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"""Response entity models for Q&A results."""
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional
from uuid import uuid4
@dataclass
class Citation:
"""A reference to source code location."""
file_path: str
start_line: int
end_line: int
def __str__(self) -> str:
"""Format as citation string."""
return f"[{self.file_path}:{self.start_line}-{self.end_line}]"
@classmethod
def parse(cls, citation_str: str) -> Optional["Citation"]:
"""Parse citation from string format [file:start-end]."""
try:
citation_str = citation_str.strip("[]")
if ":" not in citation_str:
return None
file_path, line_range = citation_str.rsplit(":", 1)
if "-" in line_range:
start, end = line_range.split("-")
return cls(
file_path=file_path,
start_line=int(start),
end_line=int(end),
)
else:
line = int(line_range)
return cls(file_path=file_path, start_line=line, end_line=line)
except (ValueError, IndexError):
return None
@dataclass
class RetrievedChunk:
"""A chunk retrieved for answering a query."""
chunk_id: str
content: str
file_path: str
start_line: int
end_line: int
relevance_score: float
chunk_type: str
name: Optional[str] = None
@property
def citation(self) -> str:
"""Get citation format."""
return f"[{self.file_path}:{self.start_line}-{self.end_line}]"
@dataclass
class Query:
"""A user's question about a repository."""
question: str
repo_id: str
id: str = field(default_factory=lambda: str(uuid4()))
timestamp: datetime = field(default_factory=datetime.now)
top_k: int = 5
@dataclass
class Response:
"""The system's answer to a query."""
answer: str
citations: list[Citation]
retrieved_chunks: list[RetrievedChunk]
grounded: bool
query_id: str = ""
confidence_score: float = 0.0
@property
def has_evidence(self) -> bool:
"""Check if response has supporting evidence."""
return len(self.retrieved_chunks) > 0
@property
def citation_count(self) -> int:
"""Count of citations in response."""
return len(self.citations)
def format_evidence(self) -> str:
"""Format evidence section for display."""
if not self.retrieved_chunks:
return "No evidence retrieved."
lines = ["## Evidence\n"]
for i, chunk in enumerate(self.retrieved_chunks, 1):
lines.append(f"### {i}. {chunk.citation} (Score: {chunk.relevance_score:.3f})")
if chunk.name:
lines.append(f"**{chunk.chunk_type}**: `{chunk.name}`\n")
lines.append("```")
lines.append(chunk.content[:500] + ("..." if len(chunk.content) > 500 else ""))
lines.append("```\n")
return "\n".join(lines)
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