feat(exp): Add made by traceloop evaluators #3491
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| Note Other AI code review bot(s) detectedCodeRabbit has detected other AI code review bot(s) in this pull request and will avoid duplicating their findings in the review comments. This may lead to a less comprehensive review. WalkthroughAdds evaluator configuration support across the SDK and many sample experiment modules. Introduces a Pydantic EvaluatorDetails model, an EvaluatorMadeByTraceloop factory, expands evaluator request payloads to include evaluator_config, and updates Experiment APIs/types to accept evaluator specs with configs. Changes
sequenceDiagram participant User as Caller participant Experiment as Experiment module participant Client as Traceloop Client participant EvaluatorSDK as Evaluator API layer participant EvalSvc as Evaluator service Note over User,Experiment: User provides evaluators (string or EvaluatorDetails) User->>Experiment: run(evaluators: List[EvaluatorSpec], ...) Experiment->>Experiment: normalize -> (slug, version, config) Experiment->>Client: create experiment run loop per task / evaluator Experiment->>EvaluatorSDK: run_experiment_evaluator(slug, input, evaluator_version, evaluator_config) EvaluatorSDK->>EvalSvc: ExecuteEvaluatorRequest {slug, version, input, evaluator_config} EvalSvc-->>EvaluatorSDK: ExecutionResponse EvaluatorSDK-->>Experiment: result end Experiment->>User: aggregated results Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes
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packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py Fixed Show fixed Hide fixed
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1. packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py:58
- Draft comment:
The 'advanced_quality_task' returns the response under the key 'completion', but later the code prints using 'text'. This key mismatch may lead to missing output in results. Consider using a consistent key (e.g., 'text' or 'completion'). - Reason this comment was not posted:
Decided after close inspection that this draft comment was likely wrong and/or not actionable: usefulness confidence = 20% vs. threshold = 50% The comment points to a potential issue: the task returns {"completion": ...} but later code accesses result.task_result.get("text", ""). This could be a real bug. However, I don't have visibility into what the Traceloop SDK does with the returned dictionary. It's possible that: 1. The SDK transforms "completion" to "text" 2. The SDK preserves the exact dictionary structure 3. This is intentional and "text" is meant to be empty (note the default value "") Since line 133 uses .get("text", "") with an empty string default, if "text" doesn't exist, it will just print an empty string - not cause an error. The code would still run, just potentially not show the response text. Without knowing the Traceloop SDK's behavior, I cannot definitively say this is a bug. The comment is speculative. I'm assuming this is a bug without knowing how the Traceloop SDK works. The code uses .get() with a default value, so it won't crash - it will just print empty strings if the key doesn't exist. This might be intentional behavior, or the SDK might transform the keys. Without access to the SDK documentation or other files, I cannot be certain this is actually a problem. The comment is speculative about SDK behavior I cannot verify. The rule states "Do NOT make speculative comments" and "you must see STRONG EVIDENCE that the comment is correct." Since the code won't crash (it uses .get() with defaults) and I don't know if the SDK transforms the data, there's no strong evidence this is actually wrong. This comment is speculative about how the Traceloop SDK handles the returned dictionary. Without being able to verify the SDK's behavior or see evidence that this causes actual problems, and given that the code uses safe .get() calls with defaults, I should delete this comment as it lacks strong evidence of being correct.
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1. packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py:67
- Draft comment:
Removed print. Consider using a logging framework (e.g., logger.debug) to log environment-specific messages. - Reason this comment was not posted:
Confidence changes required:33%<= threshold50%None
2. packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py:75
- Draft comment:
Removed print statement for local execution. Consider using a centralized logging library if logging is needed. - Reason this comment was not posted:
Confidence changes required:33%<= threshold50%None
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Actionable comments posted: 9
♻️ Duplicate comments (1)
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py (1)
131-137: Static analysis alert is a false positive.CodeQL flags line 136 for "clear-text logging of sensitive information," but this is incorrect. The code prints
security_issues['secrets_detected']which is an integer counter (e.g., "Secrets detected: 0 task(s)"), not actual secret values. The variable name happens to contain "secrets" but the value is just a count.
🧹 Nitpick comments (7)
packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py (1)
229-278:run_validation_examples()is defined but not invoked.This function is never called in the main entry point. If it's intended as an optional demo utility, consider documenting this or providing a command-line flag to invoke it. Otherwise, remove it to reduce dead code.
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py (1)
19-20: Module-level initialization may raise on missing environment variables.
