feat: 客服对话支持寒暄回复与跑题拒答

问候不再写入槽位；明显无关问题直接拒绝，知识库未命中时明确说明无法回答，并继续合理追问。

Co-authored-by: haijd <haijd@163.com>

engine/app/retrieval.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import re
 from typing import Any
 
 from .config_loader import business_config
@@ -24,6 +25,67 @@ def retrieve_for_scheme(slots: dict[str, str], bundle: list[str]) -> RetrievalRe
         return RetrievalResult([], "ragflow", warning=str(exc))
 
 
+def retrieve_question(question: str, *, page_size: int = 3, min_score: float = 0.22) -> RetrievalResult:
+    """自由问答检索；分数过低视为知识库未命中。"""
+    backend = (settings.retrieval_backend or "ragflow").lower()
+    q = (question or "").strip()
+    if not q:
+        return RetrievalResult([], backend or "none", warning="empty_query")
+    if backend == "mock":
+        return _retrieve_mock_question(q, min_score=min_score)
+    try:
+        client = RagflowClient()
+        dataset_ids = [item.strip() for item in settings.ragflow_dataset_ids.split(",") if item.strip()]
+        raw = client.retrieve(q, dataset_ids, page_size=page_size)
+        chunks = []
+        for index, item in enumerate(raw[:page_size]):
+            if not isinstance(item, dict):
+                continue
+            score = item.get("similarity")
+            try:
+                score_f = float(score) if score is not None else 0.0
+            except (TypeError, ValueError):
+                score_f = 0.0
+            if score_f < min_score:
+                continue
+            chunks.append(
+                {
+                    "id": item.get("id") or f"rf-q-{index}",
+                    "title": item.get("document_keyword")
+                    or item.get("document_name")
+                    or item.get("docnm_kwd")
+                    or "知识片段",
+                    "content": item.get("content") or item.get("content_with_weight") or "",
+                    "score": score_f,
+                }
+            )
+        warning = "" if chunks else "知识库未召回到足够相关的内容"
+        return RetrievalResult(chunks, "ragflow", warning=warning)
+    except RagflowError as exc:
+        return RetrievalResult([], "ragflow", warning=str(exc))
+
+
+def _retrieve_mock_question(question: str, *, min_score: float = 0.22) -> RetrievalResult:
+    picked: list[dict[str, Any]] = []
+    for chunk in business_config.chunks:
+        blob = f"{chunk.get('title', '')} {chunk.get('content', '')} {' '.join(chunk.get('tags') or [])}"
+        hits = sum(1 for token in re_split_tokens(question) if token and token in blob)
+        if hits:
+            score = min(0.9, 0.2 + 0.15 * hits)
+            if score >= min_score:
+                item = dict(chunk)
+                item["score"] = score
+                picked.append(item)
+        if len(picked) >= 3:
+            break
+    warning = "" if picked else "知识库未召回到足够相关的内容"
+    return RetrievalResult(picked, "mock", warning=warning)
+
+
+def re_split_tokens(text: str) -> list[str]:
+    return [t for t in re.split(r"[\s,，。；;？?！!/|]+", text) if len(t) >= 2]
+
+
 def _retrieve_mock(slots: dict[str, str], bundle: list[str]) -> RetrievalResult:
     chunks = business_config.chunks
     picked: list[dict[str, Any]] = []