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backup/server_email_v1.js

@@ -0,0 +1,84 @@
+import express from 'express';
+import { SystemMessage, HumanMessage, Runnable, LlamaCppLLM } from './src/index.js';
+import bodyParser from 'body-parser';
+import path from 'path';
+import fs from 'fs';
+
+// Classify Logic
+class EmailClassifierRunnable extends Runnable {
+    constructor(llm) {
+        super();
+        this.llm = llm;
+    }
+    async _call(input, config) {
+        // Mock fallback if model fails
+        if (!this.llm) return { category: "Error", confidence: 0, reason: "Model not initialized" };
+        
+        const messages = this._buildPrompt(input);
+        const response = await this.llm.invoke(messages, config);
+        return this._parseClassification(response.content);
+    }
+    _buildPrompt(email) {
+        return [
+            new SystemMessage(`You are an email classification assistant. Classify into: Spam, Invoice, Meeting Request, Urgent, Personal, Other. Respond in JSON like {"category": "X", "confidence": 0.9, "reason": "Y"}.`),
+            new HumanMessage(`Classify:\nSubject: ${email.subject}\nBody: ${email.body}`)
+        ];
+    }
+    _parseClassification(response) {
+        try {
+            const jsonMatch = response.match(/\{[\s\S]*\}/);
+            if (!jsonMatch) throw new Error('No JSON found');
+            return JSON.parse(jsonMatch[0]);
+        } catch (e) { return { category: 'Other', confidence: 0, reason: 'Failed to parse JSON', raw: response }; }
+    }
+}
+
+const app = express();
+const PORT = 7860;
+
+app.use(bodyParser.json());
+
+let classifier = null;
+
+async function initModel() {
+    try {
+        console.log("Loading model...");
+        // Ensure model exists
+        if (!fs.existsSync('./models/Qwen3-1.7B-Q8_0.gguf')) {
+            console.error("Model file missing!");
+            return;
+        }
+
+        const llm = new LlamaCppLLM({
+            modelPath: './models/Qwen3-1.7B-Q8_0.gguf',
+            temperature: 0.1,
+            maxTokens: 200
+        });
+        
+        // Warmup
+        await llm.invoke("Hi");
+        
+        classifier = new EmailClassifierRunnable(llm);
+        console.log("Model loaded successfully!");
+    } catch (err) {
+        console.error("Failed to load model:", err);
+    }
+}
+
+app.post('/classify', async (req, res) => {
+    if (!classifier) return res.status(503).json({ error: "Model loading or failed" });
+    try {
+        const { subject, body } = req.body;
+        const result = await classifier.invoke({ subject, body, from: 'api' });
+        res.json(result);
+    } catch (error) {
+        res.status(500).json({ error: error.message });
+    }
+});
+
+app.get('/', (req, res) => res.send('AI Email Classifier Running. POST /classify to use.'));
+
+app.listen(PORT, '0.0.0.0', () => {
+    console.log(`Server listening on ${PORT}`);
+    initModel();
+});

server.js

@@ -1,82 +1,113 @@
 import express from 'express';
-import { SystemMessage, HumanMessage, Runnable, LlamaCppLLM } from './src/index.js';
+import { SystemMessage, HumanMessage, AIMessage, LlamaCppLLM } from './src/index.js';
 import bodyParser from 'body-parser';
-import path from 'path';
 import fs from 'fs';
+import path from 'path';
 
-// Classify Logic
-class EmailClassifierRunnable extends Runnable {
-    constructor(llm) {
-        super();
-        this.llm = llm;
-    }
-    async _call(input, config) {
-        // Mock fallback if model fails
-        if (!this.llm) return { category: "Error", confidence: 0, reason: "Model not initialized" };
-        
-        const messages = this._buildPrompt(input);
-        const response = await this.llm.invoke(messages, config);
-        return this._parseClassification(response.content);
-    }
-    _buildPrompt(email) {
-        return [
-            new SystemMessage(`You are an email classification assistant. Classify into: Spam, Invoice, Meeting Request, Urgent, Personal, Other. Respond in JSON like {"category": "X", "confidence": 0.9, "reason": "Y"}.`),
-            new HumanMessage(`Classify:\nSubject: ${email.subject}\nBody: ${email.body}`)
-        ];
-    }
-    _parseClassification(response) {
-        try {
-            const jsonMatch = response.match(/\{[\s\S]*\}/);
-            if (!jsonMatch) throw new Error('No JSON found');
-            return JSON.parse(jsonMatch[0]);
-        } catch (e) { return { category: 'Other', confidence: 0, reason: 'Failed to parse JSON', raw: response }; }
-    }
-}
-
-const app = express();
+// --- CONFIGURATION ---
 const PORT = 7860;
+const LOG_FILE = 'activity.log';
+const MODEL_PATH = './models/Qwen3-1.7B-Q8_0.gguf';
 
