numpy dependency set to optional, setup.py version set to 0.2.1

localsearch/__util__/array_utils.py

@@ -1,13 +1,14 @@
 from typing import List, Dict
 
-import numpy as np
 from numpy import matmul
 from numpy.linalg import norm
 
 from localsearch import ScoredDocument
 
 
 def cosine_similarity(a, b) -> float:
+    import numpy as np
+
     a = np.expand_dims(a, axis=0) if len(a.shape) == 1 else a
     b = np.expand_dims(b, axis=0) if len(b.shape) == 1 else b
     a_norm = a / norm(a, ord=2, axis=1, keepdims=True)

localsearch/pipeline.py

@@ -3,7 +3,6 @@
 from pathlib import Path
 from typing import List, Optional
 
-import numpy as np
 from tqdm import tqdm
 
 from localsearch.__spi__.model import RankedDocument, ScoredDocument, Documents
@@ -31,6 +30,7 @@ def search(
             query: str,
             config: SearchConfig = SearchConfig()
     ) -> List[RankedDocument]:
+        import numpy as np
 
         results = flatten([reader.search_by_text(query) for reader in self.readers])
         # results = unique(results, lambda x: x.document.id)

localsearch/searcher/annoy_search.py

@@ -3,8 +3,6 @@
 from pathlib import Path
 from typing import List, Literal, Optional, Union
 
-import numpy as np
-
 from localsearch.__spi__ import Document, Encoder, IndexedDocument, ScoredDocument
 from localsearch.__spi__.types import Searcher
 from localsearch.__util__.array_utils import cosine_similarity
@@ -45,12 +43,11 @@ def __init__(self, config: AnnoyConfig, encoder: Encoder):
             raise ValueError("no annoy library found, please install localsearch[annoy]")
 
     def search_by_text(self, text: str, n: Optional[int] = None) -> List[ScoredDocument]:
+        import numpy as np
         vector = self.encoder(text)
         indices = self.index.get_nns_by_vector(vector, n or self.config.n, self.config.search_k)
         vectors = [self.index.get_item_vector(i) for i in indices]
         scores = [cosine_similarity(np.array(item), vector) for item in vectors]
-        # if not self.config.raw_data_dir:
-        #     indices = [self.id_map[e] for e in indices]
 
         documents = [self._read_document(idx) for idx in indices]
         return [ScoredDocument(s, d) for s, d in zip(scores, documents)]

requirements-dev.txt

@@ -4,4 +4,4 @@ stop-words
 simplemma
 pysbd==0.3.4
 networkx==3.1
-numpy
+numpy==1.26.4

requirements.txt

@@ -1,2 +1 @@
-numpy
 tqdm

setup.py

@@ -7,7 +7,7 @@
 
 long_description = Path(__file__).with_name("README.md").read_text()
 
-version = "0.2.0"
+version = "0.2.1"
 
 setup(
     name='localsearch',
@@ -46,7 +46,8 @@
             "tantivy@git+https://github.com/leftshiftone/tantivy-py.git#egg=tantivy",
             "stop-words==2018.7.23",
             "simplemma==0.9.1",
-            "pysbd==0.3.4"
+            "pysbd==0.3.4",
+            "numpy==1.26.4"
         ],
         'networkx': [
             "networkx==3.1"