feat(api): 新增图片问答、意图分类与任务管理功能
- 新增 SocialImageAgentService 支持朋友圈/聊天截图解析，提供图片线索提取与图谱问答建议
- 扩展 LLMService 支持多模型配置（意图分类、图片模型）与流式响应，增加思考模式控制
- 新增 /intent/classify 端点用于轻量意图分类（问答/导入/混合）以节省 token
- 新增 /tasks/{taskId} 与 /tasks/{taskId}/retry 端点用于流式任务状态查询与重试
- 前端 Dashboard 扩展人物详情显示（年龄标签、出生日期、别名、关系置信度等）
- 前端导入流程增加任务 ID 追踪与质量回放信息展示
This commit is contained in:
KOSHM-Pig
@@ -26,5 +26,8 @@ export const env = {
|
||||
LLM_BASE_URL: process.env.LLM_BASE_URL ?? "",
|
||||
LLM_API_KEY: process.env.LLM_API_KEY ?? "",
|
||||
LLM_MODEL_NAME: process.env.LLM_MODEL_NAME ?? "",
|
||||
LLM_INTENT_MODEL_NAME: process.env.LLM_INTENT_MODEL_NAME ?? "",
|
||||
LLM_IMAGE_MODEL_NAME: process.env.LLM_IMAGE_MODEL_NAME ?? "",
|
||||
LLM_THINKING_MODE: process.env.LLM_THINKING_MODE ?? "auto",
|
||||
ADMIN_PASSWORD: process.env.ADMIN_PASSWORD ?? "oncelove123"
|
||||
};
|
||||
|
||||
@@ -14,6 +14,187 @@ const sendServiceResult = async (reply, action) => {
|
||||
}
|
||||
};
|
||||
|
||||
// 内存任务存储：用于追踪流式任务状态、进度和重试信息
|
||||
const TASK_STORE_MAX = 300;
|
||||
const taskStore = new Map();
|
||||
|
||||
const nowIso = () => new Date().toISOString();
|
||||
|
||||
const trimTaskStore = () => {
|
||||
if (taskStore.size <= TASK_STORE_MAX) return;
|
||||
const entries = [...taskStore.entries()].sort(
|
||||
(a, b) => new Date(a[1].updated_at).getTime() - new Date(b[1].updated_at).getTime()
|
||||
);
|
||||
const toDelete = entries.slice(0, Math.max(taskStore.size - TASK_STORE_MAX, 0));
|
||||
toDelete.forEach(([taskId]) => taskStore.delete(taskId));
|
||||
};
|
||||
|
||||
const createTask = (type, payload) => {
|
||||
const taskId = `task_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
|
||||
const record = {
|
||||
task_id: taskId,
|
||||
type,
|
||||
status: "pending",
|
||||
retry_count: 0,
|
||||
payload: payload || {},
|
||||
last_progress: null,
|
||||
result_summary: null,
|
||||
quality_replay: null,
|
||||
error: null,
|
||||
started_at: nowIso(),
|
||||
updated_at: nowIso(),
|
||||
finished_at: null
|
||||
};
|
||||
taskStore.set(taskId, record);
|
||||
trimTaskStore();
|
||||
return record;
|
||||
};
|
||||
|
||||
const updateTask = (taskId, patch = {}) => {
|
||||
const record = taskStore.get(taskId);
|
||||
if (!record) return null;
|
||||
const next = {
|
||||
...record,
|
||||
...patch,
|
||||
updated_at: nowIso()
|
||||
};
|
||||
taskStore.set(taskId, next);
|
||||
return next;
|
||||
};
|
||||
|
||||
const buildTaskResultSummary = (type, result) => {
|
||||
if (type === "graphrag_multi_stream") {
|
||||
return {
|
||||
rounds: Number(result?.meta?.rounds || result?.rounds?.length || 0),
|
||||
retrieval_mode: result?.meta?.retrieval_mode_requested || null,
|
||||
confidence: result?.final_review?.confidence ?? null,
|
||||
answer_preview: String(result?.final_review?.answer || result?.answer || "").slice(0, 240)
|
||||
};
|
||||
}
|
||||
if (type === "analyze_stream") {
|
||||
return {
|
||||
created: result?.stats?.created || {},
|
||||
updated: result?.stats?.updated || {},
|
||||
corrected_events: Number(result?.stats?.corrected_events || 0),
|
||||
vector_indexed: Number(result?.vector_sync?.indexed || 0)
|
||||
};
|
||||
}
|
||||
return { ok: Boolean(result?.ok) };
|
||||
};
|
||||
|
||||
const buildTaskQualityReplay = (type, taskPayload, result, error = null) => {
|
||||
const replay = {
|
||||
input: {},
|
||||
output: {},
|
||||
error: error ? { message: error?.message || "internal error" } : null
|
||||
};
|
||||
if (type === "graphrag_multi_stream") {
|
||||
replay.input = {
|
||||
userId: taskPayload?.userId || "default",
|
||||
query: String(taskPayload?.query_text || taskPayload?.query || "").slice(0, 300),
|
||||
retrieval_mode: taskPayload?.retrieval_mode || "hybrid",
|
||||
max_rounds: Number(taskPayload?.max_rounds || 3),
|
||||
top_k: Number(taskPayload?.top_k || 8)
|
||||
};
|
||||
replay.output = result ? {
|
||||
confidence: result?.final_review?.confidence ?? null,
|
||||
relation_quality: result?.meta?.relation_quality || null,
|
||||
retrieval_mode_adaptive: Boolean(result?.meta?.retrieval_mode_adaptive)
|
||||
} : {};
|
||||
return replay;
|
||||
}
|
||||
if (type === "analyze_stream") {
|
||||
replay.input = {
|
||||
userId: taskPayload?.userId || "default",
|
||||
text_length: String(taskPayload?.text || "").length,
|
||||
parallelism: Number(taskPayload?.parallelism || 0) || null
|
||||
};
|
||||
replay.output = result ? {
|
||||
created: result?.stats?.created || {},
|
||||
corrected_events: Number(result?.stats?.corrected_events || 0),
|
||||
normalization: result?.normalization || null
|
||||
} : {};
|
||||
return replay;
|
||||
}
|
||||
replay.input = { type };
|
||||
replay.output = result ? { ok: Boolean(result?.ok) } : {};
|
||||
return replay;
|
||||
};
|
||||
|
||||
const safeTaskView = (record) => {
|
||||
if (!record) return null;
|
||||
return {
|
||||
task_id: record.task_id,
|
||||
type: record.type,
|
||||
status: record.status,
|
||||
retry_count: record.retry_count,
|
||||
last_progress: record.last_progress,
|
||||
result_summary: record.result_summary,
|
||||
quality_replay: record.quality_replay,
|
||||
error: record.error,
|
||||
started_at: record.started_at,
|
||||
updated_at: record.updated_at,
|
||||
finished_at: record.finished_at
|
||||
};
|
||||
};
|
||||
|
||||
const createStreamContext = (request, reply) => {
|
||||
reply.hijack();
|
||||
const raw = reply.raw;
|
||||
const requestOrigin = request.headers.origin;
|
||||
raw.setHeader('Access-Control-Allow-Origin', requestOrigin || '*');
|
||||
raw.setHeader('Vary', 'Origin');
|
||||
raw.setHeader('Access-Control-Allow-Headers', 'Content-Type, Authorization');
|
||||
raw.setHeader('Access-Control-Allow-Methods', 'POST, OPTIONS');
|
||||
raw.setHeader('Content-Type', 'text/event-stream; charset=utf-8');
|
||||
raw.setHeader('Cache-Control', 'no-cache, no-transform');
|
||||
raw.setHeader('Connection', 'keep-alive');
|
||||
raw.setHeader('X-Accel-Buffering', 'no');
|
||||
raw.flushHeaders?.();
|
||||
const runId = `run_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
|
||||
const push = (event, data) => {
|
||||
const payload = data && typeof data === 'object'
|
||||
? { run_id: runId, ...data }
|
||||
: { run_id: runId, value: data };
|
||||
raw.write(`event: ${event}\n`);
|
||||
raw.write(`data: ${JSON.stringify(payload)}\n\n`);
|
||||
};
|
||||
return { raw, runId, push };
|
||||
};
|
||||
|
||||
const streamWithGuard = async (request, reply, action) => {
|
||||
const { raw, runId, push } = createStreamContext(request, reply);
|
||||
try {
|
||||
push('meta', { run_id: runId });