Traceloop.init()at module load time means any import of this module will trigger initialization. For a sample app this is acceptable, but be aware that ifTRACELOOP_API_KEYis not set, this may cause issues at import time rather than at runtime.Consider moving initialization inside
run_security_experiment()if lazy initialization is preferred:-# Initialize Traceloop -client = Traceloop.init() +# Initialize Traceloop lazily +client = None async def run_security_experiment(): + global client + if client is None: + client = Traceloop.init()packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py (1)
166-219: Remove extraneous f-string prefixes.Multiple print statements use f-string prefixes but contain no placeholders. Consider removing the
fprefix for clarity.As per coding guidelines (Flake8 rule F541), apply this diff:
if all_metrics["perplexities"]: avg_perplexity = sum(all_metrics["perplexities"]) / len(all_metrics["perplexities"]) min_perplexity = min(all_metrics["perplexities"]) max_perplexity = max(all_metrics["perplexities"]) - print(f"Perplexity Statistics:") + print("Perplexity Statistics:") print(f" Average: {avg_perplexity:.2f}") print(f" Range: {min_perplexity:.2f} - {max_perplexity:.2f}") if avg_perplexity < 10: - print(f" Assessment: Excellent fluency and predictability") + print(" Assessment: Excellent fluency and predictability") elif avg_perplexity < 50: - print(f" Assessment: Good fluency") + print(" Assessment: Good fluency") else: - print(f" Assessment: Higher perplexity - may indicate complexity or uncertainty") + print(" Assessment: Higher perplexity - may indicate complexity or uncertainty") print() if all_metrics["goal_accuracies"]: avg_goal_accuracy = sum(all_metrics["goal_accuracies"]) / len(all_metrics["goal_accuracies"]) min_goal = min(all_metrics["goal_accuracies"]) max_goal = max(all_metrics["goal_accuracies"]) - print(f"Agent Goal Accuracy Statistics:") + print("Agent Goal Accuracy Statistics:") print(f" Average: {avg_goal_accuracy:.2%}") print(f" Range: {min_goal:.2%} - {max_goal:.2%}") if avg_goal_accuracy >= 0.8: - print(f" Assessment: Strong goal achievement") + print(" Assessment: Strong goal achievement") elif avg_goal_accuracy >= 0.6: - print(f" Assessment: Moderate goal achievement") + print(" Assessment: Moderate goal achievement") else: - print(f" Assessment: Needs improvement in goal achievement") + print(" Assessment: Needs improvement in goal achievement") print() if all_metrics["semantic_similarities"]: avg_similarity = sum(all_metrics["semantic_similarities"]) / len(all_metrics["semantic_similarities"]) min_sim = min(all_metrics["semantic_similarities"]) max_sim = max(all_metrics["semantic_similarities"]) - print(f"Semantic Similarity Statistics:") + print("Semantic Similarity Statistics:") print(f" Average: {avg_similarity:.2%}") print(f" Range: {min_sim:.2%} - {max_sim:.2%}") if avg_similarity >= 0.8: - print(f" Assessment: High semantic alignment with reference") + print(" Assessment: High semantic alignment with reference") elif avg_similarity >= 0.6: - print(f" Assessment: Moderate semantic alignment") + print(" Assessment: Moderate semantic alignment") else: - print(f" Assessment: Low semantic alignment - responses differ significantly") + print(" Assessment: Low semantic alignment - responses differ significantly") print() if all_metrics["topic_adherences"]: avg_adherence = sum(all_metrics["topic_adherences"]) / len(all_metrics["topic_adherences"]) min_adh = min(all_metrics["topic_adherences"]) max_adh = max(all_metrics["topic_adherences"]) - print(f"Topic Adherence Statistics:") + print("Topic Adherence Statistics:") print(f" Average: {avg_adherence:.2%}") print(f" Range: {min_adh:.2%} - {max_adh:.2%}") if avg_adherence >= 0.8: - print(f" Assessment: Excellent topic consistency") + print(" Assessment: Excellent topic consistency") elif avg_adherence >= 0.6: - print(f" Assessment: Moderate topic focus") + print(" Assessment: Moderate topic focus") else: - print(f" Assessment: Responses frequently drift off-topic") + print(" Assessment: Responses frequently drift off-topic") print()packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py (1)
151-177: Remove extraneous f-string prefixes.Several print statements use f-string prefixes without placeholders. Consider removing the
fprefix for clarity.As per coding guidelines (Flake8 rule F541), apply this diff:
if all_metrics["char_counts"]: avg_chars = sum(all_metrics["char_counts"]) / len(all_metrics["char_counts"]) min_chars = min(all_metrics["char_counts"]) max_chars = max(all_metrics["char_counts"]) - print(f"Character Count Statistics:") + print("Character Count Statistics:") print(f" Average: {avg_chars:.0f} chars") print(f" Range: {min_chars} - {max_chars} chars\n") if all_metrics["word_counts"]: avg_words = sum(all_metrics["word_counts"]) / len(all_metrics["word_counts"]) min_words = min(all_metrics["word_counts"]) max_words = max(all_metrics["word_counts"]) - print(f"Word Count Statistics:") + print("Word Count Statistics:") print(f" Average: {avg_words:.0f} words") print(f" Range: {min_words} - {max_words} words\n") if all_metrics["char_ratios"]: avg_char_ratio = sum(all_metrics["char_ratios"]) / len(all_metrics["char_ratios"]) - print(f"Character Ratio (Response/Reference):") + print("Character Ratio (Response/Reference):") print(f" Average: {avg_char_ratio:.2f}x") if avg_char_ratio > 1.5: print(f" Note: Responses are {avg_char_ratio:.1f}x longer than reference\n") elif avg_char_ratio < 0.5: print(f" Note: Responses are {avg_char_ratio:.1f}x shorter than reference\n") else: - print(f" Note: Response length is well-matched to reference\n") + print(" Note: Response length is well-matched to reference\n") if all_metrics["word_ratios"]: avg_word_ratio = sum(all_metrics["word_ratios"]) / len(all_metrics["word_ratios"]) - print(f"Word Ratio (Response/Reference):") + print("Word Ratio (Response/Reference):") print(f" Average: {avg_word_ratio:.2f}x")packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (3)
