242 lines
9.3 KiB
Python
242 lines
9.3 KiB
Python
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"""LLM-driven pipeline for regulatory event enrichment."""
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from __future__ import annotations
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import json
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import math
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from typing import Any
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from loguru import logger
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from app.config.settings import settings
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from app.infrastructure.embedding.openai_compatible_embedding_provider import (
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OpenAICompatibleEmbeddingProvider,
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)
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from app.services.llm.llm_factory import get_llm_client
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_EXTRACT_SYSTEM = (
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"You are a regulatory compliance expert specialising in automotive standards "
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"(GB, UN-ECE, ISO, EU). Extract structured information from regulation text. "
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"Return valid JSON only — no markdown fences, no extra keys."
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)
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_ASSESS_SYSTEM = (
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"You are an automotive compliance analyst. Given a regulation and related document excerpts, "
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"identify which documents are affected and what actions are required. "
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"Return a JSON array only."
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)
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_DIFF_SYSTEM = (
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"You are a regulatory change analyst. Given an old and new version of a regulation paragraph, "
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"classify the type of change and summarise it. "
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"Return JSON only: {\"change_type\": \"tightened|relaxed|added|removed\", \"summary\": \"...\"}"
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)
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_SIMILARITY_THRESHOLD = 0.85
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def _cosine(a: list[float], b: list[float]) -> float:
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dot = sum(x * y for x, y in zip(a, b))
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norm_a = math.sqrt(sum(x * x for x in a))
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norm_b = math.sqrt(sum(x * x for x in b))
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if norm_a == 0 or norm_b == 0:
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return 0.0
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return dot / (norm_a * norm_b)
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def _llm_json(client: Any, messages: list[dict]) -> Any:
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"""Call LLM and parse JSON response; return None on failure."""
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try:
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resp = client.chat(messages)
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text = (resp.content or "").strip()
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if text.startswith("```"):
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text = text.split("```")[1]
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if text.startswith("json"):
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text = text[4:]
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return json.loads(text)
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except Exception as exc:
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logger.warning("LLM JSON parse failed: {}", exc)
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return None
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class LlmPipeline:
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"""Three-step enrichment pipeline for crawled regulatory events."""
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def __init__(self) -> None:
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self._client = get_llm_client(
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provider=settings.llm_provider,
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model=settings.llm_model,
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)
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self._embedder = OpenAICompatibleEmbeddingProvider()
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# ------------------------------------------------------------------
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# Step 1: Structure extraction
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# ------------------------------------------------------------------
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def extract_structure(self, event: dict) -> dict:
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"""Extract obligations, deadlines, scope, penalties, impact_level from event text."""
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prompt = f"""Extract structured compliance information from this regulation:
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Standard: {event.get('standard_code', '')}
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Title: {event.get('title', '')}
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Source: {event.get('source_label', '')}
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Summary: {event.get('summary', '')}
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Tags: {', '.join(event.get('tags') or [])}
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Return JSON with exactly these keys:
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{{
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"obligations": [{{"text": "...", "deontic": "must|shall|may|prohibited", "subject": "...", "object": "...", "condition": ""}}],
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"deadlines": [{{"date": "YYYY-MM-DD or null", "description": "..."}}],
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"scope": "one sentence describing who/what this applies to",
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"penalties": "one sentence on consequences of non-compliance, or null",
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"impact_level": "high|medium|low"
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}}"""
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messages = [
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{"role": "system", "content": _EXTRACT_SYSTEM},
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{"role": "user", "content": prompt},
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]
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result = _llm_json(self._client, messages)
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if not isinstance(result, dict):
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return {
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"obligations": [],
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"deadlines": [],
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"scope": "",
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"penalties": "",
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"impact_level": "medium",
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}
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return result
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# ------------------------------------------------------------------
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# Step 2: Impact assessment
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# ------------------------------------------------------------------
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def assess_impact(self, event: dict, retrieval_service: Any) -> list[dict]:
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"""Use RAG to find affected documents and generate recommendations."""
