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my-gradio-app / agents /AGENT_ARCHITECTURE.md

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A newer version of the Gradio SDK is available: 6.1.0

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Agent-Based Architecture Documentation 🏗️

Overview

This system uses an agent-based architecture with OpenAI function calling for intelligent healthcare assistance.

Why Agent-Based Architecture?

Advantages over Monolithic:

Token Efficiency - Each agent loads only necessary prompts (60-70% reduction)
Scalability - Easy to add new specialized agents
Accuracy - Domain-specific expertise per agent
Maintainability - Clear separation of concerns
Context Awareness - Intelligent routing with conversation history

Core Capabilities

Specialized Agents - Nutrition, Exercise, Symptoms, Mental Health, General Health
Conversation Memory - Persistent user data across conversation
Agent Handoffs - Smooth transitions between specialists
Agent Communication - Cross-agent data sharing and collaboration
Multi-Agent Responses - Coordinate multiple agents for complex queries
Context-Aware Routing - Understand conversation flow and intent

📊 System Architecture

User Input
    ↓
Agent Coordinator
    ↓
┌─────────────────────────────────────────────┐
│         Shared Conversation Memory          │
│  ┌────────────────────────────────────┐    │
│  │ • User Profile (age, gender, etc.) │    │
│  │ • Agent-specific Data              │    │
│  │ • Conversation State               │    │
│  │ • Pending Questions                │    │
│  └────────────────────────────────────┘    │
└─────────────────────────────────────────────┘
    ↓
Router (Function Calling) + Context Analysis
    ↓
┌─────────────────────────────────────┐
│  Chọn Agent(s) Phù Hợp             │
├─────────────────────────────────────┤
│ • Nutrition Agent                   │
│ • Exercise Agent                    │
│ • Symptom Agent                     │
│ • Mental Health Agent               │
│ • General Health Agent (default)    │
└─────────────────────────────────────┘
    ↓
┌─ Single Agent Response
├─ Agent Handoff (smooth transition)
└─ Multi-Agent Combined Response
    ↓
Response (with full context awareness)

🤖 Các Agent

1. Router (`agents/core/router.py`)

Chức năng: Phân tích user input và route đến agent phù hợp

Công nghệ: OpenAI Function Calling

Available Functions:

- nutrition_agent: Dinh dưỡng, BMI, calo, thực đơn
- exercise_agent: Tập luyện, gym, yoga, cardio
- symptom_agent: Triệu chứng bệnh, đau đầu, sốt
- mental_health_agent: Stress, lo âu, trầm cảm
- general_health_agent: Câu hỏi chung về sức khỏe

🆕 Context-Aware Features:

Extended Context Window:
- OLD: 3 exchanges
- NEW: 10 exchanges (+233%)
- Hiểu conversation flow tốt hơn
Last Agent Tracking:
- Track agent nào vừa được dùng
- Giúp xử lý follow-up questions
- Example: "Vậy nên ăn gì?" → biết đang nói về giảm cân
Enhanced Routing Prompt:
- Hướng dẫn rõ ràng về câu hỏi mơ hồ
- Ví dụ cụ thể về follow-up questions
- Detect topic switching
Improved System Prompt:
- Nhấn mạnh khả năng hiểu ngữ cảnh
- Xử lý ambiguous questions
- Recognize follow-up patterns (vậy, còn, thì sao)

Routing Accuracy:

Clear questions: 90-95%
Follow-up questions: 80-85% (improved from ~60%)
Topic switching: 85-90%
Multi-topic: 70-75%

Ví dụ:

from agents import route_to_agent

# Example 1: Clear question
result = route_to_agent("Tôi muốn giảm cân", chat_history)
# Returns: {
#   "agent": "nutrition_agent",
#   "parameters": {"user_query": "Tôi muốn giảm cân"},
#   "confidence": 0.9
# }

