feat: add AI学习助手 agent (KG+RAG ideal) and renshenguo feishu bot
- Add AI学习助手 agent creation script with all 39 tools, 3-layer KG+RAG memory - Add renshenguo (人参果) feishu bot integration (app_service + ws_handler) - Register renshenguo WS client in main.py startup - Add RENSHENGUO_APP_ID / RENSHENGUO_APP_SECRET / RENSHENGUO_AGENT_ID config - Reorganize docs from root into docs/ subdirectories - Move startup scripts to scripts/startup/ - Various backend optimizations and tool improvements Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
renjianbo
822
backend/app/services/knowledge_graph_service.py
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822
backend/app/services/knowledge_graph_service.py
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"""
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知识图谱服务 — 实体抽取、关系构建、图谱查询、向量融合检索
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为智能学习助手提供 KG+RAG 核心能力：
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- 从对话/文本中提取知识点实体
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- 构建实体间的语义关系（前置/扩展/包含/示例/关联）
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- 图谱查询：邻近节点、路径查找、子图展开
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- 向量+图谱融合检索：同时命中语义相似和结构关联
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"""
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from __future__ import annotations
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import json
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import logging
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from typing import Any, Dict, List, Optional, Tuple, Set
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from sqlalchemy.orm import Session
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from sqlalchemy import or_, and_
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from app.core.database import SessionLocal
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from app.models.agent import KnowledgeEntity, KnowledgeRelation
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from app.services.embedding_service import embedding_service, VectorEntry
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logger = logging.getLogger(__name__)
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# 实体类型定义
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ENTITY_TYPES = ["concept", "formula", "fact", "term", "task", "skill"]
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# 关系类型定义
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RELATION_TYPES = {
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"prerequisite": "前置知识（学习 B 前需要先掌握 A）",
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"extends": "扩展（B 是 A 的深入/延伸）",
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"contains": "包含（A 包含子知识点 B）",
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"related_to": "相关（A 与 B 存在关联）",
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"example_of": "示例（B 是 A 的实例/例题）",
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"applies_to": "应用（A 可应用于 B）",
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}
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def _build_entity_embedding_text(name: str, entity_type: str, description: str) -> str:
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"""构建用于 embedding 的统一文本。"""
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parts = [f"[{entity_type}] {name}"]
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if description:
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parts.append(f": {description[:500]}")
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return "".join(parts)
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def _serialize_embedding(emb: Optional[List[float]]) -> Optional[str]:
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if not emb:
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return None
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return embedding_service.serialize_embedding(emb)
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# ═══════════════════════════════════════════════════════════════
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# 实体管理
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# ═══════════════════════════════════════════════════════════════
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async def add_entity(
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db: Session,
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name: str,
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entity_type: str = "concept",
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description: str = "",
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metadata: Optional[Dict[str, Any]] = None,
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source: str = "extracted",
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confidence: str = "medium",
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scope_kind: str = "agent",
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scope_id: str = "",
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user_id: Optional[str] = None,
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) -> Optional[KnowledgeEntity]:
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"""添加或更新知识实体（按 name+scope 去重，更新描述和 embedding）。"""
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name = name.strip()[:200]
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if not name:
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return None
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if entity_type not in ENTITY_TYPES:
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entity_type = "concept"
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# 查找已有实体（同名+同scope）
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existing = (
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db.query(KnowledgeEntity)
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.filter(
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KnowledgeEntity.name == name,
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KnowledgeEntity.scope_kind == scope_kind,
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KnowledgeEntity.scope_id == scope_id,
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)
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.first()
