Integrate dynamic AI agent with Ollama

backend/botLogic.js

@@ -10,21 +10,29 @@ class BotLogic {
     }
 
     async handleMessage(patientId, message) {
-        const context = this.states[patientId] || {};
+        if (!this.states[patientId]) {
+            this.states[patientId] = { transcript: [] };
+        }
+        
+        const context = this.states[patientId];
+        context.transcript.push({ role: 'user', content: message });
         
         // Delegate to the Agent
-        const response = await clinicAgent.processMessage(patientId, message, context);
+        const response = await clinicAgent.processMessage(patientId, context.transcript);
         
-        // Update local context/state based on agent response
+        // Update context based on agent response
-        if (response.nextStep) {
+        if (response.reply) {
-            this.states[patientId] = { lastStep: response.nextStep };
+            context.transcript.push({ role: 'assistant', content: response.reply });
-        } else {
+        }
-            // Reset or keep context as needed
+        
+        // Trim history to last 10 messages
+        if (context.transcript.length > 10) {
+            context.transcript = context.transcript.slice(-10);
         }
 
         return {
             reply: response.reply,
-            buttons: response.buttons || []
+            buttons: [] // Deprecated in favor of dynamic conversation
         };
     }
 }

backend/clinicAgent.js

@@ -7,110 +7,85 @@ const patientManager = require('./patientManager');
 const queueManager = require('./queueManager');
 const pharmacyAgent = require('./pharmacyAgent');
 const configManager = require('./configManager');
+const { OpenAI } = require('openai');
+
+const openai = new OpenAI({
+    baseURL: process.env.LLM_BASE_URL || 'http://ollama.ai-core.svc.cluster.local:11434/v1',
+    apiKey: process.env.LLM_API_KEY || 'ollama', // Ollama doesn't require a real key
+});
+const LLM_MODEL = process.env.LLM_MODEL || 'qwen2.5:3b';
 
 class ClinicAgent {
     constructor() {
         this.systemPrompt = `
         You are 'Curio AI', the assistant for Curio Health.
-        Your goal is to help patients book appointments, manage family members, and check clinic status.
+        Your goal is to help patients book appointments.
+        Be extremely concise, friendly, and natural. Do not use markdown.
+        To book an appointment, you MUST collect:
+        1. Department (e.g. Cardiology, Neurology, General)
+        2. Date (Format as YYYY-MM-DD or today/tomorrow)
+        3. Time (Format as HH:MM AM/PM)
 
-        TOOLS:
+        If any of these 3 pieces of information are missing, naturally ask the user for them.
-        - list_family: Shows all members linked to the phone.
+        Do NOT output JSON until you have ALL THREE pieces of information.
-        - add_member: Registers a new family member name.
-        - book_today: Books an appointment for today.
-        - check_status: Checks current queue position.
         
-        RULES:
+        If you have all three pieces of information, you MUST reply with ONLY a JSON block like this (no other text):
-        1. Always be polite and professional.
+        {"action": "BOOK", "department": "Cardiology", "date": "2026-05-26", "time": "10:00"}
-        2. If a patient asks for someone not in their family list, offer to add them.
-        3. If multiple people have same name, ask for clarification.
         `;
     }
 
-    async processMessage(phone, message, context = {}) {
+    async processMessage(phone, transcript) {
-        const text = message.toLowerCase();
+        console.log(`[Agent] Processing for ${phone} via Ollama`);
-        console.log(`[Agent] Processing for ${phone}: "${message}"`);
 
-        // Mock LLM Intent Extraction & Tool Execution
+        try {
-        // In a real app, this would be: const response = await llm.call({ prompt: ..., tools: [...] });
+            const response = await openai.chat.completions.create({
+                model: LLM_MODEL,
+                messages: [
+                    { role: 'system', content: this.systemPrompt },
+                    ...transcript
+                ],
+                temperature: 0.2,
+            });
 
-        const family = patientManager.getProfiles(phone);
+            const replyContent = response.choices[0].message.content.trim();
 