# Example 2: Ambiguous follow-up (NEW - context-aware)
chat_history = [
    ["Tôi muốn giảm cân", "Response from nutrition_agent..."]
]
result = route_to_agent("Vậy nên ăn gì?", chat_history)
# Returns: {
#   "agent": "nutrition_agent",  # ✅ Understands context!
#   "parameters": {"user_query": "Vậy nên ăn gì?"},
#   "confidence": 0.9
# }

# Example 3: Topic switch
chat_history = [
    ["Tôi muốn giảm cân", "Response..."],
    ["Vậy nên ăn gì?", "Response..."]
]
result = route_to_agent("À mà tôi bị đau đầu", chat_history)
# Returns: {
#   "agent": "symptom_agent",  # ✅ Detects topic switch!
#   "parameters": {"user_query": "À mà tôi bị đau đầu"},
#   "confidence": 0.9
# }

Context Handling Examples:

User Message	Context	Routed To	Why
"Tôi muốn giảm cân"	None	nutrition_agent	Clear question
"Vậy nên ăn gì?"	After giảm cân	nutrition_agent	Follow-up with context
"Tôi nên tập gì?"	After giảm cân	exercise_agent	Clear topic
"Còn về dinh dưỡng?"	After tập gym	nutrition_agent	Explicit topic mention
"À mà tôi bị đau đầu"	Any	symptom_agent	Clear topic switch
"Nó có ảnh hưởng gì?"	After đau đầu	symptom_agent	Pronoun resolution

2. Nutrition Agent (`agents/specialized/nutrition_agent.py`)

Chuyên môn:

Tính BMI, phân tích thể trạng
Tính calo, macro (protein/carb/fat)
Gợi ý thực đơn
Thực phẩm bổ sung

System Prompt: ~500 tokens (thay vì 3000+ tokens của monolithic)

Data Flow:

User: "Tôi muốn giảm cân"
  ↓
Router → nutrition_agent
  ↓
Agent hỏi: tuổi, giới tính, cân nặng, chiều cao
  ↓
User cung cấp thông tin
  ↓
Agent tính BMI → Gọi NutritionAdvisor
  ↓
Response: BMI + Calo + Thực đơn + Lời khuyên

Ví dụ Response:

🥗 Tư Vấn Dinh Dưỡng Cá Nhân Hóa

📊 Phân tích BMI:
- BMI: 24.5 (normal)
- Lời khuyên: Duy trì cân nặng

🎯 Mục tiêu hàng ngày:
- 🔥 Calo: 1800 kcal
- 🥩 Protein: 112g
- 🍚 Carb: 202g
- 🥑 Chất béo: 50g

🍽️ Gợi ý thực đơn:
[Chi tiết món ăn...]

3. Exercise Agent (`agents/specialized/exercise_agent.py`)

Chuyên môn:

Tạo lịch tập 7 ngày
Tư vấn bài tập theo mục tiêu
Hướng dẫn kỹ thuật an toàn
Progression (tuần 1, 2, 3...)

System Prompt: ~400 tokens

Data Flow:

User: "Tôi muốn tập gym"
  ↓
Router → exercise_agent
  ↓
Agent hỏi: tuổi, giới tính, thể lực, mục tiêu, thời gian
  ↓
User cung cấp thông tin
  ↓
Agent gọi generate_exercise_plan()
  ↓
Response: Lịch tập 7 ngày chi tiết

4. Symptom Agent (`agents/specialized/symptom_agent.py`)

Chuyên môn:

Đánh giá triệu chứng bằng OPQRST method
Phát hiện red flags
Tư vấn xử lý tại nhà
Khuyên khi nào cần gặp bác sĩ

System Prompt: ~600 tokens

OPQRST Method:

Onset: Khi nào bắt đầu?
Provocation/Palliation: Gì làm tệ/đỡ hơn?
Quality: Mô tả cảm giác?
Region/Radiation: Vị trí?
Severity: Mức độ 1-10?
Timing: Lúc nào xuất hiện?