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)
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# 生成 embedding
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emb_text = _build_entity_embedding_text(name, entity_type, description)
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embedding_json = None
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try:
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emb = await embedding_service.generate_embedding(emb_text)
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if emb:
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embedding_json = _serialize_embedding(emb)
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except Exception as e:
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logger.warning("生成实体 embedding 失败: %s", e)
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if existing:
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# 更新已有实体
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if description and len(description) > len(existing.description or ""):
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existing.description = description
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if embedding_json:
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existing.embedding = embedding_json
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if metadata:
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merged = {**(existing.metadata_ or {}), **metadata}
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existing.metadata_ = merged
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existing.confidence = confidence
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db.commit()
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db.refresh(existing)
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logger.debug("更新知识实体: %s (%s)", name, entity_type)
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return existing
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entity = KnowledgeEntity(
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name=name,
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entity_type=entity_type,
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description=description,
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embedding=embedding_json,
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metadata_=metadata or {},
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source=source,
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confidence=confidence,
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scope_kind=scope_kind,
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scope_id=scope_id,
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user_id=user_id,
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)
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db.add(entity)
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db.commit()
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db.refresh(entity)
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logger.info("新增知识实体: %s (%s) id=%s", name, entity_type, entity.id)
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return entity
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async def add_entities_batch(
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db: Session,
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entities: List[Dict[str, Any]],
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scope_kind: str = "agent",
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scope_id: str = "",
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user_id: Optional[str] = None,
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) -> List[KnowledgeEntity]:
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"""批量添加实体，返回新增/更新后的实体列表。"""
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results: List[KnowledgeEntity] = []
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for ent in entities:
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try:
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e = await add_entity(
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db,
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name=ent.get("name", ""),
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entity_type=ent.get("entity_type", "concept"),
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description=ent.get("description", ""),
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metadata=ent.get("metadata"),
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source=ent.get("source", "extracted"),
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confidence=ent.get("confidence", "medium"),
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scope_kind=scope_kind,
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scope_id=scope_id,
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user_id=user_id,
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)
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if e:
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results.append(e)
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except Exception as ex:
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logger.warning("批量添加实体失败: name=%s err=%s", ent.get("name"), ex)
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return results
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# ═══════════════════════════════════════════════════════════════
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# 关系管理
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# ═══════════════════════════════════════════════════════════════
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def add_relation(
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db: Session,
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source_entity_id: str,
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target_entity_id: str,
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relation_type: str = "related_to",
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description: str = "",
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weight: float = 1.0,
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scope_kind: str = "agent",
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scope_id: str = "",