|
||||
const result = await action(push, runId);
|
||||
push('done', result);
|
||||
} catch (error) {
|
||||
push('error', {
|
||||
ok: false,
|
||||
statusCode: Number(error?.statusCode) || 500,
|
||||
message: error?.message || 'internal error'
|
||||
});
|
||||
} finally {
|
||||
raw.end();
|
||||
}
|
||||
};
|
||||
|
||||
const executeTaskByType = async (service, taskRecord, onProgress = () => {}) => {
|
||||
if (!taskRecord) throw new Error("任务不存在");
|
||||
if (taskRecord.type === "graphrag_multi_stream") {
|
||||
return service.queryGraphRagMultiRound(taskRecord.payload, { onProgress });
|
||||
}
|
||||
if (taskRecord.type === "analyze_stream") {
|
||||
const payload = taskRecord.payload || {};
|
||||
return service.incrementalUpdate(payload.text, payload.userId || "default", {
|
||||
parallelism: payload.parallelism,
|
||||
expertReview: payload.expertReview,
|
||||
onProgress
|
||||
});
|
||||
}
|
||||
throw new Error(`不支持的任务类型: ${taskRecord.type}`);
|
||||
};
|
||||
|
||||
/**
|
||||
* GraphRAG 控制器：负责请求转发与响应封装。
|
||||
*/
|
||||
@@ -30,37 +211,38 @@ export const createGraphRagController = (service, multiAgentService) => ({
|
||||
sendServiceResult(reply, () => service.queryGraphRag(request.body)),
|
||||
queryGraphRagMultiRound: async (request, reply) =>
|
||||
sendServiceResult(reply, () => service.queryGraphRagMultiRound(request.body)),
|
||||
classifyIntent: async (request, reply) =>
|
||||
sendServiceResult(reply, () => service.classifyIntent(request.body)),
|
||||
queryGraphRagMultiRoundStream: async (request, reply) => {
|
||||
reply.hijack();
|
||||
const raw = reply.raw;
|
||||
const requestOrigin = request.headers.origin;
|
||||
raw.setHeader('Access-Control-Allow-Origin', requestOrigin || '*');
|
||||
raw.setHeader('Vary', 'Origin');
|
||||
raw.setHeader('Access-Control-Allow-Headers', 'Content-Type, Authorization');
|
||||
raw.setHeader('Access-Control-Allow-Methods', 'POST, OPTIONS');
|
||||
raw.setHeader('Content-Type', 'text/event-stream; charset=utf-8');
|
||||
raw.setHeader('Cache-Control', 'no-cache, no-transform');
|
||||
raw.setHeader('Connection', 'keep-alive');
|
||||
raw.setHeader('X-Accel-Buffering', 'no');
|
||||
raw.flushHeaders?.();
|
||||
const push = (event, data) => {
|
||||
raw.write(`event: ${event}\n`);
|
||||
raw.write(`data: ${JSON.stringify(data)}\n\n`);
|
||||
};
|
||||
try {
|
||||
const result = await service.queryGraphRagMultiRound(request.body, {
|
||||
onProgress: (event) => push('progress', event)
|
||||
});
|
||||
push('done', result);
|
||||
} catch (error) {
|
||||
push('error', {
|
||||
ok: false,
|
||||
statusCode: Number(error?.statusCode) || 500,
|
||||
message: error?.message || 'internal error'
|
||||
});
|
||||
} finally {
|
||||
raw.end();
|
||||
}
|
||||
const task = createTask("graphrag_multi_stream", request.body || {});
|
||||
return streamWithGuard(request, reply, async (push) => {
|
||||
updateTask(task.task_id, { status: "running" });
|
||||
push("meta", { task_id: task.task_id });
|
||||
try {
|
||||
const result = await executeTaskByType(service, task, (event) => {
|
||||
updateTask(task.task_id, { last_progress: event || null });
|
||||
push('progress', event);
|
||||
});
|
||||
updateTask(task.task_id, {
|
||||
status: "done",
|
||||
result_summary: buildTaskResultSummary(task.type, result),
|
||||
quality_replay: buildTaskQualityReplay(task.type, task.payload, result),
|
||||
finished_at: nowIso(),
|
||||
error: null
|
||||
});
|
||||
return result;
|
||||
} catch (error) {
|
||||
updateTask(task.task_id, {
|
||||
status: "error",
|
||||
quality_replay: buildTaskQualityReplay(task.type, task.payload, null, error),
|
||||
finished_at: nowIso(),
|
||||
error: {
|
||||
message: error?.message || "internal error"
|
||||
}
|
||||
});
|
||||
throw error;
|
||||
}
|
||||
});
|
||||
},
|
||||
analyzeAndIngest: async (request, reply) =>
|
||||
sendServiceResult(reply, () => service.incrementalUpdate(request.body.text, request.body.userId || 'default')),
|
||||
@@ -70,44 +252,104 @@ export const createGraphRagController = (service, multiAgentService) => ({
|
||||
limit: request.body.limit
|
||||
})),
|
||||
analyzeAndIngestStream: async (request, reply) => {
|
||||
reply.hijack();
|
||||
const raw = reply.raw;
|
||||
const requestOrigin = request.headers.origin;
|
||||
raw.setHeader('Access-Control-Allow-Origin', requestOrigin || '*');
|
||||
raw.setHeader('Vary', 'Origin');
|
||||
raw.setHeader('Access-Control-Allow-Headers', 'Content-Type, Authorization');
|
||||
raw.setHeader('Access-Control-Allow-Methods', 'POST, OPTIONS');
|
||||
raw.setHeader('Content-Type', 'text/event-stream; charset=utf-8');
|
||||
raw.setHeader('Cache-Control', 'no-cache, no-transform');
|
||||
raw.setHeader('Connection', 'keep-alive');
|
||||
raw.setHeader('X-Accel-Buffering', 'no');
|
||||
raw.flushHeaders?.();
|
||||
const push = (event, data) => {
|
||||
raw.write(`event: ${event}\n`);
|
||||
raw.write(`data: ${JSON.stringify(data)}\n\n`);
|
||||
};
|
||||
try {
|
||||
const task = createTask("analyze_stream", request.body || {});
|
||||
return streamWithGuard(request, reply, async (push) => {
|
||||
updateTask(task.task_id, { status: "running" });
|
||||
push("meta", { task_id: task.task_id });
|
||||
push('progress', { stage: 'start' });
|
||||
const result = await service.incrementalUpdate(
|
||||
request.body.text,
|
||||
request.body.userId || 'default',
|
||||
{
|
||||
parallelism: request.body.parallelism,
|
||||
expertReview: request.body.expertReview,
|
||||
onProgress: (event) => push('progress', event)
|
||||
}
|
||||
);
|
||||
push('done', result);
|
||||
} catch (error) {
|
||||
push('error', {
|
||||
ok: false,
|
||||
statusCode: Number(error?.statusCode) || 500,
|
||||
message: error?.message || 'internal error'
|
||||
});
|
||||
} finally {
|
||||
raw.end();
|
||||
}
|
||||
try {
|
||||
const result = await executeTaskByType(service, task, (event) => {
|
||||
updateTask(task.task_id, { last_progress: event || null });
|
||||
push('progress', event);
|
||||
});
|
||||
updateTask(task.task_id, {
|
||||
status: "done",
|
||||
result_summary: buildTaskResultSummary(task.type, result),
|
||||
quality_replay: buildTaskQualityReplay(task.type, task.payload, result),
|
||||
finished_at: nowIso(),
|
||||
error: null
|
||||
});
|
||||
return result;
|
||||
} catch (error) {
|
||||
updateTask(task.task_id, {
|
||||
status: "error",
|
||||
quality_replay: buildTaskQualityReplay(task.type, task.payload, null, error),
|
||||
finished_at: nowIso(),
|
||||
error: {
|
||||
message: error?.message || "internal error"
|
||||
}
