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Add a new CloudGeometry component that analyzes text input and returns statistics including word count, character count, sentence count, reading time estimate, average word/sentence length, and most frequent words with configurable stop words and minimum word length filtering. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Actionable comments posted: 2
🤖 Fix all issues with AI agents
In
`@langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py`:
- Around line 189-190: Remove the unnecessary f-string prefixes on the two
string literals (f"**Text Analytics Summary**" and f"") in text_analytics.py and
replace them with plain string literals ("**Text Analytics Summary**" and "");
do the same for the other occurrences flagged (the similar pair around lines
203-204) so only strings with actual interpolations remain as f-strings.
- Around line 133-134: The current assignment uses truthy checks that override
valid zero inputs: replace the fallbacks using explicit None checks so a
user-set 0 is preserved; specifically, change the assignments for min_len and
top_n in the method where min_len = self.min_word_length or 3 and top_n =
self.top_n_words or 10 to use conditional expressions that test "is None" (e.g.,
set min_len to 3 only if self.min_word_length is None, and set top_n to 10 only
if self.top_n_words is None) so the class fields min_word_length and top_n_words
retain 0 when explicitly provided.
🧹 Nitpick comments (3)
langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py (3)
184-186: Double computation:_compute_analytics()is called independently by each output method.When both
analyze_textandbuild_summaryoutputs are connected, the entire analysis runs twice. Consider caching the result (e.g., withfunctools.lru_cacheor a simple instance-level cache) to avoid redundant work on large texts.Sketch: simple instance-level caching
+ def _compute_analytics(self) -> dict: + if hasattr(self, "_cached_analytics"): + return self._cached_analytics text = self._get_text() ... + self._cached_analytics = analytics return analytics
98-100: Tokenizer drops all non-ASCII characters (accented letters, CJK, Cyrillic, etc.).The regex
[a-zA-Z0-9]+silently discards words like "café", "naïve", or any non-Latin text. If this component is intended for English-only text, a brief docstring or inputinfonote would be helpful. Otherwise, consider using\w+or a Unicode-aware pattern.
102-105: Sentence count may over-count for abbreviations and ellipses.Text like
"Dr. Smith went to Washington... He arrived at 5 p.m."would be split on every., producing inflated sentence counts. The[.!?]+pattern collapses consecutive punctuation (good for...), but isolated periods in abbreviations still cause false splits. This is a known limitation worth documenting, or consider a heuristic like requiring whitespace + uppercase after the delimiter.
| min_len = self.min_word_length or 3 | ||
| top_n = self.top_n_words or 10 |
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Falsy-value trap: 0 is a valid user input that will be silently overridden.
self.min_word_length or 3 falls back to 3 when the user explicitly sets min_word_length=0 (meaning "no minimum"), and self.top_n_words or 10 falls back to 10 when the user sets top_n_words=0. Use an explicit None check instead.
Proposed fix
- min_len = self.min_word_length or 3
- top_n = self.top_n_words or 10
+ min_len = self.min_word_length if self.min_word_length is not None else 3
+ top_n = self.top_n_words if self.top_n_words is not None else 10📝 Committable suggestion
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Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| min_len = self.min_word_length or 3 | |
| top_n = self.top_n_words or 10 | |
| min_len = self.min_word_length if self.min_word_length is not None else 3 | |
| top_n = self.top_n_words if self.top_n_words is not None else 10 |
🤖 Prompt for AI Agents
In
`@langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py`
around lines 133 - 134, The current assignment uses truthy checks that override
valid zero inputs: replace the fallbacks using explicit None checks so a
user-set 0 is preserved; specifically, change the assignments for min_len and
top_n in the method where min_len = self.min_word_length or 3 and top_n =
self.top_n_words or 10 to use conditional expressions that test "is None" (e.g.,
set min_len to 3 only if self.min_word_length is None, and set top_n to 10 only
if self.top_n_words is None) so the class fields min_word_length and top_n_words
retain 0 when explicitly provided.
| f"**Text Analytics Summary**", | ||
| f"", |
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Remove extraneous f prefixes on strings without placeholders.
Ruff F541 flagged these lines. The f"" prefix is misleading when there are no interpolations.
Proposed fix
lines = [
- f"**Text Analytics Summary**",
- f"",
+ "**Text Analytics Summary**",
+ "",
f"- Words: {analytics['word_count']}",
...