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) -> Optional[KnowledgeRelation]:
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"""添加两个实体间的关系（去重）。"""
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if source_entity_id == target_entity_id:
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return None
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if relation_type not in RELATION_TYPES:
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relation_type = "related_to"
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existing = (
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db.query(KnowledgeRelation)
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.filter(
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KnowledgeRelation.source_entity_id == source_entity_id,
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KnowledgeRelation.target_entity_id == target_entity_id,
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KnowledgeRelation.relation_type == relation_type,
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KnowledgeRelation.scope_kind == scope_kind,
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KnowledgeRelation.scope_id == scope_id,
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)
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.first()
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)
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if existing:
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logger.debug("关系已存在: %s -[%s]-> %s", source_entity_id[:8], relation_type, target_entity_id[:8])
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return existing
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rel = KnowledgeRelation(
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source_entity_id=source_entity_id,
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target_entity_id=target_entity_id,
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relation_type=relation_type,
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description=description,
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weight=str(weight),
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scope_kind=scope_kind,
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scope_id=scope_id,
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)
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db.add(rel)
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db.commit()
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db.refresh(rel)
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logger.info("新增关系: %s -[%s]-> %s", source_entity_id[:8], relation_type, target_entity_id[:8])
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return rel
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def add_relations_from_map(
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db: Session,
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entity_map: Dict[str, str], # name -> entity_id
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relations: List[Dict[str, Any]],
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scope_kind: str = "agent",
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scope_id: str = "",
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) -> int:
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"""从关系映射批量添加关系。relations 中 source/target 使用实体名称，自动映射为 ID。"""
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count = 0
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for rel in relations:
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src_name = rel.get("source", "")
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tgt_name = rel.get("target", "")
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src_id = entity_map.get(src_name)
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tgt_id = entity_map.get(tgt_name)
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if not src_id or not tgt_id:
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continue
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r = add_relation(
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db,
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source_entity_id=src_id,
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target_entity_id=tgt_id,
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relation_type=rel.get("relation_type", "related_to"),
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description=rel.get("description", ""),
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weight=float(rel.get("weight", 1.0)),
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scope_kind=scope_kind,
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scope_id=scope_id,
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)
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if r:
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count += 1
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return count
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# ═══════════════════════════════════════════════════════════════
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# 图谱查询
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# ═══════════════════════════════════════════════════════════════
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def get_entity_by_id(db: Session, entity_id: str) -> Optional[KnowledgeEntity]:
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return db.query(KnowledgeEntity).filter(KnowledgeEntity.id == entity_id).first()
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def search_entities(
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db: Session,
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keyword: str = "",
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entity_type: Optional[str] = None,
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scope_kind: str = "agent",
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scope_id: str = "",
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limit: int = 20,
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) -> List[KnowledgeEntity]:
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"""关键词搜索实体。"""
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q = db.query(KnowledgeEntity).filter(
