feat(engine): #873 dual-Claw event contract + SseAdapter/BufferAdapter + chat.py SSE delegate + im_llm EngineCore thin-shell (gray-gated)

- app/core/engine_event.py (#880): EngineEvent dataclass + make_* factories, single construction entry, byte-compatible wire with legacy ChatPage.tsx - app/core/claw_adapters.py (#884): SseAdapter (hot frames zero-metadata, terminal keeps trace_id) + BufferAdapter (assemble() preserves __media__) - app/api/chat.py (#881): /messages/stream now delegates to SseAdapter; wire bytes unchanged (46B delta / done+message_id) - app/api/im_llm.py (#883): IM_USE_ENGINE_CORE gray gate; _call_engine_core reuses EngineCore via BufferAdapter (default OFF, preserves media fast-path) - app/api/chat_service.py: capture 3 container-ahead fixes (stray import asyncio removed, logger.info format, LiteLLM master key)
2026-08-04 12:25:12 +08:00
1 parent 34648e4810
commit 037c965c26
@@ -181,6 +181,16 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
                        return s
                    return "（媒体生成未能返回有效内容，请重试）"

+        # ===== 1.5) EngineCore 薄壳（#873 · 灰阶门，默认关闭）=====
+        # IM_USE_ENGINE_CORE=1 时，文本回复改走与 Web Claw 同一份 EngineCore
+        # (_send_message_core)，经 BufferAdapter 缓冲装配为整条回复；上方媒体快路径
+        # 已提前返回，不受此门影响。默认 0/未设置 = 走下方既有自包含逻辑（零改动）。
+        if os.environ.get("IM_USE_ENGINE_CORE") == "1":
+            return _run_async(
+                _call_engine_core(db, user_id, prompt, platform, user),
+                timeout=300,
+            )
+
        # ===== 2) 文本回复：模型 failover + 技能自动发现 + 平台知识库注入 =====
        from ..platform_knowledge import get_platform_knowledge

@@ -413,3 +423,79 @@ def _call_real_llm(db: Session, user=None, prompt: str = "", platform: str = "im
        return f"（已尝试 {len(_try_models)} 个模型均调用失败。最后错误: {_summary}。请稍后重试。）"
    except Exception as e:
        return f"（AI 回复生成失败：{e}）"
+
+
+async def _call_engine_core(db, user_id, prompt: str, platform: str, user=None) -> "str | dict":
+    """（#873 薄壳）复用 Web Claw 同一份 EngineCore 生成 IM 回复。
+
+    单引擎原则：IM Bot 不再维护第二份「模型 failover + 技能/MCP 执行」实现，
+    而是把已发现的 skill_ids/mcp_ids 交给 _send_message_core，经 BufferAdapter
+    缓冲装配成整条回复（媒体结构 __media__ 由 BufferAdapter 原样保留）。
+    默认不启用（见 _call_real_llm 中的 IM_USE_ENGINE_CORE 灰阶门）。
+    """
+    from app.api.chat_service import _send_message_core
+    from app.api.chat_utils import SendMessageRequest
+    from app.core.claw_adapters import BufferAdapter
+    from ..models import Conversation, User
+
+    # 解析用户：IM 调用方常传 user=None，仅给 user_id
+    real_user = user
+    if real_user is None and user_id:
+        real_user = db.query(User).filter(User.id == user_id).first()
+    if real_user is None:
+        return "（EngineCore 模式：无法解析当前用户，请检查 IM 账号绑定）"
+
+    # 能力发现（与 IM 自包含逻辑同一套，claw="im"）
+    from app.core.capability_discovery import (
+        discover_mcp_servers,
+        discover_skills,
+    )
+    _query_hint = prompt
+    _marker = "用户当前问题："
+    if _marker in prompt:
+        _query_hint = prompt[prompt.rfind(_marker) + len(_marker):].strip()
+    skills = []
+    try:
+        skills = discover_skills(db, user_id, query=_query_hint, claw="im") or []
+    except Exception as _e:
+        logger.warning(f"[im_llm.engine] 技能发现失败(降级为空): {_e}")
+    mcp_servers = []
+    try:
+        mcp_servers = discover_mcp_servers(db, None, claw="im") or []
+    except Exception as _e:
+        logger.warning(f"[im_llm.engine] MCP 发现失败(降级为空): {_e}")
+
+    skill_ids = [
+        (s.get("id") if isinstance(s, dict) else getattr(s, "id", None))
+        for s in skills
+    ]
+    skill_ids = [s for s in skill_ids if s]
+    mcp_ids = [
+        (m.get("id") if isinstance(m, dict) else getattr(m, "id", None))
+        for m in mcp_servers
+    ]
+    mcp_ids = [m for m in mcp_ids if m]
+
+    # 构造本回合专用对话：EngineCore 要求已存在且归属当前用户的 Conversation
+    conv = Conversation(
+        id=gen_uuid(),
+        user_id=real_user.id,
+        title=f"IM对话-{platform}",
+        source="im_bot",
+    )
+    db.add(conv)
+    db.commit()
+
+    req = SendMessageRequest(
+        content=prompt,
+        skill_ids=skill_ids or None,
+        mcp_ids=mcp_ids or None,
+        use_tools=bool(skill_ids or mcp_ids),
+        agent_ids=None,
+        knowledge_base_ids=None,
+        attachments=None,
+    )
+
+    adapter = BufferAdapter(tenant_id=str(getattr(real_user, "tenant_id", "") or ""))
+    await _send_message_core(conv.id, req, db, real_user, stream=True, on_event=adapter)
+    return adapter.assemble()