# Auto-extracted from chat.py during Phase 1 refactor (Strangler Fig).
# Source of truth: scripts/phase1_extract.py. Do not hand-merge back.

from fastapi import APIRouter, Depends, HTTPException, Query, Response
from sqlalchemy.orm import Session
from sqlalchemy import and_, desc, asc, func
from pydantic import BaseModel, Field
from typing import Optional
import uuid
from datetime import datetime, timezone, timedelta
import re
import json
import asyncio
import math
import httpx
import os
import logging
from ..database import get_db, SessionLocal
from ..models import (



    User, Conversation, Message, AIModel, ModelProvider, TokenUsageLog,



    KnowledgeBase, KnowledgeDocument, KnowledgeDocumentChunk, EvolutionSkill,



    EvolutionTrajectory, MarketApp, AppPurchase, AppInstallation,



    MCPServer,



    gen_uuid



)
from ..auth import get_current_user
from ..platform_knowledge import get_platform_knowledge
from ..mcp_client import list_mcp_tools, call_mcp_tool
from ..middleware.log_service import LogService
from ..capability_registry import resolve_auto_model
from app.api.chat_utils import (cst_now)

def _build_knowledge_graph(db: Session, kb: KnowledgeBase) -> dict:



    """从知识库文档构建知识图谱节点和边，返回 graph_data。"""



    documents = db.query(KnowledgeDocument).filter(



        and_(



            KnowledgeDocument.knowledge_base_id == kb.id,



            KnowledgeDocument.status == "completed",



        )



    ).all()







    graph_nodes = []



    graph_edges = []







    doc_entity_types = [



        {"type": "concept", "label": "核心概念"},



        {"type": "entity", "label": "关键实体"},



        {"type": "attribute", "label": "属性特征"},



        {"type": "relation", "label": "关联关系"},



    ]







    for idx, doc in enumerate(documents):



        doc_node_id = f"doc-{doc.id[:8]}"



        graph_nodes.append({



            "id": doc_node_id, "label": doc.filename, "type": "document", "size": 18, "group": "document",



        })







        filename_base = doc.filename.rsplit(".", 1)[0]



        words = filename_base.replace("_", " ").replace("-", " ").split()







        for wi, word in enumerate(words):



            if len(word) >= 2:



                entity_type = doc_entity_types[wi % len(doc_entity_types)]



                node_id = f"ent-{doc.id[:8]}-{wi}"



                graph_nodes.append({



                    "id": node_id, "label": word, "type": entity_type["type"],



                    "size": 10 + (wi % 8), "group": entity_type["type"],



                })



                graph_edges.append({



                    "source": doc_node_id, "target": node_id,



                    "label": entity_type["label"], "weight": 0.5 + (0.5 / (wi + 1)),



                })







    for i in range(len(documents)):



        for j in range(i + 1, len(documents)):



            fi = documents[i].filename.lower().rsplit(".", 1)[0]



            fj = documents[j].filename.lower().rsplit(".", 1)[0]



            words_i = set(fi.replace("_", " ").replace("-", " ").split())



            words_j = set(fj.replace("_", " ").replace("-", " ").split())



            common = words_i & words_j



            if common and len(common) > 0:



                graph_edges.append({



                    "source": f"doc-{documents[i].id[:8]}",



                    "target": f"doc-{documents[j].id[:8]}",



                    "label": f"共有关键词: {', '.join(list(common)[:3])}",



                    "weight": min(0.9, 0.3 * len(common)),



                })







    graph_data = {



        "nodes": graph_nodes,



        "edges": graph_edges,



        "stats": {



            "total_nodes": len(graph_nodes), "total_edges": len(graph_edges),



            "document_count": len(documents),



        },



    }







    kb.knowledge_graph_count = len(graph_nodes)



    kb.updated_at = cst_now()



    return graph_data