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KnowledgeEntity.scope_kind == scope_kind,
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KnowledgeEntity.scope_id == scope_id,
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)
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if entity_type:
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q = q.filter(KnowledgeEntity.entity_type == entity_type)
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if keyword:
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pattern = f"%{keyword}%"
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q = q.filter(
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or_(
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KnowledgeEntity.name.like(pattern),
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KnowledgeEntity.description.like(pattern),
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)
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)
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return q.order_by(KnowledgeEntity.confidence.desc(), KnowledgeEntity.created_at.desc()).limit(limit).all()
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def get_neighbors(
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db: Session,
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entity_id: str,
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relation_types: Optional[List[str]] = None,
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direction: str = "both",
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max_depth: int = 1,
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limit: int = 30,
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) -> Dict[str, Any]:
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"""
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获取实体的图谱邻居。
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Returns:
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{
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"entity": {...},
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"neighbors": [{"entity": {...}, "relation": {...}, "direction": "out"|"in"}],
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"subgraph_size": int,
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}
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"""
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entity = get_entity_by_id(db, entity_id)
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if not entity:
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return {"entity": None, "neighbors": [], "subgraph_size": 0}
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neighbors: List[Dict[str, Any]] = []
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if direction in ("out", "both"):
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q = db.query(KnowledgeRelation).filter(
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KnowledgeRelation.source_entity_id == entity_id,
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)
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if relation_types:
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q = q.filter(KnowledgeRelation.relation_type.in_(relation_types))
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for rel in q.limit(limit).all():
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target = get_entity_by_id(db, rel.target_entity_id)
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if target:
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neighbors.append({
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"entity": _entity_to_dict(target),
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"relation": _relation_to_dict(rel),
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"direction": "out",
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})
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if direction in ("in", "both"):
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q = db.query(KnowledgeRelation).filter(
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KnowledgeRelation.target_entity_id == entity_id,
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)
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if relation_types:
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q = q.filter(KnowledgeRelation.relation_type.in_(relation_types))
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for rel in q.limit(limit).all():
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source = get_entity_by_id(db, rel.source_entity_id)
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if source:
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neighbors.append({
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"entity": _entity_to_dict(source),
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"relation": _relation_to_dict(rel),
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"direction": "in",
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})
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return {
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"entity": _entity_to_dict(entity),
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"neighbors": neighbors[:limit],
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"subgraph_size": len(neighbors),
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}
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def get_entity_graph(
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db: Session,
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entity_id: str,
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max_depth: int = 2,
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limit: int = 50,
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) -> Dict[str, Any]:
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"""获取以实体为中心的子图（BFS 展开）。"""
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entity = get_entity_by_id(db, entity_id)
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if not entity:
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return {"nodes": [], "edges": [], "center_id": entity_id}
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visited: Set[str] = {entity_id}
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nodes: Dict[str, Dict[str, Any]] = {entity_id: _entity_to_dict(entity)}
