Conversation
|
Warning Rate limit exceeded@KaQuMiQ has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 12 minutes and 22 seconds before requesting another review. ⌛ How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. 🚦 How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout. Please see our FAQ for further information. 📒 Files selected for processing (7)
""" WalkthroughThis change set increments the project version from "0.73.0" to "0.73.1" in Possibly related PRs
✨ Finishing Touches
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. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 5
🔭 Outside diff range comments (1)
src/draive/helpers/instruction_preparation.py (1)
24-29: Consider adding a docstringThe function lacks documentation. Since this is a helper function that prepares instructions, it would benefit from a docstring explaining its purpose, parameters, and return value.
async def prepare_instruction( instruction: InstructionDeclaration | str, /, *, guidelines: str | None = None, ) -> Instruction: + """ + Prepare a detailed instruction from a declaration or description. + + Args: + instruction: Either an InstructionDeclaration object or a string description + guidelines: Optional additional guidelines for instruction preparation + + Returns: + A prepared Instruction object + + Raises: + InstructionPreparationAmbiguity: When clarification is needed + ValueError: When instruction preparation fails + """
📜 Review details
Configuration used: CodeRabbit UI
Review profile: ASSERTIVE
Plan: Pro
📒 Files selected for processing (7)
pyproject.toml(1 hunks)src/draive/__init__.py(0 hunks)src/draive/evaluation/suite.py(4 hunks)src/draive/evaluation/value.py(2 hunks)src/draive/gemini/lmm_generation.py(2 hunks)src/draive/helpers/instruction_preparation.py(1 hunks)src/draive/helpers/instruction_refinement.py(3 hunks)
💤 Files with no reviewable changes (1)
- src/draive/init.py
🧰 Additional context used
📓 Path-based instructions (1)
`**/*.py`: Follow Ruff import ordering (standard library, third party, local) Us...
**/*.py: Follow Ruff import ordering (standard library, third party, local)
Use Python 3.12+ type features (type unions with |, generic syntax)
Use base and abstract types like Sequence or Iterable instead of concrete types
Use custom exceptions for specific errors
Format code with Ruff
Run linters (Ruff + Bandit + Pyright strict mode)
📄 Source: CodeRabbit Inference Engine (CLAUDE.md)
List of files the instruction was applied to:
src/draive/helpers/instruction_preparation.pysrc/draive/evaluation/value.pysrc/draive/gemini/lmm_generation.pysrc/draive/evaluation/suite.pysrc/draive/helpers/instruction_refinement.py
🧠 Learnings (2)
📓 Common learnings
Learnt from: KaQuMiQ
PR: miquido/draive#338
File: src/draive/lmm/__init__.py:1-2
Timestamp: 2025-06-16T10:28:07.434Z
Learning: The draive project requires Python 3.12+ as specified in pyproject.toml with "requires-python = ">=3.12"" and uses Python 3.12+ specific features like PEP 695 type aliases and generic syntax extensively throughout the codebase.
pyproject.toml (1)
Learnt from: KaQuMiQ
PR: miquido/draive#338
File: src/draive/lmm/__init__.py:1-2
Timestamp: 2025-06-16T10:28:07.434Z
Learning: The draive project requires Python 3.12+ as specified in pyproject.toml with "requires-python = ">=3.12"" and uses Python 3.12+ specific features like PEP 695 type aliases and generic syntax extensively throughout the codebase.
🧬 Code Graph Analysis (1)
src/draive/evaluation/suite.py (1)
src/draive/parameters/model.py (1)
DataModel(386-756)
🪛 Pylint (3.3.7)
src/draive/helpers/instruction_preparation.py
[convention] 24-24: Missing function or method docstring
(C0116)
src/draive/evaluation/value.py
[error] 8-8: Parsing failed: 'invalid syntax (draive.evaluation.value, line 8)'
(E0001)
🪛 Flake8 (7.2.0)
src/draive/evaluation/value.py
[error] 8-8: SyntaxError: invalid syntax
(E999)
🔇 Additional comments (10)
pyproject.toml (1)
8-8: LGTM!The minor version bump from 0.73.0 to 0.73.1 is appropriate for the interface updates and refactoring changes in this PR.
src/draive/evaluation/value.py (1)
8-19: LGTM!The addition of the "max" literal and corresponding MAX constant (1.0) is well-implemented and maintains consistency with the existing evaluation score system.