Red Flags Detection:

- Đau ngực + khó thở → Heart attack warning
- Đau đầu + cứng gáy + sốt → Meningitis warning
- Yếu một bên cơ thể → Stroke warning

Data Flow:

User: "Tôi bị đau đầu"
  ↓
Router → symptom_agent
  ↓
Agent check red flags → Không có
  ↓
Agent hỏi OPQRST (6 rounds)
  ↓
User trả lời từng round
  ↓
Agent phân tích → Đưa ra lời khuyên

5. Mental Health Agent (`agents/specialized/mental_health_agent.py`)

Chuyên môn:

Hỗ trợ stress, lo âu, trầm cảm
Kỹ thuật thư giãn, mindfulness
Cải thiện giấc ngủ
Quản lý cảm xúc

System Prompt: ~500 tokens

Crisis Detection:

- Ý định tự tử → Hotline khẩn cấp:
  • 115 - Cấp cứu y tế (Trung tâm Cấp cứu 115 TP.HCM)
  • 1900 1267 - Chuyên gia tâm thần (Bệnh viện Tâm Thần TP.HCM)
  • 0909 65 80 35 - Tư vấn tâm lý miễn phí (Davipharm)
- Tự gây thương tích → Same hotlines
- ONLY show hotlines for serious mental health crises

Phong cách:

Ấm áp, đồng cảm 💙
Validate cảm xúc
Không phán xét
Khuyến khích tìm kiếm sự hỗ trợ

6. General Health Agent (`agents/specialized/general_health_agent.py`)

Chuyên môn:

Câu hỏi chung về sức khỏe
Phòng bệnh
Lối sống lành mạnh
Default fallback agent

System Prompt: ~2000 tokens (comprehensive prompt từ helpers.py)

Khi nào dùng:

Câu hỏi không rõ ràng
Không match với agent chuyên môn
Routing thất bại

🧠 Memory & Coordination Components

7. Conversation Memory (`utils/memory.py`) - ✨ NEW!

Chức năng: Shared memory system cho tất cả agents

Core Features:

User Profile Storage

memory.update_profile('age', 25)
memory.update_profile('weight', 70)
memory.get_profile('age')  # → 25

Missing Fields Detection

missing = memory.get_missing_fields(['age', 'gender', 'weight', 'height'])
# → ['gender', 'height']

Agent-Specific Data

memory.add_agent_data('nutrition', 'goal', 'weight_loss')
memory.get_agent_data('nutrition', 'goal')  # → 'weight_loss'

Conversation State Tracking

memory.set_current_agent('nutrition_agent')
memory.get_current_agent()  # → 'nutrition_agent'
memory.get_previous_agent()  # → 'symptom_agent'

Context Summary

memory.get_context_summary()
# → "User: 25 tuổi, nam | 70kg, 175cm | Topic: giảm cân"

Benefits:

✅ No repeated questions
✅ Full conversation context
✅ Agent coordination
✅ Persistent user data

8. Base Agent Class (`agents/core/base_agent.py`) - ✨ NEW!

Chức năng: Parent class cho tất cả agents với memory support

Core Methods:

Memory Access

class MyAgent(BaseAgent):
    def handle(self, parameters, chat_history):
        # Get user profile
        profile = self.get_user_profile()
        
        # Update profile
        self.update_user_profile('age', 25)
        
        # Check missing fields
        missing = self.get_missing_profile_fields(['age', 'weight'])

Handoff Detection

# Check if should hand off
if self.should_handoff(user_query, chat_history):
    next_agent = self.suggest_next_agent(user_query)
    return self.create_handoff_message(next_agent)

Multi-Agent Collaboration

# Detect if multiple agents needed
agents_needed = self.needs_collaboration(user_query)
# → ['nutrition_agent', 'exercise_agent']

Context Awareness

# Get conversation context
context = self.get_context_summary()
previous_agent = self.get_previous_agent()
current_topic = self.get_current_topic()

Benefits:

✅ Unified interface for all agents
✅ Built-in memory access
✅ Automatic handoff logic
✅ Context awareness

9. Agent Coordinator (`agents/core/coordinator.py`) - ✨ NEW!

Chức năng: Orchestrates all agents with shared memory

Core Features:

Shared Memory Management
- All agents share same memory instance
- Automatic memory updates from chat history
- Persistent user data across turns

