Initial commit: VKUS vitrina MVP (frontend + backend).

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-29 23:13:06 +07:00
commit 862e6b7b25
113 changed files with 50949 additions and 0 deletions
@@ -0,0 +1,206 @@
+"""Оркестрация временного локального RAG и сборки ответа."""
+
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+from app.config import settings
+from app.data.knowledge_base import get_colleague_cases_for_system
+from app.models import ColleagueCase, ResponsePackage, Source
+from app.package_builder import (
+    build_warning,
+    build_why_suggested,
+    ensure_escalation_draft,
+    enrich_ticket_draft,
+)
+from app.rag.indexer import LocalRagIndex, SearchHit, detect_system_from_text
+
+if TYPE_CHECKING:
+    from app.llm.client import LlmClient
+
+_index: LocalRagIndex | None = None
+
+
+def get_index() -> LocalRagIndex:
+    global _index
+    if _index is None:
+        _index = LocalRagIndex()
+    return _index
+
+
+def init_local_rag() -> dict[str, object]:
+    """Построить индекс при старте backend."""
+    index = get_index()
+    if not settings.rag_local_enabled or settings.rag_mode != "local":
+        return {"enabled": False, "chunks": 0, "mode": settings.rag_mode}
+
+    count = index.build(settings.rag_documents_path, chunk_size=settings.rag_chunk_size)
+    return {
+        "enabled": True,
+        "mode": "local",
+        "chunks": count,
+        "documents_path": str(settings.rag_documents_path),
+    }
+
+
+def search_documents(query: str) -> list[SearchHit]:
+    if not settings.rag_local_enabled or settings.rag_mode != "local":
+        return []
+    index = get_index()
+    if not index.is_ready:
+        init_local_rag()
+    return index.search(query, top_k=settings.rag_top_k, min_score=settings.rag_min_score)
+
+
+def score_to_confidence(score: float) -> int:
+    """Нормализация cosine similarity в проценты уверенности."""
+    return min(95, max(35, int(score * 120)))
+
+
+def build_package_from_hits(
+    query: str,
+    hits: list[SearchHit],
+    *,
+    system: str | None = None,
+    conversation: list[dict[str, str]] | None = None,
+) -> ResponsePackage | None:
+    if not hits:
+        return None
+
+    top = hits[0]
+    confidence = score_to_confidence(top.score)
+    resolved_system = system or top.chunk.system or detect_system_from_text(query) or "Общее"
+
+    recommendation = _extract_recommendation(top.chunk.text)
+    steps = _extract_steps(top.chunk.text, hits)
+    sources = _unique_sources(hits)
+    colleague_cases = [
+        ColleagueCase.model_validate(case) for case in get_colleague_cases_for_system(resolved_system)
+    ]
+    priority = "Средний" if confidence >= settings.confidence_threshold else "Высокий"
+    warning = build_warning(recommendation, colleague_cases) if colleague_cases else None
+    why_suggested = build_why_suggested(
+        system=resolved_system,
+        sources=sources,
+        confidence=confidence,
+        source_title=top.chunk.source_title,
+    )
+
+    package = ResponsePackage(
+        recommendation=recommendation,
+        steps=steps,
+        sources=sources,
+        colleague_cases=colleague_cases or None,
+        warning=warning,
+        why_suggested=why_suggested,
+        confidence=confidence,
+        system=resolved_system,
+        ticket_draft=enrich_ticket_draft(
+            query,
+            resolved_system,
+            priority,
+            conversation=conversation,
+            sources=sources,
+            colleague_cases=colleague_cases,
+            recommendation=recommendation,
+        ),
+    )
+    return ensure_escalation_draft(package, query, conversation=conversation)
+
+
+def build_package_with_llm(
+    query: str,
+    hits: list[SearchHit],
+    llm_client: "LlmClient",
+    greeting: str,
+    *,
+    system: str | None = None,
+    conversation: list[dict[str, str]] | None = None,
+) -> tuple[str, ResponsePackage] | None:
+    context = "\n\n---\n\n".join(
+        f"[{h.chunk.source_title}]\n{h.chunk.text}" for h in hits[: settings.rag_top_k]
+    )
+    llm_text = llm_client.generate_with_context(query, context)
+    if not llm_text:
+        return None
+
+    package = build_package_from_hits(query, hits, system=system, conversation=conversation)
+    if package is None:
+        return None
+
+    package.recommendation = llm_text
+    if package.colleague_cases:
+        package.warning = build_warning(llm_text, package.colleague_cases)
+    if package.ticket_draft:
+        package.ticket_draft = enrich_ticket_draft(
+            query,
+            package.system or system or "Общее",
+            package.ticket_draft.priority,
+            conversation=conversation,
+            sources=package.sources,
+            colleague_cases=package.colleague_cases,
+            recommendation=llm_text,
+        )
+    package = ensure_escalation_draft(package, query, conversation=conversation)
+    threshold = settings.confidence_threshold
+    intro = (
+        f"{greeting}нашёл материалы в инструкциях и подготовил ответ:"
+        if package.confidence >= threshold
+        else (
+            f"{greeting}уверенность ниже {threshold}% — ответ может быть неполным. "
+            "Подготовил черновик заявки, его можно сразу отправить в поддержку:"
+        )
+    )
+    return intro, package
+
+
+def _extract_recommendation(text: str) -> str:
+    lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
+    if not lines:
+        return text[:400]
+    # Первая содержательная строка или абзац
+    paragraph = lines[0]
+    if len(paragraph) < 80 and len(lines) > 1:
+        paragraph = f"{paragraph} {lines[1]}"
+    return paragraph[:500]
+
+
+def _extract_steps(text: str, hits: list[SearchHit]) -> list[str]:
+    steps: list[str] = []
+    combined = "\n".join(h.chunk.text for h in hits[:3])
+
+    for line in combined.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        if re.match(r"^(\d+[\.\)]|[-•*])\s+", line):
+            steps.append(re.sub(r"^(\d+[\.\)]|[-•*])\s+", "", line))
+        elif "http" in line.lower() or line.lower().startswith("откройте"):
+            steps.append(line)
+        if len(steps) >= 6:
+            break
+
+    if not steps:
+        for line in combined.splitlines():
+            line = line.strip()
+            if len(line) > 20:
+                steps.append(line)
+            if len(steps) >= 4:
+                break
+
+    return steps[:6]
+
+
+def _unique_sources(hits: list[SearchHit]) -> list[Source]:
+    seen: set[str] = set()
+    sources: list[Source] = []
+    for hit in hits:
+        key = hit.chunk.source_path
+        if key in seen:
+            continue
+        seen.add(key)
+        sources.append(Source(title=hit.chunk.source_title, url=None))
+        if len(sources) >= 4:
+            break
+    return sources