pyr0ball
6fc8e7faa6
Citation dataclass gains bm25_score field populated from the retrieved chunk. chat.py serializes it. api.ts interface updated to include it. ChatView passes :bm25-score to CitationPanel so the Nat20 threshold check in onMounted actually has data to evaluate.
60 lines
1.6 KiB
Python
60 lines
1.6 KiB
Python
# app/services/synthesizer.py
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"""
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LLM answer synthesis over retrieved chunks.
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BSL 1.1 — requires LLMRouter (Ollama BYOK or cloud tier).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from app.services.retriever import RetrievedChunk
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_SYSTEM_PROMPT = (
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"You are a helpful document assistant. "
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"Answer the user's question using ONLY the provided document excerpts. "
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"For each claim, cite the source page as [p.N]. "
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"If the excerpts are insufficient, say so. Do not invent information."
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)
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@dataclass(frozen=True)
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class Citation:
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doc_id: str
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page_number: int
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snippet: str
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bm25_score: float
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@dataclass(frozen=True)
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class SynthesisResult:
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answer: str
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citations: tuple[Citation, ...]
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class Synthesizer:
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def __init__(self, llm) -> None: # LLMRouter
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self._llm = llm
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def synthesize(
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self,
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message: str,
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history: list[dict],
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chunks: list[RetrievedChunk],
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) -> SynthesisResult:
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context_parts = [f"[p.{c.page_number}]\n{c.text[:500]}" for c in chunks]
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context = "\n\n---\n\n".join(context_parts)
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prompt = f"Document excerpts:\n\n{context}\n\nQuestion: {message}"
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answer = self._llm.complete(prompt, system=_SYSTEM_PROMPT)
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citations = tuple(
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Citation(
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doc_id=c.doc_id,
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page_number=c.page_number,
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snippet=c.text[:200],
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bm25_score=c.bm25_score,
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)
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for c in chunks
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)
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return SynthesisResult(answer=answer, citations=citations)
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