Add under-anything knowledge dashboard
This commit is contained in:
qiaoxinjiu
@@ -0,0 +1,563 @@
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import http from "node:http";
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import fs from "node:fs";
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import path from "node:path";
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import { fileURLToPath } from "node:url";
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const __dirname = path.dirname(fileURLToPath(import.meta.url));
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const defaultStaticRoot = path.resolve(__dirname, "..", "..", "..", "..", "..", "wishfulfilled-dashboard");
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const staticRoot = process.env.RAG_STATIC_ROOT || defaultStaticRoot;
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const graphPath = process.env.RAG_GRAPH_PATH || path.join(staticRoot, "knowledge-graph.json");
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const port = Number(process.env.PORT || process.env.RAG_PORT || 8080);
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const host = process.env.HOST || "0.0.0.0";
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const MIME = {
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".html": "text/html; charset=utf-8",
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".js": "text/javascript; charset=utf-8",
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".css": "text/css; charset=utf-8",
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".json": "application/json; charset=utf-8",
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".svg": "image/svg+xml",
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".ico": "image/x-icon",
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".png": "image/png",
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".jpg": "image/jpeg",
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".jpeg": "image/jpeg",
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".woff": "font/woff",
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".woff2": "font/woff2",
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};
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const SYNONYMS = {
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回评: ["评价提交", "评价展示", "客服邀评", "评价结果追踪", "review submission", "review display", "review follow-up", "邀评", "留评"],
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黑名单: ["blacklist", "blacklist_entities", "风险", "风控", "风险案件", "risk_cases", "risk_signals", "反欺诈", "拦截"],
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测评: ["评价计划", "review plan", "评价提交", "额度统计", "测评单"],
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免评: ["KOC", "KOL", "内容发布", "引流", "带货", "exemption"],
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真实人: ["person", "person_profiles", "跨账号归并", "身份归并", "真实人归并"],
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额度: ["4/4/12", "person_quota_ledgers", "额度台账", "预占", "额度与频控"],
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对外API: ["对外 API 契约", "接口契约", "API 草案", "接口说明", "对外接口"],
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工单: ["客服工单", "support_tickets", "客服跟进", "support_followups"],
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风险: ["风险与反欺诈", "risk_signals", "risk_cases", "黑名单", "反欺诈"],
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};
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let graphCache = null;
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let graphMtime = 0;
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let chunksCache = [];
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function sendJson(res, status, data) {
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const body = JSON.stringify(data, null, 2);
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res.writeHead(status, {
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"Content-Type": "application/json; charset=utf-8",
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"Cache-Control": "no-store",
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Headers": "Content-Type, Authorization",
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"Access-Control-Allow-Methods": "GET, POST, OPTIONS",
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});
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res.end(body);
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}
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function normalizeText(value) {
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if (Array.isArray(value)) return value.join(" ");
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return typeof value === "string" ? value : "";
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}
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function stripHtml(text) {
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return text
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.replace(/<script[\s\S]*?<\/script>/gi, " ")
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.replace(/<style[\s\S]*?<\/style>/gi, " ")
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.replace(/<[^>]+>/g, " ")
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.replace(/ /g, " ")
