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chore: Optimize Logging by Replacing f-Strings with Lazy Evaluation #309

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chore: Optimize Logging by Replacing f-Strings with Lazy Evaluation #309

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d52ab0e
Chore: replace logging strings in rolling_window.py by printf-style s…
kaulson Jun 4, 2024
2c1b0be
Merge remote-tracking branch 'upstream/main' into refactor-logging
kaulson Jul 4, 2024
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56 changes: 35 additions & 21 deletions semantic_router/splitters/rolling_window.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,10 +85,10 @@ def __call__(self, docs: List[str]) -> List[DocumentSplit]:
token_count = tiktoken_length(docs[0])
if token_count > self.max_split_tokens:
logger.info(
f"Single document exceeds the maximum token limit "
f"of {self.max_split_tokens}. "
"Splitting to sentences before semantically splitting."
"Single document exceeds the maximum token limit of %s. Splitting to sentences before semantically splitting.",
self.max_split_tokens,
)

docs = split_to_sentences(docs[0])
encoded_docs = self._encode_documents(docs)
similarities = self._calculate_similarity_scores(encoded_docs)
Expand Down Expand Up @@ -130,7 +130,7 @@ def _encode_documents(self, docs: List[str]) -> np.ndarray:
batch_embeddings = self.encoder(batch_docs)
embeddings.extend(batch_embeddings)
except Exception as e:
logger.error(f"Error encoding documents {batch_docs}: {e}")
logger.error("Error encoding documents %s: %s", batch_docs, e)
raise

return np.array(embeddings)
Expand All @@ -150,12 +150,14 @@ def _calculate_similarity_scores(self, encoded_docs: np.ndarray) -> List[float]:
def _find_split_indices(self, similarities: List[float]) -> List[int]:
split_indices = []
for idx, score in enumerate(similarities):
logger.debug(f"Similarity score at index {idx}: {score}")
logger.debug("Similarity score at index %d: %f", idx, score)
if score < self.calculated_threshold:
logger.debug(
f"Adding to split_indices due to score < threshold: "
f"{score} < {self.calculated_threshold}"
"Adding to split_indices due to score < threshold: %f < %f",
score,
self.calculated_threshold,
)

# Split after the document at idx
split_indices.append(idx + 1)
return split_indices
Expand All @@ -178,7 +180,9 @@ def _find_optimal_threshold(self, docs: List[str], similarity_scores: List[float
self.calculated_threshold = (low + high) / 2
split_indices = self._find_split_indices(similarity_scores)
logger.debug(
f"Iteration {iteration}: Trying threshold: {self.calculated_threshold}"
"Iteration %d: Trying threshold: %f",
iteration,
self.calculated_threshold,
)

# Calculate the token counts for each split using the cumulative sums
Expand All @@ -192,8 +196,9 @@ def _find_optimal_threshold(self, docs: List[str], similarity_scores: List[float
# Calculate the median token count for the splits
median_tokens = np.median(split_token_counts)
logger.debug(
f"Iteration {iteration}: Median tokens per split: {median_tokens}"
"Iteration %d: Median tokens per split: %d", iteration, median_tokens
)

if (
self.min_split_tokens - self.split_tokens_tolerance
<= median_tokens
Expand All @@ -210,9 +215,11 @@ def _find_optimal_threshold(self, docs: List[str], similarity_scores: List[float
iteration += 1

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})."
"Optimal threshold %f found with median tokens (%d) in target range (%d-%d).",
self.calculated_threshold,
median_tokens,
self.min_split_tokens,
self.max_split_tokens,
)

return self.calculated_threshold
Expand All @@ -239,8 +246,9 @@ def _split_documents(

for doc_idx, doc in enumerate(docs):
doc_token_count = token_counts[doc_idx]
logger.debug(f"Accumulative token count: {current_tokens_count} tokens")
logger.debug(f"Document token count: {doc_token_count} tokens")
logger.debug("Accumulative token count: %d tokens", current_tokens_count)
logger.debug("Document token count: %d tokens", doc_token_count)

# Check if current index is a split point based on similarity
if doc_idx + 1 in split_indices:
if (
Expand All @@ -265,9 +273,11 @@ def _split_documents(
)
)
logger.debug(
f"Split finalized with {current_tokens_count} tokens due to "
f"threshold {self.calculated_threshold}."
"Split finalized with %d tokens due to threshold %f.",
current_tokens_count,
self.calculated_threshold,
)

current_split, current_tokens_count = [], 0
splits_by_threshold += 1
continue # Move to the next document after splitting
Expand All @@ -285,8 +295,9 @@ def _split_documents(
)
splits_by_max_chunk_size += 1
logger.debug(
f"Split finalized with {current_tokens_count} tokens due to "
f"exceeding token limit of {self.max_split_tokens}."
"Split finalized with %d tokens due to exceeding token limit of %d.",
current_tokens_count,
self.max_split_tokens,
)
current_split, current_tokens_count = [], 0

Expand All @@ -305,8 +316,8 @@ def _split_documents(
)
splits_by_last_split += 1
logger.debug(
f"Final split added with {current_tokens_count} "
"tokens due to remaining documents."
"Final split added with %d tokens due to remaining documents.",
current_tokens_count,
)

# Validation to ensure no tokens are lost during the split
Expand All @@ -316,8 +327,11 @@ def _split_documents(
)
if original_token_count != split_token_count:
logger.error(
f"Token count mismatch: {original_token_count} != {split_token_count}"
"Token count mismatch: %d != %d",
original_token_count,
split_token_count,
)

raise ValueError(
f"Token count mismatch: {original_token_count} != {split_token_count}"
)
Expand Down