to encode.
- :type docs: list[str]
- :return: The encoded documents.
- :rtype: list[SparseEmbedding]
"""
- res: EmbeddingResponse = self.client.embedding(input=docs, model=self.name)
+ return self.encode_queries(docs)
+
+ def encode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ res: EmbeddingResponse = self.client.embedding(
+ input=docs, model=self.name, input_type="queries"
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ def encode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ res: EmbeddingResponse = self.client.embedding(
+ input=docs, model=self.name, input_type="documents"
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ async def aencode_queries(self, docs: List[str]) -> list[SparseEmbedding]:
+ res: EmbeddingResponse = await self.async_client.embedding(
+ input=docs, model=self.name, input_type="queries"
+ )
+ embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
+ return embeds
+
+ async def aencode_documents(self, docs: List[str]) -> list[SparseEmbedding]:
+ res: EmbeddingResponse = await self.async_client.embedding(
+ input=docs, model=self.name, input_type="documents"
+ )
embeds = [SparseEmbedding.from_aurelio(r.embedding) for r in res.data]
return embeds
@@ -65,11 +86,7 @@ class AurelioSparseEncoder(SparseEncoder):
:return: The encoded documents.
:rtype: list[SparseEmbedding]
"""
- 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
+ return await self.aencode_queries(docs)
def fit(self, docs: List[str]):
"""Fit the encoder to a list of documents. AurelioSparseEncoder does not support
diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index 2442a9aeab34587a06a0cbb29a06c84c226f6817..a9b460aded5929fc25ec20c616bbf6de5870b265 100644
--- a/semantic_router/encoders/base.py
+++ b/semantic_router/encoders/base.py
@@ -3,6 +3,7 @@ from typing import Any, Coroutine, List, Optional
import numpy as np
from pydantic import BaseModel, Field, field_validator
+from semantic_router.route import Route
from semantic_router.schema import SparseEmbedding
@@ -60,25 +61,31 @@ class SparseEncoder(BaseModel):
arbitrary_types_allowed = True
def __call__(self, docs: List[str]) -> List[SparseEmbedding]:
- """Encode a list of documents. Documents must be strings, sparse encoders do not
- support other types.
+ """Sparsely encode a list of documents. Documents can be any type, but the encoder must
+ be built to handle that data type. Typically, these types are strings or
+ arrays representing images.
:param docs: The documents to encode.
- :type docs: List[str]
+ :type docs: List[Any]
:return: The encoded documents.
:rtype: List[SparseEmbedding]
"""
raise NotImplementedError("Subclasses must implement this method")
- async def acall(self, docs: List[str]) -> list[SparseEmbedding]:
- """Encode a list of documents. Documents must be strings, sparse encoders do not
- support other types.
+ def encode_queries(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Convert query texts to sparse embeddings optimized for querying"""
+ raise NotImplementedError("Subclasses must implement this method")
- :param docs: The documents to encode.
- :type docs: List[str]
- :return: The encoded documents.
- :rtype: List[SparseEmbedding]
- """
+ def encode_documents(self, docs: List[str]) -> List[SparseEmbedding]:
+ """Convert document texts to sparse embeddings optimized for storage"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_queries(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Async version of encode_queries"""
+ raise NotImplementedError("Subclasses must implement this method")
+
+ async def aencode_documents(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Async version of encode_documents"""
raise NotImplementedError("Subclasses must implement this method")
def _array_to_sparse_embeddings(
@@ -96,8 +103,16 @@ class SparseEncoder(BaseModel):
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)
+ if coords[0].size == 0:
+ # Sparse Embeddings can be all zero, if query tokens do not appear in corpus at all
+ return [SparseEmbedding(embedding=np.empty((1, 2)))]
# 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]
+
+
+class FittableMixin:
+ def fit(self, routes: list[Route]):
+ pass
diff --git a/semantic_router/encoders/bm25.py b/semantic_router/encoders/bm25.py
index 6c180179f63640734d279b3e1c387362f879a04c..5377e2e78d49b1e914536bdcbbe3eeb24ec2040a 100644
--- a/semantic_router/encoders/bm25.py
+++ b/semantic_router/encoders/bm25.py
@@ -1,73 +1,273 @@
-from typing import Any, Dict, List, Optional
+from functools import partial
+from typing import List
-from semantic_router.encoders.tfidf import TfidfEncoder
+import numpy as np
+
+from semantic_router.encoders.base import FittableMixin, SparseEncoder
from semantic_router.route import Route
from semantic_router.schema import SparseEmbedding
+from semantic_router.tokenizers import BaseTokenizer, PretrainedTokenizer
from semantic_router.utils.logger import logger
-class BM25Encoder(TfidfEncoder):
- model: Optional[Any] = None
- idx_mapping: Optional[Dict[int, int]] = None
+class BM25Encoder(SparseEncoder, FittableMixin):
+ """BM25Encoder, running a vectorized version of ATIRE BM25 algorithm
+
+ Concept:
+ - BM25 uses scoring between queries & corpus to retrieve the most relevant documents ∈ corpus
+ - most vector databases (VDB) store embedded documents and score them versus received queries for retrieval
+ - we need to break up the BM25 formula into `encode_queries` and `encode_documents`, with the latter to be stored in VDB
+ - dot product of `encode_queries(q)` and `encode_documents([D_0, D_1, ...])` is the BM25 score of the documents `[D_0, D_1, ...]` for the given query `q`
+ - we train a BM25 encoder's normalization parameters on a sufficiently large corpus to capture target language distribution
+ - these trained parameter allow us to balance TF & IDF of query & documents for retrieval (read more on how BM25 fixes issues with TF-IDF)
+
+ ATIRE Paper: https://www.cs.otago.ac.nz/research/student-publications/atire-opensource.pdf
+ Pinecone Implementation: https://github.com/pinecone-io/pinecone-text/blob/8399f9ff28c4652766c35165c0db9b0eff309077/pinecone_text/sparse/bm25_encoder.py
+
+ :param k1: normalizer parameter that limits how much a single query term `q_i ∈ q` can affect score for document `D_n`
+ :type k1: float
+ :param b: normalizer parameter that balances the effect of a single document length compared to the average document length
+ :type b: float
+ :param corpus_size: number of documents in the trained corpus
+ :type corpus_size: int, optional
+ :param _avg_doc_len: float representing the average document length in the trained corpus
+ :type _avg_doc_len: float, optional
+ :param _documents_containing_word: (1, tokenizer.vocab_size) shaped array, denoting how many documents contain `token ∈ vocab`
+ :type _documents_containing_word: class:`numpy.ndarray`, optional
+
+ """
+
type: str = "sparse"
+ k1: float = 1.5
+ b: float = 0.75
+ corpus_size: int | None = None
+ _tokenizer: BaseTokenizer | None
+ _avg_doc_len: np.float64 | float | None
+ _documents_containing_word: np.ndarray | None
def __init__(
self,
+ tokenizer: BaseTokenizer | None = None,
name: str | None = None,
+ k1: float = 1.5,
+ b: float = 0.75,
+ corpus_size: int | None = None,
+ avg_doc_len: float | None = None,
use_default_params: bool = True,
- ):
+ ) -> None:
if name is None:
name = "bm25"
super().__init__(name=name)
- try:
- from pinecone_text.sparse import BM25Encoder as encoder
- except ImportError:
- raise ImportError(
- "Please install pinecone-text to use BM25Encoder. "
- "You can install it with: `pip install 'semantic-router[hybrid]'`"
- )
- self.model = encoder()
-
- if use_default_params:
- logger.info("Downloading and initializing default sBM25 model parameters.")
