Gitea
299 lines
8.7 KiB
JavaScript
299 lines
8.7 KiB
JavaScript
require("dotenv").config();
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const postgre = require('../database/postgre');
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const express = require("express");
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const cors = require("cors");
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const OpenAI = require("openai");
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const { pipeline } = require("@xenova/transformers");
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const { QdrantClient } = require("@qdrant/js-client-rest");
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const CONFIG = {
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azure: {
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endpoint: process.env.AZURE_OPENAI_ENDPOINT,
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deployment: process.env.AZURE_DEPLOYMENT,
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apiVersion: process.env.AZURE_API_VERSION,
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apiKey: process.env.AZURE_OPENAI_KEY,
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},
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qdrant: {
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url: process.env.QDRANT_URL,
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collection: process.env.QDRANT_COLLECTION,
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},
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search: {
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topK: 20,
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minScore: 0.10,
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maxContextDocs: 10,
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},
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port: process.env.PORT || 5000,
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};
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// ─── Clients ──────────────────────────────────────────────────────────────────
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const llm = new OpenAI({
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baseURL: `${CONFIG.azure.endpoint}/openai/deployments/${CONFIG.azure.deployment}`,
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apiKey: CONFIG.azure.apiKey,
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defaultHeaders: { "api-key": CONFIG.azure.apiKey },
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defaultQuery: { "api-version": CONFIG.azure.apiVersion },
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});
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const qdrant = new QdrantClient({
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url: CONFIG.qdrant.url,
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checkCompatibility: false,
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timeout: 30000,
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});
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// ─── Embedding model (singleton, lazy-init) ───────────────────────────────────
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let _embedder = null;
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async function getEmbedder() {
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if (!_embedder) {
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console.log("Loading MiniLM model...");
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_embedder = await pipeline("feature-extraction", "Xenova/all-MiniLM-L6-v2");
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console.log("Embedding model ready");
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}
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return _embedder;
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}
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async function createEmbedding(text) {
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const model = await getEmbedder();
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const out = await model(text, { pooling: "mean", normalize: true });
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return Array.from(out.data);
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}
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// ─── Qdrant search ────────────────────────────────────────────────────────────
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async function searchQdrant(embedding, { topK, minScore, maxContextDocs } = CONFIG.search) {
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const results = await qdrant.search(CONFIG.qdrant.collection, {
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vector: embedding,
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limit: topK,
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with_payload: true,
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score_threshold: minScore,
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});
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console.log(`Qdrant returned ${results.length} results (threshold: ${minScore})`);
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return results
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.sort((a, b) => b.score - a.score)
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.slice(0, maxContextDocs);
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}
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// ─── Build LLM context string ─────────────────────────────────────────────────
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function buildContext(results) {
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return results
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.map((item, i) =>
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`[${i + 1}] File: ${item.payload?.file ?? "unknown"} | Page: ${item.payload?.page ?? "?"}\n${item.payload?.text ?? ""}`
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)
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.join("\n\n---\n\n");
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}
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const SYSTEM_PROMPT = `
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You are CPM AI Assistant.
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Rules:
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- Answer only from the provided information.
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- If the answer is not available, reply exactly:
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"❌ I could not find this information in the uploaded documents."
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- Do not make up information.
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- Do not mention documents, context, or chunks.
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- Reply in the same language and style as the user's question.
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- If the user asks in Hindi, answer in Hindi.
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- If the user asks in Hinglish, answer in Hinglish.
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- If the user asks in English, answer in English.
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Response Style:
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- Use simple and easy-to-understand language.
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- Keep answers short and clear.
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- Use headings and bullet points when helpful.
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- Highlight important words in **bold**.
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Format:
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# 📋 Topic
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## 🎯 Summary
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Short answer in the user's language.
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## ✅ Details
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- Point 1
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- Point 2
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- Point 3
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## ⚠️ Notes
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- Extra information (if available).
