diff --git a/backend/alembic/versions/010_add_global_knowledge.py b/backend/alembic/versions/010_add_global_knowledge.py new file mode 100644 index 0000000..c66a76f --- /dev/null +++ b/backend/alembic/versions/010_add_global_knowledge.py @@ -0,0 +1,45 @@ +"""add confidence and expires_at columns to global_knowledge table + +Revision ID: 010_add_global_knowledge +Revises: 009_notif_sched_feishu +Create Date: 2026-05-06 +""" +from alembic import op +import sqlalchemy as sa + + +revision = "010_add_global_knowledge" +down_revision = "009_notif_sched_feishu" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + # Add confidence column (if table doesn't exist, this is a no-op handled below) + try: + op.add_column( + "global_knowledge", + sa.Column("confidence", sa.String(20), default="medium", comment="置信度: low/medium/high"), + ) + except Exception: + pass + + # Add expires_at column + try: + op.add_column( + "global_knowledge", + sa.Column("expires_at", sa.DateTime(), nullable=True, comment="过期时间，NULL 表示永不过期"), + ) + except Exception: + pass + + +def downgrade() -> None: + try: + op.drop_column("global_knowledge", "expires_at") + except Exception: + pass + try: + op.drop_column("global_knowledge", "confidence") + except Exception: + pass diff --git a/backend/app/agent_runtime/core.py b/backend/app/agent_runtime/core.py index 21f6985..5fd87b5 100644 --- a/backend/app/agent_runtime/core.py +++ b/backend/app/agent_runtime/core.py @@ -226,6 +226,7 @@ class AgentRuntime: # LLM 直接返回文本 → 结束 self.context.add_assistant_message(content) final_text = content or "（模型未返回有效内容）" + review_score = 0.0 # 输出质量自检（默认关闭，Agent 节点可开启） if self.config.self_review_enabled and not _self_review_attempted: @@ -239,6 +240,7 @@ class AgentRuntime: tool_result=json.dumps(review, ensure_ascii=False), )) if review["passed"]: + review_score = review["score"] logger.info("self_review 通过 (%.2f >= %.2f)", review["score"], review["threshold"]) else: logger.info("self_review 未通过 (%.2f < %.2f)，追加修正", review["score"], review["threshold"]) @@ -269,7 +271,7 @@ class AgentRuntime: tool_calls_made=self.context.tool_calls_made, ) # 提取知识到全局知识池（Agent 间知识共享） - await self._extract_global_knowledge(user_input, final_text, steps) + await self._extract_global_knowledge(user_input, final_text, steps, review_score) return AgentResult( success=True, content=final_text, @@ -393,6 +395,9 @@ class AgentRuntime: iterations_used=self.context.iteration, tool_calls_made=self.context.tool_calls_made, ) + # 提取知识到全局知识池（即便截断，工具调用序列仍有参考价值） + if last_content: + await self._extract_global_knowledge(user_input, last_content, steps) if last_content: steps.append(AgentStep( iteration=self.context.iteration, @@ -512,6 +517,7 @@ class AgentRuntime: # LLM 直接返回文本 → 结束 self.context.add_assistant_message(content) final_text = content or "（模型未返回有效内容）" + review_score = 0.0 # 输出质量自检（默认关闭） if self.config.self_review_enabled and not _self_review_attempted: @@ -524,6 +530,7 @@ class AgentRuntime: "session_id": self.context.session_id, } if review["passed"]: + review_score = review["score"] logger.info("self_review 通过 (%.2f >= %.2f)", review["score"], review["threshold"]) else: logger.info("self_review 未通过 (%.2f < %.2f)，追加修正", review["score"], review["threshold"]) @@ -560,6 +567,8 @@ class AgentRuntime: iterations_used=self.context.iteration, tool_calls_made=self.context.tool_calls_made, ) + # 提取知识到全局知识池（Agent 间知识共享） + await self._extract_global_knowledge(user_input, final_text, steps, review_score) return # 有工具调用 → 先记录 assistant 消息 @@ -706,6 +715,9 @@ class AgentRuntime: iterations_used=self.context.iteration, tool_calls_made=self.context.tool_calls_made, ) + # 提取知识到全局知识池（即便截断，工具调用序列仍有参考价值） + if last_content: + await self._extract_global_knowledge(user_input, last_content, steps) yield { "type": "final", "content": last_content or "已达最大迭代次数，但模型未返回最终回答。", @@ -778,6 +790,7 @@ class AgentRuntime: async def _extract_global_knowledge( self, user_input: str, final_answer: str, steps: List[AgentStep], + self_review_score: float = 0.0, ) -> None: """从 Agent 执行结果中提取知识，写入全局知识池（Agent 间共享）。""" # 提取工具调用名称作为 tags @@ -798,11 +811,24 @@ class AgentRuntime: source_agent_id = self.config.name if self.config.name != "default_agent" else "" source_user_id = self.config.user_id or "" + # 置信度评估：基于 self_review 评分和工具执行成功数 + confidence = "medium" + if self_review_score >= 0.8: + confidence = "high" + elif self_review_score > 0 and self_review_score < 0.5: + confidence = "low" + elif tool_names and len(tool_names) >= 2: + confidence = "high" # 多工具协作通常质量更高 + # TTL: 高置信度知识有效期更长 + ttl_hours = 720 if confidence == "high" else 168 if confidence == "medium" else 24 + await self.memory.save_global_knowledge( content=content, source_agent_id=source_agent_id, source_user_id=source_user_id, tags=tags, + confidence=confidence, + ttl_hours=ttl_hours, ) async def _self_review(self, content: str, task_context: str = "") -> dict: diff --git a/backend/app/agent_runtime/memory.py b/backend/app/agent_runtime/memory.py index 61cc82f..88250c6 100644 --- a/backend/app/agent_runtime/memory.py +++ b/backend/app/agent_runtime/memory.py @@ -178,13 +178,21 @@ class AgentMemory: async def _global_knowledge_search(self, query: str = "") -> str: """从 GlobalKnowledge 表检索相关的全局知识条目。""" + from datetime import datetime from app.models.agent import GlobalKnowledge db: Optional[Session] = None try: db = SessionLocal() + now = datetime.utcnow() + + # 查询未过期的知识（expires_at IS NULL 或 expires_at > now） rows = ( db.query(GlobalKnowledge) + .filter( + (GlobalKnowledge.expires_at.is_(None)) + | (GlobalKnowledge.expires_at > now) + ) .order_by(GlobalKnowledge.created_at.desc()) .limit(50) .all() @@ -212,6 +220,7 @@ class AgentMemory: "metadata": { "source_agent_id": row.source_agent_id, "tags": row.tags or [], + "confidence": row.confidence or "medium", }, }) @@ -225,17 +234,22 @@ class AgentMemory: lines = ["## 全局知识库"] for i, m in enumerate(matched, 1): tags = m.get("metadata", {}).get("tags", []) + conf = m.get("metadata", {}).get("confidence", "medium") tag_str = f" [{', '.join(tags[:3])}]" if tags else "" - lines.append(f"{i}.{tag_str} {m.get('content_text', '')[:500]}") + conf_str = f" (置信度:{conf})" if conf != "medium" else "" + lines.append(f"{i}.{tag_str}{conf_str} {m.get('content_text', '')[:500]}") return "\n".join(lines) else: - # 无 query，返回最近 5 条全局知识 - recent = rows[:5] + # 无 query，返回最近 5 条全局知识（优先高置信度） + recent = sorted(rows, key=lambda r: ( + 0 if r.confidence == "high" else 1 if r.confidence == "medium" else 2 + ))[:5] if recent: lines = ["## 全局知识库（最近）"] for i, row in enumerate(recent, 1): tag_str = f" [{(', '.join(row.tags[:3]))}]" if row.tags else "" - lines.append(f"{i}.{tag_str} {row.content[:500]}") + conf_str = f" (置信度:{row.confidence})" if row.confidence and row.confidence != "medium" else "" + lines.append(f"{i}.{tag_str}{conf_str} {row.content[:500]}") return "\n".join(lines) return "" @@ -249,8 +263,14 @@ class AgentMemory: async def save_global_knowledge( self, content: str, source_agent_id: str = "", source_user_id: str = "", tags: Optional[List[str]] = None, + confidence: str = "medium", ttl_hours: int = 0, ) -> None: - """将知识条目写入全局知识池。""" + """将知识条目写入全局知识池（带去重、置信度、过期时间）。 + + 去重策略：对 content 取哈希，若已有相同哈希的条目则跳过。 + 过期策略：ttl_hours > 0 时设置 expires_at；0 表示永不过期。 + """ + from datetime import datetime, timedelta from app.models.agent import GlobalKnowledge if not content or len(content) < 20: @@ -260,7 +280,26 @@ class AgentMemory: try: db = SessionLocal() - # 生成 embedding + # 去重：用 content 的 MD5 哈希检查是否已存在 + import hashlib + content_hash = hashlib.md5(content[:500].encode()).hexdigest() + + # 查询最近 200 条，检查是否有相同哈希的条目 + recent = ( + db.query(GlobalKnowledge) + .order_by(GlobalKnowledge.created_at.desc()) + .limit(200) + .all() + ) + for existing in recent: + existing_hash = hashlib.md5( + (existing.content or "")[:500].encode() + ).hexdigest() + if existing_hash == content_hash: + logger.info("全局知识去重：已存在相同条目，跳过写入") + return + + # 嵌入向量 embedding_json = "" try: emb = await embedding_service.generate_embedding(content) @@ -269,18 +308,26 @@ class AgentMemory: except Exception: pass + # 过期时间 + expires_at = None + if ttl_hours > 0: + expires_at = datetime.utcnow() + timedelta(hours=ttl_hours) + record = GlobalKnowledge( content=content[:2000], embedding=embedding_json or None, source_agent_id=source_agent_id or "", source_user_id=source_user_id or "", tags=tags or [], + confidence=confidence or "medium", + expires_at=expires_at, scope_kind=self.scope_kind, scope_id=self.scope_id or "global", ) db.add(record) db.commit() - logger.info("已写入全局知识: agent=%s tags=%s", source_agent_id, tags) + logger.info("已写入全局知识: agent=%s tags=%s confidence=%s", + source_agent_id, tags, confidence) except Exception as e: logger.warning("保存全局知识失败: %s", e) if db: diff --git a/backend/app/models/agent.py b/backend/app/models/agent.py index ff5be26..33f1840 100644 --- a/backend/app/models/agent.py +++ b/backend/app/models/agent.py @@ -24,6 +24,19 @@ class Agent(Base): version = Column(Integer, default=1, comment="版本号") status = Column(String(20), default="draft", comment="状态: draft/published/running/stopped") user_id = Column(CHAR(36), ForeignKey("users.id"), comment="创建者ID") + + # 技能市场字段 + category = Column(String(50), nullable=True, comment="分类: llm/data_processing/automation/integration/other") + tags = Column(JSON, nullable=True, comment="标签列表") + thumbnail = Column(Text, nullable=True, comment="缩略图URL") + is_public = Column(Integer, default=0, comment="是否公开到市场: 0=私有 1=公开") + is_featured = Column(Integer, default=0, comment="是否精选: 0=否 1=是") + rating_avg = Column(String(10), default="0.0", comment="平均评分") + rating_count = Column(Integer, default=0, comment="评分人数") + use_count = Column(Integer, default=0, comment="被安装次数") + view_count = Column(Integer, default=0, comment="查看次数") + forked_from_id = Column(CHAR(36), nullable=True, comment="从哪个Agent Fork而来（市场安装）") + created_at = Column(DateTime, default=func.now(), comment="创建时间") updated_at = Column(DateTime, default=func.now(), onupdate=func.now(), comment="更新时间") @@ -62,6 +75,8 @@ class GlobalKnowledge(Base): source_agent_id = Column(CHAR(36), nullable=True, comment="来源 Agent ID") source_user_id = Column(CHAR(36), nullable=True, comment="来源用户 ID") tags = Column(JSON, nullable=True, comment="分类标签") + confidence = Column(String(20), default="medium", comment="置信度: low/medium/high") + expires_at = Column(DateTime, nullable=True, comment="过期时间，NULL 表示永不过期") scope_kind = Column(String(50), default="agent", comment="作用域类型") scope_id = Column(String(100), default="", comment="作用域 ID") created_at = Column(DateTime, default=func.now(), comment="创建时间") @@ -108,3 +123,37 @@ class KnowledgeRelation(Base): def __repr__(self): return f" ({self.target_entity_id})>" + + +class AgentRating(Base): + """Agent 技能市场评分/评论表""" + __tablename__ = "agent_ratings" + + id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="评分ID") + agent_id = Column(CHAR(36), nullable=False, index=True, comment="Agent ID") + user_id = Column(CHAR(36), ForeignKey("users.id"), nullable=False, comment="评分用户ID") + rating = Column(Integer, nullable=False, comment="评分 1-5") + comment = Column(Text, nullable=True, comment="评论内容") + created_at = Column(DateTime, default=func.now(), comment="评分时间") + + # 关系 + user = relationship("User", backref="agent_ratings") + + def __repr__(self): + return f"" + + +class AgentFavorite(Base): + """Agent 技能市场收藏表""" + __tablename__ = "agent_favorites" + + id = Column(CHAR(36), primary_key=True, default=lambda: str(uuid.uuid4()), comment="收藏ID") + agent_id = Column(CHAR(36), nullable=False, index=True, comment="Agent ID") + user_id = Column(CHAR(36), ForeignKey("users.id"), nullable=False, comment="收藏用户ID") + created_at = Column(DateTime, default=func.now(), comment="收藏时间") + + # 关系 + user = relationship("User", backref="agent_favorites") + + def __repr__(self): + return f""