- self.model = encoder.default()
- 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):
- indices = doc_freq["indices"]
- self.idx_mapping = {int(idx): i for i, idx in enumerate(indices)}
+ self.k1 = k1
+ self.b = b
+ self.corpus_size = corpus_size
+ self._avg_doc_len = np.float64(avg_doc_len) if avg_doc_len else None
+ if use_default_params and not tokenizer:
+ logger.info("Initializing default BM25 model parameters.")
+ self._tokenizer = PretrainedTokenizer("bert-base-uncased")
+ elif tokenizer is not None:
+ self._tokenizer = tokenizer
else:
- raise TypeError("Expected a dictionary for 'doc_freq'")
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+
+ 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 fit(self, routes: List[Route]):
+ def fit(self, routes: List[Route]) -> "BM25Encoder":
"""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.")
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ self._fit_validate(routes)
utterances = [utterance for route in routes for utterance in route.utterances]
- self.model.fit(corpus=utterances)
+ utterance_ids = self._tokenizer.tokenize(utterances, pad=True)
+ corpus = self._tf(utterance_ids)
- def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
- 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)
- elif len(docs) > 1:
- sparse_dicts = self.model.encode_documents(docs)
- else:
- raise ValueError("No documents to encode.")
+ self.corpus_size = len(utterances)
- embeds = []
- for i, output in enumerate(sparse_dicts):
- if isinstance(output, dict):
- embeds.append(SparseEmbedding.from_pinecone_dict(output))
- return embeds
+ # Calculate document lengths and average
+ doc_lengths = corpus.sum(axis=1)
+ self._avg_doc_len = doc_lengths.mean()
+
+ # Calculate document frequencies
+ documents_containing_word = np.atleast_2d((corpus > 0).sum(axis=0))
+
+ documents_containing_word[:, 0] *= 0
+ self._documents_containing_word = documents_containing_word
+
+ return self
+
+ def _tf(self, docs: np.ndarray) -> np.ndarray:
+ """Returns term frequency of query terms in trained corpus
+
+ :param docs: 2D shaped array of each document's token ids
+ :type docs: :class:`numpy.ndarray`
+ :return: Matrix where value @ (m, n) represents how many times token id `n` appears in document `m`
+ :rtype: :class:`numpy.ndarray`
+ """
+ if self._tokenizer is None:
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+ vocab_size = self._tokenizer.vocab_size
+ # `np.bincount` doesn't return a consistent shape, we need to ensure minlength
+ bincount = partial(np.bincount, minlength=vocab_size)
+ # Bincount returns element count
+ # e.g. [0, 1, 1, 3, 1] => [1, 3, 0, 1]
+ tf = np.apply_along_axis(bincount, 1, docs)
+
+ # only change the values of existing non-zero terms, so that the operation doesn't change the sparsity of the matrix, and we don't get a warning that our operation is expensive
+ # We use `0` as a padding, so ignore it's term frequency
+ tf[:, 0] *= 0 # type: ignore
+ return tf
+
+ def _df(self, queries: np.ndarray) -> np.ndarray:
+ """Returns the amount of times each token in the query appears in trained corpus
+
+ This is done in a faster, vectorized way, instead of looping through each query
+
+ :param queries: 2D shaped array of each query token ids
+ :type queries: :class:`numpy.ndarray`
+ :return: Matrix where value @ (m, n) represents how many times token id `n` in query `m` appears in the trained corpus
+ :rtype: :class:`numpy.ndarray`
+ """
+ if self._documents_containing_word is None:
+ raise ValueError(
+ "Encoder not fitted. `BM25Encoder.fit` a corpus, or `BM25Encoder.load` a pretrained encoder."
+ )
+ if self._tokenizer is None:
+ raise ValueError(
+ "Tokenizer not provided. Provide a tokenizer or set `use_default_params` to True"
+ )
+ n = queries.shape[0]
+ # Create row indices -> [[0], [1], [2], ...] (n, 1) shaped matrix
+ row_indices = np.arange(n)[:, None]
+
+ # Create a (len(queries), vocab_size) mask
+ mask = np.zeros(
+ (n, self._tokenizer.vocab_size), dtype=bool
+ ) # (n_queries, vocab_size)
+ # Fill the mask with `True` only for the token ids that appear in each query
+ mask[row_indices, queries] = True
+
+ # Repeat the `(1, vocab_size)` shaped _documents_containing_word matrix `len(queries)` times -> (len(queries), vocab_size) shape
+ # Multiply the repeated matrix with the mask to result in the document frequency of each token in each query
+ query_df = mask * self._documents_containing_word
+ return query_df
+
+ def encode_queries(self, queries: list[str]) -> list[SparseEmbedding]:
+ """Returns BM25 scores for queries using precomputed corpus scores.