184-196: Fix method signature indentation.The closing parenthesis should align with the
defkeyword per PEP 8 continuation line conventions.As per coding guidelines (Flake8 rule E125), apply this diff:
@staticmethod def word_count( - ) -> EvaluatorDetails: + ) -> EvaluatorDetails: """ Word count evaluator - counts the number of words in text. Returns: EvaluatorDetails configured for word counting """ config: Dict[str, Any] = {} return EvaluatorDetails(slug="word-count", version=None, config=config)
236-248: Fix method signature indentation.The closing parenthesis should align with the
defkeyword per PEP 8 continuation line conventions.As per coding guidelines (Flake8 rule E125), apply this diff:
@staticmethod def profanity_detector( - ) -> EvaluatorDetails: + ) -> EvaluatorDetails: """ Profanity detector evaluator - flags inappropriate language. Returns: EvaluatorDetails configured for profanity detection """ config: Dict[str, Any] = {} return EvaluatorDetails(slug="profanity-detector", version=None, config=config)
266-276: Use consistent config initialization.This method returns
config=Nonewhile all other similar methods (e.g.,char_count,word_count,profanity_detector) initializeconfigas an empty dict.Apply this diff for consistency:
@staticmethod def secrets_detector( ) -> EvaluatorDetails: """ Secrets detector evaluator - monitors for credential and key leaks. Returns: EvaluatorDetails configured for secrets detection """ - return EvaluatorDetails(slug="secrets-detector", version=None, config=None) + config: Dict[str, Any] = {} + + return EvaluatorDetails(slug="secrets-detector", version=None, config=config)
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📒 Files selected for processing (15)
packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/rag_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py(1 hunks)packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py(1 hunks)packages/sample-app/sample_app/experiment/run_research_experiment.py(1 hunks)packages/traceloop-sdk/traceloop/sdk/evaluator/__init__.py(1 hunks)packages/traceloop-sdk/traceloop/sdk/evaluator/config.py(1 hunks)packages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py(6 hunks)packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py(1 hunks)packages/traceloop-sdk/traceloop/sdk/evaluator/model.py(1 hunks)packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py(8 hunks)packages/traceloop-sdk/traceloop/sdk/experiment/model.py(1 hunks)
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📓 Path-based instructions (1)
**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
**/*.py: Store API keys only in environment variables/secure vaults; never hardcode secrets in code
Use Flake8 for code linting and adhere to its rules
Files:
packages/traceloop-sdk/traceloop/sdk/evaluator/config.pypackages/sample-app/sample_app/experiment/run_research_experiment.pypackages/sample-app/sample_app/experiment/made_by_traceloop/rag_exp.pypackages/traceloop-sdk/traceloop/sdk/evaluator/model.pypackages/traceloop-sdk/traceloop/sdk/experiment/model.pypackages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.pypackages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.pypackages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.pypackages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.pypackages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.pypackages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.pypackages/traceloop-sdk/traceloop/sdk/evaluator/__init__.pypackages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.pypackages/traceloop-sdk/traceloop/sdk/experiment/experiment.pypackages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py
🧬 Code graph analysis (8)
packages/sample-app/sample_app/experiment/made_by_traceloop/rag_exp.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (1)
agent_goal_accuracy(304-314)
packages/traceloop-sdk/traceloop/sdk/experiment/model.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/config.py (1)
EvaluatorDetails(5-20)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (2)
packages/traceloop-sdk/traceloop/sdk/__init__.py (2)
Traceloop(36-267)init(48-198)packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (2)
answer_relevancy(211-221)faithfulness(224-234)
packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (4)
char_count(159-169)word_count(185-195)char_count_ratio(172-182)word_count_ratio(198-208)
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py (1)
packages/traceloop-sdk/traceloop/sdk/__init__.py (2)
Traceloop(36-267)init(48-198)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/config.py (1)
EvaluatorDetails(5-20)
packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py (2)
packages/traceloop-sdk/traceloop/sdk/experiment/model.py (4)
RunInGithubRequest(87-97)RunInGithubResponse(100-105)TaskResult(70-75)GithubContext(78-84)packages/traceloop-sdk/traceloop/sdk/evaluator/config.py (1)
EvaluatorDetails(5-20)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/model.py (1)
ExecutionResponse(40-44)
🪛 Flake8 (7.3.0)
packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py
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packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py
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packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py
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🪛 GitHub Check: CodeQL
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py
[failure] 136-136: Clear-text logging of sensitive information
This expression logs sensitive data (secret) as clear text.