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.replace(/&/g, "&")
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.replace(/</g, "<")
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.replace(/>/g, ">");
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}
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function stripInjectedLineNumbers(text) {
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const lines = text.split("\n");
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let changed = 0;
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const cleaned = lines.map((line) => {
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const match = line.match(/^\s*\d+\|(.+)$/);
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if (!match) return line;
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changed += 1;
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return match[1];
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});
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return changed >= Math.max(3, Math.floor(lines.length / 4)) ? cleaned.join("\n") : text;
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}
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function compactKey(value) {
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return String(value || "")
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.toLowerCase()
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.replace(/[\s`*_>#|\-\[\]()（）【】《》“”"'，。、：:；;,.\/\\!?！？]+/g, "");
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}
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function normalizeForSearch(value) {
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return stripHtml(stripInjectedLineNumbers(String(value || "")))
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.toLowerCase()
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.replace(/[\t\r]+/g, " ")
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.replace(/[（）]/g, " ")
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.replace(/[【】《》“”]/g, " ")
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.replace(/[_*`>#|\[\]]/g, " ")
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.replace(/\s+/g, " ")
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.trim();
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}
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function unique(values) {
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return Array.from(new Set(values.map((v) => String(v || "").trim()).filter(Boolean)));
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}
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function tokenizeText(text) {
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const lower = normalizeForSearch(text);
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const latin = lower.match(/[a-z0-9_.\/+-]{2,}/g) || [];
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const cjkRuns = lower.match(/[\u4e00-\u9fa5]{2,}/g) || [];
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const cjkChars = lower.match(/[\u4e00-\u9fa5]/g) || [];
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const ngrams = [];
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for (let i = 0; i < cjkChars.length - 1; i += 1) ngrams.push(`${cjkChars[i]}${cjkChars[i + 1]}`);
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for (let i = 0; i < cjkChars.length - 2; i += 1) ngrams.push(`${cjkChars[i]}${cjkChars[i + 1]}${cjkChars[i + 2]}`);
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return unique([...latin, ...cjkRuns, ...ngrams, ...cjkChars.filter((ch) => !/[是什么吗呢的了和与及或在中为]/.test(ch))]);
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}
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function detectQuestionType(query) {
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const text = normalizeForSearch(query);
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if (/api|接口|契约|endpoint|对外/.test(text)) return "api";
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if (/数据|字段|表|对象|模型|schema|建表/.test(text)) return "data_model";
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if (/流程|怎么走|如何|步骤|流转|处理/.test(text)) return "process";
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if (/规则|限制|口径|额度|频控|条件/.test(text)) return "rule";
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if (/谁|负责|职责|owner|角色/.test(text)) return "responsibility";
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if (/测试|验收|用例|缺陷|上线/.test(text)) return "test";
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if (/里程碑|阶段|计划|节点/.test(text)) return "milestone";
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if (/是什么|定义|含义|意思|解释/.test(text)) return "definition";
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return "general";
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}
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function expandTerms(tokens, raw) {
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const compact = compactKey(raw);
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const expanded = [];
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for (const [term, values] of Object.entries(SYNONYMS)) {