+
+ :param queries: List of queries to encode
+ :type queries: list
+ :return: BM25 scores for each query against the corpus
+ :rtype: np.ndarray
+ """
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ if queries == []:
+ raise ValueError("No documents provided for encoding")
+
+ # Convert queries to token counts
+ queries_ids = self._tokenizer.tokenize(queries)
+ df = self._df(queries_ids) # (batch_size, vocab_size)
+ N = self.corpus_size
+ df = df + np.where(df > 0, 0.5, 0)
+ idf = np.divide(N + 1, df, out=np.zeros_like(df), where=df != 0)
+ idf = np.log(
+ idf, out=np.zeros_like(df), where=df != 0
+ ) # (batch_size, vocab_size)
+ idf_norm = np.divide(
+ idf, idf.sum(axis=1)[:, np.newaxis], out=np.zeros_like(idf), where=idf != 0
+ )
+
+ return self._array_to_sparse_embeddings(idf_norm)
+
+ def encode_documents(
+ self,
+ documents: list[str],
+ batch_size: int | None = None,
+ ) -> list[SparseEmbedding]:
+ r"""Returns document term frequency normed by itself & average trained corpus length
+ (This is the right-hand side of the BM25 equation, which gets matmul-ed with the query IDF component)
+
+ LaTeX: \frac{f(d_i, D)}{f(d_i, D) + k_1 \times (1 - b + b \times \frac{|D|}{avgdl})}
+ where:
+ f(d_i, D) is frequency of term `d_i ∈ D`
+ |D| is the document length
+ avgdl is average document length in trained corpus
+
+ :param documents: List of queries to encode
+ :type documents: list
+ :return: Encoded queries (as either sparse or dict)
+ :rtype: :class:`scipy.sparse.csr_matrix`, list[dict]
+ """
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+ if not self._tokenizer:
+ raise ValueError(
+ "BM25 encoder not initialized. Provide a tokenizer or set `use_default_params` to True"
+ )
+ if documents == []:
+ raise ValueError("No documents provided for encoding")
+ batch_size = batch_size or len(documents)
+ queries_ids = self._tokenizer.tokenize(documents, pad=True)
+ tf = self._tf(queries_ids) # (batch_size, vocab_size)
+ tf_sum = tf.sum(axis=1) # (batch_size, 1)
+ tf_normed = tf / (
+ self.k1
+ * (1.0 - self.b * self.b * (tf_sum[:, np.newaxis] / self._avg_doc_len))
+ + tf
+ ) # (batch_size, vocab_size)
+
+ return self._array_to_sparse_embeddings(tf_normed)
+
+ def model(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Encode documents using BM25, with different encoding for queries vs documents to be indexed.
+
+ :param docs: List of documents to encode
+ :param is_query: If True, use query encoding, else use document encoding
+ :return: List of sparse embeddings
+ """
+ if not self._tokenizer:
+ raise ValueError(
+ "Encoder not fitted. `BM25.index` a corpus, or `BM25.load` a pretrained encoder."
+ )
+ if (
+ self.corpus_size is None
+ or self._avg_doc_len is None
+ or self._documents_containing_word is None
+ ):
+ raise ValueError(
+ "Encoder not fitted. Please `.fit` the model on a provided corpus or load a pretrained encoder"
+ )
+
+ return self.encode_queries(docs)
+
+ def __call__(self, docs: List[str]) -> list[SparseEmbedding]:
+ return self.encode_queries(docs)
diff --git a/semantic_router/encoders/huggingface.py b/semantic_router/encoders/huggingface.py
index a62102e5f72960d8c6e3f141eb43f01c48689577..3cd973b3a64a08da1a68780e9f042e6f9f8313cb 100644
--- a/semantic_router/encoders/huggingface.py
+++ b/semantic_router/encoders/huggingface.py
@@ -1,9 +1,9 @@
"""
This module provides the HFEndpointEncoder class to embeddings models using Huggingface's endpoint.
-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
+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.
Example usage:
@@ -71,7 +71,7 @@ class HuggingFaceEncoder(DenseEncoder):
def _initialize_hf_model(self):
try:
from transformers import AutoModel, AutoTokenizer
- except ImportError:
+ except (ImportError, RuntimeError):
raise ImportError(
"Please install transformers to use HuggingFaceEncoder. "
"You can install it with: "
@@ -80,7 +80,7 @@ class HuggingFaceEncoder(DenseEncoder):
try:
import torch
- except ImportError:
+ except RuntimeError:
raise ImportError(
"Please install Pytorch to use HuggingFaceEncoder. "
"You can install it with: "
diff --git a/semantic_router/encoders/tfidf.py b/semantic_router/encoders/tfidf.py
index 656ee117e54b60a293c0b425452065258bb5db96..0d6642b7fffaf55e0e31dbe6ed85ff042148ea6b 100644
--- a/semantic_router/encoders/tfidf.py
+++ b/semantic_router/encoders/tfidf.py
@@ -5,11 +5,12 @@ from typing import Dict, List
import numpy as np
from semantic_router.encoders import SparseEncoder
+from semantic_router.encoders.base import FittableMixin
from semantic_router.route import Route
from semantic_router.schema import SparseEmbedding
-class TfidfEncoder(SparseEncoder):
+class TfidfEncoder(SparseEncoder, FittableMixin):
idf: np.ndarray = np.array([])
# TODO: add option to use default params like with BM25Encoder
word_index: Dict = {}
@@ -32,6 +33,22 @@ class TfidfEncoder(SparseEncoder):
tfidf = tf * self.idf
return self._array_to_sparse_embeddings(tfidf)
+ def encode_queries(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Encode documents using TF-IDF"""
+ return self.__call__(docs) # TF-IDF uses same method for docs and queries
+
+ def encode_documents(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Encode documents using TF-IDF"""
+ return self.__call__(docs) # TF-IDF uses same method for docs and queries
+
+ async def aencode_queries(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Async version of encode_queries"""
+ return self.__call__(docs)
+
+ async def aencode_documents(self, docs: List[str]) -> list[SparseEmbedding]:
+ """Async version of encode_documents"""
+ return self.__call__(docs)
+
def fit(self, routes: List[Route]):
"""Trains the encoder weights on the provided routes.
