refactor ai_auto_response plugin architecture

plugins/ai_auto_response/context/context_builder.py

@@ -0,0 +1,358 @@
+from __future__ import annotations
+
+import re
+from typing import Dict, List
+
+
+class ContextBuilder:
+    def __init__(self, recent_context_size: int = 30):
+        self.recent_context_size = max(int(recent_context_size or 30), 1)
+
+    def build(
+        self,
+        *,
+        room_id: str,
+        group_profile: Dict,
+        sender: str,
+        sender_name: str,
+        content: str,
+        recent_messages: List[Dict],
+        member_context: Dict,
+        member_memory_focus: List[str] | None = None,
+        trigger: Dict,
+        flow_state: str,
+        reply_mode: str,
+        vector_memories: List[Dict],
+        social_memory: Dict | None = None,
+        group_facts: Dict | None = None,
+        quote_context: Dict | None = None,
+        image_context: Dict | None = None,
+    ) -> Dict:
+        selected_messages = self._select_recent_messages(recent_messages, sender, content, quote_context or {})
+        recent_lines = []
+        for item in selected_messages:
+            msg_sender = item.get("sender_name") or item.get("sender") or "未知成员"
+            msg_content = item.get("content") or item.get("message") or ""
+            if msg_content:
+                recent_lines.append(f"{msg_sender}: {msg_content}")
+        return {
+            "group_profile": group_profile or {"room_id": room_id},
+            "speaker_profile": {
+                "wxid": sender,
+                "display_name": sender_name,
+                "member_context": member_context or {},
+            },
+            "speaker_name_clean": self._clean_display_name(sender_name),
+            "recent_message_items": self._build_recent_message_items(selected_messages),
+            "recent_messages": recent_lines,
+            "recent_summary": "",
+            "trigger_type": trigger.get("trigger_type", "none"),
+            "reply_mode": reply_mode,
+            "flow_state": flow_state,
+            "memory_prompt": self._build_member_memory_prompt(member_context, member_memory_focus or []),
+            "vector_memory_prompt": self._build_vector_memory_prompt(vector_memories),
+            "social_memory_prompt": self._build_social_memory_prompt(social_memory or {}),
+            "group_facts_prompt": self._build_group_facts_prompt(group_facts or {}),
+            "group_profile_prompt": self._build_group_profile_prompt(group_profile or {}),
+            "quote_prompt": self._build_quote_prompt(quote_context or {}),
+            "image_prompt": self._build_image_prompt(image_context or {}),
+            "image_safety_prompt": self._build_image_safety_prompt(
+                (quote_context or {}).get("image_safety") or {}
+            ),
+            "current_message": f"{sender_name}: {content}",
+        }
+
+    @staticmethod
+    def _build_recent_message_items(messages: List[Dict]) -> List[Dict]:
+        items: List[Dict] = []
+        for idx, item in enumerate(messages, start=1):
+            content = str(item.get("content") or item.get("message") or "").strip()
+            if not content:
+                continue
+            items.append({
+                "idx": idx,
+                "sender": item.get("sender_name") or item.get("sender") or "未知成员",
+                "content": content[:120],
+                "is_at": bool(item.get("is_at")),
+            })
+        return items
+
+    def _select_recent_messages(
+        self,
+        recent_messages: List[Dict],
+        current_sender: str,
+        current_content: str,
+        quote_context: Dict,
+    ) -> List[Dict]:
+        if not recent_messages:
+            return []
+        window = recent_messages[-self.recent_context_size:]
+        if len(window) <= 8:
+            return window
+
+        current_tokens = self._extract_topic_tokens(current_content)
+        quote_tokens = self._extract_topic_tokens(
+            f"{quote_context.get('title', '')} {quote_context.get('quote_body', '')}"
