Merge pull request #1657 from A-Dawn/dev

fix：优化人物画像来源限制，并改进模型选择器

src/A_memorix/core/storage/metadata_store.py

@@ -2608,7 +2608,9 @@ class MetadataStore:
         Returns:
             段落列表
         """
-        return self.query("SELECT * FROM paragraphs WHERE source = ?", (source,))
+        cursor = self._conn.cursor()
+        cursor.execute("SELECT * FROM paragraphs WHERE source = ?", (source,))
+        return [self._row_to_dict(row, "paragraph") for row in cursor.fetchall()]
 
     def get_all_sources(self) -> List[Dict[str, Any]]:
         """

src/A_memorix/core/utils/person_profile_service.py

@@ -283,7 +283,13 @@ class PersonProfileService:
             logger.warning(f"解析人物别名失败: person_id={person_id}, err={e}")
         return aliases, primary_name, memory_traits
 
-    def _collect_relation_evidence(self, aliases: List[str], limit: int = 30) -> List[Dict[str, Any]]:
+    def _collect_relation_evidence(
+        self,
+        aliases: List[str],
+        limit: int = 30,
+        *,
+        person_id: str = "",
+    ) -> List[Dict[str, Any]]:
         relation_by_hash: Dict[str, Dict[str, Any]] = {}
         for alias in aliases:
             for rel in self.metadata_store.get_relations(subject=alias, include_inactive=False):
@@ -296,6 +302,12 @@ class PersonProfileService:
                     relation_by_hash[h] = rel
 
         relations = list(relation_by_hash.values())
+        if person_id:
+            relations = [
+                rel
+                for rel in relations
+                if self._is_relation_bound_to_person(rel, person_id=person_id)
+            ]
         relations.sort(key=lambda item: float(item.get("confidence", 0.0)), reverse=True)
         relations = relations[: max(1, int(limit))]
 
@@ -312,6 +324,38 @@ class PersonProfileService:
             )
         return edges
 
+    def _is_relation_bound_to_person(
+        self,
+        relation: Dict[str, Any],
+        *,
+        person_id: str,
+    ) -> bool:
+        pid = str(person_id or "").strip()
+        if not pid:
+            return False
+
+        metadata = self._metadata_dict(relation.get("metadata"))
+        if str(metadata.get("person_id", "") or "").strip() == pid:
+            return True
+        if pid in self._list_tokens(metadata.get("person_ids")):
+            return True
+
+        source_paragraph = str(relation.get("source_paragraph", "") or "").strip()
+        if source_paragraph:
+            try:
+                paragraph = self.metadata_store.get_paragraph(source_paragraph)
+            except Exception:
+                paragraph = None
+            if isinstance(paragraph, dict):
+                payload = {
+                    "hash": source_paragraph,
+                    "source": str(paragraph.get("source", "") or ""),
+                    "metadata": self._metadata_dict(paragraph.get("metadata")),
+                }
+                return self._is_evidence_bound_to_person(payload, person_id=pid)
+
+        return False
+
     def _collect_person_fact_evidence(self, person_id: str, limit: int = 4) -> List[Dict[str, Any]]:
         token = str(person_id or "").strip()
         if not token:
@@ -346,6 +390,42 @@ class PersonProfileService:
             )
         return self._filter_stale_paragraph_evidence(evidence)
 