diff --git a/semantic_router/routers/hybrid.py b/semantic_router/routers/hybrid.py
index 6bc202f511573c3dd4bfb3d89fbd7352b6366da6..9c6680c6fa68db65c4fa433d6248f60c1934b04c 100644
--- a/semantic_router/routers/hybrid.py
+++ b/semantic_router/routers/hybrid.py
@@ -9,8 +9,8 @@ from semantic_router.encoders import (
BM25Encoder,
DenseEncoder,
SparseEncoder,
- TfidfEncoder,
)
+from semantic_router.encoders.base import FittableMixin
from semantic_router.index import BaseIndex, HybridLocalIndex
from semantic_router.llms import BaseLLM
from semantic_router.route import Route
@@ -51,6 +51,9 @@ class HybridRouter(BaseRouter):
encoder = self._get_encoder(encoder=encoder)
# initialize sparse encoder
sparse_encoder = self._get_sparse_encoder(sparse_encoder=sparse_encoder)
+ # fit sparse encoder if needed
+ if isinstance(sparse_encoder, FittableMixin) and routes:
+ sparse_encoder.fit(routes)
super().__init__(
encoder=encoder,
sparse_encoder=sparse_encoder,
@@ -63,13 +66,6 @@ class HybridRouter(BaseRouter):
)
# set alpha
self.alpha = alpha
- # 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)
def _set_score_threshold(self):
"""Set the score threshold for the HybridRouter. Unlike the base router the
@@ -92,6 +88,9 @@ class HybridRouter(BaseRouter):
:param route: The route to add.
:type route: Route
"""
+
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse Encoder not initialised.")
# TODO: merge into single method within BaseRouter
current_local_hash = self._get_hash()
current_remote_hash = self.index._read_hash()
@@ -100,6 +99,10 @@ class HybridRouter(BaseRouter):
current_remote_hash = current_local_hash
if isinstance(routes, Route):
routes = [routes]
+
+ self.routes.extend(routes)
+ if isinstance(self.sparse_encoder, FittableMixin) and self.routes:
+ self.sparse_encoder.fit(self.routes)
# create embeddings for all routes
(
route_names,
@@ -119,7 +122,6 @@ class HybridRouter(BaseRouter):
sparse_embeddings=sparse_emb,
)
- self.routes.extend(routes)
if current_local_hash.value == current_remote_hash.value:
self._write_hash() # update current hash in index
else:
@@ -136,6 +138,8 @@ class HybridRouter(BaseRouter):
:param strategy: The sync strategy to execute.
:type strategy: Dict[str, Dict[str, List[Utterance]]]
"""
+ if self.sparse_encoder is None:
+ raise ValueError("Sparse Encoder not initialised.")
if strategy["remote"]["delete"]:
data_to_delete = {} # type: ignore
for utt_obj in strategy["remote"]["delete"]:
@@ -150,7 +154,8 @@ class HybridRouter(BaseRouter):
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
+ utt.function_schemas # type: ignore
+ for utt in strategy["remote"]["upsert"]
],
metadata_list=[utt.metadata for utt in strategy["remote"]["upsert"]],
sparse_embeddings=sparse_emb,
@@ -161,6 +166,8 @@ class HybridRouter(BaseRouter):
self._local_upsert(utterances=strategy["local"]["upsert"])
# update hash
self._write_hash()
+ if isinstance(self.sparse_encoder, FittableMixin) and self.routes:
+ self.sparse_encoder.fit(self.routes)
def _get_index(self, index: Optional[BaseIndex]) -> BaseIndex:
"""Get the index.
@@ -179,7 +186,7 @@ class HybridRouter(BaseRouter):
def _get_sparse_encoder(
self, sparse_encoder: Optional[SparseEncoder]
- ) -> Optional[SparseEncoder]:
+ ) -> SparseEncoder:
"""Get the sparse encoder.
:param sparse_encoder: The sparse encoder to get.
@@ -194,31 +201,33 @@ class HybridRouter(BaseRouter):
sparse_encoder = sparse_encoder
return sparse_encoder
- def _encode(self, text: list[str]) -> tuple[np.ndarray, list[SparseEmbedding]]:
+ 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.
- :param text: The text to encode.
- :type text: list[str]
- :return: A tuple of the dense and sparse embeddings.
- :rtype: tuple[np.ndarray, list[SparseEmbedding]]
+ :param text: List of texts to encode
+ :type text: List[str]
+ :return: Tuple of dense and sparse embeddings
"""
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
+
+ # Create dense 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 = self.sparse_encoder(text)
- # xq_s = np.squeeze(xq_s)
- # convex scaling
+
+ # Create sparse vector, handling BM25's query/document distinction
+ xq_s = self.sparse_encoder.encode_documents(text)
+
+ # Convex scaling
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]
+ 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.
@@ -234,7 +243,7 @@ class HybridRouter(BaseRouter):
# 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)
+ sparse_coro = self.sparse_encoder.aencode_documents(text)
dense_vec, xq_s = await asyncio.gather(dense_coro, sparse_coro)
# create dense query vector
xq_d = np.array(dense_vec)
@@ -265,12 +274,18 @@ class HybridRouter(BaseRouter):
"""
if not self.index.is_ready():
raise ValueError("Index is not ready.")
