验证当前模型可用性」}ાડી Reviewing format? no

.env.example

@@ -11,27 +11,30 @@ FRONTEND_PORT=5174
 
 # Backend Runtime
 CORS_ORIGINS=http://localhost:5174,http://127.0.0.1:5174
-LLM_PROVIDER=openai
+LLM_PROVIDER=glm
-LLM_FALLBACK_PROVIDER=ollama
+LLM_FALLBACK_PROVIDER=glm
-OPENAI_BASE_URL=http://openclaw:18789/v1
+OPENAI_BASE_URL=https://api.edgefn.net/v1
 OPENAI_API_KEY=
-OPENCLAW_ROUTING_ENABLED=true
+OPENAI_MODEL=MiniMax-M2.5
+OPENCLAW_ROUTING_ENABLED=false
 OPENCLAW_ROUTING_RULES_PATH=/app/backend/.openclaw/pmflow-routing-rules.json
 OPENCLAW_AGENTS_DEFAULT_TIMEOUT_SECONDS=120
-LLM_MIXED_ROUTING_ENABLED=false
+LLM_MIXED_ROUTING_ENABLED=true
 LLM_LOW_TO_HIGH_ESCALATION_ENABLED=true
-LLM_ROUTING_LOW_PROVIDER=openai
+LLM_ROUTING_LOW_PROVIDER=glm
-LLM_ROUTING_LOW_MODEL=zai/glm-4-flash
+LLM_ROUTING_LOW_MODEL=GLM-4.7
 LLM_ROUTING_HIGH_PROVIDER=openai
-LLM_ROUTING_HIGH_MODEL=minimax/MiniMax-M2.5
+LLM_ROUTING_HIGH_MODEL=MiniMax-M2.5
-LLM_ROUTING_SAFETY_PROVIDER=openai
+LLM_ROUTING_SAFETY_PROVIDER=glm
-LLM_ROUTING_SAFETY_MODEL=deepseek/deepseek-chat
+LLM_ROUTING_SAFETY_MODEL=GLM-4.7
-LLM_DIRECT_EMERGENCY_ENABLED=false
+LLM_DIRECT_EMERGENCY_ENABLED=true
 RAG_DEEP_RETRIEVAL_STEPS=5
 RAG_DEEP_VECTOR_TOP_K=12
 RAG_DEEP_CONTEXT_BLOCKS=16
 RAG_DEEP_ANSWER_MAX_TOKENS=1400
 GLM_API_KEY=
+GLM_BASE_URL=https://api.edgefn.net/v1
+GLM_MODEL=GLM-4.7
 DEEPSEEK_API_KEY=
 OLLAMA_HOST=http://host.docker.internal:11434
 OLLAMA_BASE_URL=http://host.docker.internal:11434

CLAUDE.md

@@ -40,7 +40,9 @@ backend/
 │   └── utils/                 # 工具函数
 ├── alembic/                   # 数据库迁移
 │   └── versions/
-│       └── 20260310_add_feishu_outbox_reply_hash.py  # 飞书 outbox 回执哈希幂等字段
+│       ├── 20260310_add_feishu_outbox_reply_hash.py  # 飞书 outbox 回执哈希幂等字段
+│       ├── 20260311_add_pg_trgm_text_indexes.py      # pg_trgm 扩展与文本检索索引
+│       └── 20260311_add_kb_chunk_ivfflat_index.py    # knowledge_base_chunks 向量 IVFFlat 索引
 ├── requirements.txt           # Python 依赖
 ├── Dockerfile
 └── tests/

backend/alembic/versions/20260311_add_kb_chunk_ivfflat_index.py

@@ -0,0 +1,42 @@
+"""add ivfflat index for knowledge chunk embedding
+
+Revision ID: add_kb_chunk_ivfflat_index
+Revises: add_pg_trgm_text_indexes
+Create Date: 2026-03-11
+"""
+
+from __future__ import annotations
+
+from alembic import op
+
+
+revision = "add_kb_chunk_ivfflat_index"
+down_revision = "add_pg_trgm_text_indexes"
+branch_labels = None
+depends_on = None
+
+
+def _is_postgresql() -> bool:
+    bind = op.get_bind()
+    return str(getattr(getattr(bind, "dialect", None), "name", "")).lower().startswith("postgres")
+
+
+def upgrade() -> None:
+    if not _is_postgresql():
+        return
+
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_knowledge_chunks_embedding_ivfflat
+        ON knowledge_base_chunks
+        USING ivfflat (embedding vector_cosine_ops)
+        WITH (lists = 100)
+        """
+    )
+
+
+def downgrade() -> None:
+    if not _is_postgresql():
+        return
+
+    op.execute("DROP INDEX IF EXISTS ix_knowledge_chunks_embedding_ivfflat")