|
||||
});
|
||||
throw error;
|
||||
}
|
||||
});
|
||||
},
|
||||
getTaskStatus: async (request, reply) =>
|
||||
sendServiceResult(reply, async () => {
|
||||
const taskId = String(request.params.taskId || "").trim();
|
||||
if (!taskId) {
|
||||
const error = new Error("taskId 不能为空");
|
||||
error.statusCode = 400;
|
||||
throw error;
|
||||
}
|
||||
const task = taskStore.get(taskId);
|
||||
if (!task) {
|
||||
const error = new Error(`任务不存在: ${taskId}`);
|
||||
error.statusCode = 404;
|
||||
throw error;
|
||||
}
|
||||
return { ok: true, task: safeTaskView(task) };
|
||||
}),
|
||||
retryTask: async (request, reply) =>
|
||||
sendServiceResult(reply, async () => {
|
||||
const sourceTaskId = String(request.params.taskId || "").trim();
|
||||
if (!sourceTaskId) {
|
||||
const error = new Error("taskId 不能为空");
|
||||
error.statusCode = 400;
|
||||
throw error;
|
||||
}
|
||||
const sourceTask = taskStore.get(sourceTaskId);
|
||||
if (!sourceTask) {
|
||||
const error = new Error(`任务不存在: ${sourceTaskId}`);
|
||||
error.statusCode = 404;
|
||||
throw error;
|
||||
}
|
||||
const overridePayload = request.body && typeof request.body === "object" ? request.body : {};
|
||||
const nextTask = createTask(sourceTask.type, {
|
||||
...(sourceTask.payload || {}),
|
||||
...overridePayload
|
||||
});
|
||||
updateTask(nextTask.task_id, {
|
||||
status: "running",
|
||||
retry_count: Number(sourceTask.retry_count || 0) + 1,
|
||||
quality_replay: sourceTask.quality_replay || null
|
||||
});
|
||||
executeTaskByType(service, nextTask, (event) => {
|
||||
updateTask(nextTask.task_id, { last_progress: event || null });
|
||||
}).then((result) => {
|
||||
updateTask(nextTask.task_id, {
|
||||
status: "done",
|
||||
result_summary: buildTaskResultSummary(nextTask.type, result),
|
||||
quality_replay: buildTaskQualityReplay(nextTask.type, nextTask.payload, result),
|
||||
finished_at: nowIso(),
|
||||
error: null
|
||||
});
|
||||
}).catch((err) => {
|
||||
updateTask(nextTask.task_id, {
|
||||
status: "error",
|
||||
quality_replay: buildTaskQualityReplay(nextTask.type, nextTask.payload, null, err),
|
||||
finished_at: nowIso(),
|
||||
error: {
|
||||
message: err?.message || "internal error"
|
||||
}
|
||||
});
|
||||
});
|
||||
return {
|
||||
ok: true,
|
||||
source_task_id: sourceTaskId,
|
||||
task_id: nextTask.task_id,
|
||||
status: "running"
|
||||
};
|
||||
}),
|
||||
queryHistory: async (request, reply) =>
|
||||
sendServiceResult(reply, () => service.queryRelationshipHistory(
|
||||
request.body.userId || 'default',
|
||||
|
||||
@@ -262,6 +262,29 @@ export const registerGraphRagRoutes = async (app, controller) => {
|
||||
* description: 参数错误
|
||||
*/
|
||||
app.post("/query/graphrag", controller.queryGraphRag);
|
||||
/**
|
||||
* @openapi
|
||||
* /intent/classify:
|
||||
* post:
|
||||
* tags:
|
||||
* - GraphRAG
|
||||
* summary: 轻量意图分类（问答/导入/混合）用于节省 token
|
||||
* requestBody:
|
||||
* required: true
|
||||
* content:
|
||||
* application/json:
|
||||
* schema:
|
||||
* type: object
|
||||
* properties:
|
||||
* text:
|
||||
* type: string
|
||||
* responses:
|
||||
* 200:
|
||||
* description: 分类成功
|
||||
* 400:
|
||||
* description: 参数错误
|
||||
*/
|
||||
app.post("/intent/classify", controller.classifyIntent);
|
||||
/**
|
||||
* @openapi
|
||||
* /query/graphrag/multi:
|
||||
@@ -353,6 +376,53 @@ export const registerGraphRagRoutes = async (app, controller) => {
|
||||
* description: 参数错误
|
||||
*/
|
||||
app.post("/analyze/stream", controller.analyzeAndIngestStream);
|
||||
/**
|
||||
* @openapi
|
||||
* /tasks/{taskId}:
|
||||
* get:
|
||||
* tags:
|
||||
* - GraphRAG
|
||||
* summary: 查询任务状态（适用于流式问答/流式分析任务）
|
||||
* parameters:
|
||||
* - in: path
|
||||
* name: taskId
|
||||
* required: true
|
||||
* schema:
|
||||
* type: string
|
||||
* responses:
|
||||
* 200:
|
||||
* description: 查询成功
|
||||
* 404:
|
||||
* description: 任务不存在
|
||||
*/
|
||||
app.get("/tasks/:taskId", controller.getTaskStatus);
|
||||
/**
|
||||
* @openapi
|
||||
* /tasks/{taskId}/retry:
|
||||
* post:
|
||||
* tags:
|
||||
* - GraphRAG
|
||||
* summary: 重试指定任务（异步触发，立即返回新任务ID）
|
||||
* parameters:
|
||||
* - in: path
|
||||
* name: taskId
|
||||
* required: true
|
||||
* schema:
|
||||
* type: string
|
||||
* requestBody:
|
||||
* required: false
|
||||
* content:
|
||||
* application/json:
|
||||
* schema:
|
||||
* type: object
|
||||
* description: 可选覆盖原任务参数
|
||||
* responses:
|
||||
* 200:
|
||||
* description: 重试任务已启动
|
||||
* 404:
|
||||
* description: 原任务不存在
|
||||
*/
|
||||
app.post("/tasks/:taskId/retry", controller.retryTask);
|
||||
|
||||
/**
|
||||
* @openapi
|
||||
|
||||
@@ -5,7 +5,7 @@ import swaggerUi from "@fastify/swagger-ui";
|
||||
import { env } from "./config/env.js";
|
||||
import { createClients } from "./config/clients.js";
|
||||
import { createSwaggerSpec } from "./config/swagger.js";
|
||||
import { EmbeddingService, RerankService, GraphRagService, LLMService, MultiAgentService } from "./services/index.js";
|
||||
import { EmbeddingService, RerankService, GraphRagService, LLMService, MultiAgentService, SocialImageAgentService } from "./services/index.js";
|
||||
import { createGraphRagController } from "./controllers/index.js";
|
||||
import { registerRoutes } from "./routes/index.js";
|
||||
|
||||
@@ -34,12 +34,14 @@ export const createServer = async () => {
|
||||
const embeddingService = new EmbeddingService(env);
|
||||
const rerankService = new RerankService(env);
|
||||
const llmService = new LLMService(env);
|
||||
const socialImageAgentService = new SocialImageAgentService({ llmService });
|
||||
const service = new GraphRagService({
|
||||
driver: neo4jDriver,
|
||||
qdrantClient,
|
||||
embeddingService,
|
||||
rerankService,
|
||||
llmService,
|
||||
socialImageAgentService,
|
||||
env
|
||||
});
|
||||
const multiAgentService = new MultiAgentService({
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -3,3 +3,4 @@ export { RerankService } from "./rerank.service.js";
|
||||
export { GraphRagService } from "./graphrag.service.js";
|
||||
export { LLMService } from "./llm.service.js";
|
||||
export { MultiAgentService } from "./multiagent.service.js";
|
||||
export { SocialImageAgentService } from "./social-image-agent.service.js";
|
||||
|
||||
@@ -12,10 +12,76 @@ const estimateTokens = (text) => {
|
||||
|
||||
const estimateMessageTokens = (messages = []) => {
|
||||
return messages.reduce((sum, message) => {
|
||||
return sum + estimateTokens(message?.content || "") + 6;
|
||||
const content = message?.content;
|
||||
if (Array.isArray(content)) {