+ if self.sparse_encoder is None:
+ raise ValueError
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])
+ xq_d = np.array(self.encoder([text]))
+ xq_s = self.sparse_encoder.encode_queries([text])
+ vector, potential_sparse_vector = self._convex_scaling(
+ dense=xq_d, sparse=xq_s
+ )
# convert to numpy array if not already
vector = xq_reshape(vector)
if sparse_vector is None:
@@ -363,7 +378,7 @@ class HybridRouter(BaseRouter):
utterances.append(utterance.utterance)
metadata.append(utterance.metadata)
embeddings = self.encoder(utterances)
- sparse_embeddings = self.sparse_encoder(utterances)
+ sparse_embeddings = self.sparse_encoder.encode_documents(utterances)
self.index = HybridLocalIndex()
self.index.add(
embeddings=embeddings,
@@ -380,7 +395,7 @@ class HybridRouter(BaseRouter):
emb_d = np.array(self.encoder(X[i : i + batch_size]))
# TODO JB: for some reason the sparse encoder is receiving a tuple
# like `("Hello",)`
- emb_s = self.sparse_encoder(X[i : i + batch_size])
+ emb_s = self.sparse_encoder.encode_documents(X[i : i + batch_size])
Xq_d.extend(emb_d)
Xq_s.extend(emb_s)
# initial eval (we will iterate from here)
@@ -427,7 +442,7 @@ class HybridRouter(BaseRouter):
Xq_s: List[SparseEmbedding] = []
for i in tqdm(range(0, len(X), batch_size), desc="Generating embeddings"):
emb_d = np.array(self.encoder(X[i : i + batch_size]))
- emb_s = self.sparse_encoder(X[i : i + batch_size])
+ emb_s = self.sparse_encoder.encode_queries(X[i : i + batch_size])
Xq_d.extend(emb_d)
Xq_s.extend(emb_s)
diff --git a/semantic_router/schema.py b/semantic_router/schema.py
index fa6cdd2d40f50a103c69dc7bc2e0039d47f20d50..d5a68081169fd516ce68f8944002efeaffc8d809 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -5,10 +5,10 @@ from enum import Enum
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
+from aurelio_sdk.schema import SparseEmbedding as BM25SparseEmbedding
from pydantic import BaseModel, Field
from semantic_router.utils.logger import logger
-from aurelio_sdk.schema import SparseEmbedding as BM25SparseEmbedding
class EncoderType(Enum):
diff --git a/semantic_router/tokenizers.py b/semantic_router/tokenizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c1d5b34d3b6c235c541d71fc70cb95eb6e103d5
--- /dev/null
+++ b/semantic_router/tokenizers.py
@@ -0,0 +1,169 @@
+import json
+from pathlib import Path
+
+import numpy as np
+from tokenizers import Tokenizer
+from tokenizers.normalizers import Sequence
+
+
+class BaseTokenizer:
+ """Abstract Tokenizer class"""
+
+ @property
+ def vocab_size(self) -> int:
+ """Returns the vocabulary size of the tokenizer
+
+ :return: Vocabulary size of tokenizer
+ :rtype: int
+ """
+ raise NotImplementedError
+
+ @property
+ def config(self) -> dict:
+ """The tokenizer config
+
+ :return: dictionary of tokenizer config
+ :rtype: dict
+ """
+ raise NotImplementedError
+
+ def save(self, path: str | Path) -> None:
+ """Saves the configuration of the tokenizer
+
+ Saves these files:
+ - tokenizer.json: saved configuration of the tokenizer
+
+ :param path: Path to save the tokenizer to
+ :type path: str, :class:`pathlib.Path`
+ """
+ if isinstance(path, str):
+ path = Path(path)
+
+ with open(path, "w") as fp:
+ json.dump(self.config, fp)
+
+ @classmethod
+ def load(cls, path: str | Path) -> "BaseTokenizer":
+ """Returns a :class:`bm25_engine.tokenizer.BaseTokenizer` object from saved configuration
+
+ Requires these files:
+ - tokenizer.json: saved configuration of the tokenizer
+
+ :param path: Path to load the tokenizer from
+ :type path: str, :class:`pathlib.Path`
+ :returns: Configured BaseTokenizer
+ :rtype: BaseTokenizer
+ """
+ if isinstance(path, str):
+ path = Path(path)
+
+ with open(path) as fp:
+ config = json.load(fp)
+ return cls(**config)
+
+ def tokenize(self, texts: str | list[str], pad: bool = True) -> np.ndarray:
+ raise NotImplementedError
+
+
+class HashTokenizer(BaseTokenizer):
+ def __init__(self) -> None:
+ super().__init__()
+
+
+class PretrainedTokenizer(BaseTokenizer):
+ """Wrapper for HuggingFace tokenizers, representing a pretrained tokenizer (i.e. bert-base-uncased).
+ Extends the :class:`semantic_router.tokenizers.BaseTokenizer` class.
+
+ :param tokenizer: Binding for HuggingFace Rust tokenizers
+ :type tokenizer: class:`tokenizers.Tokenizer`
+ :param add_special_tokens: Whether to accept special tokens from the tokenizer (i.e. `[PAD]`)
+ :type add_special_tokens: bool
+ :param pad: Whether to pad the input to a consistent length (using `[PAD]` tokens)
+ :type pad: bool
+ :param model_ident: HuggingFace ID of the model (i.e. `bert-base-uncased`)
+ :type model_ident: str
+ """
+
+ tokenizer: Tokenizer
+ add_special_tokens: bool
+ pad: bool
+ model_ident: str
+
+ def __init__(
+ self,
+ model_ident: str,
+ custom_normalizer: Sequence | None = None,
+ add_special_tokens: bool = False,
+ pad: bool = True,
+ ) -> None:
+ """Constructor method"""
+ super().__init__()
+ self.add_special_tokens = add_special_tokens
+ self.model_ident = model_ident
+ self.tokenizer = Tokenizer.from_pretrained(model_ident)
+ self.pad = pad
+ if custom_normalizer:
+ self.tokenizer.normalizer = custom_normalizer # type: ignore
+ if pad:
+ self.tokenizer.enable_padding(direction="right", pad_id=0)
+
+ @property
+ def vocab_size(self):
+ """Returns the vocabulary size of the tokenizer
+
+ :return: Vocabulary size of tokenizer
+ :rtype: int
+ """
+ return self.tokenizer.get_vocab_size()
+
+ @property
+ def config(self) -> dict:
+ """The tokenizer config
+