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const termCompact = compactKey(term);
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if (compact.includes(termCompact) || tokens.some((token) => compactKey(token).includes(termCompact) || termCompact.includes(compactKey(token)))) {
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expanded.push(term, ...values);
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}
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}
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return unique(expanded);
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}
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function processQuery(raw) {
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const normalized = normalizeForSearch(raw);
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const compact = compactKey(raw);
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const tokens = tokenizeText(raw);
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const expandedTerms = expandTerms(tokens, raw);
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const coreTerms = unique([String(raw || "").trim(), normalized, compact, ...tokens.filter((token) => token.length >= 2)]);
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return { raw, normalized, compact, tokens, coreTerms, expandedTerms, questionType: detectQuestionType(raw) };
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}
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function loadGraph() {
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const stat = fs.statSync(graphPath);
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if (graphCache && stat.mtimeMs === graphMtime) return graphCache;
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const graph = JSON.parse(fs.readFileSync(graphPath, "utf8"));
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graphCache = graph;
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graphMtime = stat.mtimeMs;
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chunksCache = buildChunks(graph.nodes || []);
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return graph;
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}
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function nodeContent(node) {
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return stripHtml(stripInjectedLineNumbers(normalizeText(node?.knowledgeMeta?.content)));
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}
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function nodeLayer(node) {
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return normalizeText(node?.knowledgeMeta?.category) || (node?.tags || []).find((tag) => /需求|技术|测试|里程碑/.test(tag)) || "";
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}
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function detectChunkType(heading, content) {
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const text = `${heading}\n${content}`.toLowerCase();
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if (/api|接口|契约|endpoint|对外/.test(text)) return "api_contract";
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if (/数据模型|字段|表结构|对象|schema|建表/.test(text)) return "data_model";
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if (/业务规则|规则|口径|额度|频控|限制/.test(text)) return "business_rule";
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if (/流程|流转|步骤|时序|处理/.test(text)) return "process";
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if (/faq|常见问题|是什么|问答/.test(text)) return "faq";
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return "general";
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}
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function splitSections(text) {
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const source = text.trim();
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if (!source) return [];
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const sections = [];
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let currentHeading = "全文";
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let buffer = [];
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for (const line of source.split("\n")) {
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const heading = line.match(/^\s{0,3}#{1,6}\s+(.+)$/)?.[1]?.trim();
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if (heading) {
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if (buffer.length > 0) sections.push({ heading: currentHeading, text: buffer.join("\n") });
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currentHeading = heading;
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buffer = [line];
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} else {
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buffer.push(line);
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}
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}
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if (buffer.length > 0) sections.push({ heading: currentHeading, text: buffer.join("\n") });
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return sections.length ? sections : [{ heading: "全文", text: source }];
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}