Also applies to: 27-27, 53-55
src/draive/gemini/lmm_generation.py (2)
257-266: Ensure empty completion content is deliberateThe change in
src/draive/gemini/lmm_generation.py(lines 257–266) now always returns anLMMCompletion—even whencompletion_contentis empty—whereas other providers (OpenAI, vLLM, Ollama) only emit a completion when there’s actual content. Please confirm:• Empty completions from the Gemini path are acceptable and won’t break downstream logic
• All callers can safely handle anLMMCompletionwhosecontentmay be empty
• If not intended, consider re-adding a guard or error branch before returning
436-440: Gemini generator will now emit empty LMMStreamChunksChanging the branch from
elif chunk_content:toelse:means that when no content parts are collected, you’ll still yieldLMMStreamChunk.of(MultimodalContent.of(*chunk_content), eod=False)with an empty payload. Downstream consumers do not currently guard against empty
chunk.content, so please verify they handle empty chunks without side-effects or UI glitches.Review these locations:
- src/draive/gemini/lmm_generation.py lines 436–440
- src/draive/conversation/realtime/default.py around line 97
- src/draive/openai/lmm_session.py around line 365
src/draive/evaluation/suite.py (3)
102-108: LGTM!The addition of
SuiteParametersgeneric type andparametersfield toSuiteEvaluatorResultprovides better type safety and enables tracking of suite-level parameters alongside case results.
278-283: Simplified and consistent APIThe removal of overloads and unified return type
SuiteEvaluatorResultimproves API consistency. The flexible*case_parametersargument supports multiple input types elegantly.
336-349: Well-structured concurrent evaluationThe use of
gatherfor concurrent case evaluation and proper parameter handling (using suite parameters from input or falling back to stored parameters) is well implemented.src/draive/helpers/instruction_refinement.py (3)
19-87: Well-structured implementation of tree-based refinement approach!The staged approach with initialization, exploration, and finalization provides excellent separation of concerns. The parameter validation is thorough and the orchestration using
Stage.sequenceis clean.
120-169: Excellent data structure design for tree exploration!The separation between
_RefinementTreeNodeand_RefinementStateis clean, with clear responsibilities. The properties for node characteristics (is_root,is_leaf) and score access make the code more readable.
635-671: Comprehensive tree statistics for debugging and analysis!The statistics provide valuable insights into the exploration efficiency, pruning effectiveness, and the path to the best solution. The division by zero is properly handled with the ternary operator.
| is_leaf = len(node.children) == 0 | ||
| EXCEPTIONAL_THRESHOLD = 0.95 | ||
| is_exceptional = node.focused_evaluation_score > EXCEPTIONAL_THRESHOLD | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Extract exceptional threshold as a configurable parameter.
The hardcoded threshold value reduces flexibility and makes the logic less transparent.
Define the constant at module level or make it configurable:
# At module level after imports
_EXCEPTIONAL_SCORE_THRESHOLD: float = 0.95Then update the usage:
- EXCEPTIONAL_THRESHOLD = 0.95
- is_exceptional = node.focused_evaluation_score > EXCEPTIONAL_THRESHOLD
+ is_exceptional = node.focused_evaluation_score > _EXCEPTIONAL_SCORE_THRESHOLD🤖 Prompt for AI Agents
In src/draive/helpers/instruction_refinement.py around lines 619 to 622, the
exceptional threshold value is hardcoded as 0.95, reducing flexibility. Move
this constant to the module level by defining _EXCEPTIONAL_SCORE_THRESHOLD =
0.95 after the imports, then replace the inline value with this constant in the
code to improve configurability and clarity.
1 participant
No description provided.