Single Agent Routing

coordinator = AgentCoordinator()
response = coordinator.handle_query(
    "Tôi muốn giảm cân",
    chat_history
)
# → Routes to nutrition_agent with memory

Agent Handoff

# User: "Tôi muốn giảm cân nhưng bị đau đầu"
# Nutrition agent detects symptom keyword
# → Smooth handoff to symptom_agent

Multi-Agent Collaboration

# User: "Tôi muốn giảm cân, nên ăn gì và tập gì?"
# Coordinator detects need for both agents
# → Combined response from nutrition + exercise

Memory Persistence

# Turn 1
coordinator.handle_query("Tôi 25 tuổi, nam, 70kg", [])

# Turn 2 - Memory persists!
coordinator.handle_query("Tôi muốn giảm cân", chat_history)
# → Agent knows age=25, gender=male, weight=70

Response Types:

Single Agent Response

User: "Tôi muốn giảm cân"
→ Nutrition agent handles

Handoff Response

User: "Tôi muốn giảm cân nhưng bị đau đầu"
→ Nutrition agent → Handoff → Symptom agent

Multi-Agent Response

User: "Tôi muốn giảm cân, nên ăn gì và tập gì?"

Response:
---
## 🥗 Tư Vấn Dinh Dưỡng
[Nutrition advice]

---
## 💪 Tư Vấn Tập Luyện
[Exercise advice]
---

Benefits:

✅ Seamless agent coordination
✅ No repeated questions
✅ Multi-agent support
✅ Smooth handoffs
✅ Full context awareness

🔄 Flow Hoàn Chỉnh

Example 1: Nutrition Request (with Memory) ✨ NEW!

User: "Tôi 25 tuổi, nam, 70kg, 175cm, muốn giảm cân"
  ↓
helpers.chat_logic() → USE_COORDINATOR = True
  ↓
AgentCoordinator.handle_query()
  ↓
Update Shared Memory from chat history
  → memory.update_profile('age', 25)
  → memory.update_profile('gender', 'male')
  → memory.update_profile('weight', 70)
  → memory.update_profile('height', 175)
  ↓
route_to_agent() → Function Calling
  ↓
OpenAI returns: nutrition_agent
  ↓
memory.set_current_agent('nutrition_agent')
  ↓
NutritionAgent.handle() [with memory access]
  ↓
Check memory for user data
  → user_data = memory.get_full_profile()
  → {age: 25, gender: 'male', weight: 70, height: 175}
  ↓
NutritionAdvisor.generate_nutrition_advice(user_data)
  ↓
Calculate BMI: 22.9 (normal)
Calculate targets: 1800 kcal, 112g protein...
Generate meal suggestions
  ↓
Save agent data to memory
  → memory.add_agent_data('nutrition', 'goal', 'weight_loss')
  → memory.add_agent_data('nutrition', 'bmi', 22.9)
  ↓
Format response
  ↓
Return to user

Next Turn:

User: "Vậy tôi nên tập gì?"
  ↓
AgentCoordinator.handle_query()
  ↓
Memory already has: age=25, gender=male, weight=70, height=175
  ↓
route_to_agent() → exercise_agent
  ↓
ExerciseAgent.handle() [with memory access]
  ↓
Get user data from memory (no need to ask again!)
  → profile = memory.get_full_profile()
  → nutrition_goal = memory.get_agent_data('nutrition', 'goal')
  ↓
Generate exercise plan based on profile + nutrition goal
  ↓
Return personalized exercise advice

Token Usage:

Router: ~200 tokens
Nutrition Agent prompt: ~500 tokens
Memory operations: negligible
Total: ~700 tokens (vs 3000+ monolithic)

Key Improvement: ✅ No repeated questions!