This expression logs sensitive data (secret) as clear text.
This expression logs sensitive data (secret) as clear text.
🪛 Ruff (0.14.7)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py
22-22: PEP 484 prohibits implicit Optional
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packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py
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🔇 Additional comments (15)
packages/sample-app/sample_app/experiment/run_research_experiment.py (2)
49-53: Redundant data:textduplicatescompletion.Both
completionandtextcontain the sameanswervalue. If this duplication is intentional for evaluator compatibility (e.g., different evaluators expecting different field names), consider adding a brief inline comment explaining why both fields are needed.
17-21: LGTM!API keys are correctly retrieved from environment variables, following the coding guidelines for secure credential handling.
packages/traceloop-sdk/traceloop/sdk/evaluator/config.py (1)
5-20: LGTM!Clean Pydantic model with proper type hints, sensible defaults, and clear documentation including usage examples.
packages/sample-app/sample_app/experiment/made_by_traceloop/rag_exp.py (1)
43-65: LGTM!OpenAI API key is securely retrieved from environment variable, and the async client usage is correct.
packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py (1)
31-32: LGTM!API key is correctly retrieved from environment variable, adhering to the coding guidelines for secure credential handling.
packages/traceloop-sdk/traceloop/sdk/evaluator/model.py (1)
16-23: LGTM!The new
evaluator_configfield is appropriately typed asOptional[Dict[str, Any]]with aNonedefault, maintaining backward compatibility with existing code.packages/traceloop-sdk/traceloop/sdk/evaluator/__init__.py (1)
1-9: LGTM!Clean package initialization that properly exposes the new
EvaluatorDetailsandEvaluatorMadeByTraceloopclasses alongside the existingEvaluator. The__all__list is correctly updated to reflect the public API.packages/traceloop-sdk/traceloop/sdk/experiment/model.py (1)
2-6: LGTM!The
EvaluatorSpectype alias provides a clean, flexible API that accepts either a simple string slug or a fullEvaluatorDetailsconfiguration object. This is a sensible design that maintains backward compatibility while enabling richer evaluator configurations.packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py (1)
23-34: LGTM!The
generate_responsefunction correctly retrieves the OpenAI API key from environment variables viaos.getenv("OPENAI_API_KEY"), adhering to the coding guidelines for secure secret handling.packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (1)
23-37: LGTM!The API key is correctly retrieved from the environment variable, and the function properly handles the optional context by conditionally adding a system message.
packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py (1)
23-34: LGTM!The
generate_responsefunction correctly retrieves the OpenAI API key from the environment variable, following secure secret handling practices.packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py (2)
130-148: Well-structured evaluator configuration support.The implementation cleanly handles both string slugs and
EvaluatorDetailsobjects, converting them to normalized tuples for consistent processing throughout the experiment execution.
175-200: Proper threading of evaluator configuration.The
evaluator_configis correctly unpacked from the tuple and passed to bothrun_experiment_evaluatorandtrigger_experiment_evaluator, maintaining consistency across synchronous and fire-and-forget execution paths.packages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py (2)
24-44: Clean extension of evaluator request builder.The
evaluator_configparameter is properly added to the request builder signature and passed through toExecuteEvaluatorRequestconstruction.
67-107: Consistent evaluator configuration support in execution path.The
evaluator_configparameter is correctly threaded through the synchronous execution path, with appropriate documentation updates.
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1. packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py:105
- Draft comment:
Typo in the comment: 'ddataset' should be 'dataset'. - Reason this comment was not posted:
Comment was on unchanged code.
2. packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py:90
- Draft comment:
Typo in the comment: 'ddataset' should be corrected to 'dataset'. - Reason this comment was not posted:
Comment was on unchanged code.
3. packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py:94
- Draft comment:
The comment still has a typo ('ddataset'); consider fixing it to 'dataset'. - Reason this comment was not posted:
Comment was on unchanged code.
4. packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py:95
- Draft comment:
Fix the typo in the comment ('ddataset' → 'dataset'). - Reason this comment was not posted:
Comment was on unchanged code.
5. packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py:85
- Draft comment:
The comment still uses 'ddataset' instead of 'dataset'—please correct it. - Reason this comment was not posted:
Comment was on unchanged code.
6. packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py:121
- Draft comment:
Removal of the detailed evaluator configuration print statements reduces debug visibility. Consider adding a conditional verbose flag if these logs are useful for troubleshooting. - Reason this comment was not posted:
Decided after close inspection that this draft comment was likely wrong and/or not actionable: usefulness confidence = 15% vs. threshold = 50% The comment is suggesting that the PR author "consider" adding a verbose flag, which is speculative and not a clear code change requirement. The comment uses the word "consider" which makes it a suggestion rather than identifying a definite issue. The removed logging code appears to be debug/informational output, and the PR author intentionally removed it - presumably because they didn't find it necessary. The comment is essentially second-guessing the author's decision without strong evidence that this is wrong. This violates the rule about not making speculative comments and not asking the author to confirm their intention. The removed logging might have been genuinely useful for debugging, and the comment could be providing valuable feedback about maintaining observability in the codebase. Perhaps there's a team standard about logging that I'm not aware of. Even if logging is valuable, the comment uses "consider" which makes it a suggestion rather than identifying a clear issue. The rules explicitly state not to make speculative comments or ask the author to confirm their intention. If the logging was critical, it would be caught in testing or code review by humans who understand the context better. This comment should be deleted. It's speculative (uses "consider"), doesn't identify a definite issue, and second-guesses an intentional removal by the author without strong evidence that it's wrong. The author clearly decided this logging wasn't necessary.
7. packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py:105
- Draft comment:
Typographical error: consider changing 'ddataset' to 'dataset' in the comment. - Reason this comment was not posted:
Comment was on unchanged code.
8. packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py:90
- Draft comment:
Typo in comment: "Set a ddataset slug that exists in the traceloop platform" should be "Set a dataset slug that exists in the traceloop platform". - Reason this comment was not posted:
Comment was on unchanged code.
9. packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py:94
- Draft comment:
Typographical error: The comment says 'ddataset' which is likely a typo. Consider changing it to 'dataset'. - Reason this comment was not posted:
Comment was on unchanged code.
10. packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py:95
- Draft comment:
Typo: "ddataset" on line 95 should be "dataset". - Reason this comment was not posted:
Comment was on unchanged code.
11. packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py:85
- Draft comment:
Typo: "ddataset" should be spelled as "dataset" in the comment. - Reason this comment was not posted:
Comment was on unchanged code.
12. packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py:128
- Draft comment:
Typographical error: "ddataset" appears in the comment. It should be "dataset". - Reason this comment was not posted:
Comment was on unchanged code.
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♻️ Duplicate comments (5)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (2)
22-22: Use explicit type hint for optional parameter.The
contextparameter defaults toNonebut lacks an explicitOptionalor union type hint, violating PEP 484.This issue was previously flagged. Apply this diff:
-async def generate_response(prompt: str, context: str = None) -> str: +async def generate_response(prompt: str, context: str | None = None) -> str:
45-48: Pass context to generate_response for faithfulness evaluation.The
contextis extracted from the row but not passed togenerate_response, which accepts it as an optional parameter. This omission affects the faithfulness evaluator's ability to assess whether the response is grounded in the provided context.This issue was previously flagged. Apply this diff:
question = row.get("question", "This is a demo question") context = row.get("context", "This is a demo context") # Generate response - completion = await generate_response(question) + completion = await generate_response(question, context)packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py (1)
79-84: Guard againstNoneconfig when accessing.items().The
EvaluatorDetails.configfield is typed asOptional[Dict[str, Any]]. While current factory methods setconfig, accessing.items()onNonewould raise anAttributeError.This issue was previously flagged. Apply this diff:
print("Running experiment with content safety evaluators:") for evaluator in evaluators: - config_str = ", ".join(f"{k}={v}" for k, v in evaluator.config.items() if k != "description") + config_str = "" + if evaluator.config: + config_str = ", ".join(f"{k}={v}" for k, v in evaluator.config.items() if k != "description") print(f" - {evaluator.slug}") if config_str: print(f" Config: {config_str}")packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py (2)
24-36: Extract and return logprobs data for perplexity evaluation.The function enables
logprobs=Truebut returns only the message content without extractingresponse.choices[0].logprobs. The perplexity evaluator requires the actual logprobs data structure. Additionally, the response access lacks error handling.This issue was previously flagged. Apply this diff:
-async def generate_response(prompt: str, max_tokens: int = 300) -> str: - """Generate a response using OpenAI with logprobs for perplexity measurement""" +from typing import Tuple, Any + +async def generate_response(prompt: str, max_tokens: int = 300) -> Tuple[str, Any]: + """Generate a response using OpenAI with logprobs for perplexity measurement + + Returns: + Tuple of (content, logprobs) where logprobs is the raw logprobs data structure + """ openai_client = AsyncOpenAI(api_key=os.getenv("OPENAI_API_KEY")) response = await openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=max_tokens, logprobs=True, # Enable logprobs for perplexity calculation ) - return response.choices[0].message.content + if not response.choices: + raise ValueError("No response generated by OpenAI") + + choice = response.choices[0] + content = choice.message.content + if content is None: + raise ValueError("OpenAI returned empty content") + + return content, choice.logprobs
53-59: Use actual logprobs data instead of completion text.Currently, the
"logprobs"key is assigned the completion text, but the perplexity evaluator expects the actual logprobs data structure from the OpenAI response. This depends on fixinggenerate_response()to return logprobs.This issue was previously flagged. After fixing
generate_response(), apply this diff:# Generate response - completion = await generate_response(question) + completion, logprobs_data = await generate_response(question) # Return data for evaluation # Different evaluators expect different fields return { - "logprobs": completion, # For perplexity + "logprobs": logprobs_data, # For perplexity "question": question, "completion": completion, # Standard completion field "reference": reference_answer, # For semantic similarity comparison "reference_topics": topic, # For topic adherence }
🧹 Nitpick comments (12)
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py (2)
84-84: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Per Python conventions and static analysis recommendations, prefix them with underscores to indicate they are intentionally unused.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run( dataset_slug="security", # Set a ddataset slug that exists in the traceloop platform dataset_version="v1", task=security_task, evaluators=evaluators, experiment_slug="security-evaluators-exp", stop_on_error=False, wait_for_results=True, )