|
||||
const merged = content.map((part) => {
|
||||
if (!part || typeof part !== "object") return "";
|
||||
if (typeof part.text === "string") return part.text;
|
||||
if (typeof part.image_url === "string") return part.image_url;
|
||||
if (part.image_url && typeof part.image_url === "object" && typeof part.image_url.url === "string") return part.image_url.url;
|
||||
if (part.source && typeof part.source === "object" && typeof part.source.data === "string") return part.source.data.slice(0, 64);
|
||||
return "";
|
||||
}).join(" ");
|
||||
return sum + estimateTokens(merged) + 6;
|
||||
}
|
||||
return sum + estimateTokens(content || "") + 6;
|
||||
}, 0);
|
||||
};
|
||||
|
||||
const normalizeContentPart = (part) => {
|
||||
if (typeof part === "string") {
|
||||
return { type: "text", text: part };
|
||||
}
|
||||
if (!part || typeof part !== "object") {
|
||||
return null;
|
||||
}
|
||||
if (typeof part.type !== "string" || !part.type.trim()) {
|
||||
if (typeof part.text === "string") {
|
||||
return { ...part, type: "text" };
|
||||
}
|
||||
return null;
|
||||
}
|
||||
if (part.type === "image_url" && typeof part.image_url === "string") {
|
||||
return { ...part, image_url: { url: part.image_url } };
|
||||
}
|
||||
return part;
|
||||
};
|
||||
|
||||
const normalizeMessagesForChat = (messages = []) => {
|
||||
const hasArrayContent = messages.some((message) => Array.isArray(message?.content));
|
||||
if (!hasArrayContent) return messages;
|
||||
return messages.map((message) => {
|
||||
const content = message?.content;
|
||||
if (!Array.isArray(content)) {
|
||||
return message;
|
||||
}
|
||||
const normalizedContent = content.map(normalizeContentPart).filter(Boolean);
|
||||
return {
|
||||
...message,
|
||||
content: normalizedContent.length ? normalizedContent : [{ type: "text", text: "" }]
|
||||
};
|
||||
});
|
||||
};
|
||||
|
||||
const extractDeltaText = (chunk = {}) => {
|
||||
const choices = Array.isArray(chunk?.choices) ? chunk.choices : [];
|
||||
if (!choices.length) return "";
|
||||
const delta = choices[0]?.delta;
|
||||
if (!delta || typeof delta !== "object") return "";
|
||||
if (typeof delta.content === "string") return delta.content;
|
||||
if (Array.isArray(delta.content)) {
|
||||
return delta.content
|
||||
.map((item) => {
|
||||
if (typeof item === "string") return item;
|
||||
if (item && typeof item === "object" && typeof item.text === "string") return item.text;
|
||||
return "";
|
||||
})
|
||||
.join("");
|
||||
}
|
||||
if (typeof delta.reasoning_content === "string") return delta.reasoning_content;
|
||||
return "";
|
||||
};
|
||||
|
||||
const parseJsonObject = (content) => {
|
||||
if (!content || typeof content !== "string") {
|
||||
throw new Error("empty content");
|
||||
@@ -257,19 +323,65 @@ export class LLMService {
|
||||
this.baseUrl = (env.LLM_BASE_URL ?? "").replace(/\/+$/, "");
|
||||
this.apiKey = env.LLM_API_KEY ?? "";
|
||||
this.model = env.LLM_MODEL_NAME ?? "";
|
||||
this.intentModel = env.LLM_INTENT_MODEL_NAME ?? "";
|
||||
this.imageModel = env.LLM_IMAGE_MODEL_NAME ?? "";
|
||||
this.thinkingMode = String(env.LLM_THINKING_MODE ?? "auto").trim().toLowerCase();
|
||||
}
|
||||
|
||||
isEnabled() {
|
||||
return Boolean(this.baseUrl && this.apiKey && this.model);
|
||||
}
|
||||
|
||||
async chat(messages, temperature = 0.7) {
|
||||
if (!this.isEnabled()) {
|
||||
throw createHttpError(400, "LLM 服务未配置，请提供 LLM_BASE_URL/LLM_API_KEY/LLM_MODEL_NAME");
|
||||
isModelEnabled(modelName = "") {
|
||||
return Boolean(this.baseUrl && this.apiKey && String(modelName || "").trim());
|
||||
}
|
||||
|
||||
isIntentEnabled() {
|
||||
return this.isModelEnabled(this.intentModel || this.model);
|
||||
}
|
||||
|
||||
isImageEnabled() {
|
||||
return this.isModelEnabled(this.imageModel || this.model);
|
||||
}
|
||||
|
||||
async chat(messages, temperature = 0.7, options = {}) {
|
||||
return this.chatWithModel(messages, temperature, this.model, 4096, options);
|
||||
}
|
||||
|
||||
async chatImage(messages, temperature = 0.7, options = {}) {
|
||||
return this.chatWithModel(messages, temperature, this.imageModel || this.model, 4096, options);
|
||||
}
|
||||
|
||||
async chatStream(messages, temperature = 0.7, options = {}, onToken = () => {}) {
|
||||
return this.chatWithModelStream(messages, temperature, this.model, 4096, options, onToken);
|
||||
}
|
||||
|
||||
async chatWithModel(messages, temperature = 0.7, model = this.model, maxTokens = 4096, options = {}) {
|
||||
if (!this.baseUrl || !this.apiKey) {
|
||||
throw createHttpError(400, "LLM 服务未配置，请提供 LLM_BASE_URL/LLM_API_KEY");
|
||||
}
|
||||
if (!String(model || "").trim()) {
|
||||
throw createHttpError(400, "LLM 模型未配置，请提供 LLM_MODEL_NAME 或 LLM_INTENT_MODEL_NAME");
|
||||
}
|
||||
|
||||
const promptTokensEstimated = estimateMessageTokens(messages);
|
||||
console.log(`[TOKEN] model=${this.model} prompt_tokens_estimated=${promptTokensEstimated} max_tokens=4096`);
|
||||
const normalizedMessages = normalizeMessagesForChat(messages);
|
||||
const promptTokensEstimated = estimateMessageTokens(normalizedMessages);
|
||||
const normalizedModeRaw = String(options?.thinking || this.thinkingMode || "auto").trim().toLowerCase();
|
||||
const thinkingMode = ["on", "off", "auto"].includes(normalizedModeRaw) ? normalizedModeRaw : "auto";
|
||||
const isQwen3Family = /qwen\s*3|qwen3/i.test(String(model || ""));
|
||||
const autoEnableThinking = isQwen3Family && promptTokensEstimated >= 1600 && Number(maxTokens || 0) >= 900;
|
||||
const enableThinking = thinkingMode === "on" ? true : thinkingMode === "off" ? false : autoEnableThinking;
|
||||
console.log(`[TOKEN] model=${model} prompt_tokens_estimated=${promptTokensEstimated} max_tokens=${maxTokens} thinking_mode=${thinkingMode} thinking_enabled=${enableThinking}`);
|
||||
|
||||
const payload = {
|
||||
model,
|
||||
messages: normalizedMessages,
|
||||
temperature: temperature,
|
||||
max_tokens: maxTokens
|
||||
};
|
||||
if (isQwen3Family) {
|
||||
payload.extra_body = { enable_thinking: enableThinking };
|
||||
}
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/chat/completions`, {
|
||||
method: "POST",
|
||||
@@ -277,12 +389,7 @@ export class LLMService {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${this.apiKey}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: this.model,
|
||||
messages: messages,
|
||||
temperature: temperature,
|
||||
max_tokens: 4096
|
||||
})
|
||||
body: JSON.stringify(payload)
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
@@ -296,11 +403,102 @@ export class LLMService {