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function buildChunks(nodes) {
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return nodes.flatMap((node) => {
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const content = nodeContent(node);
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const titleBlock = [node.name, node.summary, node.filePath, (node.tags || []).join(" ")].join("\n");
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const sections = splitSections(content || titleBlock);
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return sections.map((section, index) => {
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const sectionText = section.text.trim() || titleBlock;
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const normalizedContent = normalizeForSearch(sectionText);
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return {
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chunkId: `${node.id}#${index}`,
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nodeId: node.id,
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docTitle: node.name || node.id,
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docPath: node.filePath || node.id,
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sectionTitle: section.heading,
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content: sectionText,
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normalizedContent,
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compactContent: compactKey(sectionText),
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tags: node.tags || [],
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summary: node.summary || "",
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chunkType: detectChunkType(section.heading, sectionText),
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layer: nodeLayer(node),
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};
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});
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});
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}
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function countOccurrences(haystack, needle) {
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if (!needle) return 0;
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return haystack.split(needle).length - 1;
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}
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function fieldScore(field, query, fieldName, multiplier = 1) {
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const normalized = normalizeForSearch(field);
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const compact = compactKey(field);
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const reasons = [];
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let score = 0;
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const rawNeedles = unique([query.normalized, String(query.raw || "").toLowerCase()].filter((v) => v.length >= 2));
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for (const needle of rawNeedles) {
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if (normalized.includes(needle)) {
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const add = 70 * multiplier;
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score += add;
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reasons.push({ type: "exact", field: fieldName, message: `${fieldName} 原文命中：${needle}`, score: add });
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}
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}
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if (query.compact && compact.includes(query.compact)) {
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const add = 80 * multiplier;
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score += add;
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reasons.push({ type: "compact", field: fieldName, message: `${fieldName} 去标点命中`, score: add });
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}
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let tokenHits = 0;
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for (const term of query.coreTerms) {
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const normalizedTerm = normalizeForSearch(term);
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const compactTerm = compactKey(term);
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if (!normalizedTerm && !compactTerm) continue;
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const occurrences = Math.max(countOccurrences(normalized, normalizedTerm), countOccurrences(compact, compactTerm));
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if (occurrences > 0) {
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tokenHits += 1;
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score += Math.min(occurrences, 4) * Math.min(Math.max(compactTerm.length, normalizedTerm.length), 10) * multiplier;
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}
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}
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if (tokenHits > 0) reasons.push({ type: "token", field: fieldName, message: `${fieldName} 关键词覆盖 ${tokenHits} 项`, score: tokenHits * multiplier });
