Conversation
|
Caution Review failedThe pull request is closed. WalkthroughThis change introduces a comprehensive reference document for the Draive framework, a Python 3.12+ functional programming framework for LLM applications. The document details architectural principles, strict typing rules, and essential imports. It distinguishes between state and data models, introduces protocols for service interfaces, and provides usage examples for dependency injection, provider setup, authentication, and multi-provider abstraction. The reference covers context management, the Stage API for AI workflows, evaluation systems, LLM generation, tool integration, multimodal content handling, conversation management, RAG patterns, resource management, utilities, advanced patterns, and error handling. It also includes complete application and testing examples, and enumerates best practices and rules. Additionally, a minor code cleanup was made by removing a generic type parameter from a state instantiation in the instruction refinement helper. Estimated code review effort🎯 4 (Complex) | ⏱️ ~40 minutes
Possibly related PRs
Note 🔌 MCP (Model Context Protocol) integration is now available in Early Access!Pro users can now connect to remote MCP servers under the Integrations page to get reviews and chat conversations that understand additional development context. 📜 Recent review detailsConfiguration used: CodeRabbit UI 📒 Files selected for processing (3)
✨ Finishing Touches
🧪 Generate unit tests
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. 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: 4
📜 Review details
Configuration used: CodeRabbit UI
Review profile: ASSERTIVE
Plan: Pro
📒 Files selected for processing (1)
llms.txt(1 hunks)
🧰 Additional context used
🧠 Learnings (1)
📓 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.
Learnt from: KaQuMiQ
PR: miquido/draive#327
File: src/draive/helpers/instruction_preparation.py:28-34
Timestamp: 2025-05-28T17:41:57.460Z
Learning: The draive project uses and requires Python 3.12+, so PEP-695 generic syntax with square brackets (e.g., `def func[T: Type]()`) is valid and should be used instead of the older TypeVar approach.
🪛 LanguageTool
llms.txt
[style] ~41-~41: To elevate your writing, try using a synonym here.
Context: ...Reasoning**: LLM application quality is hard to ensure without systematic evaluation...
(HARD_TO)
There was a problem hiding this comment.
Actionable comments posted: 8
♻️ Duplicate comments (5)
llms.txt (5)
876-913: Remove duplicated “Evaluation System → Built-in Evaluators” sectionThe block at Lines 1376–1407 duplicates earlier content (Lines 876–913). Keep a single authoritative section to avoid divergence.
-## Evaluation System - -### Built-in Evaluators -```python -... (lines 1376-1407 content) -``` +<!-- Removed duplicated “Evaluation System → Built-in Evaluators” section; already covered earlier -->Also applies to: 1376-1407
1427-1437: Invalid Python: inline class definition inside function argumentsDefining a class directly in the call to
ModelGeneration.generate()is invalid. Define the DataModel above, then pass it.- evaluation = await ModelGeneration.generate( - class CodeEvaluation(DataModel): - readability_score: float = Field(description="Score 0-1 for readability") - efficiency_score: float = Field(description="Score 0-1 for efficiency") - correctness_score: float = Field(description="Score 0-1 for correctness") - overall_score: float = Field(description="Overall score 0-1") - feedback: str = Field(description="Detailed feedback") - , - instruction=f"Evaluate this {language} code based on: {', '.join(criteria)}", - input=code, - ) + class CodeEvaluation(DataModel): + readability_score: float = Field(description="Score 0-1 for readability") + efficiency_score: float = Field(description="Score 0-1 for efficiency") + correctness_score: float = Field(description="Score 0-1 for correctness") + overall_score: float = Field(description="Overall score 0-1") + feedback: str = Field(description="Detailed feedback") + + evaluation = await ModelGeneration.generate( + CodeEvaluation, + instruction=f"Evaluate this {language} code based on: {', '.join(criteria)}", + input=code, + )
1848-1853: Replace unsafe eval in calculator tool with a safe evaluatorExample code should not normalize unsafe patterns.
@tool async def calculator(expression: str) -> float: """Calculate mathematical expressions.""" - # Safe evaluation logic - return eval(expression) # Use safe_eval in production + # Safe evaluation logic (avoid eval) + from simpleeval import simple_eval # lightweight safe evaluator + return float(simple_eval(expression))Optionally add a short warning paragraph above the snippet explaining risks of eval and safe alternatives.
2099-2109: Invalid Python: inline DataModel in generate() call (ArticleSummaryData)Same issue as above; define class separately and pass it to the call.