backend/alembic/versions/20260311_add_pg_trgm_text_indexes.py

@@ -0,0 +1,76 @@
+"""add pg_trgm text indexes for project search
+
+Revision ID: add_pg_trgm_text_indexes
+Revises: add_feishu_outbox_reply_hash
+Create Date: 2026-03-11
+"""
+
+from __future__ import annotations
+
+from alembic import op
+
+
+revision = "add_pg_trgm_text_indexes"
+down_revision = "add_feishu_outbox_reply_hash"
+branch_labels = None
+depends_on = None
+
+
+def _is_postgresql() -> bool:
+    bind = op.get_bind()
+    return str(getattr(getattr(bind, "dialect", None), "name", "")).lower().startswith("postgres")
+
+
+def upgrade() -> None:
+    if not _is_postgresql():
+        return
+
+    op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_projects_name_trgm
+        ON projects USING GIN (name gin_trgm_ops)
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_projects_title_trgm
+        ON projects USING GIN (title gin_trgm_ops)
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_projects_description_trgm
+        ON projects USING GIN (description gin_trgm_ops)
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_signals_title_trgm
+        ON signals USING GIN (title gin_trgm_ops)
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_signals_content_trgm
+        ON signals USING GIN (content gin_trgm_ops)
+        """
+    )
+    op.execute(
+        """
+        CREATE INDEX IF NOT EXISTS ix_tasks_title_trgm
+        ON tasks USING GIN (title gin_trgm_ops)
+        """
+    )
+
+
+def downgrade() -> None:
+    if not _is_postgresql():
+        return
+
+    op.execute("DROP INDEX IF EXISTS ix_tasks_title_trgm")
+    op.execute("DROP INDEX IF EXISTS ix_signals_content_trgm")
+    op.execute("DROP INDEX IF EXISTS ix_signals_title_trgm")
+    op.execute("DROP INDEX IF EXISTS ix_projects_description_trgm")
+    op.execute("DROP INDEX IF EXISTS ix_projects_title_trgm")
+    op.execute("DROP INDEX IF EXISTS ix_projects_name_trgm")

backend/app/services/adaptive_rag_service.py

@@ -24,7 +24,7 @@ from datetime import datetime, timezone
 from typing import Any, Optional
 
 import httpx
-from sqlalchemy import and_, delete, func, or_, select
+from sqlalchemy import and_, bindparam, delete, func, or_, select, text
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ..config.ai_prompts import PROMPT_RAG_ANSWER, PROMPT_RAG_DB_PROJECT_QA, PROMPT_RAG_INTENT_CLASSIFICATION
@@ -4490,7 +4490,147 @@ class AdaptiveRAGService:
         source_types = self._map_domains_to_source_types(domains)
         if not source_types:
             return []
+        limit = max(1, min(int(top_k or DEFAULT_TOP_K), 40))
+        if self._is_postgresql_session():
+            pg_rows = await self._retrieve_vector_context_pg(
+                question_embedding=question_embedding,
+                source_types=source_types,
+                project_id=project_id,
+                source_chat_id=source_chat_id,
+                limit=limit,
+            )
+            if pg_rows is not None:
+                return pg_rows
 