]
...
if analytics["top_words"]:
- lines.append(f"")
- lines.append(f"**Top Words:**")
+ lines.append("")
+ lines.append("**Top Words:**")Also applies to: 203-204
🧰 Tools
🪛 Ruff (0.15.0)
[error] 189-189: f-string without any placeholders
Remove extraneous f prefix
(F541)
[error] 190-190: f-string without any placeholders
Remove extraneous f prefix
(F541)
🤖 Prompt for AI Agents
In
`@langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py`
around lines 189 - 190, Remove the unnecessary f-string prefixes on the two
string literals (f"**Text Analytics Summary**" and f"") in text_analytics.py and
replace them with plain string literals ("**Text Analytics Summary**" and "");
do the same for the other occurrences flagged (the similar pair around lines
203-204) so only strings with actual interpolations remain as f-strings.
|
@coderabbitai Review all security issues and flag them if any |
|
Tip For best results, initiate chat on the files or code changes. 🧩 Analysis chain🏁 Script executed: Length of output: 10069
🚨 Security Issues Identified1. CRITICAL: Resource Exhaustion / Denial of ServiceLocation: Issue: No input length validation on the text being analyzed. An attacker could provide:
Impact:
Recommendation: MAX_TEXT_LENGTH = 1_000_000 # 1MB character limit
def _get_text(self) -> str:
raw = self.input_text
if hasattr(raw, "text"):
raw = raw.text
text = str(raw)
if len(text) > MAX_TEXT_LENGTH:
raise ValueError(f"Input text exceeds maximum length of {MAX_TEXT_LENGTH} characters")
return text2. MEDIUM: Integer Parameter ValidationLocation: Lines 34-50 ( Issue: No validation on integer inputs:
Impact:
Recommendation: def _get_word_frequencies(self, words: list[str]) -> list[dict]:
stop_words = self._get_stop_words()
min_len = max(0, self.min_word_length or 3) # Ensure non-negative
top_n = min(max(1, self.top_n_words or 10), 100) # Clamp between 1-100
# ... rest of implementation3. LOW: Regex Performance ConcernLocation: Line 95 - Issue: The pattern Impact: Minor CPU performance degradation with crafted input Recommendation: def _count_paragraphs(self, text: str) -> int:
# Limit whitespace matching to prevent regex slowdown
paragraphs = re.split(r'\n\s{0,100}\n', text)
paragraphs = [p.strip() for p in paragraphs if p.strip()]
return len(paragraphs)✅ Security Positives
📋 Recommended ActionsPriority 1 (Critical):
Priority 2 (Medium):
Priority 3 (Low):
Would you like me to generate a complete implementation with all security fixes applied? |
|
@coderabbitai please do add the required changes |
|
Tip For best results, initiate chat on the files or code changes.
Task: Implement Security Fixes for TextAnalytics Component
💡 Use |
|
@coderabbitai review |
✅ Actions performedReview triggered.
|
WalkthroughIntroduces TextAnalyticsComponent, a new text analysis component that processes input text to compute metrics including word count, character counts, sentence/paragraph counts, reading time estimates, and top word frequencies. Also updates the cloudgeometry module to expose this new component for public access. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes 🚥 Pre-merge checks | ✅ 2 | ❌ 2❌ Failed checks (2 warnings)
✅ Passed checks (2 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing touches
🧪 Generate unit tests (beta)
⚔️ Resolve merge conflicts (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
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Actionable comments posted: 1
🤖 Fix all issues with AI agents
In
`@langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py`:
- Around line 92-96: The _get_text method currently returns unbounded text and
must enforce a size guard to prevent resource-exhaustion; convert raw to a
string (as done now), then check its length against a defined MAX_TEXT_LENGTH
constant (add module-level MAX_TEXT_LENGTH, e.g. an explicit byte/char limit)
and if the input exceeds it either raise a specific exception (ValueError or a
custom InvalidInputError) or truncate safely before returning; implement the
check inside _get_text (after the hasattr(raw, "text") handling) and reference
the MAX_TEXT_LENGTH symbol in the validation and error message.
🧹 Nitpick comments (3)
langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py (3)
150-157:_compute_analytics()runs the full analysis from scratch on every call — doubled work when both outputs are used.Both
analyze_text()andbuild_summary()independently invoke_compute_analytics(), re-running all regex work, tokenization, and frequency counting. If both outputs are wired in a flow, this is redundant.Consider caching the result for the lifetime of a single component run (e.g., via a simple instance-level cache or by computing once and feeding the result into both output methods).