|
||||
const completionTokens = usage.completion_tokens;
|
||||
const totalTokens = usage.total_tokens;
|
||||
console.log(
|
||||
`[TOKEN] model=${this.model} prompt_tokens_actual=${promptTokens ?? "n/a"} completion_tokens=${completionTokens ?? "n/a"} total_tokens=${totalTokens ?? "n/a"}`
|
||||
`[TOKEN] model=${model} prompt_tokens_actual=${promptTokens ?? "n/a"} completion_tokens=${completionTokens ?? "n/a"} total_tokens=${totalTokens ?? "n/a"}`
|
||||
);
|
||||
return data;
|
||||
}
|
||||
|
||||
async chatWithModelStream(messages, temperature = 0.7, model = this.model, maxTokens = 4096, options = {}, onToken = () => {}) {
|
||||
if (!this.baseUrl || !this.apiKey) {
|
||||
throw createHttpError(400, "LLM 服务未配置，请提供 LLM_BASE_URL/LLM_API_KEY");
|
||||
}
|
||||
if (!String(model || "").trim()) {
|
||||
throw createHttpError(400, "LLM 模型未配置，请提供 LLM_MODEL_NAME 或 LLM_INTENT_MODEL_NAME");
|
||||
}
|
||||
|
||||
const normalizedMessages = normalizeMessagesForChat(messages);
|
||||
const promptTokensEstimated = estimateMessageTokens(normalizedMessages);
|
||||
const normalizedModeRaw = String(options?.thinking || this.thinkingMode || "auto").trim().toLowerCase();
|
||||
const thinkingMode = ["on", "off", "auto"].includes(normalizedModeRaw) ? normalizedModeRaw : "auto";
|
||||
const isQwen3Family = /qwen\s*3|qwen3/i.test(String(model || ""));
|
||||
const autoEnableThinking = isQwen3Family && promptTokensEstimated >= 1600 && Number(maxTokens || 0) >= 900;
|
||||
const enableThinking = thinkingMode === "on" ? true : thinkingMode === "off" ? false : autoEnableThinking;
|
||||
|
||||
const payload = {
|
||||
model,
|
||||
messages: normalizedMessages,
|
||||
temperature: temperature,
|
||||
max_tokens: maxTokens,
|
||||
stream: true
|
||||
};
|
||||
if (isQwen3Family) {
|
||||
payload.extra_body = { enable_thinking: enableThinking };
|
||||
}
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/chat/completions`, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
Authorization: `Bearer ${this.apiKey}`
|
||||
},
|
||||
body: JSON.stringify(payload)
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
throw createHttpError(response.status, `LLM 请求失败：${errorText}`);
|
||||
}
|
||||
|
||||
const reader = response.body?.getReader?.();
|
||||
if (!reader) {
|
||||
const fallback = await response.json().catch(() => null);
|
||||
const text = String(fallback?.choices?.[0]?.message?.content || "");
|
||||
if (text) onToken(text, text, { done: true });
|
||||
return { text, done: true };
|
||||
}
|
||||
|
||||
const decoder = new TextDecoder("utf-8");
|
||||
let buffer = "";
|
||||
let fullText = "";
|
||||
let done = false;
|
||||
while (!done) {
|
||||
const readResult = await reader.read();
|
||||
done = Boolean(readResult?.done);
|
||||
if (!readResult?.value) continue;
|
||||
buffer += decoder.decode(readResult.value, { stream: true });
|
||||
const lines = buffer.split(/\r?\n/);
|
||||
buffer = lines.pop() || "";
|
||||
for (const line of lines) {
|
||||
const trimmed = String(line || "").trim();
|
||||
if (!trimmed || !trimmed.startsWith("data:")) continue;
|
||||
const dataPayload = trimmed.slice(5).trim();
|
||||
if (!dataPayload || dataPayload === "[DONE]") continue;
|
||||
try {
|
||||
const chunk = JSON.parse(dataPayload);
|
||||
const deltaText = extractDeltaText(chunk);
|
||||
if (deltaText) {
|
||||
fullText += deltaText;
|
||||
onToken(deltaText, fullText, chunk);
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
}
|
||||
if (buffer.trim().startsWith("data:")) {
|
||||
const dataPayload = buffer.trim().slice(5).trim();
|
||||
if (dataPayload && dataPayload !== "[DONE]") {
|
||||
try {
|
||||
const chunk = JSON.parse(dataPayload);
|
||||
const deltaText = extractDeltaText(chunk);
|
||||
if (deltaText) {
|
||||
fullText += deltaText;
|
||||
onToken(deltaText, fullText, chunk);
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
}
|
||||
return { text: fullText, done: true };
|
||||
}
|
||||
|
||||
async _analyzeTextSinglePass(text, existingContext) {
|
||||
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。
|
||||
|
||||
@@ -319,7 +517,13 @@ export class LLMService {
|
||||
"name": "人物名称",
|
||||
"summary": "人物描述",
|
||||
"role": "用户 | 恋爱对象 | 朋友 | 家人 | 同事 | 其他",
|
||||
"gender": "male | female | unknown"
|
||||
"gender": "male | female | unknown",
|
||||
"birth_date_utc": "YYYY-MM-DDT00:00:00.000Z 或 null",
|
||||
"occupation": "职业（可选）",
|
||||
"education_background": "教育背景（可选）",
|
||||
"residential_status": "常住状态/所在城市（可选）",
|
||||
"consent_status": "granted | denied | revoked | unknown",
|
||||
"data_source": "ingest | user_input | manual | import | llm_extract | system"
|
||||
}
|
||||
],
|
||||
"organizations": [
|
||||
@@ -372,6 +576,8 @@ export class LLMService {
|
||||
8. 长度控制：persons.summary 不超过 100 字；events.summary 不超过 120 字；relations.summary 不超过 80 字
|
||||
9. 人物性别：person.gender 只允许 male/female/unknown，无法确认时填 unknown
|
||||
10. 若文本出现“某人毕业于/就读于/来自某大学”，需创建该大学 organization，并补充关系（如 STUDIED_AT/ALUMNUS_OF）
|
||||
11. 出现生日/出生年份时，person.birth_date_utc 必须写成 UTC ISO（如 2001-05-20T00:00:00.000Z）；只有年份时用该年 01-01
|
||||
12. person.consent_status 未明确时填 unknown；person.data_source 默认填 llm_extract
|
||||
|
||||
只返回 JSON，不要有其他文字。`;
|
||||
|
||||
@@ -535,6 +741,60 @@ export class LLMService {
|
||||
}
|
||||
}
|
||||
|
||||
async classifyIntent(text, options = {}) {
|
||||
const rawText = typeof text === "string" ? text.trim() : "";
|
||||
if (!rawText) {
|
||||
return {
|
||||
intent: "qa",
|
||||
should_ingest: false,
|
||||
should_query: true,
|
||||
confidence: 0.52,
|
||||
reason: "empty_text",
|
||||
model: this.intentModel || this.model
|
||||
};
|
||||
}
|
||||
const model = this.intentModel || this.model;
|
||||
const maxTokens = Math.min(Math.max(Number(options?.maxTokens || 220), 80), 400);
|
||||
const result = await this.chatWithModel([
|
||||
{
|
||||
role: "system",
|
||||
content: `你是“意图分类器”，只输出 JSON：
|
||||
{"intent":"qa|ingest|mixed|unknown","confidence":0.0,"reason":"一句话","should_ingest":true|false,"should_query":true|false}
|
||||
规则：
|
||||
1) qa：主要是提问/求分析
|
||||
2) ingest：主要是陈述新增事实，意图写入图谱
|
||||
3) mixed：既有新增事实又要求回答
|
||||
4) unknown：无法判定时保守返回
|
||||
5) 结果要保守，不要过度推断`
|
||||
},
|
||||
{
|
||||
role: "user",
|
||||
content: rawText
|
||||
}
|
||||
], 0, model, maxTokens, { thinking: "off" });
|
||||
const content = result?.choices?.[0]?.message?.content || "";
|
||||
let parsed;
|
||||
try {
|
||||
parsed = parseJsonObject(content);
|
||||
} catch {
|
||||
parsed = {};
|
||||
}
|
||||
const intentRaw = String(parsed?.intent || "").trim().toLowerCase();