85-85: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="security", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="security", # Set a dataset slug that exists in the traceloop platformpackages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (2)
94-94: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Prefix them with underscores to indicate they are intentionally unused.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(
95-95: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="quality", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="quality", # Set a dataset slug that exists in the traceloop platformpackages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py (2)
89-89: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Prefix them with underscores.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(
90-90: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="content-safety", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="content-safety", # Set a dataset slug that exists in the traceloop platformpackages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py (2)
104-104: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Prefix them with underscores.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(
105-105: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="quality", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="quality", # Set a dataset slug that exists in the traceloop platformpackages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py (2)
93-93: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Prefix them with underscores.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(
94-94: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="metrics", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="metrics", # Set a dataset slug that exists in the traceloop platformpackages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py (2)
127-127: Prefix unused variables with underscore.The variables
resultsanderrorsare unpacked but never used. Prefix them with underscores.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(
128-128: Fix typo in comment.The comment contains "ddataset" which should be "dataset".
Apply this diff:
- dataset_slug="validation", # Set a ddataset slug that exists in the traceloop platform + dataset_slug="validation", # Set a dataset slug that exists in the traceloop platform
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packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (4)
EvaluatorMadeByTraceloop(5-340)profanity_detector(237-247)toxicity_detector(38-52)sexism_detector(250-264)
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packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (2)
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packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py (1)
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packages/sample-app/sample_app/experiment/made_by_traceloop/content_safety_exp.py
89-89: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
89-89: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
packages/sample-app/sample_app/experiment/made_by_traceloop/advanced_quality_exp.py
104-104: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
104-104: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py
22-22: PEP 484 prohibits implicit Optional
Convert to T | None
(RUF013)
94-94: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
94-94: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
packages/sample-app/sample_app/experiment/made_by_traceloop/metrics_exp.py
93-93: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
93-93: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
packages/sample-app/sample_app/experiment/made_by_traceloop/validation_exp.py
127-127: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
127-127: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
packages/sample-app/sample_app/experiment/made_by_traceloop/security_exp.py
84-84: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
84-84: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
(RUF059)
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1. packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py:48
- Draft comment:
Passes 'context' to generate_response to include additional context. Confirm that generate_response properly handles the context parameter. - Reason this comment was not posted:
Confidence changes required:0%<= threshold50%None
2. packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py:185
- Draft comment:
Refactor: Simplified word_count method signature by removing the unnecessary line break within the parentheses. - Reason this comment was not posted:
Confidence changes required:0%<= threshold50%None
3. packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py:237
- Draft comment:
Refactor: Simplified profanity_detector method signature by removing the unnecessary line break within the parentheses. - Reason this comment was not posted:
Confidence changes required:0%<= threshold50%None
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♻️ Duplicate comments (2)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (2)
22-22: PEP 484: Use explicit type union for optional parameters.The
contextparameter defaults toNonebut the type hint isstr. This violates PEP 484 and should explicitly indicate the parameter can beNone.Apply this diff:
-async def generate_response(prompt: str, context: str = None) -> str: +async def generate_response(prompt: str, context: str | None = None) -> str:
30-37: Add error handling for OpenAI API response.The function accesses
response.choices[0].message.contentwithout validating the response structure, which could raiseIndexErrororAttributeErrorat runtime if the API returns an unexpected response.Apply this diff:
response = await openai_client.chat.completions.create( model="gpt-3.5-turbo", messages=messages, temperature=0.7, max_tokens=200, ) - return response.choices[0].message.content + if not response.choices: + raise ValueError("No response generated by OpenAI") + + content = response.choices[0].message.content + if content is None: + raise ValueError("OpenAI returned empty content") + + return content
🧹 Nitpick comments (3)
packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py (1)
94-94: Consider prefixing unused variables with underscore.The unpacked variables
resultsanderrorsare not used after the experiment run. If they're intentionally unused, prefix them with underscores to indicate this explicitly.Apply this diff:
- results, errors = await client.experiment.run( + _results, _errors = await client.experiment.run(packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (2)
12-18: Remove unused import from docstring example.The example imports
Predefinedon line 13 but never uses it, which may confuse developers.Apply this diff to remove the unused import:
Example: - >>> from traceloop.sdk.evaluator import Predefined - >>> >>> evaluators = [ ... EvaluatorMadeByTraceloop.pii_detector(probability_threshold=0.8), ... EvaluatorMadeByTraceloop.toxicity_detector(threshold=0.7),
21-68: Consider adding threshold validation.The
pii_detector,toxicity_detector, andprompt_injectionmethods document that thresholds should be in the range 0.0-1.0, but no validation enforces this constraint. Invalid values could cause runtime errors or unexpected behavior downstream.Consider adding validation like:
@staticmethod def pii_detector( probability_threshold: float = 0.5, ) -> EvaluatorDetails: """ PII (Personally Identifiable Information) detector evaluator. Args: probability_threshold: Minimum probability threshold for detecting PII (0.0-1.0) Returns: EvaluatorDetails configured for PII detection """ if not 0.0 <= probability_threshold <= 1.0: raise ValueError(f"probability_threshold must be between 0.0 and 1.0, got {probability_threshold}") config: Dict[str, Any] = {"probability_threshold": probability_threshold} return EvaluatorDetails(slug="pii-detector", version=None, config=config)Apply similar validation to
toxicity_detector(line 38),prompt_injection(line 55), andsexism_detector(line 248).
