chore/ 精简展示AI活动的日志

api/app/agents/chat/helpers.py

@@ -1,5 +1,6 @@
 """聊天 Agent 共享工具：历史获取、格式化、存储"""
 
+import hashlib
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, List
@@ -68,12 +69,28 @@ async def get_history_messages(conversation_id: str) -> List[Any]:
     return _lc_messages_from_rows(_human_ai_rows(history))
 
 
-def format_history_string(messages: List[Any]) -> str:
+def _sha12_utf8(text: str) -> str:
+    return hashlib.sha256((text or "").encode("utf-8")).hexdigest()[:12]
+
+
+def format_history_string(
+    messages: List[Any], *, omit_system_body: bool = False
+) -> str:
     """将 LangChain 消息列表格式化为调试日志用多段文本（含 System，不静默跳过）。"""
     history_parts: list[str] = []
     for msg in messages:
         if isinstance(msg, SystemMessage):
-            history_parts.append(f"System: {msg.content}")
+            if omit_system_body:
+                c = (
+                    (msg.content or "")
+                    if isinstance(msg.content, str)
+                    else str(msg.content)
+                )
+                history_parts.append(
+                    f"System: <omitted total_len={len(c)} sha12={_sha12_utf8(c)}>"
+                )
+            else:
+                history_parts.append(f"System: {msg.content}")
         elif isinstance(msg, HumanMessage):
             history_parts.append(f"Human: {msg.content}")
         elif isinstance(msg, AIMessage):

api/app/agents/chat/interview_agent.py

@@ -176,7 +176,10 @@ class InterviewAgent:
             log_agent_payload(
                 logger,
                 "InterviewAgent.generate_response.prompt",
-                format_history_string(messages),
+                format_history_string(
+                    messages,
+                    omit_system_body=settings.agent_log_omit_system_message_body,
+                ),
             )
             chat_llm = self.llm.bind(max_tokens=reply_plan.max_tokens)
             prompt_chars = _message_contents_char_count(messages)
@@ -276,7 +279,10 @@ class InterviewAgent:
             log_agent_payload(
                 logger,
                 "InterviewAgent.opening.prompt",
-                format_history_string(messages),
+                format_history_string(
+                    messages,
+                    omit_system_body=settings.agent_log_omit_system_message_body,
+                ),
             )
             opening_llm = self.llm.bind(max_tokens=settings.chat_opening_max_tokens)
             prompt_chars = _message_contents_char_count(messages)

api/app/agents/chat/orchestrator.py

@@ -58,38 +58,46 @@ async def _fetch_interview_memory_evidence(
     from app.features.memory.service import MemoryService
 
     if not settings.chat_memory_retrieval_enabled:
+        logger.debug(
+            "event=chat_memory_retrieval_skip reason=disabled user_id={}", user_id
+        )
         return ""
     msg = (user_message or "").strip()
     if not msg:
+        logger.debug(
+            "event=chat_memory_retrieval_skip reason=empty user_id={}", user_id
+        )
         return ""
     if (
         settings.chat_memory_retrieval_require_substantive
         and not should_run_chat_stage_memory_heavy_work(msg)
     ):
+        logger.debug(
+            "event=chat_memory_retrieval_skip reason=not_substantive user_id={}",
+            user_id,
+        )
         return ""
     try:
         emb = get_embedding_provider()
         ms = MemoryService(db, embedding_provider=emb)
         bundle = await ms.retrieve(user_id, msg, top_k=settings.chat_memory_top_k)
         bd = bundle.model_dump()
-        vector_ok = emb.is_available()
-        logger.info(
-            "memory_evidence_retrieved user_id={} chunks={} facts={} summaries={} timeline={} stories={} vector_ok={}",
-            user_id,
-            len(bd.get("relevant_chunks") or []),
-            len(bd.get("relevant_facts") or []),
-            len(bd.get("relevant_summaries") or []),
-            len(bd.get("timeline_hints") or []),
-            len(bd.get("relevant_stories") or []),
-            vector_ok,
-        )
         text = format_evidence_chunks_for_prompt(bd)
         t = (text or "").strip()
         if not t:
+            logger.debug(
+                "event=memory_evidence_for_prompt user_id={} formatted_chars=0",
+                user_id,
+            )
             return ""
         max_c = settings.chat_memory_evidence_max_chars
         if len(t) > max_c:
-            return t[: max_c - 3] + "..."
+            t = t[: max_c - 3] + "..."
+        logger.info(
+            "event=memory_evidence_for_prompt user_id={} formatted_chars={}",
+            user_id,
+            len(t),
+        )
         return t
     except Exception as e:
         try:

api/app/agents/chat/profile_agent.py

@@ -188,7 +188,10 @@ class ProfileAgent:
             log_agent_payload(
                 logger,
                 "ProfileAgent.followup.prompt",
-                format_history_string(messages),
+                format_history_string(
+                    messages,
+                    omit_system_body=settings.agent_log_omit_system_message_body,
+                ),
             )
             prompt_chars = _message_contents_char_count(messages)
             logger.info(
@@ -246,7 +249,12 @@ class ProfileAgent:
             else:
                 messages.append(HumanMessage(content="（请说出资料收集开场白。）"))
             log_agent_payload(
-                logger, "ProfileAgent.greeting.prompt", format_history_string(messages)
+                logger,
+                "ProfileAgent.greeting.prompt",
+                format_history_string(
+                    messages,
+                    omit_system_body=settings.agent_log_omit_system_message_body,
+                ),
             )
             prompt_chars = _message_contents_char_count(messages)
             logger.info(