64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
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from __future__ import annotations
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from dataclasses import asdict, dataclass, field
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from typing import Any, Dict, List, Optional
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@dataclass
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class CodeFunctionEvidence:
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node_id: str
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name: str
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qualified_name: str
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file: str
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start_line: Optional[int] = None
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end_line: Optional[int] = None
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signature: str = ""
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summary: str = ""
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logic_flow: str = ""
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code_snippet: str = ""
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calls: List[str] = field(default_factory=list)
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called_by: List[str] = field(default_factory=list)
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includes: List[str] = field(default_factory=list)
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embedding_model: str = ""
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embedding_dim: int = 0
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embedding_available: bool = True
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raw: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@dataclass
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class CodeSearchHit:
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evidence: CodeFunctionEvidence
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similarity: float
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distance: float
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rank: int
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def to_dict(self) -> Dict[str, Any]:
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data = self.evidence.to_dict()
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data.update(
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{
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"similarity": self.similarity,
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"distance": self.distance,
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"rank": self.rank,
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}
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)
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return data
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@dataclass
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class CodeGraphContext:
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node_id: str
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callers: List[CodeFunctionEvidence] = field(default_factory=list)
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callees: List[CodeFunctionEvidence] = field(default_factory=list)
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call_chains: List[str] = field(default_factory=list)
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def to_dict(self) -> Dict[str, Any]:
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return {
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"node_id": self.node_id,
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"callers": [item.to_dict() for item in self.callers],
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"callees": [item.to_dict() for item in self.callees],
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"call_chains": self.call_chains,
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}
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