+ :return: dictionary of tokenizer config
+ :rtype: dict
+ """
+ return {
+ "model_ident": self.model_ident,
+ "add_special_tokens": self.add_special_tokens,
+ "pad": self.pad,
+ }
+
+ def tokenize(self, texts: str | list[str], pad: bool = True) -> np.ndarray:
+ """Tokenizes a string or list of strings into a 2D :class:`numpy.ndarray` of token ids
+
+ :param texts: Texts to be tokenized
+ :type texts: str, list
+ :param pad: unused here (configured in the constructor)
+ :type pad: bool
+ :return: 2D numpy array representing token ids
+ :rtype: class:`numpy.ndarray`
+ """
+ if isinstance(texts, str):
+ texts = [texts]
+
+ encodings = self.tokenizer.encode_batch_fast(
+ texts, add_special_tokens=self.add_special_tokens
+ )
+
+ return np.array([e.ids for e in encodings])
+
+
+class TokenizerFactory:
+ """Tokenizer factory class"""
+
+ @staticmethod
+ def get(type_: str, **tokenizer_kwargs) -> BaseTokenizer:
+ r"""Get a configured :class:`bm25_engine.tokenizer.BaseTokenizer`
+
+ :param type_: Tokenizer type to instantiate
+ :type type_: str
+ :param \**kwargs: kwargs to be passed to Tokenizer constructor
+ :return: Tokenizer
+ :rtype: `bm25_engine.tokenizer.BaseTokenizer`
+ """
+ match type_:
+ case "pretrained":
+ return PretrainedTokenizer(**tokenizer_kwargs)
+ case _:
+ return PretrainedTokenizer(**tokenizer_kwargs)
diff --git a/tests/functional/encoders/test_bm25_functional.py b/tests/functional/encoders/test_bm25_functional.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e8fc1114a3243f673dd1cf120604bacce8c1eaa
--- /dev/null
+++ b/tests/functional/encoders/test_bm25_functional.py
@@ -0,0 +1,55 @@
+import numpy as np
+import pytest
+
+from semantic_router import Route
+from semantic_router.encoders.bm25 import BM25Encoder
+
+UTTERANCES = [
+ "A high weight in tf–idf is reached by a high term frequency",
+ "(in the given document) and a low document frequency of the term",
+ "in the whole collection of documents; the weights hence tend to filter",
+ "out common terms. Since the ratio inside the idf's log function is always",
+ "greater than or equal to 1, the value of idf (and tf–idf) is greater than or equal",
+ "to 0. As a term appears in more documents, the ratio inside the logarithm approaches",
+ "1, bringing the idf and tf–idf closer to 0.",
+]
+QUERIES = ["weights", "ratio logarithm"]
+
+
+@pytest.fixture
+def bm25_encoder():
+ sparse_encoder = BM25Encoder(use_default_params=True)
+ sparse_encoder.fit([Route(name="test_route", utterances=UTTERANCES)])
+ return sparse_encoder
+
+
+class TestBM25Encoder:
+ def _sparse_to_vector(self, sparse_embedding, vocab_size):
+ """Re-constructs the full (sparse_embedding.shape[0], vocab_size) array"""
+ return (
+ np.eye(vocab_size)[sparse_embedding[:, 0].astype(np.uint).tolist()]
+ * np.atleast_2d(sparse_embedding[:, 1]).T
+ ).sum(axis=0)
+
+ def test_bm25_scoring(self, bm25_encoder):
+ vocab_size = bm25_encoder._tokenizer.vocab_size
+ expected = np.array(
+ [
+ [0.00000, 0.00000, 0.54575, 0.00000, 0.00000, 0.00000, 0.00000],
+ [0.00000, 0.00000, 0.00000, 0.18864, 0.00000, 0.67897, 0.00000],
+ ]
+ )
+ q_e = np.stack(
+ [
+ self._sparse_to_vector(v.embedding, vocab_size=vocab_size)
+ for v in bm25_encoder.encode_queries(QUERIES)
+ ]
+ )
+ d_e = np.stack(
+ [
+ self._sparse_to_vector(v.embedding, vocab_size=vocab_size)
+ for v in bm25_encoder.encode_documents(UTTERANCES)
+ ]
+ )
+ scores = q_e @ d_e.T
+ assert np.allclose(scores, expected, rtol=1e-4), expected
diff --git a/tests/unit/encoders/test_bm25.py b/tests/unit/encoders/test_bm25.py
index 6c2133482f8a54f543b199d5de58963439e61dca..408412eb5b64e9306279d1f7eaf61889e7d25dd3 100644
--- a/tests/unit/encoders/test_bm25.py
+++ b/tests/unit/encoders/test_bm25.py
@@ -1,12 +1,9 @@
-import nltk
import numpy as np
import pytest
from semantic_router.encoders import BM25Encoder
from semantic_router.route import Route
-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.",
@@ -18,7 +15,7 @@ UTTERANCES = [
@pytest.fixture
def bm25_encoder():
- sparse_encoder = BM25Encoder(use_default_params=False)
+ sparse_encoder = BM25Encoder(use_default_params=True)
sparse_encoder.fit(
[
Route(
@@ -44,11 +41,11 @@ def routes():
class TestBM25Encoder:
def test_initialization(self, bm25_encoder):
- assert bm25_encoder.model is not None
+ assert bm25_encoder._tokenizer is not None
def test_fit(self, bm25_encoder, routes):
bm25_encoder.fit(routes)
- assert bm25_encoder.model is not None
+ assert bm25_encoder._tokenizer is not None
def test_fit_with_strings(self, bm25_encoder):
route_strings = ["test a", "test b", "test c"]
@@ -74,11 +71,51 @@ class TestBM25Encoder:
), "Each item in result should be an array"
def test_call_method_with_uninitialized_model_or_mapping(self, bm25_encoder):
- bm25_encoder.model = None
+ bm25_encoder._tokenizer = None
with pytest.raises(ValueError):
bm25_encoder(["test"])
def test_fit_with_uninitialized_model(self, bm25_encoder, routes):
- bm25_encoder.model = None
+ bm25_encoder._tokenizer = None
with pytest.raises(ValueError):
bm25_encoder.fit(routes)
+
+ def test_encode_queries(self, bm25_encoder):
+ queries = ["quick brown", "lazy dog", "hello world"]
+ results = bm25_encoder.encode_queries(queries)
+
+ assert len(results) == len(queries)
+ assert all([isinstance(result.embedding, np.ndarray) for result in results])
+
+ def test_encode_queries_empty_list(self, bm25_encoder):
+ with pytest.raises(ValueError, match="No documents provided for encoding"):
+ bm25_encoder.encode_queries([])
+