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answer_relevancy(210-220)faithfulness(223-233)
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packages/sample-app/sample_app/experiment/made_by_traceloop/quality_exp.py
22-22: PEP 484 prohibits implicit Optional
Convert to T | None
(RUF013)
94-94: Unpacked variable results is never used
Prefix it with an underscore or any other dummy variable pattern
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94-94: Unpacked variable errors is never used
Prefix it with an underscore or any other dummy variable pattern
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packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (1)
1-2: LGTM!The imports are appropriate and all are used throughout the file.
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1. packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py:273
- Draft comment:
Using an empty dict for config in secrets_detector ensures consistency with other evaluator methods. This is a good practice. - Reason this comment was not posted:
Comment did not seem useful. Confidence is useful =0%<= threshold50%This comment is purely informative and does not provide any actionable feedback or suggestions for improvement. It simply states that using an empty dict for config ensures consistency, which does not align with the rules for useful comments.
2. packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py:128
- Draft comment:
Adding a detailed type annotation for evaluator_details improves clarity and consistency in code. - Reason this comment was not posted:
Comment did not seem useful. Confidence is useful =0%<= threshold50%This comment is purely informative, as it praises the addition of a detailed type annotation without providing any actionable feedback or suggestions. It does not align with the rules for good comments, which should offer specific suggestions or highlight potential issues.
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⚠️ Outside diff range comments (1)
packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py (1)
96-103: Avoid silently ignoring unsupported evaluator specs in_run_locallyThe new normalization only handles
strandEvaluatorDetails. If a caller accidentally passes any other type (including a legacy(slug, version)tuple), that entry is silently dropped: no evaluator will run and there’s no error, which is hard to debug and could be a behavioral regression.It would be safer to fail fast on unsupported types so misconfigurations are obvious to users.
@@ - evaluators: Optional[List[EvaluatorSpec]] = None, + evaluators: Optional[List[EvaluatorSpec]] = None, @@ - # Convert evaluators to tuples of (slug, version, config) + # Convert evaluators to tuples of (slug, version, config) evaluator_details: Optional[List[Tuple[str, Optional[str], Optional[Dict[str, Any]]]]] = None if evaluators: evaluator_details = [] for evaluator in evaluators: if isinstance(evaluator, str): # Simple string slug evaluator_details.append((evaluator, None, None)) elif isinstance(evaluator, EvaluatorDetails): # EvaluatorDetails object with config evaluator_details.append((evaluator.slug, evaluator.version, evaluator.config)) + else: + raise TypeError( + f"Unsupported evaluator spec {evaluator!r}; " + "expected str or EvaluatorDetails." + )Optionally (if you still want to support legacy
(slug, version)tuples) you could extend the branch instead of raising, but in that case you should also updateEvaluatorSpecto include the tuple form.Also applies to: 130-148, 173-196
♻️ Duplicate comments (1)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (1)
101-122: Enforce the documented requirement forschema_stringinjson_validator.The docstring says
schema_stringis "required if enable_schema_validation is True", but the implementation allowsenable_schema_validation=Truewithschema_string=None. That breaks the documented contract and can push the error downstream instead of failing fast.Add a guard that raises a clear
ValueErrorwhenenable_schema_validationisTrueandschema_stringis not provided:def json_validator( enable_schema_validation: bool = False, schema_string: Optional[str] = None, ) -> EvaluatorDetails: @@ - Returns: - EvaluatorDetails configured for JSON validation - """ - config: Dict[str, Any] = { - "enable_schema_validation": enable_schema_validation, - } - if schema_string: - config["schema_string"] = schema_string + Returns: + EvaluatorDetails configured for JSON validation + """ + if enable_schema_validation and not schema_string: + raise ValueError("schema_string is required when enable_schema_validation is True") + + config: Dict[str, Any] = { + "enable_schema_validation": enable_schema_validation, + } + if schema_string: + config["schema_string"] = schema_string
🧹 Nitpick comments (4)
packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (2)
12-18: Clean up the example import to avoid confusion.The docstring example imports
Predefinedbut then usesEvaluatorMadeByTraceloopdirectly and never usesPredefined. Consider either importingEvaluatorMadeByTraceloopin the example or actually demonstrating howPredefinedrelates to this class, so the usage pattern is unambiguous.