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`.trim();
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async function askLLM(question, context) {
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const completion = await llm.chat.completions.create({
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model: CONFIG.azure.deployment,
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temperature: 0,
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max_tokens: 1500,
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "user", content: `Context:\n${context}\n\nQuestion:\n${question}` },
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],
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});
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return completion.choices[0].message.content;
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}
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const health = async (req, res) => {
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res.json({ status: "ok", model: CONFIG.azure.deployment, collection: CONFIG.qdrant.collection });
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}
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const ask = async (req, res) => {
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const { question } = req.body ?? {};
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if (!question?.trim()) {
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return res.status(400).json({ success: false, error: "question is required" });
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}
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const t0 = Date.now();
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try {
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const embedding = await createEmbedding(question.trim());
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const results = await searchQdrant(embedding);
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if (!results.length) {
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return res.json({
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success: true,
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question,
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answer: "❌ I could not find this information in the uploaded documents.",
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sources: [],
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ms: Date.now() - t0,
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});
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}
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const context = buildContext(results);
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const answer = await askLLM(question, context);
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return res.json({
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success: true,
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question,
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answer,
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sources: results.map(r => ({
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score: +r.score.toFixed(4),
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file: r.payload?.file,
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page: r.payload?.page,
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chunk: r.payload?.chunk,
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})),
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ms: Date.now() - t0,
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});
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} catch (err) {
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console.error("❌ /ask error:", err);
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return res.status(500).json({ success: false, error: err.message });
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}
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}
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const askstream = async (req, res) => {
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const { question } = req.body ?? {};
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const user_id = req.user.id;
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console.log("Received question:", user_id);
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if (!question?.trim()) {
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return res.status(400).json({ success: false, error: "question is required" });
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}
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res.setHeader("Content-Type", "text/event-stream");
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res.setHeader("Cache-Control", "no-cache");
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res.setHeader("Connection", "keep-alive");
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res.flushHeaders();
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const send = (event, data) => res.write(`event: ${event}\ndata: ${JSON.stringify(data)}\n\n`);
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try {
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send("status", { message: "🔍 Searching documents..." });
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const embedding = await createEmbedding(question.trim());
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const results = await searchQdrant(embedding);
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if (!results.length) {
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send("token", { token: "❌", isWord: true });
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send("token", { token: "I", isWord: true });
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send("token", { token: "could", isWord: true });
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send("token", { token: "not", isWord: true });
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send("token", { token: "find", isWord: true });
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send("token", { token: "this", isWord: true });
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send("token", { token: "information", isWord: true });
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send("token", { token: "in", isWord: true });
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send("token", { token: "the", isWord: true });
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send("token", { token: "uploaded", isWord: true });
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send("token", { token: "documents.", isWord: true });
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send("done", { sources: [] });
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const result = await postgre.query(
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'insert into useraskquestion (user_id, questions) values ($1, $2)',
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[user_id, question]
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);
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return res.end();
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}
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const result = await postgre.query(
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'insert into useraskquestion (user_id, questions,status) values ($1, $2, $3)',
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[user_id, question,'1']
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);
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const sources = results.map(r => ({
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score: +r.score.toFixed(4),
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file: r.payload?.file,
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page: r.payload?.page,
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chunk: r.payload?.chunk,
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}));
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send("sources", { sources });
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send("status", { message: "💬 Generating answer..." });
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const context = buildContext(results);
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const stream = await llm.chat.completions.create({
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model: CONFIG.azure.deployment,
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temperature: 0,
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max_tokens: 1500,
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stream: true,
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messages: [
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{ role: "system", content: SYSTEM_PROMPT },
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{ role: "user", content: `Context:\n${context}\n\nQuestion:\n${question}` },
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],
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});
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// Buffer to handle tokens that may be split mid-word
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let wordBuffer = "";
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for await (const chunk of stream) {
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const rawToken = chunk.choices[0]?.delta?.content ?? "";
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if (!rawToken) continue;
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wordBuffer += rawToken;
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// Split on whitespace — emit complete words, keep trailing partial
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// We preserve newlines/markdown as separate tokens so markdown renders correctly
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const parts = wordBuffer.split(/(\s+)/);
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// Last element might be an incomplete word — buffer it
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wordBuffer = parts.pop() ?? "";
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for (const part of parts) {
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if (part) {
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send("token", { token: part, isWord: /\S/.test(part) });
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}
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}
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}
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// Flush any remaining buffered text
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if (wordBuffer) {
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send("token", { token: wordBuffer, isWord: true });
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}
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send("done", { sources });
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} catch (err) {
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console.error("❌ /ask/stream error:", err);
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send("error", { error: err.message });
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}
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res.end();
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}
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module.exports = { ask, askstream, health };
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