+ def test_encode_queries_unfitted(self):
+ encoder = BM25Encoder(use_default_params=True)
+ with pytest.raises(ValueError, match="Encoder not fitted"):
+ encoder.encode_queries(["test query"])
+
+ def test_encode_documents(self, bm25_encoder):
+ documents = ["quick brown", "lazy dog", "hello world"]
+ results = bm25_encoder.encode_documents(documents)
+
+ assert len(results) == len(documents)
+ assert all([isinstance(result.embedding, np.ndarray) for result in results])
+
+ def test_encode_documents_empty_list(self, bm25_encoder):
+ with pytest.raises(ValueError, match="No documents provided for encoding"):
+ bm25_encoder.encode_documents([])
+
+ def test_encode_documents_unfitted(self):
+ encoder = BM25Encoder(use_default_params=True)
+ with pytest.raises(ValueError, match="Encoder not fitted"):
+ encoder.encode_documents(["test document"])
+
+ def test_encode_documents_batch_size(self, bm25_encoder):
+ documents = ["quick brown", "lazy dog", "hello world", "test document"]
+ batch_size = 2
+ results = bm25_encoder.encode_documents(documents, batch_size=batch_size)
+
+ assert len(results) == len(documents)
+ assert all(isinstance(result.embedding, np.ndarray) for result in results)
diff --git a/tests/unit/encoders/test_huggingface.py b/tests/unit/encoders/test_huggingface.py
index 1677a450037285885034b73efbc9e26dd4d2b68f..76f28f5bb8564de0c434c778b09a80200c913b4d 100644
--- a/tests/unit/encoders/test_huggingface.py
+++ b/tests/unit/encoders/test_huggingface.py
@@ -26,7 +26,9 @@ class TestHuggingFaceEncoder:
with pytest.raises(ImportError) as error:
HuggingFaceEncoder()
- assert "Please install Pytorch to use HuggingFaceEncoder" in str(error.value)
+ assert "Please install transformers to use HuggingFaceEncoder" in str(
+ error.value
+ )
@pytest.mark.skipif(
os.environ.get("RUN_HF_TESTS") is None, reason="Set RUN_HF_TESTS=1 to run"
diff --git a/tests/unit/test_router.py b/tests/unit/test_router.py
index 5f90be3d1662e95d13a6ae3e9bfd60663f6c0dea..fca6735a76bc46a7122d328130aaa9909f4e688c 100644
--- a/tests/unit/test_router.py
+++ b/tests/unit/test_router.py
@@ -282,9 +282,7 @@ def get_test_encoders():
def get_test_routers():
- routers = [SemanticRouter]
- if importlib.util.find_spec("pinecone_text") is not None:
- routers.append(HybridRouter)
+ routers = [SemanticRouter, HybridRouter]
return routers
diff --git a/tests/unit/test_sync.py b/tests/unit/test_sync.py
index be99f282b2b839df9ff8e2614bdb384a2a1240bb..9f41108b2c334ac51af3376aac215c7f95f7f7ab 100644
--- a/tests/unit/test_sync.py
+++ b/tests/unit/test_sync.py
@@ -318,9 +318,7 @@ def get_test_indexes():
def get_test_routers():
- routers = [SemanticRouter]
- if importlib.util.find_spec("pinecone_text") is not None:
- routers.append(HybridRouter)
+ routers = [SemanticRouter, HybridRouter]
return routers
diff --git a/tests/unit/test_tokenizers.py b/tests/unit/test_tokenizers.py
new file mode 100644
index 0000000000000000000000000000000000000000..4757ffb82f4fef5ac2b241002cce88f20a13ce3a
--- /dev/null
+++ b/tests/unit/test_tokenizers.py
@@ -0,0 +1,98 @@
+import json
+import tempfile
+
+import numpy as np
+import pytest
+
+from semantic_router.tokenizers import (
+ BaseTokenizer,
+ PretrainedTokenizer,
+)
+
+
+class TestBaseTokenizer:
+ def test_abstract_methods(self):
+ class ConcreteTokenizer(BaseTokenizer):
+ pass
+
+ tokenizer = ConcreteTokenizer()
+ with pytest.raises(NotImplementedError):
+ _ = tokenizer.vocab_size
+ with pytest.raises(NotImplementedError):
+ _ = tokenizer.config
+ with pytest.raises(NotImplementedError):
+ tokenizer.tokenize("test")
+
+ def test_save_load(self):
+ class ConcreteTokenizer(BaseTokenizer):
+ def __init__(self, test_param) -> None:
+ self.test_param = test_param
+ super().__init__()
+
+ @property
+ def vocab_size(self):
+ return 100
+
+ @property
+ def config(self):
+ return {"test_param": self.test_param}
+
+ def tokenize(self, texts, pad=True):
+ pass
+
+ with tempfile.NamedTemporaryFile(suffix=".json") as tmp:
+ tokenizer = ConcreteTokenizer(test_param="value")
+ tokenizer.save(tmp.name)
+
+ loaded = ConcreteTokenizer.load(tmp.name)
+ assert isinstance(loaded, ConcreteTokenizer)
+ with open(tmp.name) as f:
+ saved_config = json.load(f)
+ assert saved_config == {"test_param": "value"}
+
+
+class TestPretrainedTokenizer:
+ @pytest.fixture
+ def tokenizer(self):
+ return PretrainedTokenizer("bert-base-uncased")
+
+ def test_initialization(self, tokenizer):
+ assert tokenizer.model_ident == "bert-base-uncased"
+ assert tokenizer.add_special_tokens is False
+ assert tokenizer.pad is True
+
+ def test_vocab_size(self, tokenizer):
+ assert isinstance(tokenizer.vocab_size, int)
+ assert tokenizer.vocab_size > 0
+
+ def test_config(self, tokenizer):
+ config = tokenizer.config
+ assert isinstance(config, dict)
+ assert "model_ident" in config
+ assert "add_special_tokens" in config
+ assert "pad" in config
+
+ def test_tokenize_single_text(self, tokenizer):
+ text = "Hello world"
+ tokens = tokenizer.tokenize(text)
+ assert isinstance(tokens, np.ndarray)
+ assert tokens.ndim == 2
+ assert tokens.shape[0] == 1 # One sequence
+ assert tokens.shape[1] > 0 # At least one token
+
+ def test_tokenize_multiple_texts(self, tokenizer):
+ texts = ["Hello world", "Testing tokenization"]
+ tokens = tokenizer.tokenize(texts)
+ assert isinstance(tokens, np.ndarray)
+ assert tokens.ndim == 2
+ assert tokens.shape[0] == 2 # Two sequences
+
+ def test_save_load_cycle(self, tokenizer):
+ with tempfile.NamedTemporaryFile(suffix=".json") as tmp:
+ tokenizer.save(tmp.name)
+ loaded = PretrainedTokenizer.load(tmp.name)
+
+ assert isinstance(loaded, PretrainedTokenizer)
+ assert loaded.model_ident == tokenizer.model_ident
+ assert loaded.add_special_tokens == tokenizer.add_special_tokens
+ assert loaded.pad == tokenizer.pad
diff --git a/uv.lock b/uv.lock
index d0bdc658d5ac61e6057cee985c022e83dc4434d0..1daed64e35d7cc5058f114d412cbe737a7e22b30 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1556,15 +1556,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980", size = 20256 },
]
-[[package]]
-name = "joblib"
-version = "1.4.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/64/33/60135848598c076ce4b231e1b1895170f45fbcaeaa2c9d5e38b04db70c35/joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e", size = 2116621 }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6", size = 301817 },
-]
-
[[package]]
name = "jupyter-client"
version = "8.6.3"
@@ -1955,6 +1946,7 @@ wheels = [
]
[[package]]
+<<<<<<< HEAD
name = "nltk"
version = "3.9.1"
source = { registry = "https://pypi.org/simple" }
@@ -2001,6 +1993,24 @@ wheels = [
]
[[package]]
+||||||| parent of bb324f0 (feat: implements vectorised sparse BM25 encoder)
+name = "nltk"
+version = "3.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "click" },
+ { name = "joblib" },
+ { name = "regex" },
+ { name = "tqdm" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/3c/87/db8be88ad32c2d042420b6fd9ffd4a149f9a0d7f0e86b3f543be2eeeedd2/nltk-3.9.1.tar.gz", hash = "sha256:87d127bd3de4bd89a4f81265e5fa59cb1b199b27440175370f7417d2bc7ae868", size = 2904691 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/4d/66/7d9e26593edda06e8cb531874633f7c2372279c3b0f46235539fe546df8b/nltk-3.9.1-py3-none-any.whl", hash = "sha256:4fa26829c5b00715afe3061398a8989dc643b92ce7dd93fb4585a70930d168a1", size = 1505442 },
+]
+
+[[package]]
+=======
+>>>>>>> bb324f0 (feat: implements vectorised sparse BM25 encoder)
name = "numpy"
version = "1.26.4"
source = { registry = "https://pypi.org/simple" }
@@ -2358,24 +2368,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/3b/1d/a21fdfcd6d022cb64cef5c2a29ee6691c6c103c4566b41646b080b7536a5/pinecone_plugin_interface-0.0.7-py3-none-any.whl", hash = "sha256:875857ad9c9fc8bbc074dbe780d187a2afd21f5bfe0f3b08601924a61ef1bba8", size = 6249 },
]
-[[package]]
-name = "pinecone-text"
-version = "0.9.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
- { name = "mmh3" },
- { name = "nltk" },
- { name = "numpy" },
- { name = "python-dotenv" },
- { name = "requests" },
- { name = "types-requests" },
- { name = "wget" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/97/cc/06edd3aefaf566894fb4f6a8c55c5d3990660a7b822706ba1c86c1faa093/pinecone_text-0.9.0.tar.gz", hash = "sha256:f8244a9bc090ee2fc904053a2415cfc114fd8bbfbc9a6ba8b0581c84990d0239", size = 20635 }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/3f/7d/c3596a064f11ca1c26c57d1a7022f8b6e724cbf50e8c365ddfb869d47ca7/pinecone_text-0.9.0-py3-none-any.whl", hash = "sha256:7a1ad2e282d10b59f0288a2a4f4b8c583a77378172232fae573cf398a3c44240", size = 23440 },
-]
-
[[package]]
name = "platformdirs"
version = "4.3.6"
@@ -3400,7 +3392,6 @@ all = [
{ name = "mistralai" },
{ name = "pillow", marker = "python_full_version < '3.13'" },
{ name = "pinecone" },
- { name = "pinecone-text" },
{ name = "qdrant-client", version = "1.12.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
{ name = "qdrant-client", version = "1.13.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
{ name = "tokenizers", marker = "python_full_version < '3.13'" },
@@ -3424,9 +3415,6 @@ fastembed = [
google = [
{ name = "google-cloud-aiplatform" },
]
-hybrid = [
- { name = "pinecone-text" },
-]
local = [
{ name = "llama-cpp-python", marker = "python_full_version < '3.13'" },
{ name = "tokenizers", marker = "python_full_version < '3.13'" },
@@ -3482,7 +3470,6 @@ requires-dist = [
{ name = "openai", specifier = ">=1.10.0,<2.0.0" },
{ name = "pillow", marker = "python_full_version < '3.13' and extra == 'vision'", specifier = ">=10.2.0,<11.0.0" },
{ name = "pinecone", marker = "extra == 'pinecone'", specifier = ">=5.0.0,<6.0.0" },
- { name = "pinecone-text", marker = "extra == 'hybrid'", specifier = ">=0.7.1" },
{ name = "psycopg2-binary", specifier = ">=2.9.9,<3" },
{ name = "pydantic", specifier = ">=2.10.2,<3" },
{ name = "pydoc-markdown", marker = "python_full_version < '3.12' and extra == 'docs'", specifier = ">=4.8.2" },
@@ -3494,7 +3481,6 @@ requires-dist = [
{ name = "semantic-router", extras = ["cohere"], marker = "extra == 'all'" },
{ name = "semantic-router", extras = ["fastembed"], marker = "extra == 'all'" },
{ name = "semantic-router", extras = ["google"], marker = "extra == 'all'" },
- { name = "semantic-router", extras = ["hybrid"], marker = "extra == 'all'" },
{ name = "semantic-router", extras = ["local"], marker = "extra == 'all'" },
{ name = "semantic-router", extras = ["mistralai"], marker = "extra == 'all'" },
{ name = "semantic-router", extras = ["pinecone"], marker = "extra == 'all'" },
@@ -3965,12 +3951,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/fd/84/fd2ba7aafacbad3c4201d395674fc6348826569da3c0937e75505ead3528/wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859", size = 34166 },
]
-[[package]]
-name = "wget"
-version = "3.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip", hash = "sha256:35e630eca2aa50ce998b9b1a127bb26b30dfee573702782aa982f875e3f16061", size = 10857 }
-
[[package]]
name = "win32-setctime"
version = "1.2.0"