feat: 新增多用户支持、关系历史查询与恋爱决策建议功能
- 新增用户服务，支持多用户数据隔离与认证 - 新增关系历史查询接口，支持按冲突、积极、时间线等类型过滤 - 新增恋爱决策建议接口，基于图谱分析生成关系健康报告 - 优化前端图谱可视化，增加节点详情面板、图例和边标签显示 - 改进文本分析逻辑，支持实体去重和情感标注 - 新增完整流程测试脚本，验证分析、入库、查询全链路
This commit is contained in:
KOSHM-Pig
@@ -21,12 +21,20 @@ export const createGraphRagController = (service) => ({
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health: async (_request, reply) => reply.send({ ok: true }),
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ready: async (_request, reply) => sendServiceResult(reply, () => service.ready()),
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bootstrap: async (_request, reply) => sendServiceResult(reply, () => service.bootstrap()),
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getGraphStats: async (_request, reply) => sendServiceResult(reply, () => service.getGraphStats()),
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getGraphStats: async (request, reply) => sendServiceResult(reply, () => service.getGraphStats(request.query.userId || 'default')),
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ingest: async (request, reply) => sendServiceResult(reply, () => service.ingest(request.body)),
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queryTimeline: async (request, reply) =>
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sendServiceResult(reply, () => service.queryTimeline(request.body)),
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queryGraphRag: async (request, reply) =>
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sendServiceResult(reply, () => service.queryGraphRag(request.body)),
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analyzeAndIngest: async (request, reply) =>
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sendServiceResult(reply, () => service.analyzeAndIngest(request.body.text))
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sendServiceResult(reply, () => service.incrementalUpdate(request.body.text, request.body.userId || 'default')),
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queryHistory: async (request, reply) =>
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sendServiceResult(reply, () => service.queryRelationshipHistory(
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request.body.userId || 'default',
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request.body.queryType || 'all',
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request.body.limit || 20
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)),
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getAdvice: async (request, reply) =>
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sendServiceResult(reply, () => service.getRelationshipAdvice(request.body.userId || 'default'))
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});
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@@ -245,4 +245,53 @@ export const registerGraphRagRoutes = async (app, controller) => {
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*/
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app.post("/query/graphrag", controller.queryGraphRag);
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app.post("/analyze", controller.analyzeAndIngest);
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/**
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* @openapi
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* /query/history:
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* post:
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* tags:
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* - GraphRAG
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* summary: 查询恋爱关系历史（支持按类型过滤）
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* requestBody:
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* required: true
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* content:
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* application/json:
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* schema:
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* type: object
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* properties:
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* userId:
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* type: string
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* queryType:
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* type: string
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* enum: [all, conflicts, positive, third_party, timeline]
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* limit:
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* type: integer
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* responses:
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* 200:
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* description: 查询成功
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*/
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app.post("/query/history", controller.queryHistory);
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/**
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* @openapi
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* /query/advice:
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* post:
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* tags:
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* - GraphRAG
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* summary: 获取恋爱决策建议（基于图谱分析）
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* requestBody:
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* required: true
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* content:
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* application/json:
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* schema:
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* type: object
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* properties:
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* userId:
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* type: string
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* responses:
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* 200:
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* description: 建议生成成功
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*/
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app.post("/query/advice", controller.getAdvice);
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};
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File diff suppressed because it is too large
Load Diff
@@ -4,6 +4,52 @@ const createHttpError = (statusCode, message) => {
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return error;
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};
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/**
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* 文本预处理
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*/
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const preprocessText = (text) => {
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return text
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.replace(/\r\n/g, '\n')
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.replace(/\r/g, '\n')
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.replace(/\n{3,}/g, '\n\n')
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.split('\n')
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.map(line => line.trim())
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.filter(line => line.length > 0)
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.join('\n')
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.trim();
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};
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/**
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* 将文本分块（适合长文本）
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*/
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const splitTextIntoChunks = (text, chunkSize = 500, overlap = 50) => {