+    @staticmethod
+    def _metadata_dict(value: Any) -> Dict[str, Any]:
+        return dict(value) if isinstance(value, dict) else {}
+
+    @staticmethod
+    def _list_tokens(value: Any) -> List[str]:
+        if value is None:
+            return []
+        if isinstance(value, (list, tuple, set)):
+            return [str(item or "").strip() for item in value if str(item or "").strip()]
+        token = str(value or "").strip()
+        return [token] if token else []
+
+    def _is_evidence_bound_to_person(
+        self,
+        item: Dict[str, Any],
+        *,
+        person_id: str,
+    ) -> bool:
+        """画像证据必须显式绑定到 person_id，避免别名全局召回串人。"""
+        pid = str(person_id or "").strip()
+        if not pid:
+            return False
+
+        metadata = self._metadata_dict(item.get("metadata"))
+        source = str(item.get("source", "") or metadata.get("source", "") or "").strip()
+        if source == f"person_fact:{pid}":
+            return True
+
+        if str(metadata.get("person_id", "") or "").strip() == pid:
+            return True
+        if pid in self._list_tokens(metadata.get("person_ids")):
+            return True
+
+        return False
+
     @staticmethod
     def _source_type_from_source(source: str) -> str:
         token = str(source or "").strip()
@@ -360,7 +440,7 @@ class PersonProfileService:
         paragraph_hash: str,
         metadata: Dict[str, Any],
     ) -> Tuple[Dict[str, Any], str]:
-        merged = dict(metadata or {})
+        merged = self._metadata_dict(metadata)
         source = str(merged.get("source", "") or "").strip()
         try:
             paragraph = self.metadata_store.get_paragraph(paragraph_hash)
@@ -458,9 +538,11 @@ class PersonProfileService:
                             "score": 0.0,
                             "content": str(para.get("content", ""))[:180],
                             "source": str(para.get("source", "") or ""),
-                            "metadata": dict(para.get("metadata", {}) or {}),
+                            "metadata": self._metadata_dict(para.get("metadata")),
                         }
                     )
+                    if not self._is_evidence_bound_to_person(fallback[-1], person_id=person_id):
+                        fallback.pop()
             return self._filter_stale_paragraph_evidence(fallback[:top_k])
 
         per_alias_top_k = max(2, int(top_k / max(1, len(alias_queries))))
@@ -483,21 +565,22 @@ class PersonProfileService:
                 h = str(getattr(item, "hash_value", "") or "")
                 if not h or h in seen_hash:
                     continue
-                seen_hash.add(h)
                 metadata, source = self._enrich_paragraph_evidence_metadata(
                     h,
-                    dict(getattr(item, "metadata", {}) or {}),
+                    self._metadata_dict(getattr(item, "metadata", {})),
-                )
-                evidence.append(
-                    {
-                        "hash": h,
-                        "type": str(getattr(item, "result_type", "")),
-                        "score": float(getattr(item, "score", 0.0) or 0.0),
-                        "content": str(getattr(item, "content", "") or "")[:220],
-                        "source": source,
-                        "metadata": metadata,
-                    }
                 )
+                payload = {
+                    "hash": h,
+                    "type": str(getattr(item, "result_type", "")),
+                    "score": float(getattr(item, "score", 0.0) or 0.0),
+                    "content": str(getattr(item, "content", "") or "")[:220],
+                    "source": source,
+                    "metadata": metadata,
+                }
+                if not self._is_evidence_bound_to_person(payload, person_id=person_id):
+                    continue
+                seen_hash.add(h)
+                evidence.append(payload)
         evidence.sort(key=lambda x: x.get("score", 0.0), reverse=True)
         return self._filter_stale_paragraph_evidence(evidence[:top_k])
 
@@ -640,7 +723,7 @@ class PersonProfileService:
         if not aliases and person_keyword:
             aliases = [person_keyword.strip()]
             primary_name = person_keyword.strip()
-        relation_edges = self._collect_relation_evidence(aliases, limit=max(10, top_k * 2))
+        relation_edges = self._collect_relation_evidence(aliases, limit=max(10, top_k * 2), person_id=pid)
         vector_evidence = await self._collect_vector_evidence(aliases, top_k=max(4, top_k), person_id=pid)
 
         evidence_ids = [

src/A_memorix/core/utils/summary_importer.py

@@ -16,7 +16,7 @@ import traceback
 from src.common.logger import get_logger
 from src.services import llm_service as llm_api
 from src.services import message_service as message_api
-from src.config.config import global_config, model_config as host_model_config
+from src.config.config import config_manager, global_config
 from src.config.model_configs import TaskConfig
 