+        )
+        focus_tokens = current_tokens | quote_tokens
+        quote_sender_name = str(quote_context.get("quote_sender_name", "") or "").strip().lower()
+
+        scored: List[tuple[int, int, Dict]] = []
+        for idx, item in enumerate(window):
+            score = self._message_relevance(
+                item,
+                current_sender=current_sender,
+                focus_tokens=focus_tokens,
+                quote_sender_name=quote_sender_name,
+            )
+            if score > 0:
+                scored.append((score, idx, item))
+
+        # 总是保留尾部几条，维持现场感；再拼上与当前话题最相关的消息。
+        tail_indexes = set(range(max(len(window) - 4, 0), len(window)))
+        keep_indexes = set(tail_indexes)
+        for _, idx, _ in sorted(scored, key=lambda x: (-x[0], -x[1]))[:10]:
+            keep_indexes.add(idx)
+
+        selected = [window[idx] for idx in sorted(keep_indexes)]
+        if len(selected) < 6:
+            return window[-6:]
+        return selected[-12:]
+
+    @classmethod
+    def _message_relevance(
+        cls,
+        item: Dict,
+        *,
+        current_sender: str,
+        focus_tokens: set[str],
+        quote_sender_name: str,
+    ) -> int:
+        content = str(item.get("content") or item.get("message") or "").strip()
+        if not content:
+            return 0
+        sender = str(item.get("sender", "") or "")
+        sender_name = str(item.get("sender_name", "") or "").strip().lower()
+        score = 0
+
+        if sender == current_sender:
+            score += 3
+        if quote_sender_name and quote_sender_name in sender_name:
+            score += 3
+        if item.get("is_at"):
+            score += 1
+
+        if focus_tokens:
+            tokens = cls._extract_topic_tokens(content)
+            overlap = focus_tokens & tokens
+            score += min(len(overlap) * 2, 6)
+            if overlap and cls._looks_like_question_or_answer(content):
+                score += 2
+        elif sender == current_sender:
+            score += 1
+
+        if cls._looks_like_question_or_answer(content):
+            score += 1
+        return score
+
+    @staticmethod
+    def _looks_like_question_or_answer(content: str) -> bool:
+        text = str(content or "").strip().lower()
+        if not text:
+            return False
+        patterns = [
+            r"\?$", r"？$", r"怎么", r"如何", r"为啥", r"为什么", r"能不能", r"可以吗",
+            r"报错", r"试试", r"先", r"然后", r"配置", r"日志", r"接口", r"原因",
+        ]
+        return any(re.search(pattern, text, flags=re.IGNORECASE) for pattern in patterns)
+
+    @staticmethod
+    def _extract_topic_tokens(content: str) -> set[str]:
+        text = str(content or "").lower()
+        tokens = set(re.findall(r"[a-z0-9_\\-]{3,}", text))
+        keywords = [
+            "openclaw", "qdrant", "ollama", "docker", "python", "api", "插件", "机器人", "模型",
+            "日志", "配置", "报错", "部署", "联网", "图片", "记忆", "群聊", "dota", "战绩",
+        ]
+        for keyword in keywords:
+            if keyword in text:
+                tokens.add(keyword)
+        return tokens
+
+    @staticmethod
+    def _clean_display_name(sender_name: str) -> str:
+        import re
+
+        text = str(sender_name or "").strip()
+        if not text:
+            return ""
+        text = re.sub(r"\s+", "", text)
+        text = re.sub(r"[^\u4e00-\u9fffA-Za-z0-9_]", "", text)
+        return text[:8]
+
+    @staticmethod
+    def _build_member_memory_prompt(member_context: Dict, focus_lines: List[str] | None = None) -> str:
+        if not member_context:
+            return "暂无稳定成员画像。"
+        meta = member_context.get("meta", {}) or {}
+        topics = member_context.get("topics_of_interest", []) or []
+        recent_focus = member_context.get("recent_focus", []) or []
+        common_scenarios = ContextBuilder._stringify_items(meta.get("common_scenarios", []), 4)
+        skills = ContextBuilder._stringify_items(meta.get("skill_profile", []), 5)