+        return await self._retrieve_vector_context_legacy(
+            question_embedding=question_embedding,
+            source_types=source_types,
+            project_id=project_id,
+            source_chat_id=source_chat_id,
+            limit=limit,
+        )
+
+    def _is_postgresql_session(self) -> bool:
+        try:
+            bind = self.db.get_bind()
+        except Exception:
+            return False
+        dialect_name = str(getattr(getattr(bind, "dialect", None), "name", "")).lower()
+        return dialect_name.startswith("postgres")
+
+    @staticmethod
+    def _vector_literal(values: list[float]) -> str:
+        normalized: list[str] = []
+        for raw in values:
+            try:
+                normalized.append(f"{float(raw):.8f}")
+            except Exception:
+                normalized.append("0.0")
+        return "[" + ",".join(normalized) + "]"
+
+    async def _retrieve_vector_context_pg(
+        self,
+        *,
+        question_embedding: list[float],
+        source_types: list[str],
+        project_id: Optional[int],
+        source_chat_id: Optional[str],
+        limit: int,
+    ) -> Optional[list[RetrievedItem]]:
+        query_vec = self._vector_literal(question_embedding)
+        scoped_project_id = int(project_id) if project_id is not None else 0
+        scoped_source_chat_id = str(source_chat_id or "").strip()
+        stmt = text(
+            """
+            SELECT
+                id,
+                source_type,
+                content,
+                project_id,
+                "metadata" AS chunk_metadata,
+                1 - (embedding <=> CAST(:query_vec AS vector)) AS similarity
+            FROM knowledge_base_chunks
+            WHERE tenant_id = :tenant_id
+              AND source_type IN :source_types
+              AND embedding IS NOT NULL
+              AND (
+                    :project_scope = FALSE
+                    OR (source_type IN ('feishu', 'pmflow') AND project_id = :project_id)
+                    OR (source_type NOT IN ('feishu', 'pmflow') AND (project_id = :project_id OR project_id IS NULL))
+              )
+              AND (
+                    :source_chat_id = ''
+                    OR source_type <> 'feishu'
+                    OR COALESCE("metadata"->>'chat_id', '') = ''
+                    OR "metadata"->>'chat_id' = :source_chat_id
+              )
+            ORDER BY embedding <=> CAST(:query_vec AS vector)
+            LIMIT :limit
+            """
+        ).bindparams(bindparam("source_types", expanding=True))
+        try:
+            rows = (
+                await self.db.execute(
+                    stmt,
+                    {
+                        "tenant_id": self.tenant_id,
+                        "source_types": list(source_types),
+                        "project_scope": project_id is not None,
+                        "project_id": scoped_project_id,
+                        "source_chat_id": scoped_source_chat_id,
+                        "query_vec": query_vec,
+                        "limit": int(limit),
+                    },
+                )
+            ).mappings().all()
+        except Exception:
+            return None
+
+        scored: list[RetrievedItem] = []
+        for row in rows:
+            source_type = str(row.get("source_type") or "doc")
+            meta = self._safe_dict(row.get("chunk_metadata")).copy()
+            chunk_project_id_raw = row.get("project_id")
+            chunk_project_id = int(chunk_project_id_raw) if chunk_project_id_raw is not None else None
+            if chunk_project_id is not None:
+                meta.setdefault("project_id", chunk_project_id)
+
+            if source_type == "pmflow":
+                target_url = str(meta.get("target_url") or "").strip()
+                if not target_url:
+                    target_url = self._build_pmflow_target_url(
+                        record_type=str(meta.get("record_type") or ""),
+                        record_id=meta.get("record_id"),
+                        project_id=meta.get("project_id"),
+                    )
+                    if target_url:
+                        meta["target_url"] = target_url
+
+            score = self._to_float(row.get("similarity"), 0.0)
+            if score <= 0:
+                continue
+            label = self._source_label(source_type, meta)
+            scored.append(
+                RetrievedItem(
+                    source=source_type,
+                    content=str(row.get("content") or "")[:1200],
+                    score=score,
+                    source_label=label,
+                    metadata=meta,
+                )
+            )
+        scored.sort(key=lambda x: x.score, reverse=True)
+        return scored[:limit]
+
+    async def _retrieve_vector_context_legacy(
+        self,
+        *,
+        question_embedding: list[float],
+        source_types: list[str],
+        project_id: Optional[int],
+        source_chat_id: Optional[str],
+        limit: int,
+    ) -> list[RetrievedItem]:
         stmt = select(KnowledgeBaseChunk).where(
             and_(
                 KnowledgeBaseChunk.tenant_id == self.tenant_id,
@@ -4498,7 +4638,8 @@ class AdaptiveRAGService:
             )
         )
 
-        chunks = (await self.db.execute(stmt.limit(500))).scalars().all()
+        scan_limit = max(120, min(1200, limit * 20))
+        chunks = (await self.db.execute(stmt.limit(scan_limit))).scalars().all()
         scored: list[RetrievedItem] = []
         for chunk in chunks:
             meta = self._safe_dict(chunk.chunk_metadata).copy()
@@ -4507,8 +4648,6 @@ class AdaptiveRAGService:
                 meta.setdefault("project_id", chunk_project_id)
 
             if project_id is not None:
-                # 防止跨项目泄漏：Feishu/PMFlow 来源必须严格匹配项目组；
-                # 文档/URL 来源可读取全局（project_id 为空）和当前项目数据。
                 if chunk.source_type in {"feishu", "pmflow"}:
                     if chunk_project_id != int(project_id):
                         continue
@@ -4543,7 +4682,6 @@ class AdaptiveRAGService:
                 )
             )
         scored.sort(key=lambda x: x.score, reverse=True)
-        limit = max(1, min(int(top_k or DEFAULT_TOP_K), 40))
         return scored[:limit]
 