 from ..storage import (
@@ -150,36 +150,57 @@ class SummaryImporter:
         return True
 
     def _normalize_summary_model_selectors(self, raw_value: Any) -> List[str]:
-        """标准化 summarization.model_name 配置（vNext 仅接受字符串数组）。"""
+        """标准化 summarization.model_name 配置。"""
         if raw_value is None:
             return ["auto"]
         if isinstance(raw_value, list):
             selectors = [str(x).strip() for x in raw_value if str(x).strip()]
             return selectors or ["auto"]
+        if isinstance(raw_value, str):
+            selector = raw_value.strip()
+            if selector:
+                logger.warning("summarization.model_name 建议使用 List[str]，当前字符串配置已兼容处理。")
+                return [selector]
+            return ["auto"]
         raise ValueError(
-            "summarization.model_name 在 vNext 必须为 List[str]。"
+            "summarization.model_name 必须为 List[str] 或 str。"
             " 请执行 scripts/release_vnext_migrate.py migrate。"
         )
 
     def _pick_default_summary_task(self, available_tasks: Dict[str, TaskConfig]) -> Tuple[Optional[str], Optional[TaskConfig]]:
         """
         选择总结默认任务，避免错误落到 embedding 任务。
-        优先级：memory > utils > planner；不再顺延到 replyer 或其他任务。
+        优先级：replyer > utils > planner > tool_use > 其他非 embedding。
         """
-        preferred = ("memory", "utils", "planner")
+        preferred = ("replyer", "utils", "planner", "tool_use")
         for name in preferred:
             cfg = available_tasks.get(name)
             if cfg and cfg.model_list:
                 return name, cfg
 
+        for name, cfg in available_tasks.items():
+            if name != "embedding" and cfg.model_list:
+                return name, cfg
+
+        for name, cfg in available_tasks.items():
+            if cfg.model_list:
+                return name, cfg
+
         return None, None
 
-    def _resolve_summary_model_config(self) -> Optional[TaskConfig]:
+    @staticmethod
+    def _current_model_dict() -> Dict[str, Any]:
+        try:
+            return getattr(config_manager.get_model_config(), "models_dict", {}) or {}
+        except Exception as exc:
+            logger.warning(f"读取当前模型字典失败: {exc}")
+            return {}
+
+    def _resolve_summary_model_config(self) -> Optional[Tuple[str, TaskConfig]]:
         """
-        解析 summarization.model_name 为 TaskConfig。
+        解析 summarization.model_name 为 (task_name, TaskConfig)。
         支持：
         - "auto"
-        - "memory"（任务名）
         - "replyer"（任务名）
         - "some-model-name"（具体模型名）
         - ["utils:model1", "utils:model2", "replyer"]（数组混合语法）
@@ -192,16 +213,18 @@ class SummaryImporter:
         # 避免默认值本身触发类型校验异常。
         raw_cfg = self.plugin_config.get("summarization", {}).get("model_name", ["auto"])
         selectors = self._normalize_summary_model_selectors(raw_cfg)
-        _default_task_name, default_task_cfg = self._pick_default_summary_task(available_tasks)
+        default_task_name, default_task_cfg = self._pick_default_summary_task(available_tasks)
 
-        selected_models: List[str] = []
         base_cfg: Optional[TaskConfig] = None
-        model_dict = getattr(host_model_config, "models_dict", {})
+        base_task_name: Optional[str] = None
+        model_dict = self._current_model_dict()
 