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return { score, reasons };
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}
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function semanticScore(chunk, query) {
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const reasons = [];
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const terms = unique([...query.tokens, ...query.expandedTerms.flatMap(tokenizeText)]).filter((term) => term.length >= 2);
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if (!terms.length) return { score: 0, reasons };
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const targetTokens = new Set(tokenizeText(`${chunk.docTitle} ${chunk.sectionTitle} ${chunk.normalizedContent}`));
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let overlap = 0;
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for (const term of terms) {
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const compact = compactKey(term);
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if (targetTokens.has(term) || chunk.compactContent.includes(compact) || compactKey(`${chunk.docTitle}${chunk.sectionTitle}`).includes(compact)) overlap += 1;
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}
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const ratio = overlap / Math.max(terms.length, 1);
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const score = ratio * 90;
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if (score > 0) reasons.push({ type: "semantic", message: `语义/同义词覆盖 ${(ratio * 100).toFixed(0)}%`, score });
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return { score, reasons };
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}
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function directoryBoost(chunk, query) {
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const p = chunk.docPath || "";
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const base = p.includes("05_需求文档") ? 15 : p.includes("01_业务流程") ? 12 : p.includes("07_技术文档") ? 10 : p.includes("08_测试相关") ? 8 : p.includes("06_里程碑") ? 6 : p.includes("02_项目管理流程") ? 5 : p.includes("04_Agent检索") ? 4 : p.includes("03_规范与模板") ? 1 : 0;
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if (query.questionType === "api" && p.includes("07_技术文档")) return base + 8;
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if ((query.questionType === "definition" || query.questionType === "rule") && p.includes("05_需求文档")) return base + 5;
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if (query.questionType === "test" && p.includes("08_测试相关")) return base + 12;
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return base;
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}
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function chunkTypeBoost(chunk, query) {
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if (query.questionType === "api" && chunk.chunkType === "api_contract") return 18;
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if (query.questionType === "data_model" && chunk.chunkType === "data_model") return 18;
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if (query.questionType === "process" && chunk.chunkType === "process") return 14;
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if (query.questionType === "rule" && chunk.chunkType === "business_rule") return 14;
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if (query.questionType === "definition" && (chunk.chunkType === "faq" || /概述|目标|说明/.test(chunk.sectionTitle))) return 10;
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return 0;
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}
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function makeSnippet(text, query) {
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const compact = String(text || "").replace(/\s+/g, " ").trim();
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if (!compact) return "";
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const lower = compact.toLowerCase();
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const compactLower = compactKey(compact);
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const terms = unique([query.normalized, query.raw, query.compact, ...query.coreTerms, ...query.expandedTerms]);
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const hit = terms.map((term) => {
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const plainIndex = lower.indexOf(normalizeForSearch(term));
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if (plainIndex >= 0) return plainIndex;
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const compactIndex = compactLower.indexOf(compactKey(term));
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return compactIndex >= 0 ? Math.min(compactIndex, compact.length - 1) : -1;
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}).filter((i) => i >= 0).sort((a, b) => a - b)[0];
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const start = Math.max(0, (hit ?? 0) - 110);
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const snippet = compact.slice(start, start + 420);
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return `${start > 0 ? "…" : ""}${snippet}${start + 420 < compact.length ? "…" : ""}`;
