chore/ 精简展示AI活动的日志

api/.env.example

@@ -22,6 +22,11 @@ LOG_LEVEL=INFO
 # LOG_AGENT_VERBOSE=0
 # DEBUG 下 prompt/响应预览最大字符数
 # AGENT_LOG_MAX_CHARS=4096
+# DEBUG 下访谈/资料：省略 SystemMessage 正文（仅 total_len+sha12）；0/false=打出全文
+# AGENT_LOG_OMIT_SYSTEM_MESSAGE_BODY=1
+# DEBUG 下超长单段 *.prompt：总长超过下一项时，先跳过前 N 字符再预览（0=不跳过）
+# AGENT_LOG_JSON_PROMPT_PREFIX_CHARS=0
+# AGENT_LOG_JSON_PROMPT_PREFIX_ONLY_IF_LEN_GT=4000
 # 第三方 stdlib logging（空=自动：LOG_LEVEL 为 DEBUG/TRACE 时 Celery→INFO、httpx/httpcore→WARNING，减少刷屏）
 # CELERY_LOG_LEVEL=
 # HTTPX_LOG_LEVEL=

api/.env.production

@@ -20,6 +20,11 @@ LOG_LEVEL=INFO
 # LOG_AGENT_VERBOSE=0
 # DEBUG 下 prompt/响应预览最大字符数
 # AGENT_LOG_MAX_CHARS=4096
+# DEBUG 下访谈/资料：省略 SystemMessage 正文（仅 total_len+sha12）；0/false=打出全文
+# AGENT_LOG_OMIT_SYSTEM_MESSAGE_BODY=1
+# DEBUG 下超长单段 *.prompt：总长超过下一项时，先跳过前 N 字符再预览（0=不跳过）
+# AGENT_LOG_JSON_PROMPT_PREFIX_CHARS=0
+# AGENT_LOG_JSON_PROMPT_PREFIX_ONLY_IF_LEN_GT=4000
 # 第三方 stdlib logging（空=自动：LOG_LEVEL 为 DEBUG/TRACE 时 Celery→INFO、httpx/httpcore→WARNING，减少刷屏）
 # CELERY_LOG_LEVEL=
 # HTTPX_LOG_LEVEL=

api/.env.staging

@@ -9,6 +9,18 @@
 # Logging（loguru sink 最低级别：TRACE / DEBUG / INFO / WARNING / ERROR / CRITICAL）
 # =============================================================================
 LOG_LEVEL=INFO
+# Agent 单行 INFO 摘要（耗时、路由、段落规模）；与 LOG_LEVEL 独立
+# LOG_AGENT_VERBOSE=0
+# DEBUG 下 prompt/响应预览最大字符数
+# AGENT_LOG_MAX_CHARS=4096
+# DEBUG 下访谈/资料：省略 SystemMessage 正文（仅 total_len+sha12）；0/false=打出全文
+# AGENT_LOG_OMIT_SYSTEM_MESSAGE_BODY=1
+# DEBUG 下超长单段 *.prompt：总长超过下一项时，先跳过前 N 字符再预览（0=不跳过）
+# AGENT_LOG_JSON_PROMPT_PREFIX_CHARS=0
+# AGENT_LOG_JSON_PROMPT_PREFIX_ONLY_IF_LEN_GT=4000
+# 第三方 stdlib logging（空=自动）
+# CELERY_LOG_LEVEL=
+# HTTPX_LOG_LEVEL=
 
 # =============================================================================
 # LLM / DeepSeek

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(

api/app/core/agent_logging.py

@@ -6,6 +6,10 @@ Agent / LLM 诊断日志：耗时、输入输出规模、截断预览。
   便于生产环境在不把全局日志调到 DEBUG 的情况下排查 Agent 性能与路径。
 