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const chunks = [];
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let start = 0;
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while (start < text.length) {
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const end = Math.min(start + chunkSize, text.length);
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let chunkEnd = end;
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// 尝试在句子边界处切断
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if (end < text.length) {
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const lastPeriod = text.lastIndexOf('.', end);
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const lastQuestion = text.lastIndexOf('?', end);
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const lastExclamation = text.lastIndexOf('!', end);
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const lastNewline = text.lastIndexOf('\n', end);
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const breakPoint = Math.max(lastPeriod, lastQuestion, lastExclamation, lastNewline);
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if (breakPoint > start + chunkSize / 2) {
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chunkEnd = breakPoint + 1;
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}
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}
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chunks.push(text.slice(start, chunkEnd).trim());
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start = chunkEnd - overlap;
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}
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return chunks;
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};
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export class LLMService {
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constructor(env) {
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this.baseUrl = (env.LLM_BASE_URL ?? "").replace(/\/+$/, "");
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@@ -29,7 +75,8 @@ export class LLMService {
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body: JSON.stringify({
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model: this.model,
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messages: messages,
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temperature: temperature
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temperature: temperature,
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max_tokens: 4096
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})
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});
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@@ -42,34 +89,100 @@ export class LLMService {
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return data;
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}
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async analyzeText(text) {
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/**
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* 分析文本并提取详细的实体和关系（MiroFish 风格）
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* @param {string} text - 用户输入的文本
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* @param {object} existingEntities - 现有实体列表（用于识别是否已存在）
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*/
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async analyzeText(text, existingEntities = {}) {
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if (!text?.trim()) {
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throw createHttpError(400, "分析文本不能为空");
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}
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const systemPrompt = `你是一个实体关系分析专家。请分析用户输入的文本，提取人物、事件、主题、关系。
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const existingContext = existingEntities.persons?.length > 0 || existingEntities.organizations?.length > 0
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? `
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## 输出格式
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## 已有实体列表（极其重要！）
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**如果文本中提到的人/组织已存在于下方列表中，必须复用相同的 ID，不要创建新实体！**
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已有的人物:
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${(existingEntities.persons || []).map(p => `- ID: "${p.id}", 名字："${p.name}", 描述：${p.summary}`).join('\n')}
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已有的组织:
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${(existingEntities.organizations || []).map(o => `- ID: "${o.id}", 名字："${o.name}", 描述：${o.summary}`).join('\n')}
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**代词解析指南**:
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- "我女朋友" = 已有实体中的"女朋友"（如果有）
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- "她" = 根据上下文推断指代哪个女性角色
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- "他" = 根据上下文推断指代哪个男性角色
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- "丽丽" = 如果已有实体中有"丽丽"，复用 ID`
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: '';
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const systemPrompt = `你是一个恋爱关系知识图谱构建专家。从用户输入的文本中提取实体和关系，用于后续的恋爱决策建议。
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## 核心原则
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1. **重点关注**：用户本人（"我"）和恋爱对象（女朋友/男朋友/心仪对象）需要详细记录
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2. **其他人物**：朋友、闺蜜、同事等只需要记录基本信息（名字、与用户的关系）
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3. **事件细节**：记录争吵、约会、礼物、重要对话等影响关系的事件
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4. **情感线索**：提取情绪变化、态度、期望等软性信息
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## 输出格式（严格 JSON）
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{
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"persons": [{"id": "p1", "name": "人物名称", "description": "人物描述"}],
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"events": [{"id": "e1", "type": "事件类型", "summary": "事件摘要", "occurred_at": "ISO 时间", "participants": ["p1"], "topics": ["t1"], "importance": 5}],
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"topics": [{"id": "t1", "name": "主题名称"}],
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"relations": [{"source": "p1", "target": "p2", "type": "关系类型", "description": "关系描述"}]
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"persons": [
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{
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"id": "p1",
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"name": "人物名称",
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"summary": "人物描述",
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"role": "用户 | 恋爱对象 | 朋友 | 家人 | 同事 | 其他"
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}
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],
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"events": [
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{
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"id": "e1",
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"type": "事件类型",
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"summary": "事件描述（包含情感细节）",
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"occurred_at": "ISO 时间",
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"participants": ["p1"],
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"emotional_tone": "positive | neutral | negative",
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"importance": 1-10
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}
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],
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"topics": [
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{
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"id": "t1",
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"name": "主题名称"
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}
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],
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"relations": [
