diff --git a/semantic_router/encoders/base.py b/semantic_router/encoders/base.py
index 1bcf3d9d970e044e60f6449768b2a0b409218491..dcfe9aa322addd8e6f5cdae77c958d97fce403b9 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 SparseEncoder(BaseModel):
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 4eac26e79c591cf5a0aafba42b426a75c3bfd476..0ec36499dabee336a3857efffbf9f67bb9a58248 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 @@ class BM25Encoder(SparseEncoder):
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 @@ class BM25Encoder(SparseEncoder):
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 1bec7b9f9508f76d33091d6a2c79de11e72e84ca..d9d97a473eadb06e12e0f34fa8c82cfefa6caf57 100644
--- a/semantic_router/encoders/tfidf.py
+++ b/semantic_router/encoders/tfidf.py
@@ -8,6 +8,7 @@ from numpy.linalg import norm
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 @@ class TfidfEncoder(SparseEncoder):
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 @@ class TfidfEncoder(SparseEncoder):
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 9596af5061a7bf2aef3b902d1540fcd604645e82..616416e701e432e4ce1220a00697d5256d2c5bcf 100644
--- a/semantic_router/schema.py
+++ b/semantic_router/schema.py
@@ -419,23 +419,28 @@ class SparseEmbedding(BaseModel):
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 {