-        def _append_models(models: List[str]):
+        def _find_task_for_model(model_name: str) -> Tuple[Optional[str], Optional[TaskConfig]]:
-            for model_name in models:
+            for task_name, task_cfg in available_tasks.items():
-                if model_name and model_name not in selected_models:
+                task_models = [str(item).strip() for item in (getattr(task_cfg, "model_list", []) or []) if str(item).strip()]
-                    selected_models.append(model_name)
+                if model_name in task_models:
+                    return task_name, task_cfg
+            return None, None
 
         for raw_selector in selectors:
             selector = raw_selector.strip()
@@ -210,9 +233,9 @@ class SummaryImporter:
 
             if selector.lower() == "auto":
                 if default_task_cfg:
-                    _append_models(default_task_cfg.model_list)
                     if base_cfg is None:
                         base_cfg = default_task_cfg
+                        base_task_name = default_task_name
                 continue
 
             if ":" in selector:
@@ -226,42 +249,60 @@ class SummaryImporter:
 
                 if base_cfg is None:
                     base_cfg = task_cfg
+                    base_task_name = task_name
 
                 if not model_name or model_name.lower() == "auto":
-                    _append_models(task_cfg.model_list)
                     continue
 
-                if model_name in model_dict or model_name in task_cfg.model_list:
+                if model_name in task_cfg.model_list:
-                    _append_models([model_name])
+                    logger.info(
+                        f"总结模型选择器 '{selector}' 已定位到任务 '{task_name}'；"
+                        "当前 LLM 服务按任务候选列表执行，不单独覆盖具体模型。"
+                    )
                 else:
-                    logger.warning(f"总结模型选择器 '{selector}' 的模型 '{model_name}' 不存在，已跳过")
+                    logger.warning(f"总结模型选择器 '{selector}' 的模型 '{model_name}' 不在任务 '{task_name}' 中，已跳过")
                 continue
 
             task_cfg = available_tasks.get(selector)
             if task_cfg:
-                _append_models(task_cfg.model_list)
                 if base_cfg is None:
                     base_cfg = task_cfg
+                    base_task_name = selector
                 continue
 
             if selector in model_dict:
-                _append_models([selector])
+                task_name, task_cfg = _find_task_for_model(selector)
+                if task_name and task_cfg:
+                    if base_cfg is None:
+                        base_cfg = task_cfg
+                        base_task_name = task_name
+                    logger.info(
+                        f"总结模型选择器 '{selector}' 已映射到任务 '{task_name}'；"
+                        "当前 LLM 服务按任务候选列表执行，不单独覆盖具体模型。"
+                    )
+                    continue
+                logger.warning(f"总结模型选择器 '{selector}' 未归属于任何任务，已跳过")
                 continue
 
             logger.warning(f"总结模型选择器 '{selector}' 无法识别，已跳过")
 
-        if not selected_models:
+        if base_cfg is None or not base_task_name:
             if default_task_cfg:
-                _append_models(default_task_cfg.model_list)
                 if base_cfg is None:
                     base_cfg = default_task_cfg
+                    base_task_name = default_task_name
+            else:
+                base_task_name, first_cfg = next(iter(available_tasks.items()))
+                if base_cfg is None:
+                    base_cfg = first_cfg
 
-        if not selected_models:
+        if base_cfg is None or not base_task_name:
             return None
 
-        template_cfg = base_cfg or default_task_cfg or TaskConfig()
+        template_cfg = base_cfg
-        return TaskConfig(
+        task_name_to_use = base_task_name
-            model_list=selected_models,
+        return task_name_to_use, TaskConfig(
+            model_list=list(template_cfg.model_list),
             max_tokens=template_cfg.max_tokens,
             temperature=template_cfg.temperature,
             slow_threshold=template_cfg.slow_threshold,
@@ -331,12 +372,13 @@ class SummaryImporter:
                 chat_history=chat_history_text
             )
 
-            model_config_to_use = self._resolve_summary_model_config()
+            resolved_model = self._resolve_summary_model_config()
-            if model_config_to_use is None:
+            if resolved_model is None:
                 return False, "未找到可用的总结模型配置"
-            task_name_to_use = llm_api.resolve_task_name_from_model_config(model_config_to_use)
+            task_name_to_use, model_config_to_use = resolved_model
 
             logger.info(f"正在为流 {stream_id} 执行总结，消息条数: {len(messages)}")
+            logger.info(f"总结模型任务: {task_name_to_use}")
             logger.info(f"总结模型候选列表: {model_config_to_use.model_list}")
 
             result = await llm_api.generate(