pyr0ball
347b391c6e
- Strengthen synthesizer system prompt: hard 'respond with exactly' constraint instead of soft 'say so'; removes any wiggle room for the model to supplement from training data - Add early return in synthesize() when chunks is empty (belt-and-suspenders alongside the existing guard in chat.py) - Add MIN_SIGNAL threshold (0.01) in retriever: if the top combined score is below the threshold, return empty so the caller's no-results path fires instead of sending noise chunks to the LLM
73 lines
2.2 KiB
Python
73 lines
2.2 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 document assistant. "
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"Answer questions using ONLY the document excerpts provided. "
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"Cite every claim with the source page as [p.N]. "
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"If the excerpts do not contain the answer, respond with exactly: "
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"'I could not find an answer to that question in the indexed documents.' "
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"Do NOT use knowledge from outside the provided excerpts. "
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"Do NOT speculate, infer, or guess beyond what is explicitly stated."
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)
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_NO_RESULTS_ANSWER = (
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"I could not find any relevant passages in the indexed documents for that question. "
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"Try rephrasing, or check that the relevant document has been ingested."
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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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if not chunks:
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return SynthesisResult(answer=_NO_RESULTS_ANSWER, citations=())
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# 1500 chars (~300 words) per chunk: enough to capture definitions that
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# appear mid-paragraph without blowing past a 32k-context model's limit.
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context_parts = [f"[p.{c.page_number}]\n{c.text[:1500]}" 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[:400],
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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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