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{
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"source": "p1",
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"target": "p2",
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"type": "关系类型",
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"summary": "关系描述"
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}
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]
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}
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## 注意
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- 时间用 ISO 格式，如文本没明确时间用当前时间
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- importance 是重要性评分 1-10
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- 关系类型：PARTICIPATES_IN, ABOUT, LOVES, FIGHTS_WITH, GIVES, PROPOSES_TO 等
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- 如果文本涉及"我"，推断另一个角色（如"她"）
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- 即使文本很短也要提取信息，不要返回空数组
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## 关系类型参考
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- 用户与恋爱对象：LOVES, DATING, MARRIED_TO, ENGAGED_TO, BROKEN_UP_WITH, CONFLICT_WITH
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- 用户与他人：FRIENDS_WITH, COLLEAGUE_OF, CLASSMATE_OF, SIBLING_OF
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- 恋爱对象与他人：FRIENDS_WITH, COLLEAGUE_OF, FAMILY_OF, CONFLICT_WITH
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## 重要规则
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1. **用户识别**："我"=用户本人，固定 ID 为"user"
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2. **恋爱对象**："女朋友/男朋友/她/他"=恋爱对象，固定 ID 为"partner"
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3. **其他人物**：不需要详细描述，只记录名字和与核心人物的关系
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4. **实体去重**：如果文本中提到的人已存在于"已有实体"列表中，**复用相同的 ID**
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5. **时间标准化**：occurred_at 使用 ISO 格式
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6. **情感标注**：emotional_tone 标注事件的情感倾向（positive/neutral/negative）
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只返回 JSON，不要有其他文字。`;
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const messages = [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: text }
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{ role: "user", content: `${existingContext}\n\n## 待分析文本\n${text}` }
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];
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console.log("[DEBUG] LLM request messages:", JSON.stringify(messages));
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const result = await this.chat(messages, 0.3);
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@@ -87,33 +200,6 @@ export class LLMService {
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throw createHttpError(500, `LLM 返回格式错误：${content.substring(0, 200)}`);
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}
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if (this.isEmptyAnalysis(parsed)) {
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const retryMessages = [
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{
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role: "system",
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content: "你是信息抽取器。必须输出非空 JSON：{persons:[{id,name,description}],events:[{id,type,summary,occurred_at,participants,topics,importance}],topics:[{id,name}],relations:[{source,target,type,description}]}"
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},
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{
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role: "user",
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content: `从下列文本提取实体关系，至少给出 2 个 persons、1 个 event、1 个 relation，且仅返回 JSON：${text}`
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}
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];
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const retryResult = await this.chat(retryMessages, 0.2);
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const retryContent = retryResult?.choices?.[0]?.message?.content;
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if (!retryContent) {
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return parsed;
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}
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try {
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const retryJsonMatch = retryContent.match(/\{[\s\S]*\}/);
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const retryParsed = retryJsonMatch ? JSON.parse(retryJsonMatch[0]) : JSON.parse(retryContent);
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if (!this.isEmptyAnalysis(retryParsed)) {
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return retryParsed;
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}
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} catch (_) {
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return parsed;
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}
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}
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return parsed;
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}
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@@ -124,4 +210,67 @@ export class LLMService {
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&& (!Array.isArray(data.topics) || data.topics.length === 0)
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&& (!Array.isArray(data.relations) || data.relations.length === 0);
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}
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/**
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* 基于图谱数据生成恋爱决策建议
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*/
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async generateRelationshipAdvice(data) {
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const systemPrompt = `你是一个恋爱关系咨询师，擅长分析情侣关系模式并给出专业建议。
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请根据以下数据生成恋爱建议：
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1. 事件统计：各类事件的数量和情感倾向
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2. 最近事件：最近发生的 10 个事件
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3. 第三方影响：朋友/家人对关系的参与程度
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## 输出格式（JSON）
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{
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"relationship_health": "healthy | neutral | concerning",
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"summary": "关系状态总结（100 字以内）",
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"patterns": [
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{
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"pattern": "识别出的模式（如'频繁争吵'、'缺乏沟通'）",
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"evidence": "支持该模式的事件或数据",
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"suggestion": "针对性建议"
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}
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],
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"third_party_influence": "第三方影响分析",
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"action_items": [
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"具体可执行的建议 1",
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"具体可执行的建议 2",
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"具体可执行的建议 3"
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],
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"positive_notes": "关系中积极的方面（鼓励）"
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}`;
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const userMessage = `
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## 事件统计
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${JSON.stringify(data.eventStats, null, 2)}
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## 最近事件
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${JSON.stringify(data.recentEvents, null, 2)}