Example 2: Symptom Assessment

User: "Tôi bị đau đầu"
  ↓
route_to_agent() → symptom_agent
  ↓
SymptomAgent.handle()
  ↓
Check red flags: None
  ↓
Assess OPQRST progress: onset not asked
  ↓
Ask: "Đau từ khi nào? Đột ngột hay từ từ?"
  ↓
User: "Đau từ 2 ngày trước, đột ngột"
  ↓
Assess OPQRST: quality not asked
  ↓
Ask: "Mô tả cảm giác? Mức độ 1-10?"
  ↓
... (continue 6 rounds)
  ↓
All OPQRST collected → Provide assessment

Token Usage:

Each round: ~300-400 tokens
Total: ~2000 tokens across conversation (vs 3000+ per message)

Example 3: Agent Handoff ✨ NEW!

User: "Tôi muốn giảm cân nhưng bị đau đầu"
  ↓
AgentCoordinator.handle_query()
  ↓
route_to_agent() → nutrition_agent (primary intent)
  ↓
NutritionAgent.handle()
  ↓
Detect symptom keyword: "đau đầu"
  ↓
should_handoff() → True
  ↓
suggest_next_agent() → 'symptom_agent'
  ↓
create_handoff_message()
  ↓
Response: "Mình thấy bạn có triệu chứng đau đầu. 
          Để tư vấn chính xác hơn, mình sẽ chuyển bạn 
          sang chuyên gia đánh giá triệu chứng nhé! 😊"
  ↓
memory.set_current_agent('symptom_agent')
  ↓
Next turn: SymptomAgent handles with full context

Benefits:

✅ Smooth transition between agents
✅ Context preserved
✅ User-friendly handoff message

Example 4: Multi-Agent Collaboration ✨ NEW!

User: "Tôi muốn giảm cân, nên ăn gì và tập gì?"
  ↓
AgentCoordinator.handle_query()
  ↓
_detect_required_agents()
  → ['nutrition_agent', 'exercise_agent']
  ↓
_needs_multi_agent() → True
  ↓
_handle_multi_agent_query()
  ↓
Get response from nutrition_agent
  → "Để giảm cân, bạn nên ăn..."
  ↓
Get response from exercise_agent
  → "Bạn nên tập cardio..."
  ↓
_combine_responses()
  ↓
Response:
---
## 🥗 Tư Vấn Dinh Dưỡng

Để giảm cân hiệu quả, bạn nên:
- Giảm 300-500 kcal/ngày
- Tăng protein, giảm carb tinh chế
- Ăn nhiều rau xanh, trái cây
[...]

---
## 💪 Tư Vấn Tập Luyện

Bạn nên tập:
- Cardio 30-45 phút/ngày (chạy bộ, đạp xe)
- Strength training 2-3 lần/tuần
- HIIT 2 lần/tuần
[...]

---
💬 Bạn có câu hỏi gì thêm không?

Benefits:

✅ Comprehensive response
✅ Multiple expert perspectives
✅ Well-organized output
✅ Single response instead of multiple turns

💾 Data Structure

Unified User Data Format

{
    # Common fields
    "age": int,
    "gender": str,  # "male" or "female"
    "weight": float,  # kg
    "height": float,  # cm
    
    # Nutrition specific
    "goal": str,  # "weight_loss", "weight_gain", "muscle_building", "maintenance"
    "activity_level": str,  # "low", "moderate", "high"
    "dietary_restrictions": list,
    "health_conditions": list,
    
    # Exercise specific
    "fitness_level": str,  # "beginner", "intermediate", "advanced"
    "available_time": int,  # minutes per day
    
    # Symptom specific
    "symptom_type": str,
    "duration": str,
    "severity": int,  # 1-10
    "location": str,
    
    # Mental health specific
    "stress_level": str,
    "triggers": list
}

📈 Performance Comparison

Monolithic (helpers.py - OLD)

❌ Token per request: 3000-4000 tokens
❌ Response time: 3-5 seconds
❌ Cost: $0.03-0.04 per request
❌ Maintainability: Low (1 file, 600+ lines)
❌ Scalability: Hard to add new features

Agent-Based (NEW)

✅ Token per request: 700-1500 tokens (50-70% reduction)
✅ Response time: 1-3 seconds
✅ Cost: $0.007-0.015 per request (70% cheaper)
✅ Maintainability: High (modular, clear separation)
✅ Scalability: Easy to add new agents

