feat(engine-models): Add pydantic models for custom grammar detection engine

[JayGhiya]

User description

This commit introduces a set of Pydantic models for the custom grammar
detection engine. The key changes are:

• Defines enums for TargetLevel, LocatorStrategy, and Concept to represent the various concepts used in the refactored engine.
• Introduces the ConstructQueryConfig model to represent the language-specific configuration for constructing the concept queries.
• Defines the FeatureSpec model to represent the strongly-typed feature specification from the schema.
• Adds the Detection and DetectionResult models to represent the results of feature detection in source code.
• Introduces specialized detection information models for AnnotationLikeInfo, CallExpressionInfo, and InheritanceInfo.

These models provide a structured and type-safe way to work with the custom grammar detection engine, improving maintainability and robustness of the codebase.

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PR Type

Enhancement

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Description

• Add comprehensive Pydantic models for custom grammar detection engine

• Introduce structural signature models for source code analysis

• Add SQLModel framework metadata models with PostgreSQL support

• Migrate build system from Poetry to uv/hatch

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Diagram Walkthrough

flowchart LR
  A["Engine Models"] --> B["Detection Engine"]
  C["Structural Models"] --> D["Code Analysis"]
  E["Framework Models"] --> F["PostgreSQL Storage"]
  G["Build System"] --> H["uv/hatch Migration"]

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File Walkthrough

	Relevant files

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[huly-for-github]

Connected to Huly®: UNOPL-799

[qodo-code-review]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 No relevant tests

🔒 No security concerns identified

⚡ Recommended focus areas for review Duplicate File This file appears to be a duplicate of the structural signature models that are already defined in the base_models/structural_signature.py file. The models have slight differences which could lead to confusion and maintenance issues. """Pydantic models for file structural signatures.""" from typing import List, Optional from pydantic import BaseModel, Field class ImportInfo(BaseModel): """Information about import statements in a file.""" start_line: int = Field(..., description="Starting line number of import block") end_line: int = Field(..., description="Ending line number of import block") imports: List[str] = Field(..., description="Array of import statement strings") class VariableInfo(BaseModel): """Information about a global variable declaration.""" start_line: int = Field(..., description="Starting line number of variable declaration") end_line: int = Field(..., description="Ending line number of variable declaration") signature: str = Field(..., description="Complete variable declaration line(s)") class FunctionInfo(BaseModel): """Information about a function or method.""" start_line: int = Field(..., description="Line number where the function starts") end_line: int = Field(..., description="Line number where the function ends") signature: str = Field(..., description="Complete function declaration including decorators, def/async def, parameters, return type") docstring: Optional[str] = Field(None, description="Function docstring") class ClassInfo(BaseModel): """Information about a class definition.""" start_line: int = Field(..., description="Line number where the class starts") end_line: int = Field(..., description="Line number where the class ends") signature: str = Field(..., description="Complete class declaration including decorators and inheritance") docstring: Optional[str] = Field(None, description="Class docstring") class_variables: List[VariableInfo] = Field(default_factory=list, description="Class-level variables") methods: List[FunctionInfo] = Field(default_factory=list, description="Class methods") nested_classes: List['ClassInfo'] = Field(default_factory=list, description="Nested class definitions") class StructuralSignature(BaseModel): """ Structural signature of a source code file. This model captures the high-level structure and outline of a source file, including module-level constructs, imports, functions, classes, and their positions within the file. """ module_docstring: Optional[str] = Field(None, description="Module-level docstring") imports: Optional[ImportInfo] = Field(default=None, description="Import statements block") global_variables: List[VariableInfo] = Field(default_factory=list, description="Module-level variables") functions: List[FunctionInfo] = Field(default_factory=list, description="Module-level functions") classes: List[ClassInfo] = Field(default_factory=list, description="Class definitions") # Additional metadata total_lines: Optional[int] = Field(None, description="Total number of lines in the file") has_main_block: bool = Field(False, description="Whether the file has if __name__ == '__main__' block") encoding: Optional[str] = Field("utf-8", description="File encoding") # Update forward references for nested classes ClassInfo.model_rebuild() Model Inconsistency The StructuralSignature model is missing the imports field that exists in the duplicate file, and ClassInfo uses 'vars' instead of 'class_variables'. These inconsistencies between the two versions could cause integration issues. vars: List[VariableInfo] = Field(default_factory=list, description="Class and instance variables") methods: List[FunctionInfo] = Field(default_factory=list, description="Class methods") nested_classes: List['ClassInfo'] = Field(default_factory=list, description="Nested class declarations") class StructuralSignature(BaseModel): """ Structural signature of a source code file. This model captures the high-level structure and outline of a source file, including module-level constructs, functions, classes, and their positions within the file. """ module_docstring: Optional[str] = Field(None, description="Module-level docstring") global_variables: List[VariableInfo] = Field(default_factory=list, description="Module-level variables") functions: List[FunctionInfo] = Field(default_factory=list, description="Module-level functions") classes: List[ClassInfo] = Field(default_factory=list, description="Class definitions") Hardcoded Path The src configuration contains a hardcoded absolute path specific to a user's local machine, which will not work in other environments and should be made relative or removed. src = ["/Users/jayghiya/Documents/unoplat/unoplat-codebase-understanding/unoplat-code-confluence-commons"] line-length = 88

[qodo-code-review]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix conflicting field name The field name vars conflicts with Python's built-in vars() function and differs from the original class_variables field name. This could cause naming conflicts and breaks compatibility with existing code. unoplat-code-confluence-commons/unoplat_code_confluence_commons/base_models/structural_signature.py [35] -vars: List[VariableInfo] = Field(default_factory=list, description="Class and instance variables") +class_variables: List[VariableInfo] = Field(default_factory=list, description="Class-level variables") Apply / Chat Suggestion importance[1-10]: 7 __ Why: The suggestion correctly identifies that the field name vars shadows a Python built-in function, which is poor practice and could lead to confusion or bugs.	Medium
Update

[JayGhiya]

ok this has been a lesson. i had given access to agent to run commands of act (Tool we use to run github action locally) it added bind and removed all uncommitted changes of our experiment with pydantic ai agent all files gone revoking access for that command