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obligations = event.get("obligations") or []
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obligation_texts = " ".join(o.get("text", "") for o in obligations[:3])
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query = f"{event.get('standard_code', '')} {event.get('title', '')} {obligation_texts}"
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try:
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chunks = retrieval_service.retrieve(query=query, top_k=5)
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except Exception as exc:
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logger.warning("RAG retrieval failed: {}", exc)
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return []
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if not chunks:
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return []
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seen: set[str] = set()
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doc_excerpts: list[dict] = []
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for chunk in chunks:
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if chunk.doc_id not in seen:
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seen.add(chunk.doc_id)
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doc_excerpts.append({
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"doc_id": chunk.doc_id,
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"doc_name": chunk.doc_title,
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"score": round(float(chunk.score if chunk.score is not None else 0), 4),
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"snippet": (chunk.text or "")[:300],
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"clause": getattr(chunk, "section_title", "") or "",
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})
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context = "\n".join(
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f"[{d['doc_name']} {d['clause']}] score={d['score']}: {d['snippet']}"
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for d in doc_excerpts
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)
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prompt = f"""Regulation: {event.get('standard_code')} — {event.get('title')}
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Obligations: {obligation_texts or event.get('summary', '')}
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Affected documents found in knowledge base:
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{context}
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For each document, assess impact and recommend action. Return JSON array:
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[{{"doc_id":"...","doc_name":"...","score":0.0,"key_clauses":"...","recommendation":"one sentence action"}}]"""
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messages = [
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{"role": "system", "content": _ASSESS_SYSTEM},
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{"role": "user", "content": prompt},
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]
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result = _llm_json(self._client, messages)
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if isinstance(result, list):
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score_map = {d["doc_id"]: d["score"] for d in doc_excerpts}
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for item in result:
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if isinstance(item, dict) and item.get("doc_id") in score_map:
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item["score"] = score_map[item["doc_id"]]
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return result
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return doc_excerpts
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# ------------------------------------------------------------------
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# Step 3: Semantic diff
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# ------------------------------------------------------------------
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def compute_diff(self, old_text: str, new_text: str) -> dict:
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"""Compare old and new regulation text; return changed sections and summary."""
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old_paras = [p.strip() for p in old_text.split("\n") if p.strip()]
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new_paras = [p.strip() for p in new_text.split("\n") if p.strip()]
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if not old_paras or not new_paras:
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return {"changed_sections": [], "change_summary": "No comparable text."}
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all_paras = old_paras + new_paras
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try:
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all_embeddings = self._embedder.embed_texts(all_paras)
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except Exception as exc:
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logger.warning("Embedding for diff failed: {}", exc)
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return {"changed_sections": [], "change_summary": "Diff unavailable (embedding error)."}
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old_embeddings = all_embeddings[: len(old_paras)]
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new_embeddings = all_embeddings[len(old_paras):]
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changed_sections: list[dict] = []
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max_len = max(len(old_paras), len(new_paras))
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for i in range(max_len):
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if i >= len(old_paras):
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# New paragraph added
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changed_sections.append({
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"old_text": "",
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"new_text": new_paras[i][:300],
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"similarity": 0.0,
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"change_type": "added",
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"summary": "New paragraph added.",
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})
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continue
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if i >= len(new_paras):
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# Old paragraph removed
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changed_sections.append({
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"old_text": old_paras[i][:300],
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"new_text": "",
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"similarity": 0.0,
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"change_type": "removed",
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"summary": "Paragraph removed.",
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})
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continue
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# Both exist — compare via embeddings
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sim = _cosine(old_embeddings[i], new_embeddings[i])
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if sim < _SIMILARITY_THRESHOLD:
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messages = [
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{"role": "system", "content": _DIFF_SYSTEM},
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{"role": "user", "content": f"OLD: {old_paras[i][:500]}\nNEW: {new_paras[i][:500]}"},
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]
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classification = _llm_json(self._client, messages) or {}
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changed_sections.append({
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"old_text": old_paras[i][:300],
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"new_text": new_paras[i][:300],
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"similarity": round(sim, 3),
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"change_type": classification.get("change_type", "modified"),
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"summary": classification.get("summary", ""),
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})
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if not changed_sections:
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change_summary = "No substantive changes detected between versions."
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else:
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types = [s["change_type"] for s in changed_sections]
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change_summary = (
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f"{len(changed_sections)} paragraph(s) changed: "
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+ ", ".join(f"{t}" for t in set(types))
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+ ". "
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+ (changed_sections[0].get("summary", "") if changed_sections else "")
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)
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return {"changed_sections": changed_sections, "change_summary": change_summary}
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