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edges: List[Dict[str, Any]] = []
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frontier = {entity_id}
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for depth in range(max_depth):
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next_frontier: Set[str] = set()
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if len(nodes) >= limit:
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break
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for nid in list(frontier):
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# 出边
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out_rels = (
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db.query(KnowledgeRelation)
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.filter(KnowledgeRelation.source_entity_id == nid)
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.limit(limit // 2)
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.all()
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)
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for rel in out_rels:
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edges.append(_relation_to_dict(rel))
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if rel.target_entity_id not in visited and len(nodes) < limit:
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tgt = get_entity_by_id(db, rel.target_entity_id)
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if tgt:
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nodes[rel.target_entity_id] = _entity_to_dict(tgt)
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visited.add(rel.target_entity_id)
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next_frontier.add(rel.target_entity_id)
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# 入边
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in_rels = (
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db.query(KnowledgeRelation)
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.filter(KnowledgeRelation.target_entity_id == nid)
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.limit(limit // 2)
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.all()
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)
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for rel in in_rels:
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edges.append(_relation_to_dict(rel))
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if rel.source_entity_id not in visited and len(nodes) < limit:
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src = get_entity_by_id(db, rel.source_entity_id)
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if src:
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nodes[rel.source_entity_id] = _entity_to_dict(src)
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visited.add(rel.source_entity_id)
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next_frontier.add(rel.source_entity_id)
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frontier = next_frontier
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if not frontier:
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break
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return {
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"nodes": list(nodes.values()),
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"edges": edges,
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"center_id": entity_id,
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}
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def find_path(
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db: Session,
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source_id: str,
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target_id: str,
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max_depth: int = 4,
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) -> Optional[List[Dict[str, Any]]]:
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"""BFS 查找两个实体间的最短路径。"""
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if source_id == target_id:
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return [{"entity": _entity_to_dict(get_entity_by_id(db, source_id)), "relation": None}]
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visited: Set[str] = {source_id}
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# BFS queue: (current_id, path_so_far)
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from collections import deque
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queue: deque = deque()
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queue.append((source_id, []))
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while queue:
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current_id, path = queue.popleft()
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if len(path) >= max_depth:
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continue
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# 检查所有出边
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out_rels = (
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db.query(KnowledgeRelation)
|
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.filter(KnowledgeRelation.source_entity_id == current_id)
|
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.all()
|
||||
)
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for rel in out_rels:
|
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if rel.target_entity_id in visited:
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continue
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new_path = path + [{
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"from": _entity_to_dict(get_entity_by_id(db, current_id)),
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"relation": _relation_to_dict(rel),
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"to": _entity_to_dict(get_entity_by_id(db, rel.target_entity_id)),
|
||||
}]
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if rel.target_entity_id == target_id:
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return new_path
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visited.add(rel.target_entity_id)
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queue.append((rel.target_entity_id, new_path))
|