-        // Scenario 1: Greeting + Profile Check
+            // Try to parse JSON intent
-        if (text.includes("hi") || text.includes("hello")) {
+            try {
-            if (family.length === 0) {
+                // Remove potential markdown code blocks if the LLM wrapped it
-                return {
+                const cleanJson = replyContent.replace(/```json/g, '').replace(/```/g, '').trim();
-                    reply: "Hello! I'm Curio AI. I don't see a profile for this number yet. What is your full name so I can get you started?",
+                const intent = JSON.parse(cleanJson);
-                    nextStep: "AWAIT_REGISTRATION"
-                };
-            }
-            return {
-                reply: `Hello again! I see profiles for ${family.map(p => p.name).join(', ')}. Who can I help you with today?`,
-                buttons: [...family.map(p => p.name), "+ Add New Member"]
-            };
-        }
                 
-        // Scenario 2: Adding a Member
+                if (intent.action === 'BOOK') {
-        if (context.lastStep === "AWAIT_REGISTRATION" || text.includes("add new member")) {
+                    // Extract HH:MM
-            if (text.includes("add new member")) {
+                    let timeVal = "10:00"; // fallback
-                return { reply: "Sure! What is the name of the family member you'd like to add?" };
+                    const timeMatch = intent.time.match(/(\d{1,2}:\d{2})/);
-            }
+                    if (timeMatch) timeVal = timeMatch[1];
-            const newId = patientManager.addProfile(phone, { name: message, relation: "Family" });
-            return {
-                reply: `Perfect! I've added ${message} to your family account. Would you like to book an appointment for them today?`,
-                buttons: ["Book for Today", "Check Queue"]
-            };
-        }
                     
-        // Scenario 3: Booking Intent
+                    // Parse date
-        if (text.includes("book") || text.includes("today")) {
+                    let dateVal = new Date().toISOString().split('T')[0];
-            return {
+                    if (intent.date && intent.date.match(/^\d{4}-\d{2}-\d{2}$/)) {
-                reply: "I can help with that. Which time works best for you?",
+                        dateVal = intent.date;
-                buttons: ["10:00 AM", "11:00 AM", "06:00 PM"],
+                    }
-                nextStep: "AWAIT_TIME"
-            };
-        }
                     
-        // Scenario 3.1: Handling time selection
+                    // Book the token using Dr. Sharma's Cardiology ("sharma-clinic")
-        if (context.lastStep === "AWAIT_TIME") {
+                    // In a real system, we'd map intent.department to tenantId
-            let time = "";
+                    const result = await queueManager.bookOnline(dateVal, timeVal, phone, 'sharma-clinic');
-            if (text.includes("1") || text.includes("10")) time = "10:00";
+                    if (result.success) {
-            else if (text.includes("2") || text.includes("11")) time = "11:00";
+                        return { reply: `Awesome! Your appointment for ${intent.department} is booked. Your token is ${result.token}. You should receive a confirmation message shortly.` };
-            else if (text.includes("3") || text.includes("6") || text.includes("06")) time = "18:00";
+                    } else {
-
+                        return { reply: `Sorry, ${result.message}` };
-            if (time) {
+                    }
-                const today = new Date().toISOString().split('T')[0];
-                const result = await queueManager.bookOnline(today, time, phone, 'sharma-clinic');
-                if (result.success) {
-                    return { reply: `Awesome! Your token is ${result.token}. You should receive a confirmation message shortly.` };
-                } else {
-                    return { reply: `Sorry, ${result.message}` };
                 }
+            } catch (e) {
+                // Not JSON, just regular conversational text
+                return { reply: replyContent };
             }
-        }
             
