refactor ai_auto_response plugin architecture

plugins/ai_auto_response/context/conversation_hints.py

@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, List
+
+
+TECH_OVERLAP_KEYWORDS = [
+    "报错", "日志", "配置", "接口", "插件", "部署", "docker", "python", "openclaw", "机器人", "qdrant", "ollama",
+]
+ANSWER_KEYWORDS = [
+    "先", "然后", "重启", "配置", "日志", "接口", "看一下", "试试", "排查",
+    "报错", "原因", "因为", "改成", "装", "部署", "重现", "检查", "确认",
+]
+
+
+def build_conversation_hints(
+    recent_messages: List[Dict],
+    current_sender: str,
+    current_content: str,
+    quote_context: Dict[str, Any],
+    bot_name: str,
+) -> Dict[str, Any]:
+    previous_messages = list(recent_messages[:-1]) if recent_messages else []
+    recent_window = previous_messages[-4:]
+    solver_count = 0
+    solver_senders = set()
+    current_tokens = extract_overlap_tokens(current_content)
+    for item in recent_window:
+        sender = str(item.get("sender", "") or "")
+        if not sender or sender == current_sender:
+            continue
+        content = str(item.get("content") or item.get("message") or "").strip().lower()
+        if looks_like_answer(content) and has_topic_overlap(current_tokens, content):
+            solver_count += 1
+            solver_senders.add(sender)
+
+    previous_same_sender_directed = False
+    same_sender_recent_count = 0
+    bot_name_lower = str(bot_name or "").lower()
+    for item in reversed(previous_messages[-6:]):
+        sender = str(item.get("sender", "") or "")
+        if sender != current_sender:
+            continue
+        same_sender_recent_count += 1
+        content = str(item.get("content") or item.get("message") or "").strip().lower()
+        if bool(item.get("is_at")) or (bot_name_lower and bot_name_lower in content):
+            previous_same_sender_directed = True
+            break
+
+    quote_targets_bot = False
+    quote_sender_name = str(quote_context.get("quote_sender_name", "") or "").strip().lower()
+    if quote_sender_name and bot_name_lower and bot_name_lower in quote_sender_name:
+        quote_targets_bot = True
+
+    return {
+        "has_recent_human_solver": solver_count >= 2 and len(solver_senders) >= 1,
+        "solver_count": solver_count,
+        "previous_same_sender_directed": previous_same_sender_directed,
+        "same_sender_recent_count": same_sender_recent_count,
+        "quote_targets_bot": quote_targets_bot,
+    }
+
+
+def looks_like_answer(content: str) -> bool:
+    if not content:
+        return False
+    if len(content) >= 18:
+        return True
+    return any(keyword in content for keyword in ANSWER_KEYWORDS)
+
+
+def extract_overlap_tokens(content: str) -> set[str]:
+    text = str(content or "").lower()
+    tokens = set(re.findall(r"[a-z0-9_\\-]{3,}", text))
+    for keyword in TECH_OVERLAP_KEYWORDS:
+        if keyword in text:
+            tokens.add(keyword)
+    return tokens
+
+
+def has_topic_overlap(current_tokens: set[str], previous_content: str) -> bool:
+    if not current_tokens:
+        return False
+    previous_tokens = extract_overlap_tokens(previous_content)
+    return bool(current_tokens & previous_tokens)