- summary_result = await ModelGeneration.generate( - class ArticleSummaryData(DataModel): - summary: str = Field(description="Concise summary in 2-3 sentences") - key_points: Sequence[str] = Field(description="3-5 key points from the article") - reading_time: int = Field(description="Estimated reading time in minutes") - , - instruction="Analyze this article and provide a comprehensive summary", - input=f"Title: {article.title}\n\nContent: {article.content}", - ) + class ArticleSummaryData(DataModel): + summary: str = Field(description="Concise summary in 2-3 sentences") + key_points: Sequence[str] = Field(description="3-5 key points from the article") + reading_time: int = Field(description="Estimated reading time in minutes") + + summary_result = await ModelGeneration.generate( + ArticleSummaryData, + instruction="Analyze this article and provide a comprehensive summary", + input=f"Title: {article.title}\n\nContent: {article.content}", + )
2430-2437: Consolidate repeated “Performance and Scalability Rules” (Section 15 duplicates Section 11)Merge into a single authoritative list to avoid drift.
-### 15. Performance and Scalability Rules -... -**(duplicate of Section 11 content)** +<!-- Removed duplicate. Consolidate any unique bullets into Section 11 above. -->Also applies to: 2400-2405
| Stage.completion( | ||
| "Format as executive summary with action items", | ||
| instruction="User markdown formatting for the result." | ||
| ) | ||
| ) |
There was a problem hiding this comment.
🧹 Nitpick (assertive)
Typo in instruction text
“User markdown formatting” → “Use markdown formatting”.
- instruction="User markdown formatting for the result."
+ instruction="Use markdown formatting for the result."📝 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.
| Stage.completion( | |
| "Format as executive summary with action items", | |
| instruction="User markdown formatting for the result." | |
| ) | |
| ) | |
| Stage.completion( | |
| "Format as executive summary with action items", | |
| instruction="Use markdown formatting for the result." | |
| ) | |
| ) |
🤖 Prompt for AI Agents
In llms.txt around lines 637 to 641, there is a typo in the instruction text
where it says "User markdown formatting for the result." Change "User" to "Use"
so the instruction reads "Use markdown formatting for the result."
llms.txt
Outdated
| @tool | ||
| async def analyze_data(data: Sequence[float]) -> DataModel: | ||
| """Analyze numerical data and return structured results.""" | ||
| class StatisticalAnalysis(DataModel): | ||
| mean: float | ||
| median: float | ||
| std_dev: float | ||
| count: int | ||
|
|
||
| return StatisticalAnalysis( | ||
| mean=sum(data) / len(data), | ||
| median=sorted(data)[len(data) // 2], | ||
| std_dev=calculate_std_dev(data), | ||
| count=len(data), | ||
| ) | ||
| ``` |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve analyze_data: correct median for even-length and remove undefined calculate_std_dev
Use statistics from stdlib; current median calc is wrong for even-length inputs and std dev helper is undefined.
@tool
async def analyze_data(data: Sequence[float]) -> DataModel:
"""Analyze numerical data and return structured results."""
class StatisticalAnalysis(DataModel):
mean: float
median: float
std_dev: float
count: int
-
- return StatisticalAnalysis(
- mean=sum(data) / len(data),
- median=sorted(data)[len(data) // 2],
- std_dev=calculate_std_dev(data),
- count=len(data),
- )
+ from statistics import mean as _mean, median as _median, pstdev as _pstdev
+ n = len(data)
+ return StatisticalAnalysis(
+ mean=_mean(data) if n else 0.0,
+ median=_median(data) if n else 0.0,
+ std_dev=_pstdev(data) if n > 1 else 0.0,
+ count=n,
+ )📝 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.
| @tool | |
| async def analyze_data(data: Sequence[float]) -> DataModel: | |
| """Analyze numerical data and return structured results.""" | |
| class StatisticalAnalysis(DataModel): | |
| mean: float | |
| median: float | |
| std_dev: float | |
| count: int | |
| return StatisticalAnalysis( | |
| mean=sum(data) / len(data), | |
| median=sorted(data)[len(data) // 2], | |
| std_dev=calculate_std_dev(data), | |
| count=len(data), | |
| ) | |
| ``` | |
| @tool | |
| async def analyze_data(data: Sequence[float]) -> DataModel: | |
| """Analyze numerical data and return structured results.""" | |
| class StatisticalAnalysis(DataModel): | |
| mean: float | |
| median: float | |
| std_dev: float | |
| count: int | |
| from statistics import mean as _mean, median as _median, pstdev as _pstdev | |
| n = len(data) | |
| return StatisticalAnalysis( | |
| mean=_mean(data) if n else 0.0, | |
| median=_median(data) if n else 0.0, | |
| std_dev=_pstdev(data) if n > 1 else 0.0, | |
| count=n, | |
| ) |
🤖 Prompt for AI Agents
In llms.txt around lines 1134 to 1149, the median calculation is incorrect for
even-length data sequences and the function calculate_std_dev is undefined.
Replace the manual median calculation with the median function from the Python
statistics module to handle both even and odd lengths correctly. Also, import
and use statistics.stdev for standard deviation instead of the undefined
calculate_std_dev function. Ensure to add the necessary import statement for the
statistics module at the top of the file.
KaQuMiQ
force-pushed
the
feature/llms
branch
2 times, most recently
from
fe12906 to
1ce3733
Compare
1 participant
No description provided.