-app.use(bodyParser.json());
+// --- LOGGING UTILS ---
+function log(type, content) {
+    const timestamp = new Date().toISOString();
+    const entry = `[${timestamp}] [${type}] ${JSON.stringify(content)}
+`;
+    console.log(entry.trim());
+    fs.appendFileSync(LOG_FILE, entry);
+}
 
-let classifier = null;
+// --- AI ENGINE ---
+let llm = null;
+let defaultSystemPrompt = "You are a helpful and intelligent AI assistant. You answer concisely and accurately.";
 
 async function initModel() {
     try {
-        console.log("Loading model...");
-        // Ensure model exists
-        if (!fs.existsSync('./models/Qwen3-1.7B-Q8_0.gguf')) {
-            console.error("Model file missing!");
+        log('SYSTEM', 'Loading model...');
+        if (!fs.existsSync(MODEL_PATH)) {
+            log('ERROR', `Model not found at ${MODEL_PATH}`);
             return;
         }
 
-        const llm = new LlamaCppLLM({
-            modelPath: './models/Qwen3-1.7B-Q8_0.gguf',
-            temperature: 0.1,
-            maxTokens: 200
+        llm = new LlamaCppLLM({
+            modelPath: MODEL_PATH,
+            temperature: 0.7, // Higher for creativity
+            maxTokens: 1024   // Longer responses
         });
         
         // Warmup
-        await llm.invoke("Hi");
-        
-        classifier = new EmailClassifierRunnable(llm);
-        console.log("Model loaded successfully!");
+        await llm.invoke("Hello");
+        log('SYSTEM', 'Model loaded successfully. Ready to serve.');
     } catch (err) {
-        console.error("Failed to load model:", err);
+        log('FATAL', err.message);
     }
 }
 
-app.post('/classify', async (req, res) => {
-    if (!classifier) return res.status(503).json({ error: "Model loading or failed" });
+// --- SERVER SETUP ---
+const app = express();
+app.use(bodyParser.json());
+
+// 1. CHAT ENDPOINT (Main Interaction)
+app.post('/chat', async (req, res) => {
+    if (!llm) return res.status(503).json({ error: "System initializing..." });
+
     try {
-        const { subject, body } = req.body;
-        const result = await classifier.invoke({ subject, body, from: 'api' });
-        res.json(result);
+        const { message, history = [], system_instruction } = req.body;
+        
+        // Construct Message Chain
+        const messages = [];
+        
+        // 1. System Prompt (Training/Instruction)
+        messages.push(new SystemMessage(system_instruction || defaultSystemPrompt));
+
+        // 2. Chat History (Short-term memory)
+        history.forEach(msg => {
+            if (msg.role === 'user') messages.push(new HumanMessage(msg.content));
+            if (msg.role === 'ai') messages.push(new AIMessage(msg.content));
+        });
+
+        // 3. Current Message
+        messages.push(new HumanMessage(message));
+
+        log('REQUEST', { message, instruction: system_instruction || "default" });
+
+        // Invoke AI
+        const response = await llm.invoke(messages);
+        
+        log('RESPONSE', response.content);
+        res.json({ reply: response.content });
+
     } catch (error) {
+        log('ERROR', error.message);
         res.status(500).json({ error: error.message });
     }
 });
 
-app.get('/', (req, res) => res.send('AI Email Classifier Running. POST /classify to use.'));
+// 2. CONFIG ENDPOINT (Set default behavior)
+app.post('/config', (req, res) => {
+    const { system_prompt, temperature } = req.body;
+    if (system_prompt) {
+        defaultSystemPrompt = system_prompt;
+        log('CONFIG', `Updated default system prompt to: ${system_prompt.substring(0, 50)}...`);
+    }
+    // Note: Temperature change requires LLM reload in current simple wrapper, skipping for now
+    res.json({ success: true, current_system_prompt: defaultSystemPrompt });
+});
+
+// 3. LOGS ENDPOINT (Realtime Monitoring)
+app.get('/logs', (req, res) => {
+    if (fs.existsSync(LOG_FILE)) {
+        const logs = fs.readFileSync(LOG_FILE, 'utf-8');
+        // Return last 100 lines
+        const lines = logs.trim().split('\n').slice(-100).join('\n');
+        res.send(`<pre>${lines}</pre>`);
+    } else {
+        res.send('No logs yet.');
+    }
+});
+
+app.get('/', (req, res) => res.send('AI Agent v2 (Chat Mode) Running.'));
 
 app.listen(PORT, '0.0.0.0', () => {
     console.log(`Server listening on ${PORT}`);