|
||||
const allowed = new Set(["qa", "ingest", "mixed", "unknown"]);
|
||||
const intent = allowed.has(intentRaw) ? intentRaw : "unknown";
|
||||
const confidence = Math.min(Math.max(Number(parsed?.confidence ?? 0), 0), 1);
|
||||
const shouldIngest = parsed?.should_ingest === true || intent === "ingest" || intent === "mixed";
|
||||
const shouldQuery = parsed?.should_query === false ? false : intent !== "ingest";
|
||||
return {
|
||||
intent,
|
||||
should_ingest: shouldIngest,
|
||||
should_query: shouldQuery,
|
||||
confidence,
|
||||
reason: String(parsed?.reason || "").slice(0, 120),
|
||||
model
|
||||
};
|
||||
}
|
||||
|
||||
async adjudicateEventCorrections(text, recentEvents = [], options = {}) {
|
||||
const rawText = typeof text === "string" ? text.trim() : "";
|
||||
if (!rawText) return { decisions: [] };
|
||||
|
||||
@@ -0,0 +1,272 @@
|
||||
const createHttpError = (statusCode, message) => {
|
||||
const error = new Error(message);
|
||||
error.statusCode = statusCode;
|
||||
return error;
|
||||
};
|
||||
|
||||
const tryParseJson = (rawText) => {
|
||||
const text = String(rawText || "").trim();
|
||||
if (!text) return null;
|
||||
const candidates = [text];
|
||||
const fenced = text.match(/```(?:json)?\s*([\s\S]*?)```/i);
|
||||
if (fenced?.[1]) candidates.push(fenced[1].trim());
|
||||
const startIdx = text.indexOf("{");
|
||||
const endIdx = text.lastIndexOf("}");
|
||||
if (startIdx >= 0 && endIdx > startIdx) candidates.push(text.slice(startIdx, endIdx + 1));
|
||||
for (const candidate of candidates) {
|
||||
try {
|
||||
return JSON.parse(candidate);
|
||||
} catch {}
|
||||
}
|
||||
return null;
|
||||
};
|
||||
|
||||
const normalizeScene = (value) => {
|
||||
const raw = String(value || "").trim().toLowerCase();
|
||||
if (["moments", "wechat_moments", "朋友圈"].includes(raw)) return "moments";
|
||||
if (["wechat_chat", "chat", "聊天", "微信聊天记录"].includes(raw)) return "wechat_chat";
|
||||
return "auto";
|
||||
};
|
||||
|
||||
const normalizeDataUrl = (value) => {
|
||||
const text = String(value || "").trim();
|
||||
if (!text || !text.startsWith("data:image/")) return "";
|
||||
if (!text.includes(";base64,")) return "";
|
||||
return text;
|
||||
};
|
||||
|
||||
const normalizeImageUrl = (value) => {
|
||||
const text = String(value || "").trim();
|
||||
if (!text) return "";
|
||||
if (/^https?:\/\//i.test(text)) return text;
|
||||
return "";
|
||||
};
|
||||
|
||||
const compactText = (value, maxLen = 600) => {
|
||||
const text = String(value || "").replace(/\s+/g, " ").trim();
|
||||
if (!text) return "";
|
||||
return text.length > maxLen ? `${text.slice(0, maxLen - 1)}…` : text;
|
||||
};
|
||||
|
||||
const parseDataUrl = (dataUrl = "") => {
|
||||
const raw = String(dataUrl || "");
|
||||
const match = raw.match(/^data:(image\/[a-zA-Z0-9.+-]+);base64,(.+)$/);
|
||||
if (!match) return { mediaType: "", base64: "" };
|
||||
return {
|
||||
mediaType: match[1] || "",
|
||||
base64: match[2] || ""
|
||||
};
|
||||
};
|
||||
|
||||
export class SocialImageAgentService {
|
||||
constructor({ llmService }) {
|
||||
this.llmService = llmService;
|
||||
this.maxImageChars = 2_600_000;
|
||||
}
|
||||
|
||||
isEnabled() {
|
||||
if (typeof this.llmService?.isImageEnabled === "function") {
|
||||
return Boolean(this.llmService.isImageEnabled());
|
||||
}
|
||||
return Boolean(this.llmService?.isEnabled?.());
|
||||
}
|
||||
|
||||
_readImageInput(payload = {}) {
|
||||
const image = payload.image_payload && typeof payload.image_payload === "object"
|
||||
? payload.image_payload
|
||||
: payload.image && typeof payload.image === "object"
|
||||
? payload.image
|
||||
: {};
|
||||
const dataUrl = normalizeDataUrl(
|
||||
image.data_url
|
||||
|| image.dataUrl
|
||||
|| payload.image_data_url
|
||||
|| payload.imageDataUrl
|
||||
);
|
||||
const imageUrl = normalizeImageUrl(
|
||||
image.url
|
||||
|| image.image_url
|
||||
|| payload.image_url
|
||||
);
|
||||
const fileName = String(image.file_name || image.fileName || payload.image_file_name || "").trim();
|
||||
const scene = normalizeScene(payload.image_scene || image.scene || payload.scene);
|
||||
return { dataUrl, imageUrl, fileName, scene };
|
||||
}
|
||||
|
||||
_buildSystemPrompt(scene = "auto") {
|
||||
const sceneHint = scene === "moments"
|
||||
? "当前图片偏向朋友圈动态，请重点提取发帖人、互动对象、时间线线索、情绪倾向、评论互动与配图语义。"
|
||||
: scene === "wechat_chat"
|
||||
? "当前图片偏向微信聊天记录，请重点提取对话双方、关键句、冲突/和好信号、时间点、主动方与边界表达。"
|
||||
: "请先识别是朋友圈还是微信聊天记录，再按对应维度提取。";
|
||||
return `你是图谱问答的图片理解Agent，擅长解析朋友圈截图/微信聊天截图并转成可检索线索。
|
||||
${sceneHint}
|
||||
朋友圈模式下，请优先提取：发帖人身份、被@对象、评论关系、情绪强度、关系状态变化。
|
||||
微信聊天模式下，请优先提取：说话人轮次、关键原句、冲突信号、修复信号、时间顺序与关系边界。
|
||||
微信聊天角色识别规则：
|
||||
1) 绿色聊天气泡默认判定为 self（我方）；
|
||||
2) 白色聊天气泡默认判定为 other（对方）；
|
||||
3) 若颜色不清晰，再结合左右位置、头像位置、昵称与时间分布判断；
|
||||
4) 无法确认时标记 unknown，禁止强行猜测。
|
||||
只返回 JSON，不要输出任何额外文字。格式：
|
||||
{
|
||||
"scene":"moments|wechat_chat|unknown",
|
||||
"confidence":0-1,
|
||||
"scene_focus":["本图最关键的3-5个观察点"],
|
||||
"summary":"图片核心摘要（120字内）",
|
||||
"extracted_text":"OCR关键信息，可为空",
|
||||
"chat_turns":[{"speaker":"self|other|unknown","text":"一句话","cue":"green|white|left|right|avatar"}],
|
||||
"key_entities":["人物/群聊/地点"],
|
||||
"key_events":["关键事件线索"],
|
||||
"risk_signals":["冲突/误解/边界风险信号"],
|
||||
"relation_hints":["关系线索"],
|
||||
"suggested_question":"可用于图谱问答的建议问题（尽量具体）"
|
||||
}`;
|
||||
}
|
||||
|
||||
_isMissingImageSignal(raw = "", parsed = {}) {
|
||||
const text = `${String(raw || "")}\n${String(parsed?.summary || "")}`.toLowerCase();
|
||||
return /未提供(截图|图片)|无法进行视觉解析|无可见图像|看不到图片|cannot\s+see\s+the\s+image|no\s+image\s+provided|image\s+not\s+provided/.test(text);
|
||||
}
|
||||
|
||||
_buildUserContextParts(scene = "auto", queryText = "") {
|
||||
return [
|
||||
{ type: "text", text: `场景偏好: ${scene}` },
|
||||
{ type: "text", text: `用户补充问题: ${queryText || "未提供文本问题，请基于图片生成可检索问题"}` },
|
||||
{ type: "text", text: "请输出标准JSON。" }
|
||||
];
|
||||
}
|
||||
|
||||
_buildImageContentVariants({ dataUrl = "", imageUrl = "", contextParts = [] } = {}) {
|
||||
if (imageUrl) {
|
||||
return [
|
||||
[...contextParts, { type: "image_url", image_url: { url: imageUrl } }],
|
||||
[...contextParts, { type: "image_url", image_url: imageUrl }]
|
||||
];
|
||||
}
|
||||
if (!dataUrl) return [contextParts];
|
||||
return [
|
||||
[...contextParts, { type: "image_url", image_url: { url: dataUrl } }],
|
||||
[...contextParts, { type: "image_url", image_url: dataUrl }]
|
||||
];
|
||||
}
|
||||
|
||||
async analyzeForGraphQa(payload = {}) {
|
||||
if (!this.isEnabled()) {
|
||||