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}
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function searchChunks(queryText, topK = 16) {
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loadGraph();
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const query = processQuery(queryText);
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if (!query.normalized && !query.compact) return { query, hits: [] };
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const weights = query.questionType === "api" ? { exact: 0.45, fuzzy: 0.25, semantic: 0.1 } : query.questionType === "definition" || query.questionType === "process" ? { exact: 0.25, fuzzy: 0.2, semantic: 0.3 } : { exact: 0.3, fuzzy: 0.2, semantic: 0.25 };
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const hits = chunksCache.map((chunk) => {
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const title = fieldScore(`${chunk.docTitle}\n${chunk.docPath}`, query, "docTitle", 4);
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const section = fieldScore(chunk.sectionTitle, query, "sectionTitle", 6);
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const content = fieldScore(chunk.normalizedContent, query, "content", 1);
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const tags = fieldScore((chunk.tags || []).join(" "), query, "tags", 3);
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const semantic = semanticScore(chunk, query);
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const exact = title.score + section.score + tags.score + content.score;
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const fuzzy = (chunk.compactContent.includes(query.compact) ? 80 : 0) + content.score * 0.2;
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const titleBoost = title.score > 0 ? 12 : 0;
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const sectionBoost = section.score > 0 ? 18 : 0;
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const dirBoost = directoryBoost(chunk, query);
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const typeBoost = chunkTypeBoost(chunk, query);
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const score = exact * weights.exact + fuzzy * weights.fuzzy + semantic.score * weights.semantic + titleBoost + sectionBoost + dirBoost + typeBoost;
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const reasons = [...title.reasons, ...section.reasons, ...tags.reasons, ...content.reasons.slice(0, 3), ...semantic.reasons];
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if (dirBoost) reasons.push({ type: "directory", message: `目录权重：${(chunk.docPath || "").split("/")[0] || ""}`, score: dirBoost });
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if (typeBoost) reasons.push({ type: "chunkType", message: `章节类型匹配：${chunk.chunkType}`, score: typeBoost });
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return {
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chunkId: chunk.chunkId,
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nodeId: chunk.nodeId,
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docTitle: chunk.docTitle,
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docPath: chunk.docPath,
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sectionTitle: chunk.sectionTitle,
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chunkType: chunk.chunkType,
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layer: chunk.layer,
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score,
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scores: { exact, fuzzy, semantic: semantic.score, titleBoost, sectionBoost, directoryBoost: dirBoost, chunkTypeBoost: typeBoost },
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snippet: makeSnippet(chunk.content, query),
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evidenceContent: chunk.content.slice(0, 2600),
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reasons,
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};
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}).filter((hit) => hit.score > 10 && hit.snippet).sort((a, b) => b.score - a.score).reduce((acc, hit) => {
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const key = `${hit.nodeId}:${hit.sectionTitle}`;
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if (!acc.some((item) => `${item.nodeId}:${item.sectionTitle}` === key)) acc.push(hit);
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return acc;
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}, []).slice(0, topK);
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return { query, hits };
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}
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function evidenceDecision(question, hits) {
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if (!String(question || "").trim()) return { allowed: false, confidence: "none", reason: "请输入问题。" };
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if (!hits.length) return { allowed: false, confidence: "none", reason: "没有检索到相关证据。" };
|
||||
if (hits[0].score >= 75) return { allowed: true, confidence: "high", reason: "Top1 证据分数达到阈值。" };
|
||||
if (hits[0].score >= 60 && (hits[1]?.score || 0) >= 50) return { allowed: true, confidence: "medium", reason: "多个证据片段达到中等相关。" };