     # ------------------------------------------------------------------

backend/app/services/llm.py

@@ -13,6 +13,7 @@ import asyncio
 from dataclasses import dataclass
 import hashlib
 import json
+import logging
 import os
 import re
 import time
@@ -38,6 +39,8 @@ except Exception:  # pragma: no cover
     REDIS_AVAILABLE = False
     redis_async = None
 
+logger = logging.getLogger(__name__)
+
 SUPPORTED_PROVIDERS = {"deepseek", "glm", "openai", "ollama"}
 OPENCLAW_PRIMARY_GATEWAY_URL = "http://openclaw:18789/v1"
 OPENCLAW_DEFAULT_LOW_MODEL = "zai/glm-4-flash"
@@ -626,6 +629,7 @@ class LLMService:
         return await self._generate_structured_with_db(db, prompt, **call_kwargs)
 
     async def _generate_with_db(self, db: AsyncSession, prompt: str, **kwargs: Any) -> str:
+        started_at = time.perf_counter()
         tenant_id = int(kwargs.get("tenant_id") or 1)
         function_name = str(kwargs.get("function_name") or "llm_generate")
         temperature = float(kwargs.get("temperature") or 0.2)
@@ -639,6 +643,20 @@ class LLMService:
         prompt_profile = {"role_name": "", "system_prompt": ""}
         merged_prompt = self._merge_prompt_with_profile(prompt=prompt, prompt_profile=prompt_profile)
         safety_attempted = False
+
+        def _log_stub_failure(error_obj: Exception) -> None:
+            elapsed_ms = (time.perf_counter() - started_at) * 1000
+            provider = str(engine_meta.get("provider") or "")
+            model = str(engine_meta.get("model") or "")
+            logger.warning(
+                "[LLM_FAIL] 生成失败 → 兜底 stub function=%s provider=%s model=%s error=%r duration_ms=%.0f",
+                function_name,
+                provider,
+                model,
+                error_obj,
+                elapsed_ms,
+            )
+
         try:
             if use_prompt_profile:
                 prompt_profile = await self._resolve_active_prompt_profile(db, tenant_id=tenant_id)
@@ -743,6 +761,7 @@ class LLMService:
                 status = "failed_fast"
                 response_text = self._stub_response(prompt)
                 _runtime_log("LLM", f"生成快速失败，已跳过升级重试：{exc}")
+                _log_stub_failure(exc)
                 return response_text
             if self._is_low_tier_function(function_name):
                 if self._prefer_fast_local_fallback(function_name):
@@ -850,7 +869,7 @@ class LLMService:
 
             status = "failed"
             response_text = self._stub_response(prompt)
-            _runtime_log("LLM", f"生成失败，已返回兜底 stub：{exc}")
+            _log_stub_failure(upgraded_exc)
             return response_text
         finally:
             try:

docker-compose.dev.yml

@@ -65,19 +65,20 @@ services:
       OPENCLAW_ROUTING_RULES_PATH: ${OPENCLAW_ROUTING_RULES_PATH:-/app/backend/.openclaw/pmflow-routing-rules.json}
       LLM_MIXED_ROUTING_ENABLED: ${LLM_MIXED_ROUTING_ENABLED:-false}
       LLM_LOW_TO_HIGH_ESCALATION_ENABLED: ${LLM_LOW_TO_HIGH_ESCALATION_ENABLED:-true}
-      LLM_ROUTING_LOW_PROVIDER: ${LLM_ROUTING_LOW_PROVIDER:-openai}
+      LLM_ROUTING_LOW_PROVIDER: ${LLM_ROUTING_LOW_PROVIDER:-glm}
-      LLM_ROUTING_LOW_MODEL: ${LLM_ROUTING_LOW_MODEL:-zai/glm-4-flash}
+      LLM_ROUTING_LOW_MODEL: ${LLM_ROUTING_LOW_MODEL:-GLM-4.7}
       LLM_ROUTING_HIGH_PROVIDER: ${LLM_ROUTING_HIGH_PROVIDER:-openai}
-      LLM_ROUTING_HIGH_MODEL: ${LLM_ROUTING_HIGH_MODEL:-minimax/MiniMax-M2.5}
+      LLM_ROUTING_HIGH_MODEL: ${LLM_ROUTING_HIGH_MODEL:-MiniMax-M2.5}
-      LLM_ROUTING_SAFETY_PROVIDER: ${LLM_ROUTING_SAFETY_PROVIDER:-openai}
+      LLM_ROUTING_SAFETY_PROVIDER: ${LLM_ROUTING_SAFETY_PROVIDER:-glm}
-      LLM_ROUTING_SAFETY_MODEL: ${LLM_ROUTING_SAFETY_MODEL:-deepseek/deepseek-chat}
+      LLM_ROUTING_SAFETY_MODEL: ${LLM_ROUTING_SAFETY_MODEL:-GLM-4.7}
       LLM_DIRECT_EMERGENCY_ENABLED: ${LLM_DIRECT_EMERGENCY_ENABLED:-false}
       LLM_REQUEST_TIMEOUT: ${LLM_REQUEST_TIMEOUT:-90}
       AUTO_CREATE_TABLES: "true"
       GLM_API_KEY: ${GLM_API_KEY:-}
       GLM_BASE_URL: ${GLM_BASE_URL:-https://open.bigmodel.cn/api/paas/v4}
-      GLM_MODEL: ${GLM_MODEL:-glm-4-flash}
+      GLM_MODEL: ${GLM_MODEL:-GLM-4.7}
       OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      OPENAI_MODEL: ${OPENAI_MODEL:-MiniMax-M2.5}
       DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY:-}
       OLLAMA_HOST: ${OLLAMA_HOST:-http://host.docker.internal:11434}
       OLLAMA_BASE_URL: ${OLLAMA_BASE_URL:-http://host.docker.internal:11434}

