chore: collapse multi-line argument definitions into single lines across multiple modules

pr/memory/fact_extractor.py

@@ -169,9 +169,7 @@ class FactExtractor:
         sentence_count = len(re.split(r"[.!?]", text))
 
         urls = re.findall(r"https?://[^\s]+", text)
-        email_addresses = re.findall(
-            r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", text
-        )
+        email_addresses = re.findall(r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b", text)
         dates = re.findall(
             r"\b\d{1,2}[-/]\d{1,2}[-/]\d{2,4}\b|\b\d{4}[-/]\d{1,2}[-/]\d{1,2}\b", text
         )

pr/memory/semantic_index.py

@@ -38,8 +38,7 @@ class SemanticIndex:
             self.idf_scores = {token: 1.0 for token in token_doc_count}
         else:
             self.idf_scores = {
-                token: math.log(doc_count / count)
-                for token, count in token_doc_count.items()
+                token: math.log(doc_count / count) for token, count in token_doc_count.items()
             }
 
     def add_document(self, doc_id: str, text: str):
@@ -51,8 +50,7 @@ class SemanticIndex:
 
         tf_scores = self._compute_tf(tokens)
         self.doc_vectors[doc_id] = {
-            token: tf_scores.get(token, 0) * self.idf_scores.get(token, 0)
-            for token in tokens
+            token: tf_scores.get(token, 0) * self.idf_scores.get(token, 0) for token in tokens
         }
 
     def remove_document(self, doc_id: str):
@@ -67,8 +65,7 @@ class SemanticIndex:
         query_tf = self._compute_tf(query_tokens)
 
         query_vector = {
-            token: query_tf.get(token, 0) * self.idf_scores.get(token, 0)
-            for token in query_tokens
+            token: query_tf.get(token, 0) * self.idf_scores.get(token, 0) for token in query_tokens
         }
 
         scores = []
@@ -79,9 +76,7 @@ class SemanticIndex:
         scores.sort(key=lambda x: x[1], reverse=True)
         return scores[:top_k]
 
-    def _cosine_similarity(
-        self, vec1: Dict[str, float], vec2: Dict[str, float]
-    ) -> float:
+    def _cosine_similarity(self, vec1: Dict[str, float], vec2: Dict[str, float]) -> float:
         dot_product = sum(
             vec1.get(token, 0) * vec2.get(token, 0) for token in set(vec1) | set(vec2)
         )