+        problem_solving = ContextBuilder._stringify_items(meta.get("problem_solving_profile", []), 4)
+        stable_traits = ContextBuilder._stringify_items(meta.get("stable_traits", []), 4)
+        habits = ContextBuilder._stringify_items(meta.get("habit_patterns", []), 4)
+        expression_profile = ContextBuilder._stringify_items(meta.get("expression_profile", []), 4)
+        reply_entry = ContextBuilder._stringify_items(meta.get("reply_entry_profile", []), 4)
+        reply_prefs = ContextBuilder._stringify_items(meta.get("long_term_reply_preferences", []), 4)
+        recent_state = ContextBuilder._stringify_items(meta.get("recent_state", []), 4)
+        reply_taboos = ContextBuilder._stringify_items(meta.get("reply_taboos", []), 3)
+        lines = [
+            f"成员摘要：{member_context.get('summary_text', '')}".strip(),
+            f"互动风格：{member_context.get('interaction_style', '')}".strip(),
+            f"回复偏好：{member_context.get('response_style_hint', '')}".strip(),
+            f"本次相关记忆：{'；'.join((focus_lines or [])[:4])}" if focus_lines else "",
+            f"长期主题：{', '.join(topics[:5])}" if topics else "",
+            f"近期关注：{', '.join(recent_focus[:4])}" if recent_focus else "",
+            f"常见发言场景：{common_scenarios}" if common_scenarios else "",
+            f"技能侧重点：{skills}" if skills else "",
+            f"处理问题方式：{problem_solving}" if problem_solving else "",
+            f"稳定特征：{stable_traits}" if stable_traits else "",
+            f"习惯模式：{habits}" if habits else "",
+            f"表达标记：{expression_profile}" if expression_profile else "",
+            f"有效接话点：{reply_entry}" if reply_entry else "",
+            f"长期回复偏好：{reply_prefs}" if reply_prefs else "",
+            f"近期状态：{recent_state}" if recent_state else "",
+            f"气质倾向：{meta.get('temperament_tendency', '')}".strip(),
+            f"群内角色：{meta.get('group_role', '')}".strip(),
+            f"回复禁忌：{reply_taboos}" if reply_taboos else "",
+        ]
+        return "\n".join([line for line in lines if line])
+
+    @staticmethod
+    def _stringify_items(items: List | str, limit: int) -> str:
+        if isinstance(items, str):
+            return items.strip()
+        values: List[str] = []
+        for item in items[:limit]:
+            if isinstance(item, dict):
+                value = str(
+                    item.get("name")
+                    or item.get("label")
+                    or item.get("value")
+                    or item.get("text")
+                    or ""
+                ).strip()
+            else:
+                value = str(item or "").strip()
+            if value and value not in values:
+                values.append(value)
+        return ", ".join(values)
+
+    @staticmethod
+    def _build_vector_memory_prompt(vector_memories: List[Dict]) -> str:
+        if not vector_memories:
+            return ""
+        lines = []
+        for item in vector_memories[:2]:
+            summary = item.get("content_summary") or item.get("summary_text") or item.get("text") or ""
+            memory_type = item.get("memory_type", "memory")
+            if summary:
+                lines.append(f"[{memory_type}] {summary}")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _build_social_memory_prompt(social_memory: Dict) -> str:
+        prompt = str((social_memory or {}).get("prompt", "") or "").strip()
+        return prompt
+
+    @staticmethod
+    def _build_group_facts_prompt(group_facts: Dict) -> str:
+        return str((group_facts or {}).get("prompt", "") or "").strip()
+
+    @staticmethod
+    def _build_group_profile_prompt(group_profile: Dict) -> str:
+        if not group_profile:
+            return "当前群没有特殊知识域限制。"
+        focus = ", ".join(group_profile.get("knowledge_focus", [])[:6])
+        boundaries = ", ".join(group_profile.get("topic_boundaries", [])[:6])
+        summary = str(group_profile.get("group_memory_summary", "") or "").replace("\n", " ").strip()
+        if len(summary) > 120:
+            summary = summary[:117] + "..."
