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Add instruction generation tool#392

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KaQuMiQ merged 1 commit intomainfrom
feature/generate
Aug 7, 2025
Merged

Add instruction generation tool#392
KaQuMiQ merged 1 commit intomainfrom
feature/generate

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@KaQuMiQ KaQuMiQ commented Aug 7, 2025

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📥 Commits

Reviewing files that changed from the base of the PR and between e549bb9 and f69a0b2.

📒 Files selected for processing (2)
  • src/draive/helpers/instruction_refinement.py (4 hunks)
  • tools/generate_instruction.py (1 hunks)

Walkthrough

This pull request makes two primary changes. First, it removes the generic type parameter from type annotations of _RefinementTreeNode in several functions within src/draive/helpers/instruction_refinement.py, simplifying the type signatures without altering logic or control flow. Second, it introduces a new standalone script, tools/generate_instruction.py, which generates actionable LLM instructions from task descriptions using the draive library. The script supports multiple AI providers, handles API key loading, command-line parsing, and outputs the generated instructions.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~15–20 minutes

Possibly related PRs

  • Update instruction refinement #357: Refactors the instruction refinement process and updates _explore_node, overlapping with this PR's changes to type annotations and function signatures in the same area.
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Actionable comments posted: 4

📜 Review details

Configuration used: CodeRabbit UI
Review profile: ASSERTIVE
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 5a13842 and e549bb9.

📒 Files selected for processing (2)
  • src/draive/helpers/instruction_refinement.py (4 hunks)
  • tools/generate_instruction.py (1 hunks)
🧰 Additional context used
📓 Path-based instructions (1)
**/*.py

📄 CodeRabbit Inference Engine (CLAUDE.md)

**/*.py: Use absolute imports from draive package
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
Use NumPy docstring convention for all functions, classes, and methods
Skip module-level docstrings unless explicitly requested
Include sections: Parameters, Returns, Raises, Notes (if needed) in docstrings
ALWAYS use Sequence[T] instead of list[T], Mapping[K,V] instead of dict[K,V], and Set[T] instead of set[T] for collections in State, Config and DataModel classes
Immutable updates to State, Config, and DataModel classes should be performed through the .updated() method

Files:

  • src/draive/helpers/instruction_refinement.py
  • tools/generate_instruction.py
🔇 Additional comments (8)
src/draive/helpers/instruction_refinement.py (5)

290-291: LGTM! Type annotation simplification improves readability.

The removal of the generic type parameter from _RefinementTreeNode is consistent with the class definition and makes the code cleaner without affecting functionality.


301-301: LGTM! Consistent type annotation cleanup.

The type annotation simplification aligns with the other changes in this file and maintains consistency across the codebase.


307-307: LGTM! Proper use of concrete dict type.

Using dict[UUID, _RefinementTreeNode] is appropriate here since this is a mutable local variable, not a parameter type that should use Mapping.


359-365: LGTM! Function signature simplification.

The removal of the generic parameter from the function signature and return type makes the code more readable while maintaining the same functionality.


387-387: LGTM! Consistent local variable typing.

Using dict[UUID, _RefinementTreeNode] for the local variable is appropriate and consistent with the simplified type annotations throughout the file.

tools/generate_instruction.py (3)

57-67: LGTM! Proper absolute imports from draive package.

The imports follow the coding guidelines by using absolute imports from the draive package and are properly organized.


114-175: LGTM! Well-structured CLI with proper error handling.

The main function demonstrates good practices:

  • Proper argument parsing with helpful examples
  • Robust error handling for various scenarios
  • Clean stdin/argument handling logic
  • Appropriate exit codes for different error conditions

104-104: No action needed: valid Gemini model name

The alias gemini-2.5-pro is indeed the correct identifier for the latest stable “Pro” model in Google’s Gemini API. You can continue using it as-is.

• Location: tools/generate_instruction.py line 104
• Model alias: gemini-2.5-pro [latest stable Pro model]

@KaQuMiQ KaQuMiQ changed the title Add instruction generation toop Add instruction generation tool Aug 7, 2025
@KaQuMiQ KaQuMiQ merged commit 81f49db into main Aug 7, 2025
4 of 5 checks passed
@KaQuMiQ KaQuMiQ deleted the feature/generate branch August 7, 2025 13:47
@coderabbitai coderabbitai bot mentioned this pull request Aug 8, 2025
@coderabbitai coderabbitai bot mentioned this pull request Oct 1, 2025
@coderabbitai coderabbitai bot mentioned this pull request Oct 21, 2025
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