Merge pull request #220 from aurelio-labs/simonas/reduce-logging-in-splitter

chore: Reduction of logs for splitter

semantic_router/splitters/rolling_window.py

@@ -39,6 +39,7 @@ class RollingWindowSplitter(BaseSplitter):
     def __init__(
         self,
         encoder: BaseEncoder,
+        name="rolling_window_splitter",
         threshold_adjustment=0.01,
         dynamic_threshold: bool = True,
         window_size=5,
@@ -46,7 +47,7 @@ class RollingWindowSplitter(BaseSplitter):
         max_split_tokens=300,
         split_tokens_tolerance=10,
         plot_splits=False,
-        name="rolling_window_splitter",
+        enable_statistics=False,
     ):
         super().__init__(name=name, encoder=encoder)
         self.calculated_threshold: float
@@ -58,6 +59,7 @@ class RollingWindowSplitter(BaseSplitter):
         self.min_split_tokens = min_split_tokens
         self.max_split_tokens = max_split_tokens
         self.split_tokens_tolerance = split_tokens_tolerance
+        self.enable_statistics = enable_statistics
         self.statistics: SplitStatistics
 
     def __call__(self, docs: List[str]) -> List[DocumentSplit]:
@@ -88,8 +90,13 @@ class RollingWindowSplitter(BaseSplitter):
             self.calculated_threshold = self.encoder.score_threshold
         split_indices = self._find_split_indices(similarities=similarities)
         splits = self._split_documents(docs, split_indices, similarities)
-        self.plot_similarity_scores(similarities, split_indices, splits)
-        logger.info(self.statistics)
+
+        if self.plot_splits:
+            self.plot_similarity_scores(similarities, split_indices, splits)
+
+        if self.enable_statistics:
+            print(self.statistics)
+
         return splits
 
     def _encode_documents(self, docs: List[str]) -> np.ndarray:
@@ -174,7 +181,7 @@ class RollingWindowSplitter(BaseSplitter):
                 logger.debug(f"Iteration {iteration}: Adjusting low to {low}")
             iteration += 1
 
-        logger.info(
+        logger.debug(
             f"Optimal threshold {self.calculated_threshold} found "
             f"with median tokens ({median_tokens}) in target range "
             f"({self.min_split_tokens}-{self.max_split_tokens})."
@@ -325,9 +332,7 @@ class RollingWindowSplitter(BaseSplitter):
             )
             return
 
-        if not self.plot_splits:
-            return
-        fig, axs = plt.subplots(2, 1, figsize=(12, 12))  # Adjust for two plots
+        _, axs = plt.subplots(2, 1, figsize=(12, 12))  # Adjust for two plots
 
         # Plot 1: Similarity Scores
         axs[0].plot(similarities, label="Similarity Scores", marker="o")