158-339: Optional: DRY up simple “no-config” evaluators.Many evaluators (
char_count,char_count_ratio,word_count,word_count_ratio,answer_relevancy,faithfulness,profanity_detector,secrets_detector,sql_validator,semantic_similarity,agent_goal_accuracy,topic_adherence,perplexity) all buildconfig: Dict[str, Any] = {}and return anEvaluatorDetailswith just a different slug. If this list grows, you might consider a tiny helper to reduce repetition and keep slugs centralized, e.g._simple(slug: str) -> EvaluatorDetails.packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py (2)
38-63: Clarifyevaluatorsparameter to match newEvaluatorSpeccontractThe type hint now allows
EvaluatorSpec, but the docstring still says “List of evaluator slugs to run”, which hides the support forEvaluatorDetails(with version/config). Updating the docstring will make the new API surface clearer.@@ - evaluators: List of evaluator slugs to run + evaluators: List of evaluator specs to run. Each item can be a + string evaluator slug or an EvaluatorDetails instance with + optional version/config.
251-255: Align_run_in_githubdocstring and behavior withEvaluatorSpecand fail-fast on bad specsHere,
evaluatorsis also typed asOptional[List[EvaluatorSpec]], and the slug extraction handles onlystrandEvaluatorDetails. The docstring, however, still claims support for “(slug, version) tuples”, which no longer matches the implementation and may confuse callers. Also, unsupported types are silently ignored, similar to_run_locally.I recommend (a) updating the docstring to describe the actual supported forms and (b) failing fast on unsupported specs.
@@ - evaluators: List of evaluator slugs or (slug, version) tuples to run + evaluators: List of evaluator specs to run. Each item can be a + string evaluator slug or an EvaluatorDetails instance with + optional version/config. For GitHub runs, only the slug is + currently used. @@ - evaluator_slugs = None - if evaluators: - evaluator_slugs = [] - for evaluator in evaluators: - if isinstance(evaluator, str): - evaluator_slugs.append(evaluator) - elif isinstance(evaluator, EvaluatorDetails): - evaluator_slugs.append(evaluator.slug) + evaluator_slugs = None + if evaluators: + evaluator_slugs = [] + for evaluator in evaluators: + if isinstance(evaluator, str): + evaluator_slugs.append(evaluator) + elif isinstance(evaluator, EvaluatorDetails): + evaluator_slugs.append(evaluator.slug) + else: + raise TypeError( + f"Unsupported evaluator spec {evaluator!r}; " + "expected str or EvaluatorDetails." + )This makes the GitHub path’s behavior explicit and keeps configuration/version semantics for that path clearly documented (currently config/version are accepted via
EvaluatorDetailsbut effectively ignored here).Also applies to: 347-355
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packages/traceloop-sdk/traceloop/sdk/evaluator/evaluators_made_by_traceloop.py (1)
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EvaluatorDetails(5-20)
packages/traceloop-sdk/traceloop/sdk/experiment/experiment.py (2)
packages/traceloop-sdk/traceloop/sdk/evaluator/config.py (1)
EvaluatorDetails(5-20)packages/traceloop-sdk/traceloop/sdk/evaluator/evaluator.py (1)
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Fixes TLP-1296
feat(instrumentation): ...orfix(instrumentation): ....Important
Add experiment templates and evaluator factories for various evaluations, standardize task output, and enhance evaluator configuration handling.
advanced_quality_exp.py,content_safety_exp.py,metrics_exp.py,quality_exp.py,security_exp.py, andvalidation_exp.pyto demonstrate various evaluators.EvaluatorMadeByTraceloopinevaluators_made_by_traceloop.pyfor creating evaluators with configurations likepii_detector,toxicity_detector,regex_validator, etc.textinrun_research_experiment.py.EvaluatorDetailsinconfig.pyfor evaluator configuration.ExecuteEvaluatorRequestinmodel.pyto includeevaluator_config.run_experiment_evaluator()andtrigger_experiment_evaluator()inevaluator.pyto handleevaluator_config.run()and_run_locally()inexperiment.pyto supportEvaluatorSpecwith configuration.This description was created by
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New Features
SDK Changes
Bug Fixes
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