♻️ Sketch: compute-once pattern
+ def _get_or_compute_analytics(self) -> dict: + if not hasattr(self, "_cached_analytics"): + self._cached_analytics = self._compute_analytics() + return self._cached_analytics + def analyze_text(self) -> Data: """Return full analytics as a Data object.""" - analytics = self._compute_analytics() + analytics = self._get_or_compute_analytics() self.status = f"{analytics['word_count']} words, {analytics['sentence_count']} sentences" return Data(data=analytics) def build_summary(self) -> Message: """Return a human-readable summary message.""" - analytics = self._compute_analytics() + analytics = self._get_or_compute_analytics()
155-155: Avoid allocating a full string copy just to count non-space characters.
text.replace(" ", "")creates a complete copy of the input string only to measure its length. For large inputs this is wasteful. A subtraction is O(n) with no extra allocation.♻️ Proposed fix
- char_no_spaces = len(text.replace(" ", "")) + char_no_spaces = char_count - text.count(" ")
188-206: Minor: uselist.extendfor the top-words section and drop unnecessaryfprefixes.Ruff PERF401 flags the append-in-loop pattern on line 205–206. Combining this with the
f""prefix cleanup:♻️ Proposed fix
lines = [ - f"**Text Analytics Summary**", - f"", + "**Text Analytics Summary**", + "", f"- Words: {analytics['word_count']}", f"- Characters: {analytics['character_count']} ({analytics['character_count_no_spaces']} without spaces)", f"- Sentences: {analytics['sentence_count']}", f"- Paragraphs: {analytics['paragraph_count']}", f"- Avg word length: {analytics['average_word_length']} chars", f"- Avg sentence length: {analytics['average_sentence_length']} words", ] if self.include_reading_time and "reading_time" in analytics: lines.append(f"- Reading time: {analytics['reading_time']['display']}") if analytics["top_words"]: - lines.append(f"") - lines.append(f"**Top Words:**") - for item in analytics["top_words"]: - lines.append(f" - \"{item['word']}\" — {item['count']}x ({item['percentage']}%)") + lines.append("") + lines.append("**Top Words:**") + lines.extend( + f" - \"{item['word']}\" — {item['count']}x ({item['percentage']}%)" + for item in analytics["top_words"] + )
| def _get_text(self) -> str: | ||
| raw = self.input_text | ||
| if hasattr(raw, "text"): | ||
| raw = raw.text | ||
| return str(raw) |
There was a problem hiding this comment.
Missing input-length validation — resource-exhaustion / DoS risk.
_get_text accepts arbitrarily large inputs with no size guard. Downstream processing (regex tokenization, Counter, text.replace(...) copy, paragraph-splitting regex) will consume unbounded CPU and memory. Add an early length check here, since this is the single entry point for all text.
🛡️ Proposed fix
+ MAX_TEXT_LENGTH = 1_000_000 # class-level constant
+
def _get_text(self) -> str:
raw = self.input_text
if hasattr(raw, "text"):
raw = raw.text
- return str(raw)
+ text = str(raw)
+ if len(text) > self.MAX_TEXT_LENGTH:
+ msg = f"Input text exceeds maximum allowed length of {self.MAX_TEXT_LENGTH:,} characters."
+ raise ValueError(msg)
+ return text📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def _get_text(self) -> str: | |
| raw = self.input_text | |
| if hasattr(raw, "text"): | |
| raw = raw.text | |
| return str(raw) | |
| MAX_TEXT_LENGTH = 1_000_000 # class-level constant | |
| def _get_text(self) -> str: | |
| raw = self.input_text | |
| if hasattr(raw, "text"): | |
| raw = raw.text | |
| text = str(raw) | |
| if len(text) > self.MAX_TEXT_LENGTH: | |
| msg = f"Input text exceeds maximum allowed length of {self.MAX_TEXT_LENGTH:,} characters." | |
| raise ValueError(msg) | |
| return text |
🤖 Prompt for AI Agents
In
`@langbuilder/src/backend/base/langbuilder/components/cloudgeometry/text_analytics.py`
around lines 92 - 96, The _get_text method currently returns unbounded text and
must enforce a size guard to prevent resource-exhaustion; convert raw to a
string (as done now), then check its length against a defined MAX_TEXT_LENGTH
constant (add module-level MAX_TEXT_LENGTH, e.g. an explicit byte/char limit)
and if the input exceeds it either raise a specific exception (ValueError or a
custom InvalidInputError) or truncate safely before returning; implement the
check inside _get_text (after the hasattr(raw, "text") handling) and reference
the MAX_TEXT_LENGTH symbol in the validation and error message.
1 participant
Add a new CloudGeometry component that analyzes text input and returns statistics including word count, character count, sentence count, reading time estimate, average word/sentence length, and most frequent words with configurable stop words and minimum word length filtering.
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