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## 第三方参与
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${JSON.stringify(data.thirdParty, null, 2)}
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请分析这段恋爱关系的健康状况，并给出专业建议。`;
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const result = await this.chat([
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: userMessage }
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], 0.5);
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const content = result?.choices?.[0]?.message?.content;
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try {
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const jsonMatch = content.match(/\{[\s\S]*\}/);
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return jsonMatch ? JSON.parse(jsonMatch[0]) : JSON.parse(content);
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} catch (e) {
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return {
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relationship_health: 'neutral',
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summary: content || '无法生成详细分析',
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patterns: [],
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action_items: ['建议与伴侣坦诚沟通', '关注彼此的情感需求'],
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positive_notes: '每段关系都有起伏，重要的是共同努力'
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};
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}
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||||
}
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||||
}
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171
OnceLove/oncelove-graphrag/api/src/services/user.service.js
Normal file
171
OnceLove/oncelove-graphrag/api/src/services/user.service.js
Normal file
@@ -0,0 +1,171 @@
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import { randomUUID } from 'crypto';
|
||||
|
||||
/**
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* 用户服务：管理用户 UUID 和认证
|
||||
*/
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||||
export class UserService {
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||||
constructor(driver) {
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||||
this.driver = driver;
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||||
}
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||||
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||||
/**
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||||
* 创建或获取用户
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||||
*/
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||||
async getOrCreateUser(token) {
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const session = this.driver.session();
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||||
try {
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// 尝试通过 token 查找用户
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||||
const result = await session.run(
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||||
`MATCH (u:User {token: $token}) RETURN u`,
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{ token }
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||||
);
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||||
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||||
if (result.records.length > 0) {
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const user = result.records[0].get('u');
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return {
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id: user.properties.id,
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||||
token: user.properties.token,
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||||
createdAt: user.properties.created_at
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||||
};
|
||||
}
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||||
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// 创建新用户
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||||
const userId = randomUUID();
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||||
const createdAt = new Date().toISOString();
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||||
|
||||
await session.run(
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||||
`CREATE (u:User {id: $id, token: $token, created_at: $created_at})`,
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||||
{ id: userId, token, created_at: createdAt }
|
||||
);
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||||
|
||||
return { id: userId, token, createdAt };
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||||
} finally {
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||||
await session.close();
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||||
}
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||||
}
|
||||
|
||||
/**
|
||||
* 验证用户 token
|
||||
*/
|
||||
async validateUser(token) {
|
||||
if (!token) return null;
|
||||
|
||||
const session = this.driver.session();
|
||||
try {
|
||||
const result = await session.run(
|
||||
`MATCH (u:User {token: $token}) RETURN u`,
|
||||
{ token }
|
||||
);
|
||||
|
||||
if (result.records.length > 0) {
|
||||
const user = result.records[0].get('u');
|
||||
return {
|
||||
id: user.properties.id,
|
||||
token: user.properties.token
|
||||
};
|
||||
}
|
||||
return null;
|
||||
} finally {
|
||||
await session.close();
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取用户图谱统计
|
||||
*/
|
||||
async getUserGraphStats(userId) {
|
||||
const session = this.driver.session();
|
||||
try {
|
||||
const result = await session.run(
|
||||
`
|
||||
MATCH (p:Person {user_id: $userId})
|
||||
RETURN p.id AS id, p.name AS name, 'person' AS type, null AS occurred_at
|
||||
LIMIT 200
|
||||
`,
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const persons = result.records.map(r => ({
|
||||
id: r.get('id'),
|
||||
name: r.get('name'),
|
||||
type: r.get('type'),
|
||||
occurred_at: r.get('occurred_at')
|
||||
}));
|
||||
|
||||
const events = await session.run(
|
||||
`
|
||||
MATCH (e:Event {user_id: $userId})
|
||||
RETURN e.id AS id, e.summary AS name, 'event' AS type, e.occurred_at AS occurred_at
|
||||
LIMIT 200
|
||||
`,
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const topics = await session.run(
|
||||
`
|
||||
MATCH (t:Topic {user_id: $userId})
|
||||
RETURN t.name AS id, t.name AS name, 'topic' AS type, null AS occurred_at
|
||||
LIMIT 100
|
||||
`,
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const nodes = [
|
||||
...persons,
|
||||
...events.records.map(r => ({
|
||||
id: r.get('id'),
|
||||
name: r.get('name'),
|
||||
type: r.get('type'),
|
||||
occurred_at: r.get('occurred_at')
|
||||
})),
|
||||
...topics.records.map(r => ({
|
||||
id: r.get('id'),
|
||||
name: r.get('name'),
|
||||
type: r.get('type'),
|
||||
occurred_at: r.get('occurred_at')
|
||||
}))
|
||||
];
|
||||
|
||||
// 查询关系
|
||||
const personEventRels = await session.run(
|
||||
`
|
||||
MATCH (p:Person {user_id: $userId})-[:PARTICIPATES_IN]->(e:Event {user_id: $userId})
|
||||
RETURN p.id AS source, e.id AS target, 'PARTICIPATES_IN' AS type
|
||||
LIMIT 500
|
||||
`,
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const eventTopicRels = await session.run(
|
||||
`
|
||||
MATCH (e:Event {user_id: $userId})-[:ABOUT]->(t:Topic {user_id: $userId})
|
||||
RETURN e.id AS source, t.name AS target, 'ABOUT' AS type
|
||||
LIMIT 300
|
||||
`,
|
||||
{ userId }
|
||||
);
|
||||
|
||||
const links = [
|
||||
...personEventRels.records.map(r => ({
|
||||
source: r.get('source'),
|
||||
target: r.get('target'),
|
||||
type: r.get('type')
|
||||
})),
|
||||
...eventTopicRels.records.map(r => ({
|
||||
source: r.get('source'),
|
||||
target: r.get('target'),
|
||||
type: r.get('type')
|
||||
}))
|
||||
];
|
||||
|
||||
return {
|
||||
ok: true,
|
||||
nodes,
|
||||
links,
|
||||
total: nodes.length
|
||||
};
|
||||
} finally {
|
||||
await session.close();
|
||||
}
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user