docker-compose.yml

@@ -65,19 +65,20 @@ services:
       OPENCLAW_ROUTING_RULES_PATH: ${OPENCLAW_ROUTING_RULES_PATH:-/app/backend/.openclaw/pmflow-routing-rules.json}
       LLM_MIXED_ROUTING_ENABLED: ${LLM_MIXED_ROUTING_ENABLED:-false}
       LLM_LOW_TO_HIGH_ESCALATION_ENABLED: ${LLM_LOW_TO_HIGH_ESCALATION_ENABLED:-true}
-      LLM_ROUTING_LOW_PROVIDER: ${LLM_ROUTING_LOW_PROVIDER:-openai}
+      LLM_ROUTING_LOW_PROVIDER: ${LLM_ROUTING_LOW_PROVIDER:-glm}
-      LLM_ROUTING_LOW_MODEL: ${LLM_ROUTING_LOW_MODEL:-zai/glm-4-flash}
+      LLM_ROUTING_LOW_MODEL: ${LLM_ROUTING_LOW_MODEL:-GLM-4.7}
       LLM_ROUTING_HIGH_PROVIDER: ${LLM_ROUTING_HIGH_PROVIDER:-openai}
-      LLM_ROUTING_HIGH_MODEL: ${LLM_ROUTING_HIGH_MODEL:-minimax/MiniMax-M2.5}
+      LLM_ROUTING_HIGH_MODEL: ${LLM_ROUTING_HIGH_MODEL:-MiniMax-M2.5}
-      LLM_ROUTING_SAFETY_PROVIDER: ${LLM_ROUTING_SAFETY_PROVIDER:-openai}
+      LLM_ROUTING_SAFETY_PROVIDER: ${LLM_ROUTING_SAFETY_PROVIDER:-glm}
-      LLM_ROUTING_SAFETY_MODEL: ${LLM_ROUTING_SAFETY_MODEL:-deepseek/deepseek-chat}
+      LLM_ROUTING_SAFETY_MODEL: ${LLM_ROUTING_SAFETY_MODEL:-GLM-4.7}
       LLM_DIRECT_EMERGENCY_ENABLED: ${LLM_DIRECT_EMERGENCY_ENABLED:-false}
       LLM_REQUEST_TIMEOUT: ${LLM_REQUEST_TIMEOUT:-90}
       AUTO_CREATE_TABLES: "true"
       GLM_API_KEY: ${GLM_API_KEY:-}
       GLM_BASE_URL: ${GLM_BASE_URL:-https://open.bigmodel.cn/api/paas/v4}
-      GLM_MODEL: ${GLM_MODEL:-glm-4-flash}
+      GLM_MODEL: ${GLM_MODEL:-GLM-4.7}
       OPENAI_API_KEY: ${OPENAI_API_KEY:-}
+      OPENAI_MODEL: ${OPENAI_MODEL:-MiniMax-M2.5}
       DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY:-}
       OLLAMA_HOST: ${OLLAMA_HOST:-http://host.docker.internal:11434}
       OLLAMA_BASE_URL: ${OLLAMA_BASE_URL:-http://host.docker.internal:11434}