|
||||
return { allowed: false, confidence: "low", reason: "证据分数不足。" };
|
||||
}
|
||||
|
||||
function localAnswer(question, hits) {
|
||||
const decision = evidenceDecision(question, hits);
|
||||
if (!decision.allowed) return "暂无该需求描述。";
|
||||
const top = hits.slice(0, 5);
|
||||
return [
|
||||
`结论：已基于本地知识库检索到可用证据。${decision.reason}`,
|
||||
"",
|
||||
"相关依据：",
|
||||
...top.map((hit, index) => `${index + 1}. ${hit.docTitle}${hit.sectionTitle ? ` / ${hit.sectionTitle}` : ""}\n ${hit.snippet}\n 来源：${hit.docPath}`),
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
function detectProvider({ endpoint, apiKey, provider }) {
|
||||
if (provider && provider !== "auto") return provider;
|
||||
const raw = String(endpoint || "").toLowerCase();
|
||||
if (String(apiKey || "").startsWith("plan-") || raw.includes("/plan/v1")) return "routin-plan";
|
||||
return "openai";
|
||||
}
|
||||
|
||||
function normalizeOpenAiEndpoint(endpoint) {
|
||||
const raw = String(endpoint || "").trim();
|
||||
if (!raw) return "";
|
||||
if (/\/chat\/completions\/?$/.test(raw)) return raw;
|
||||
return `${raw.replace(/\/+$/, "")}/chat/completions`;
|
||||
}
|
||||
|
||||
function normalizePlanEndpoint(endpoint) {
|
||||
const raw = String(endpoint || "").trim();
|
||||
let base = raw || "https://api.routin.ai/plan/v1";
|
||||
base = base.replace(/\/chat\/completions\/?$/, "").replace(/\/messages\/?$/, "").replace(/\/+$/, "");
|
||||
if (!base.includes("/plan/v1")) base = base.replace(/\/v1$/, "/plan/v1");
|
||||
return `${base}/messages`;
|
||||
}
|
||||
|
||||
function extractPlanMessageText(data) {
|
||||
if (data && typeof data === "object") {
|
||||
const content = data.content;
|
||||
if (Array.isArray(content)) {
|
||||
const texts = content
|
||||
.map((part) => typeof part === "string" ? part : part?.text)
|
||||
.filter((part) => typeof part === "string" && part.trim());
|
||||
if (texts.length) return texts.join("");
|
||||
}
|
||||
if (typeof content === "string") return content;
|
||||
}
|
||||
return "";
|
||||
}
|
||||
|
||||
function extractAnswer(data, protocol) {
|
||||
if (protocol === "routin-plan") {
|
||||
const planText = extractPlanMessageText(data);
|
||||
if (planText) return planText;
|
||||
}
|
||||
return data?.choices?.[0]?.message?.content
|
||||
|| data?.message?.content
|
||||
|| data?.response
|
||||
|| data?.data?.answer
|
||||
|| data?.answer
|
||||
|| extractPlanMessageText(data);
|
||||
}
|
||||
|
||||
async function callLlm({ endpoint, model, apiKey, provider = "auto", question, hits, timeoutMs = 60000 }) {
|
||||
const protocol = detectProvider({ endpoint, apiKey, provider });
|
||||
const requestEndpoint = protocol === "routin-plan" ? normalizePlanEndpoint(endpoint) : normalizeOpenAiEndpoint(endpoint);
|
||||
const evidence = hits.slice(0, 8).map((hit, index) => `[${index + 1}]\n来源：${hit.docPath}\n章节：${hit.sectionTitle}\n内容：${hit.snippet}`).join("\n\n");
|
||||
const systemPrompt = "你是如愿知识库问答助手。你只能基于提供的知识库片段回答，不得使用外部知识，不得编造文档中没有的内容。如果证据不足，请只回答：暂无该需求描述。回答末尾必须列出来源文件。";
|
||||
const userPrompt = `用户问题：\n${question}\n\n检索证据：\n${evidence}\n\n请用中文结构化回答。`;
|
||||
const messages = protocol === "routin-plan"
|
||||
? [{ role: "user", content: `${systemPrompt}\n\n${userPrompt}` }]
|
||||
: [{ role: "system", content: systemPrompt }, { role: "user", content: userPrompt }];
|
||||
const body = protocol === "routin-plan"
|
||||
? { model, messages, max_tokens: 4096, temperature: 0.7 }
|
||||
: { model, messages, temperature: 0.1, stream: false };
|
||||
const controller = new AbortController();
|
||||
const timer = setTimeout(() => controller.abort(), timeoutMs);
|
||||
const startedAt = Date.now();
|
||||
try {
|
||||
const response = await fetch(requestEndpoint, {
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
...(apiKey ? { Authorization: `Bearer ${apiKey}` } : {}),
|
||||
},
|
||||
body: JSON.stringify(body),
|
||||
signal: controller.signal,
|
||||
});
|
||||
const text = await response.text();
|
||||
let data = null;
|
||||
try { data = text ? JSON.parse(text) : null; } catch {}
|
||||
if (!response.ok) {
|
||||
const remoteMessage = data?.error?.message || data?.message || text.slice(0, 500);
|
||||
const err = new Error(`模型接口返回 ${response.status} ${response.statusText}${remoteMessage ? `：${remoteMessage}` : ""}`);
|
||||
err.status = response.status;
|
||||
err.endpoint = requestEndpoint;
|
||||
err.elapsedMs = Date.now() - startedAt;
|
||||
err.protocol = protocol;
|
||||
throw err;
|
||||
}
|
||||
const answer = extractAnswer(data, protocol);
|
||||
if (typeof answer !== "string" || !answer.trim()) {
|
||||
const err = new Error("模型返回成功，但响应结构里没有可解析的回答内容。支持 OpenAI choices[0].message.content、RoutIn content[]/content、message.content / response / answer。原始返回：" + text.slice(0, 500));
|
||||
err.endpoint = requestEndpoint;
|
||||
err.elapsedMs = Date.now() - startedAt;
|
||||
err.protocol = protocol;
|
||||
throw err;
|
||||
}
|
||||
return { answer: answer.trim(), endpoint: requestEndpoint, elapsedMs: Date.now() - startedAt, rawShape: Object.keys(data || {}), protocol };
|
||||
} finally {
|
||||
clearTimeout(timer);
|
||||
}
|
||||
}
|
||||
|
||||
async function readBody(req) {
|
||||
const chunks = [];
|
||||
for await (const chunk of req) chunks.push(chunk);
|
||||