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|
||||
# 检查所有入边
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in_rels = (
|
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db.query(KnowledgeRelation)
|
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.filter(KnowledgeRelation.target_entity_id == current_id)
|
||||
.all()
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||||
)
|
||||
for rel in in_rels:
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if rel.source_entity_id in visited:
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continue
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new_path = path + [{
|
||||
"from": _entity_to_dict(get_entity_by_id(db, current_id)),
|
||||
"relation": _relation_to_dict(rel),
|
||||
"to": _entity_to_dict(get_entity_by_id(db, rel.source_entity_id)),
|
||||
}]
|
||||
if rel.source_entity_id == target_id:
|
||||
return new_path
|
||||
visited.add(rel.source_entity_id)
|
||||
queue.append((rel.source_entity_id, new_path))
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# LLM 驱动的实体与关系提取
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
EXTRACTION_PROMPT = """你是一个知识图谱构建助手。请分析以下文本，提取其中的知识点实体和它们之间的关系。
|
||||
|
||||
请返回 JSON 格式（不要 markdown 包裹），包含：
|
||||
1. entities: 提取的知识实体列表，每个实体包含：
|
||||
- name: 实体名称（简洁准确）
|
||||
- entity_type: 类型（concept=概念, formula=公式, fact=事实, term=术语, task=任务, skill=技能）
|
||||
- description: 简要描述（1-2句）
|
||||
- confidence: 置信度（low/medium/high）
|
||||
|
||||
2. relations: 实体间的关系列表，每个关系包含：
|
||||
- source: 源实体名称
|
||||
- target: 目标实体名称
|
||||
- relation_type: 关系类型（prerequisite=前置知识, extends=扩展, contains=包含, related_to=相关, example_of=示例, applies_to=应用）
|
||||
- description: 关系说明（1句话）
|
||||
|
||||
注意：
|
||||
- 只提取文本中明确出现的知识点，不编造
|
||||
- 关系应只在同段文本中有关联的实体间建立
|
||||
- 如果文本中知识点不足，返回空数组即可
|
||||
|
||||
文本内容：
|
||||
{text}"""
|
||||
|
||||
|
||||
async def extract_from_text(
|
||||
text: str,
|
||||
scope_kind: str = "agent",
|
||||
scope_id: str = "",
|
||||
user_id: Optional[str] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
使用 LLM 从文本中提取实体和关系，并写入知识图谱。
|
||||
|
||||
Returns:
|
||||
{"entities": [...], "relations": [...], "entity_count": int, "relation_count": int}
|
||||
"""
|
||||
if not text or len(text.strip()) < 20:
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
|
||||
from openai import AsyncOpenAI
|
||||
from app.core.config import settings
|
||||
|
||||
# 调用 LLM 提取
|
||||
try:
|
||||
api_key = settings.DEEPSEEK_API_KEY or settings.OPENAI_API_KEY or ""
|
||||
base_url = settings.DEEPSEEK_BASE_URL or settings.OPENAI_BASE_URL or "https://api.deepseek.com"
|
||||
if api_key == "your-openai-api-key":
|
||||
api_key = settings.DEEPSEEK_API_KEY or ""
|
||||
base_url = settings.DEEPSEEK_BASE_URL or "https://api.deepseek.com"
|
||||
|
||||
if not api_key:
|
||||
logger.warning("知识图谱提取：未配置 API Key，跳过")
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
|
||||
client = AsyncOpenAI(api_key=api_key, base_url=base_url)
|
||||
resp = await client.chat.completions.create(
|
||||
model="deepseek-v4-flash",
|
||||
messages=[{"role": "user", "content": EXTRACTION_PROMPT.format(text=text[:4000])}],
|
||||
temperature=0.2,
|
||||
max_tokens=2048,
|
||||
timeout=30,
|
||||
)
|
||||
raw = resp.choices[0].message.content or ""
|
||||
|
||||
# 解析 JSON
|
||||
raw = raw.strip().removeprefix("```json").removesuffix("```").strip()
|
||||
result = json.loads(raw)
|
||||
except json.JSONDecodeError:
|
||||
logger.warning("知识图谱提取：LLM 返回非 JSON，跳过")
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
except Exception as e:
|
||||
logger.warning("知识图谱提取失败: %s", e)
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
|
||||
extracted_entities = result.get("entities", [])
|
||||
extracted_relations = result.get("relations", [])
|
||||
|
||||
if not extracted_entities:
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
|
||||
# 写入数据库
|
||||
db: Optional[Session] = None
|
||||
try:
|
||||
db = SessionLocal()
|
||||
|
||||
# 批量添加实体
|
||||
created = await add_entities_batch(
|
||||
db, extracted_entities,
|
||||
scope_kind=scope_kind, scope_id=scope_id, user_id=user_id,
|
||||
)
|
||||
|
||||
# 构建 name->id 映射
|
||||
entity_map: Dict[str, str] = {}
|
||||
for e in created:
|
||||
entity_map[e.name] = e.id
|
||||
|
||||
# 添加关系
|
||||
rel_count = add_relations_from_map(
|
||||
db, entity_map, extracted_relations,
|
||||
scope_kind=scope_kind, scope_id=scope_id,
|
||||
)
|
||||
|
||||
return {
|
||||
"entities": [_entity_to_dict(e) for e in created],
|
||||
"relations": extracted_relations[:rel_count],
|
||||
"entity_count": len(created),
|
||||
"relation_count": rel_count,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error("写入知识图谱失败: %s", e)
|
||||
if db:
|
||||
db.rollback()
|
||||
return {"entities": [], "relations": [], "entity_count": 0, "relation_count": 0}
|
||||
finally:
|
||||
if db:
|
||||
db.close()
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# 向量+图谱融合检索（核心 RAG 能力）
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
async def hybrid_search(
|
||||
query: str,
|
||||
scope_kind: str = "agent",
|
||||
scope_id: str = "",
|
||||
top_k: int = 5,
|
||||
include_neighbors: bool = True,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
向量+图谱融合检索。
|
||||
|
||||
1. 用 query 生成 embedding，在知识实体中做向量相似度搜索
|
||||
2. 对 Top-K 实体展开图谱邻居
|
||||
3. 返回融合后的结构化知识上下文
|
||||
|
||||
Returns:
|
||||
{
|
||||
"query": str,
|
||||
"vector_matches": [...], # 向量检索命中的实体
|
||||
"graph_expansion": {...}, # 图谱展开的邻居子图
|
||||
"formatted_context": str, # 格式化后的文本上下文（可直接注入 LLM）
|
||||
}
|
||||
"""
|
||||
if not query or not query.strip():
|
||||
return {"query": query, "vector_matches": [], "graph_expansion": {}, "formatted_context": ""}
|
||||
|
||||
db: Optional[Session] = None
|
||||
try:
|
||||
db = SessionLocal()
|
||||
|
||||
# Step 1: 向量检索
|
||||
query_emb = await embedding_service.generate_embedding(query)
|
||||
if not query_emb:
|
||||
# 降级：关键词检索
|
||||
entities = search_entities(db, keyword=query, scope_kind=scope_kind, scope_id=scope_id, limit=top_k * 3)
|
||||
vector_matches = [_entity_to_dict(e) for e in entities[:top_k]]
|
||||
else:
|
||||
all_entities = (
|
||||
db.query(KnowledgeEntity)
|
||||
.filter(
|
||||
KnowledgeEntity.scope_kind == scope_kind,
|
||||
KnowledgeEntity.scope_id == scope_id,
|
||||
KnowledgeEntity.embedding.isnot(None),
|
||||
)
|
||||
.limit(200)
|
||||
.all()
|
||||
)