throw createHttpError(400, "LLM 服务未配置，无法启用图片问答");
|
||||
}
|
||||
const { dataUrl, imageUrl, fileName, scene } = this._readImageInput(payload);
|
||||
if (!dataUrl && !imageUrl) {
|
||||
throw createHttpError(400, "缺少图片输入，请提供 image_payload.data_url 或 image_url");
|
||||
}
|
||||
if (dataUrl && dataUrl.length > this.maxImageChars) {
|
||||
throw createHttpError(413, "图片体积过大，请压缩后重试");
|
||||
}
|
||||
const queryText = String(payload.query_text || payload.query || "").trim();
|
||||
const contextParts = this._buildUserContextParts(scene, queryText);
|
||||
const variants = this._buildImageContentVariants({ dataUrl, imageUrl, contextParts });
|
||||
let raw = "";
|
||||
let parsed = {};
|
||||
let lastError = null;
|
||||
for (let i = 0; i < variants.length; i += 1) {
|
||||
try {
|
||||
const response = await this.llmService.chatImage([
|
||||
{ role: "system", content: this._buildSystemPrompt(scene) },
|
||||
{ role: "user", content: variants[i] }
|
||||
], 0.2, { thinking: "off" });
|
||||
raw = response?.choices?.[0]?.message?.content || "";
|
||||
parsed = tryParseJson(raw) || {};
|
||||
const missingSignal = this._isMissingImageSignal(raw, parsed);
|
||||
if (missingSignal && i < variants.length - 1) {
|
||||
continue;
|
||||
}
|
||||
break;
|
||||
} catch (error) {
|
||||
lastError = error;
|
||||
if (i >= variants.length - 1) {
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (!raw && lastError) {
|
||||
throw lastError;
|
||||
}
|
||||
if (this._isMissingImageSignal(raw, parsed)) {
|
||||
throw createHttpError(422, "图片未被模型正确识别，请尝试 Ctrl+V 粘贴截图或更换支持视觉的模型");
|
||||
}
|
||||
const finalScene = ["moments", "wechat_chat", "unknown"].includes(String(parsed.scene || "").trim().toLowerCase())
|
||||
? String(parsed.scene || "").trim().toLowerCase()
|
||||
: "unknown";
|
||||
const confidenceRaw = Number(parsed.confidence);
|
||||
const confidence = Number.isFinite(confidenceRaw) ? Math.max(0, Math.min(1, confidenceRaw)) : 0.6;
|
||||
const summary = compactText(parsed.summary, 180);
|
||||
const extractedText = compactText(parsed.extracted_text, 1200);
|
||||
const keyEntities = (Array.isArray(parsed.key_entities) ? parsed.key_entities : [])
|
||||
.map((item) => compactText(item, 40))
|
||||
.filter(Boolean)
|
||||
.slice(0, 10);
|
||||
const chatTurns = (Array.isArray(parsed.chat_turns) ? parsed.chat_turns : [])
|
||||
.map((item) => {
|
||||
const speakerRaw = String(item?.speaker || "").trim().toLowerCase();
|
||||
const speaker = ["self", "other", "unknown"].includes(speakerRaw) ? speakerRaw : "unknown";
|
||||
return {
|
||||
speaker,
|
||||
text: compactText(item?.text, 120),
|
||||
cue: compactText(item?.cue, 24)
|
||||
};
|
||||
})
|
||||
.filter((item) => item.text)
|
||||
.slice(0, 20);
|
||||
const keyEvents = (Array.isArray(parsed.key_events) ? parsed.key_events : [])
|
||||
.map((item) => compactText(item, 80))
|
||||
.filter(Boolean)
|
||||
.slice(0, 8);
|
||||
const sceneFocus = (Array.isArray(parsed.scene_focus) ? parsed.scene_focus : [])
|
||||
.map((item) => compactText(item, 80))
|
||||
.filter(Boolean)
|
||||
.slice(0, 6);
|
||||
const riskSignals = (Array.isArray(parsed.risk_signals) ? parsed.risk_signals : [])
|
||||
.map((item) => compactText(item, 80))
|
||||
.filter(Boolean)
|
||||
.slice(0, 6);
|
||||
const relationHints = (Array.isArray(parsed.relation_hints) ? parsed.relation_hints : [])
|
||||
.map((item) => compactText(item, 80))
|
||||
.filter(Boolean)
|
||||
.slice(0, 8);
|
||||
const suggestedQuestion = compactText(parsed.suggested_question, 200);
|
||||
const imageDigest = [
|
||||
summary ? `图片摘要：${summary}` : "",
|
||||
chatTurns.length
|
||||
? `聊天分轨：${chatTurns.slice(0, 8).map((item) => `${item.speaker === "self" ? "我" : item.speaker === "other" ? "对方" : "未知"}:${item.text}`).join(" | ")}`
|
||||
: "",
|
||||
sceneFocus.length ? `场景观察：${sceneFocus.join("；")}` : "",
|
||||
keyEntities.length ? `关键实体：${keyEntities.join("、")}` : "",
|
||||
keyEvents.length ? `事件线索：${keyEvents.join("；")}` : "",
|
||||
riskSignals.length ? `风险信号：${riskSignals.join("；")}` : "",
|
||||
relationHints.length ? `关系线索：${relationHints.join("；")}` : "",
|
||||
extractedText ? `OCR摘录：${extractedText.slice(0, 260)}` : ""
|
||||
].filter(Boolean).join("\n");
|
||||
const queryTextForQa = [
|
||||
queryText,
|
||||
suggestedQuestion,
|
||||
imageDigest ? `以下是图片提取线索，请结合图谱回答：\n${imageDigest}` : ""
|
||||
].filter(Boolean).join("\n\n");
|
||||
return {
|
||||
ok: true,
|
||||
scene: finalScene,
|
||||
confidence,
|
||||
file_name: fileName || null,
|
||||
summary,
|
||||
extracted_text: extractedText,
|
||||
chat_turns: chatTurns,
|
||||
scene_focus: sceneFocus,
|
||||
key_entities: keyEntities,
|
||||
key_events: keyEvents,
|
||||
risk_signals: riskSignals,
|
||||
relation_hints: relationHints,
|
||||
suggested_question: suggestedQuestion,
|
||||
image_digest: imageDigest,
|
||||
query_text_for_qa: queryTextForQa || queryText || suggestedQuestion || "请基于图片线索给出关系判断"
|
||||
};
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -122,6 +122,64 @@
|
||||
<span class="detail-label">性别</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.gender || 'unknown' }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.age_label">
|
||||
<span class="detail-label">年龄标签</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.age_label }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.birth_date_utc">
|
||||
<span class="detail-label">出生日期(UTC)</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.birth_date_utc }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.aliases?.length">
|
||||
<span class="detail-label">别名</span>
|
||||
<span class="detail-value detail-tags">
|
||||
<span class="detail-tag" v-for="(alias, idx) in selectedDetail.data.aliases.slice(0, 8)" :key="`${selectedDetail.data.id}_alias_${idx}`">
|
||||
{{ alias }}
|
||||
</span>
|
||||
</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.person_role">
|
||||
<span class="detail-label">人物角色</span>
|
||||
<span class="detail-value">{{ formatPersonRole(selectedDetail.data.person_role) }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.relation_to_user">
|
||||
<span class="detail-label">与用户关系</span>
|
||||
<span class="detail-value">
|
||||
{{ selectedDetail.data.relation_to_user }}
|
||||
<template v-if="Number.isFinite(Number(selectedDetail.data.relation_confidence))">
|
||||
· {{ formatPercent(selectedDetail.data.relation_confidence) }}
|
||||
</template>
|
||||
</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.occupation">
|
||||