-        // Scenario 3.5: Medicine Query
+            return { reply: replyContent };
-        if (text.includes("have") || text.includes("medicine") || text.includes("syrup") || text.includes("tablet")) {
+        } catch (error) {
-            const pharmacyReply = pharmacyAgent.handlePatientQuery(message);
+            console.error('[ClinicAgent] LLM Error:', error.message);
-            return {
+            return { reply: "I'm having trouble connecting to my AI brain right now. Please try again in a moment." };
-                reply: "💊 *Pharmacy Update*: " + pharmacyReply,
-                buttons: ["Book for Today", "Check Other Meds"]
-            };
         }
-
-        // Scenario 4: Natural Language Match for Family Members
-        const matchedMember = family.find(p => text.includes(p.name.toLowerCase()));
-        if (matchedMember) {
-            return {
-                reply: `I've switched context to ${matchedMember.name}. What would you like to do for them?`,
-                buttons: ["Book for Today", "View History"]
-            };
-        }
-
-        // Fallback to "Smart" LLM reply
-        return {
-            reply: "I'm not sure how to help with that yet, but I'm learning! You can ask me to book an appointment or add a family member."
-        };
     }
 }
 

k8s/app.yaml

@@ -34,6 +34,10 @@ spec:
           value: "rN5RpKAbFVCv0G45ehluf1JbTWt2dgNW"
         - name: TWILIO_WHATSAPP_FROM
           value: "whatsapp:+14155238886"
+        - name: LLM_BASE_URL
+          value: "http://ollama.ai-core.svc.cluster.local:11434/v1"
+        - name: LLM_MODEL
+          value: "qwen2.5:3b"
         livenessProbe:
           httpGet:
             path: /

node_modules/.package-lock.json

@@ -1163,6 +1163,27 @@
         "node": ">= 0.8"
       }
     },
+    "node_modules/openai": {
+      "version": "6.39.0",
+      "resolved": "https://registry.npmjs.org/openai/-/openai-6.39.0.tgz",
+      "integrity": "sha512-O61LIsimY3acVabwvomwFhwrnN36yvHY2quIfy9keEcFytGgWeV35yLHQ6NVMLSBxRpHmcg2yuhCnlu2HT4pLQ==",
+      "license": "Apache-2.0",
+      "bin": {
+        "openai": "bin/cli"
+      },
+      "peerDependencies": {
+        "ws": "^8.18.0",
+        "zod": "^3.25 || ^4.0"
+      },
+      "peerDependenciesMeta": {
+        "ws": {
+          "optional": true
+        },
+        "zod": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/parseurl": {
       "version": "1.3.3",
       "resolved": "https://registry.npmjs.org/parseurl/-/parseurl-1.3.3.tgz",

package-lock.json

@@ -13,6 +13,7 @@
         "dotenv": "^17.4.2",
         "express": "^4.18.2",
         "jsonwebtoken": "^9.0.3",
+        "openai": "^6.39.0",
         "pg": "^8.21.0",
         "twilio": "^6.0.2",
         "uuid": "^14.0.0"
@@ -1177,6 +1178,27 @@
         "node": ">= 0.8"
       }
     },
+    "node_modules/openai": {
+      "version": "6.39.0",
+      "resolved": "https://registry.npmjs.org/openai/-/openai-6.39.0.tgz",
+      "integrity": "sha512-O61LIsimY3acVabwvomwFhwrnN36yvHY2quIfy9keEcFytGgWeV35yLHQ6NVMLSBxRpHmcg2yuhCnlu2HT4pLQ==",
+      "license": "Apache-2.0",
+      "bin": {
+        "openai": "bin/cli"
+      },
+      "peerDependencies": {
+        "ws": "^8.18.0",
+        "zod": "^3.25 || ^4.0"
+      },
+      "peerDependenciesMeta": {
+        "ws": {
+          "optional": true
+        },
+        "zod": {
+          "optional": true
+        }
+      }
+    },
     "node_modules/parseurl": {
       "version": "1.3.3",
       "resolved": "https://registry.npmjs.org/parseurl/-/parseurl-1.3.3.tgz",

package.json

@@ -13,6 +13,7 @@
     "dotenv": "^17.4.2",
     "express": "^4.18.2",
     "jsonwebtoken": "^9.0.3",
+    "openai": "^6.39.0",
     "pg": "^8.21.0",
     "twilio": "^6.0.2",
     "uuid": "^14.0.0"