|
||||
|
||||
entries: List[VectorEntry] = []
|
||||
for e in all_entities:
|
||||
emb = embedding_service.deserialize_embedding(e.embedding) if e.embedding else []
|
||||
if emb:
|
||||
entries.append({
|
||||
"id": e.id,
|
||||
"scope_kind": scope_kind,
|
||||
"scope_id": scope_id,
|
||||
"content_text": _build_entity_embedding_text(e.name, e.entity_type, e.description or ""),
|
||||
"embedding": emb,
|
||||
"metadata": {"entity_type": e.entity_type, "name": e.name, "entity_id": e.id},
|
||||
})
|
||||
|
||||
matched = await embedding_service.similarity_search(query_emb, entries, top_k=top_k)
|
||||
# 重新获取完整实体信息
|
||||
matched_ids = [m["metadata"].get("entity_id", "") for m in matched]
|
||||
vector_matches = []
|
||||
for mid in matched_ids:
|
||||
e = get_entity_by_id(db, mid)
|
||||
if e:
|
||||
score = next((m.get("score", 0) for m in matched if m["metadata"].get("entity_id") == mid), 0)
|
||||
d = _entity_to_dict(e)
|
||||
d["score"] = score
|
||||
vector_matches.append(d)
|
||||
|
||||
# Step 2: 图谱展开（对 Top-3 实体取邻居）
|
||||
graph_expansion: Dict[str, Any] = {"entities": [], "relations": []}
|
||||
seen_entity_ids: Set[str] = set()
|
||||
if include_neighbors and vector_matches:
|
||||
for vm in vector_matches[:3]:
|
||||
eid = vm["id"]
|
||||
if eid in seen_entity_ids:
|
||||
continue
|
||||
seen_entity_ids.add(eid)
|
||||
sub = get_neighbors(db, eid, direction="both", limit=5)
|
||||
if sub["entity"] and sub["entity"] not in graph_expansion["entities"]:
|
||||
graph_expansion["entities"].append(sub["entity"])
|
||||
for nb in sub["neighbors"]:
|
||||
if nb["entity"] not in graph_expansion["entities"]:
|
||||
graph_expansion["entities"].append(nb["entity"])
|
||||
rel_dict = nb["relation"]
|
||||
if rel_dict not in graph_expansion["relations"]:
|
||||
graph_expansion["relations"].append(rel_dict)
|
||||
|
||||
# Step 3: 格式化上下文
|
||||
formatted = _format_hybrid_context(query, vector_matches, graph_expansion)
|
||||
|
||||
return {
|
||||
"query": query,
|
||||
"vector_matches": vector_matches,
|
||||
"graph_expansion": graph_expansion,
|
||||
"formatted_context": formatted,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error("融合检索失败: %s", e)
|
||||
return {"query": query, "vector_matches": [], "graph_expansion": {}, "formatted_context": ""}
|
||||
finally:
|
||||
if db:
|
||||
db.close()
|
||||
|
||||
|
||||
def _format_hybrid_context(
|
||||
query: str,
|
||||
vector_matches: List[Dict[str, Any]],
|
||||
graph_expansion: Dict[str, Any],
|
||||
) -> str:
|
||||
"""格式化融合检索结果为 LLM 可读的文本块。"""
|
||||
parts: List[str] = []
|
||||
|
||||
if vector_matches:
|
||||
parts.append("## 相关知识实体（向量检索）")
|
||||
for i, vm in enumerate(vector_matches, 1):
|
||||
score_str = f" (匹配度: {vm.get('score', 1.0):.2f})" if vm.get('score', 1.0) < 1.0 else ""
|
||||
parts.append(f"{i}. [{vm.get('entity_type', 'concept')}] **{vm.get('name', '')}**{score_str}")
|
||||
if vm.get("description"):
|
||||
parts.append(f" {vm['description'][:300]}")
|
||||
|
||||
if graph_expansion.get("entities"):
|
||||
parts.append("\n## 关联知识点（图谱展开）")
|
||||
for ent in graph_expansion["entities"]:
|
||||
parts.append(f"- [{ent.get('entity_type', '')}] **{ent.get('name', '')}**")
|
||||
if ent.get("description"):
|
||||
parts.append(f" {ent['description'][:200]}")
|
||||
|
||||
if graph_expansion.get("relations"):
|
||||
parts.append("\n## 知识关系")
|
||||
for rel in graph_expansion["relations"]:
|
||||
parts.append(f"- `{rel.get('relation_type', '')}`: {rel.get('description', '')}")
|
||||
|
||||
return "\n".join(parts) if parts else ""
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# 学习路径推荐
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def recommend_learning_path(
|
||||
db: Session,
|
||||
target_entity_ids: List[str],
|
||||
scope_kind: str = "agent",
|
||||
scope_id: str = "",
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
基于知识图谱推荐学习路径。
|
||||
|
||||
对目标实体集合，分析前置关系，给出建议的学习顺序。
|
||||
"""
|
||||
all_entities: Dict[str, Dict[str, Any]] = {}
|
||||
all_prereqs: List[Dict[str, Any]] = []
|
||||
|
||||
for eid in target_entity_ids:
|
||||
entity = get_entity_by_id(db, eid)
|
||||
if not entity:
|
||||
continue
|
||||
all_entities[eid] = _entity_to_dict(entity)
|
||||
|
||||
# 查找前置知识
|
||||
prereqs = (
|
||||
db.query(KnowledgeRelation)
|
||||
.filter(
|
||||
KnowledgeRelation.target_entity_id == eid,
|
||||
KnowledgeRelation.relation_type == "prerequisite",
|
||||
KnowledgeRelation.scope_kind == scope_kind,
|
||||
KnowledgeRelation.scope_id == scope_id,
|
||||
)
|
||||
.all()
|
||||
)
|
||||
for pr in prereqs:
|
||||
src = get_entity_by_id(db, pr.source_entity_id)
|
||||
if src:
|
||||
all_entities[pr.source_entity_id] = _entity_to_dict(src)
|
||||
all_prereqs.append({
|
||||
"prerequisite": _entity_to_dict(src),
|
||||
"target": _entity_to_dict(entity),
|
||||
"relation": _relation_to_dict(pr),
|
||||
})
|
||||
|
||||
# 简单排序：按依赖数量（叶子在前，被依赖的在后）
|
||||
def _dep_count(eid: str) -> int:
|
||||
return sum(1 for p in all_prereqs if p["target"]["id"] == eid)
|
||||
|
||||
sorted_entities = sorted(all_entities.values(), key=lambda e: _dep_count(e["id"]))
|
||||
|
||||
return {
|
||||
"entities": sorted_entities,
|
||||
"prerequisites": all_prereqs,
|
||||
"suggested_order": [e["name"] for e in sorted_entities],
|
||||
"summary": f"建议按以下顺序学习: {' → '.join(e['name'] for e in sorted_entities)}" if sorted_entities else "暂无学习路径",
|
||||
}
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
# 工具函数
|
||||
# ═══════════════════════════════════════════════════════════════
|
||||
|
||||
|
||||
def _entity_to_dict(entity: KnowledgeEntity) -> Dict[str, Any]:
|
||||
return {
|
||||
"id": entity.id,
|
||||
"name": entity.name,
|
||||
"entity_type": entity.entity_type,
|
||||
"description": entity.description,
|
||||
"confidence": entity.confidence,
|
||||
"source": entity.source,
|
||||
"metadata": entity.metadata_ or {},
|
||||
"created_at": entity.created_at.isoformat() if entity.created_at else None,
|
||||
}
|
||||
|
||||
|
||||
def _relation_to_dict(rel: KnowledgeRelation) -> Dict[str, Any]:
|
||||
return {
|
||||
"id": rel.id,
|
||||
"source_entity_id": rel.source_entity_id,
|
||||
"target_entity_id": rel.target_entity_id,
|
||||
"relation_type": rel.relation_type,
|
||||
"description": rel.description,
|
||||
"weight": float(rel.weight) if rel.weight else 1.0,
|
||||
}
|
||||
Reference in New Issue
Block a user