<span class="detail-label">职业</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.occupation }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.education_background">
|
||||
<span class="detail-label">教育背景</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.education_background }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person' && selectedDetail.data.residential_status">
|
||||
<span class="detail-label">常住状态</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.residential_status }}</span>
|
||||
</div>
|
||||
<div class="detail-row" v-if="selectedDetail.data.type === 'person'">
|
||||
<span class="detail-label">属性标签</span>
|
||||
<span class="detail-value detail-tags">
|
||||
<span class="detail-tag" v-if="selectedDetail.data.data_source">
|
||||
来源: {{ formatDataSource(selectedDetail.data.data_source) }}
|
||||
</span>
|
||||
<span class="detail-tag" v-if="selectedDetail.data.consent_status">
|
||||
同意状态: {{ formatConsentStatus(selectedDetail.data.consent_status) }}
|
||||
</span>
|
||||
<span class="detail-tag" v-if="selectedDetail.data.privacy_level">
|
||||
隐私级别: {{ selectedDetail.data.privacy_level }}
|
||||
</span>
|
||||
<span class="detail-tag" v-if="Number.isFinite(Number(selectedDetail.data.data_quality))">
|
||||
数据质量: {{ formatPercent(selectedDetail.data.data_quality) }}
|
||||
</span>
|
||||
</span>
|
||||
</div>
|
||||
<div class="detail-row">
|
||||
<span class="detail-label">ID</span>
|
||||
<span class="detail-value">{{ selectedDetail.data.id || '-' }}</span>
|
||||
@@ -1052,6 +1110,51 @@ const formatDateTime = (value) => {
|
||||
return String(value)
|
||||
}
|
||||
|
||||
const formatPercent = (value) => {
|
||||
const num = Number(value)
|
||||
if (!Number.isFinite(num)) return '-'
|
||||
const ratio = num <= 1 ? num * 100 : num
|
||||
return `${Math.max(0, Math.min(100, ratio)).toFixed(1)}%`
|
||||
}
|
||||
|
||||
const formatPersonRole = (value) => {
|
||||
const raw = String(value || '').trim().toLowerCase()
|
||||
const map = {
|
||||
self: '本人',
|
||||
partner: '伴侣',
|
||||
ex: '前任',
|
||||
friend: '朋友',
|
||||
colleague: '同事',
|
||||
other: '其他',
|
||||
unknown: '未知'
|
||||
}
|
||||
return map[raw] || value || '未知'
|
||||
}
|
||||
|
||||
const formatDataSource = (value) => {
|
||||
const raw = String(value || '').trim().toLowerCase()
|
||||
const map = {
|
||||
ingest: '导入',
|
||||
user_input: '用户输入',
|
||||
manual: '手动',
|
||||
import: '导入',
|
||||
llm_extract: '模型抽取',
|
||||
system: '系统'
|
||||
}
|
||||
return map[raw] || (value || '未知')
|
||||
}
|
||||
|
||||
const formatConsentStatus = (value) => {
|
||||
const raw = String(value || '').trim().toLowerCase()
|
||||
const map = {
|
||||
granted: '已同意',
|
||||
denied: '拒绝',
|
||||
revoked: '已撤回',
|
||||
unknown: '未知'
|
||||
}
|
||||
return map[raw] || (value || '未知')
|
||||
}
|
||||
|
||||
const clearSelection = () => {
|
||||
selectedDetail.value = null
|
||||
resetSelectionStyles?.()
|
||||
@@ -1071,6 +1174,26 @@ const formatIngestProgress = (event) => {
|
||||
return `阶段: ${stage}`
|
||||
}
|
||||
|
||||
const formatQualityReplayLine = (qualityReplay) => {
|
||||
if (!qualityReplay || typeof qualityReplay !== 'object') return ''
|
||||
const output = qualityReplay.output || {}
|
||||
const relationQuality = output.relation_quality || null
|
||||
const normalization = output.normalization || null
|
||||
if (relationQuality) {
|
||||
const avg = Number(relationQuality.average_score || 0)
|
||||
const stable = Number(relationQuality.stable_count || 0)
|
||||
const risk = Number(relationQuality.conflict_risk_count || 0)
|
||||
return `质量回放: 关系均分 ${(avg * 100).toFixed(1)}% · 稳定 ${stable} · 风险 ${risk}`
|
||||
}
|
||||
if (normalization) {
|
||||
return `质量回放: 人物别名合并 ${Number(normalization.person_alias_merged || 0)} · 组织别名合并 ${Number(normalization.organization_alias_merged || 0)} · 关系去重 ${Number(normalization.relation_deduped || 0)}`
|
||||
}
|
||||
if (typeof output.confidence === 'number') {
|
||||
return `质量回放: 置信度 ${(Number(output.confidence) * 100).toFixed(1)}%`
|
||||
}
|
||||
return ''
|
||||
}
|
||||
|
||||
const handleIngest = async () => {
|
||||
if (!ingestText.value) return
|
||||
ingestLoading.value = true
|
||||
@@ -1086,6 +1209,7 @@ const handleIngest = async () => {
|
||||
})
|
||||
}
|
||||
try {
|
||||
let ingestTaskId = ''
|
||||
logAt('开始提交导入请求...', 'info')
|
||||
const payload = {
|
||||
text: ingestText.value,
|
||||
@@ -1126,6 +1250,11 @@ const handleIngest = async () => {
|
||||
}
|
||||
if (eventName === 'progress') {
|
||||
logAt(formatIngestProgress(payload), 'info')
|
||||
} else if (eventName === 'meta') {
|
||||
if (payload?.task_id) {
|
||||
ingestTaskId = payload.task_id
|
||||
logAt(`任务已创建: ${ingestTaskId}`, 'info')
|
||||
}
|
||||
} else if (eventName === 'done') {
|
||||
doneResult = payload
|
||||
} else if (eventName === 'error') {
|
||||
@@ -1144,12 +1273,29 @@ const handleIngest = async () => {
|
||||
if (buffer.trim()) parseSseBlock(buffer)
|
||||
|
||||
if (streamError) {
|
||||
if (ingestTaskId) {
|
||||
logAt(`可通过 /tasks/${ingestTaskId} 查询失败详情`, 'error')
|
||||
}
|
||||
throw new Error(streamError)
|
||||
}
|
||||
if (!doneResult?.ok) {
|
||||
throw new Error(doneResult?.error || doneResult?.message || '分析失败')
|
||||
}
|
||||
logAt(`分析成功: ${doneResult.stats?.created?.persons || 0} 人, ${doneResult.stats?.created?.organizations || 0} 组织, ${doneResult.stats?.created?.events || 0} 事件, ${doneResult.stats?.created?.topics || 0} 主题`, 'success')
|
||||
if (ingestTaskId) {
|
||||
logAt(`任务完成: ${ingestTaskId}`, 'success')
|
||||
try {
|
||||
const taskRes = await fetch(`${apiBase}/tasks/${encodeURIComponent(ingestTaskId)}`, {
|
||||
method: 'GET',
|
||||
headers: { 'Content-Type': 'application/json' }
|
||||
})
|
||||
const taskData = await taskRes.json()
|
||||
if (taskRes.ok && taskData?.ok && taskData?.task?.quality_replay) {
|
||||
const replayLine = formatQualityReplayLine(taskData.task.quality_replay)
|
||||
if (replayLine) logAt(replayLine, 'info')
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
ingestText.value = ''
|
||||
renderGraph()
|
||||
} catch (e) {
|
||||
@@ -1747,6 +1893,23 @@ input:checked + .slider:before {
|
||||
word-break: break-word;
|
||||
}
|
||||
|
||||
.detail-tags {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.detail-tag {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
border: 1px solid #cccccc;
|
||||
border-radius: 999px;
|
||||
padding: 2px 8px;
|
||||
font-size: 12px;
|
||||
color: #333333;
|
||||
background: #ffffff;
|
||||
}
|
||||
|
||||
.summary-value { line-height: 1.55; }
|
||||
|
||||
.panel-grid {
|
||||
|
||||
Reference in New Issue
Block a user