 敏感内容：DEBUG 下会记录用户相关文本截断预览，生产环境请勿长期开启 DEBUG。
+
+配置（节选）：``AGENT_LOG_OMIT_SYSTEM_MESSAGE_BODY``（默认 true）省略聊天 System 正文，仅打 len+sha12；
+``AGENT_LOG_JSON_PROMPT_PREFIX_CHARS`` + ``AGENT_LOG_JSON_PROMPT_PREFIX_ONLY_IF_LEN_GT`` 在 DEBUG 下跳过
+超长单段 prompt 的前缀再预览。
 """
 
 from __future__ import annotations
@@ -96,10 +100,23 @@ def log_agent_payload(
     """在 DEBUG 下记录文本长度与截断预览。"""
     if not agent_verbose_enabled():
         return
-    preview = truncate_for_log(text, max_chars=max_chars)
+    raw = text or ""
+    total_len = len(raw)
+    preview_source = raw
+    extra_note = ""
+    if (
+        label.endswith(".prompt")
+        and settings.agent_log_json_prompt_prefix_chars > 0
+        and total_len > settings.agent_log_json_prompt_prefix_only_if_len_gt
+    ):
+        skip = settings.agent_log_json_prompt_prefix_chars
+        preview_source = raw[skip:]
+        extra_note = f" skipped_prefix_chars={skip}"
+    preview = truncate_for_log(preview_source, max_chars=max_chars)
     logger.debug(
-        "agent_payload {} total_len={} preview={}",
+        "agent_payload {} total_len={}{} preview={}",
         label,
-        len(text or ""),
+        total_len,
+        extra_note,
         preview,
     )

api/app/core/config.py

@@ -188,6 +188,14 @@ class Settings(BaseSettings):
     log_agent_verbose: bool = False
     # AGENT_LOG_MAX_CHARS：DEBUG 下记录 prompt/响应预览时的最大字符数
     agent_log_max_chars: int = Field(default=4096, ge=256, le=100_000)
+    # AGENT_LOG_OMIT_SYSTEM_MESSAGE_BODY：DEBUG 下访谈/资料聊天日志省略 System 正文（仅 len+sha12）
+    agent_log_omit_system_message_body: bool = True
+    # AGENT_LOG_JSON_PROMPT_PREFIX_CHARS：DEBUG 下 *.prompt 总长超过下项时再跳过前 N 字符后预览（0=不跳过）
+    agent_log_json_prompt_prefix_chars: int = Field(default=0, ge=0, le=500_000)
+    # AGENT_LOG_JSON_PROMPT_PREFIX_ONLY_IF_LEN_GT：触发“跳过前缀”的最小 prompt 长度
+    agent_log_json_prompt_prefix_only_if_len_gt: int = Field(
+        default=4000, ge=0, le=2_000_000
+    )
     # 第三方 stdlib logging（空=自动：LOG_LEVEL 为 DEBUG/TRACE 时 Celery→INFO、httpx/httpcore→WARNING）
     celery_log_level: str = ""
     httpx_log_level: str = ""
@@ -201,6 +209,18 @@ class Settings(BaseSettings):
             return False
         return str(v).strip().lower() in ("1", "true", "yes", "on")
 
+    @field_validator("agent_log_omit_system_message_body", mode="before")
+    @classmethod
+    def _coerce_agent_log_omit_system_message_body(cls, v: object) -> bool:
+        if isinstance(v, bool):
+            return v
+        if v is None:
+            return True
+        s = str(v).strip().lower()
+        if s in ("0", "false", "no", "off"):
+            return False
+        return True
+
     # ── Misc ─────────────────────────────────────────────────
     enable_test_subscription: int = 0
     enable_test_plan: str = ""  # "1" / "true" / "yes" 为 True

api/app/features/memory/service.py

@@ -70,16 +70,20 @@ class MemoryService:
 
         await self._db.flush()
 
+        from app.core.config import settings
+
+        vectors_written = 0
         # Embedding: 若有 provider 则写入
         if self._embedding and chunk_records:
             texts = [c for _, c in chunk_records]
             embeddings = await self._embedding.embed_texts(texts)
             for (chunk_id, _), emb in zip(chunk_records, embeddings):
                 if emb:
+                    vectors_written += 1
                     await update_chunk_embedding(self._db, chunk_id, emb)
 
+        enrichment_ok: bool | None = None
         try:
-            from app.core.config import settings
             from app.core.dependencies import get_llm_provider_fast
             from app.features.memory.enrichment import enrich_memory_after_ingest_async
 