+        lines = [
+            f"群模式：{group_profile.get('mode', 'social')}",
+            f"知识域：{group_profile.get('knowledge_domain', 'general')}",
+            f"配置知识域：{group_profile.get('configured_domain', 'general')}",
+            f"历史推断知识域：{group_profile.get('group_memory_domain', 'general')}",
+            f"回答风格：{group_profile.get('reply_style', '自然短句')}",
+            f"互动调性：{group_profile.get('interaction_tone', '自然群友感')}",
+            f"幽默强度：{group_profile.get('humor_style', '轻微')}",
+            f"嘴硬程度：{group_profile.get('sharpness_style', '轻微嘴硬，不刻薄')}",
+            f"表达松弛度：{group_profile.get('expressiveness_style', '克制')}",
+            f"称呼强度：{group_profile.get('address_style', '低频称呼，默认直接接话')}",
+            f"知识重点：{focus}" if focus else "",
+            f"群长期摘要：{summary}" if summary else "",
+            f"历史推断社交风格：{ContextBuilder._build_style_summary(group_profile.get('group_memory_style', {}))}"
+            if group_profile.get("group_memory_style")
+            else "",
+            f"边界提醒：{boundaries}" if boundaries else "",
+            f"人格叠加：{group_profile.get('persona_overlay', '')}".strip(),
+        ]
+        return "\n".join([line for line in lines if line])
+
+    @staticmethod
+    def _build_style_summary(style_profile: Dict) -> str:
+        if not style_profile:
+            return ""
+        return " / ".join(
+            [
+                str(style_profile.get("interaction_tone", "") or "").strip(),
+                str(style_profile.get("humor_style", "") or "").strip(),
+                str(style_profile.get("sharpness_style", "") or "").strip(),
+                str(style_profile.get("expressiveness_style", "") or "").strip(),
+            ]
+        ).strip(" /")
+
+    @staticmethod
+    def _build_quote_prompt(quote_context: Dict) -> str:
+        if not quote_context:
+            return ""
+        quote_type = quote_context.get("quote_type_label", "引用消息")
+        quote_sender = quote_context.get("quote_sender_name", "") or "未知成员"
+        quote_body = quote_context.get("quote_body", "") or ""
+        title = quote_context.get("title", "") or ""
+        lines = [
+            f"用户这次是在引用消息后发言。",
+            f"引用类型：{quote_type}",
+            f"被引用发送者：{quote_sender}",
+            f"图片附件：已附带原图" if quote_context.get("has_image_attachment") else "",
+            f"引用标题：{title}" if title else "",
+            f"被引用内容：{quote_body}" if quote_body else "",
+        ]
+        return "\n".join([line for line in lines if line])
+
+    @staticmethod
+    def _build_image_prompt(image_context: Dict) -> str:
+        if not image_context:
+            return ""
+        lines = [
+            "已附带最近一张群图片作为上下文。",
+            f"图片发送者：{image_context.get('sender_name', '未知成员')}",
+            f"图片说明：{image_context.get('hint', '')}" if image_context.get("hint") else "",
+        ]
+        return "\n".join([line for line in lines if line])
+
+    @staticmethod
+    def _build_image_safety_prompt(image_safety: Dict) -> str:
+        if not image_safety or not image_safety.get("suspected"):
+            return ""
+        if image_safety.get("has_visual_context"):
+            return "当前发言疑似是在评论图片，但本次已附带图片上下文，可以基于图片谨慎理解。"
+        reason = str(image_safety.get("reason", "") or "").strip()
+        lines = [
+            "当前发言疑似是在评论图片，但你这次没有看到图片本身。",
+            f"原因：{reason}" if reason else "",
+            "不要假装看过图，不要直接评价画面细节、人物状态、构图、文字内容或颜色元素。",
+            "如果要回，只能轻微承认信息不足，或请对方引用图片/补一句文字说明，再继续。",
+        ]
+        return "\n".join([line for line in lines if line])