const raw = Buffer.concat(chunks).toString("utf8");
|
||||
return raw ? JSON.parse(raw) : {};
|
||||
}
|
||||
|
||||
async function handleApi(req, res, url) {
|
||||
if (req.method === "OPTIONS") return sendJson(res, 200, { ok: true });
|
||||
if (url.pathname === "/api/rag/health") {
|
||||
const graph = loadGraph();
|
||||
return sendJson(res, 200, { ok: true, graphPath, staticRoot, documentCount: (graph.nodes || []).length, chunkCount: chunksCache.length, llmProxy: true });
|
||||
}
|
||||
if (url.pathname === "/api/rag/search" && req.method === "POST") {
|
||||
const body = await readBody(req);
|
||||
const { query, hits } = searchChunks(body.query || "", body.topK || 16);
|
||||
const decision = evidenceDecision(body.query || "", hits);
|
||||
return sendJson(res, 200, { ok: true, query, decision, answer: localAnswer(body.query || "", hits), hits });
|
||||
}
|
||||
if (url.pathname === "/api/rag/answer" && req.method === "POST") {
|
||||
const body = await readBody(req);
|
||||
const requestStatus = { stage: "search", startedAt: new Date().toISOString() };
|
||||
const { query, hits } = searchChunks(body.query || "", body.topK || 16);
|
||||
const decision = evidenceDecision(body.query || "", hits);
|
||||
if (!decision.allowed) return sendJson(res, 200, { ok: true, mode: "llm", requestStatus: { ...requestStatus, stage: "evidence_rejected" }, query, decision, answer: "暂无该需求描述。", hits });
|
||||
if (!body.llm?.enabled) return sendJson(res, 200, { ok: true, mode: "local", requestStatus: { ...requestStatus, stage: "local_answer" }, query, decision, answer: localAnswer(body.query || "", hits), hits });
|
||||
try {
|
||||
const result = await callLlm({ endpoint: body.llm.endpoint, model: body.llm.model, apiKey: body.llm.apiKey, question: body.query || "", hits });
|
||||
return sendJson(res, 200, { ok: true, mode: "llm", requestStatus: { ...requestStatus, stage: "llm_done", endpoint: result.endpoint, elapsedMs: result.elapsedMs, rawShape: result.rawShape }, query, decision, answer: result.answer, hits });
|
||||
} catch (error) {
|
||||
return sendJson(res, 502, {
|
||||
ok: false,
|
||||
mode: "llm",
|
||||
requestStatus: { ...requestStatus, stage: "llm_failed", endpoint: error.endpoint || (detectProvider({ endpoint: body.llm?.endpoint, apiKey: body.llm?.apiKey, provider: body.llm?.provider }) === "routin-plan" ? normalizePlanEndpoint(body.llm?.endpoint) : normalizeOpenAiEndpoint(body.llm?.endpoint)), elapsedMs: error.elapsedMs, protocol: error.protocol },
|
||||
query,
|
||||
decision,
|
||||
answer: localAnswer(body.query || "", hits),
|
||||
hits,
|
||||
error: {
|
||||
message: error instanceof Error ? error.message : String(error),
|
||||
name: error?.name,
|
||||
status: error?.status,
|
||||
},
|
||||
});
|
||||
}
|
||||
}
|
||||
if (url.pathname === "/api/llm/test" && req.method === "POST") {
|
||||
const body = await readBody(req);
|
||||
try {
|
||||
const result = await callLlm({ endpoint: body.endpoint, model: body.model, apiKey: body.apiKey, question: "连接测试", hits: [{ docPath: "测试", sectionTitle: "连接测试", snippet: "请只回答：连接成功。", score: 100 }] });
|
||||
return sendJson(res, 200, { ok: true, endpoint: result.endpoint, elapsedMs: result.elapsedMs, answer: result.answer });
|
||||
} catch (error) {
|
||||
return sendJson(res, 502, { ok: false, endpoint: error.endpoint || (detectProvider({ endpoint: body.endpoint, apiKey: body.apiKey, provider: body.provider }) === "routin-plan" ? normalizePlanEndpoint(body.endpoint) : normalizeOpenAiEndpoint(body.endpoint)), protocol: error.protocol, elapsedMs: error.elapsedMs, error: { message: error instanceof Error ? error.message : String(error), name: error?.name, status: error?.status } });
|
||||
}
|
||||
}
|
||||
return sendJson(res, 404, { ok: false, error: "API not found" });
|
||||
}
|
||||
|
||||
function serveStatic(req, res, url) {
|
||||
let pathname = decodeURIComponent(url.pathname);
|
||||
if (pathname === "/") pathname = "/index.html";
|
||||
const requested = path.normalize(path.join(staticRoot, pathname));
|
||||
if (!requested.startsWith(staticRoot)) {
|
||||
res.writeHead(403);
|
||||
res.end("Forbidden");
|
||||
return;
|
||||
}
|
||||
const filePath = fs.existsSync(requested) && fs.statSync(requested).isFile() ? requested : path.join(staticRoot, "index.html");
|
||||
const ext = path.extname(filePath).toLowerCase();
|
||||
res.writeHead(200, { "Content-Type": MIME[ext] || "application/octet-stream", "Cache-Control": ext === ".html" ? "no-store" : "no-cache" });
|
||||
fs.createReadStream(filePath).pipe(res);
|
||||
}
|
||||
|
||||
const server = http.createServer(async (req, res) => {
|
||||
const url = new URL(req.url || "/", `http://${req.headers.host || "localhost"}`);
|
||||
try {
|
||||
if (url.pathname.startsWith("/api/")) return await handleApi(req, res, url);
|
||||
return serveStatic(req, res, url);
|
||||
} catch (error) {
|
||||
return sendJson(res, 500, { ok: false, error: error instanceof Error ? error.message : String(error) });
|
||||
}
|
||||
});
|
||||
|
||||
server.listen(port, host, () => {
|
||||
loadGraph();
|
||||
console.log(`RAG dashboard server listening on http://${host}:${port}`);
|
||||
console.log(`Static root: ${staticRoot}`);
|
||||
console.log(`Graph path: ${graphPath}`);
|
||||
});
|
||||
Reference in New Issue
Block a user