@@ -88,12 +92,28 @@ class MemoryService:
                 await enrich_memory_after_ingest_async(
                     self._db, user_id, source.id, llm
                 )
+                enrichment_ok = True
         except Exception as e:
+            if settings.memory_enrichment_enabled:
+                enrichment_ok = False
             logger.warning(
                 "memory enrichment 跳过: {} exc_type={}", e, type(e).__name__
             )
 
         await self._db.commit()
+        emb_ok = self._embedding.is_available() if self._embedding else False
+        logger.info(
+            "event=memory_ingest_done user_id={} conversation_id={} source_id={} "
+            "chunks={} vectors_written={} embedding_available={} enrichment_enabled={} enrichment_ok={}",
+            user_id,
+            conversation_id,
+            source.id,
+            len(chunk_records),
+            vectors_written,
+            emb_ok,
+            settings.memory_enrichment_enabled,
+            enrichment_ok,
+        )
         return source.id
 
     async def retrieve(
@@ -104,7 +124,23 @@ class MemoryService:
 
         retriever = HybridRetriever(self._db, embedding_provider=self._embedding)
         raw = await retriever.retrieve(user_id=user_id, query=query, top_k=top_k)
-        return EvidenceBundle.model_validate(raw)
+        bundle = EvidenceBundle.model_validate(raw)
+        bd = bundle.model_dump()
+        vec_ok = self._embedding.is_available() if self._embedding else False
+        logger.info(
+            "event=memory_retrieve_done user_id={} query_len={} top_k={} "
+            "chunks={} facts={} summaries={} timeline={} stories={} vector_ok={}",
+            user_id,
+            len((query or "").strip()),
+            top_k,
+            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 []),
+            vec_ok,
+        )
+        return bundle
 
     async def exclude_chunk(
         self, user_id: str, chunk_id: str, *, reason: str = ""
@@ -215,29 +251,51 @@ def ingest_transcript_sync(
         session.flush()
         chunk_records.append((chunk.id, content))
 
+    from app.core.config import settings
+
+    vectors_written = 0
+    embedding_available = False
     try:
         embedding_provider = get_embedding_provider()
+        if embedding_provider is not None:
+            embedding_available = embedding_provider.is_available()
         if chunk_records and embedding_provider is not None:
             texts = [content for _, content in chunk_records]
             embeddings = embedding_provider.embed_texts_sync(texts)
             for (chunk_id, _), emb in zip(chunk_records, embeddings):
                 if emb:
+                    vectors_written += 1
                     update_chunk_embedding_sync(session, chunk_id, emb)
     except Exception as e:
         logger.warning(
             "memory embedding 跳过(sync): {} exc_type={}", e, type(e).__name__
         )
 
+    enrichment_ok: bool | None = None
     try:
-        from app.core.config import settings
         from app.features.memory.enrichment import enrich_memory_after_ingest_sync
 
         if settings.memory_enrichment_enabled:
             enrich_memory_after_ingest_sync(session, user_id, source.id, llm=None)
+            enrichment_ok = True
     except Exception as e:
+        if settings.memory_enrichment_enabled:
+            enrichment_ok = False
         logger.warning(
             "memory enrichment 跳过(sync): {} exc_type={}", e, type(e).__name__
         )
 
     session.commit()
+    logger.info(
+        "event=memory_ingest_done user_id={} conversation_id={} source_id={} "
+        "chunks={} vectors_written={} embedding_available={} enrichment_enabled={} enrichment_ok={} sync=1",
+        user_id,
+        conversation_id,
+        source.id,
+        len(chunk_records),
+        vectors_written,
+        embedding_available,
+        settings.memory_enrichment_enabled,
+        enrichment_ok,
+    )
     return source.id

api/tests/test_interview_prompts.py

@@ -195,3 +195,17 @@ def test_format_history_string_includes_system_for_debug_logs() -> None:
     assert "System: SYS_INSTRUCTIONS" in s
     assert "Human: hi" in s
     assert "Assistant: hello" in s
+
+
+def test_format_history_string_omit_system_body() -> None:
+    s = format_history_string(
+        [
+            SystemMessage(content="SYS_INSTRUCTIONS"),
+            HumanMessage(content="hi"),
+        ],
+        omit_system_body=True,
+    )
+    assert "SYS_INSTRUCTIONS" not in s
+    assert "System: <omitted total_len=16" in s
+    assert "sha12=" in s
+    assert "Human: hi" in s