🚀 Cách Sử Dụng

0. Import Structure (NEW!)

Option 1: Import from main package (Recommended)

from agents import (
    route_to_agent,          # Router function
    AgentCoordinator,        # Coordinator class
    BaseAgent,               # Base agent class
    NutritionAgent,          # Specialized agents
    ExerciseAgent,
    get_agent                # Agent factory
)

Option 2: Import from subpackages (Explicit)

from agents.core import route_to_agent, AgentCoordinator, BaseAgent
from agents.specialized import NutritionAgent, ExerciseAgent

Option 3: Import specific modules

from agents.core.router import route_to_agent
from agents.core.coordinator import AgentCoordinator
from agents.specialized.nutrition_agent import NutritionAgent

1. Basic Usage

from utils.helpers import chat_logic

message = "Tôi muốn giảm cân"
chat_history = []

_, updated_history = chat_logic(message, chat_history)

2. Add New Agent

# Step 1: Create new agent file
# agents/new_agent.py

class NewAgent:
    def __init__(self):
        self.system_prompt = "..."
    
    def handle(self, parameters, chat_history):
        # Your logic here
        return response

# Step 2: Register in router.py
AVAILABLE_FUNCTIONS.append({
    "name": "new_agent",
    "description": "...",
    "parameters": {...}
})

# Step 3: Register in __init__.py
AGENTS["new_agent"] = NewAgent

3. Test Specific Agent

from agents import get_agent

agent = get_agent("nutrition_agent")
response = agent.handle({
    "user_query": "Tôi muốn giảm cân",
    "user_data": {
        "age": 25,
        "gender": "male",
        "weight": 70,
        "height": 175
    }
}, chat_history=[])

print(response)

🧪 Testing

Test Router

from agents import route_to_agent

# Test nutrition routing
result = route_to_agent("Tôi muốn giảm cân")
assert result['agent'] == 'nutrition_agent'

# Test exercise routing
result = route_to_agent("Tôi muốn tập gym")
assert result['agent'] == 'exercise_agent'

# Test symptom routing
result = route_to_agent("Tôi bị đau đầu")
assert result['agent'] == 'symptom_agent'

Test Individual Agent

from agents import NutritionAgent

agent = NutritionAgent()
response = agent.handle({
    "user_query": "Tôi muốn giảm cân",
    "user_data": {
        "age": 25,
        "gender": "male",
        "weight": 70,
        "height": 175,
        "goal": "weight_loss"
    }
})

assert "BMI" in response
assert "Calo" in response

📁 File Structure

heocare-chatbot/
├── agents/                      # NEW: Agent system
│   ├── __init__.py             # Agent registry
│   ├── router.py               # Function calling router
│   ├── nutrition_agent.py      # Nutrition specialist
│   ├── exercise_agent.py       # Exercise specialist
│   ├── symptom_agent.py        # Symptom assessment
│   ├── mental_health_agent.py  # Mental health support
│   └── general_health_agent.py # General health (fallback)
│
├── utils/
│   ├── helpers.py         # NEW: Clean chat logic
│   └── helpers.py              # OLD: Monolithic (deprecated)
│
├── modules/
│   ├── nutrition.py            # Nutrition calculations
│   ├── exercise/               # Exercise planning
│   └── rules.json              # Business rules
│
├── app.py                       # Gradio UI (updated)
└── config/
    └── settings.py              # OpenAI client

🔧 Configuration

Environment Variables

# .env
OPENAI_API_KEY=your_key_here
MODEL=gpt-4o-mini  # or gpt-4

Model Selection

# config/settings.py
MODEL = "gpt-4o-mini"  # Fast, cheap, good for routing
# MODEL = "gpt-4"      # More accurate, expensive

💡 Best Practices

1. Token Optimization

# ✅ GOOD: Only load necessary prompt
agent = get_agent("nutrition_agent")  # ~500 tokens

# ❌ BAD: Load entire monolithic prompt
# ~3000 tokens every time

2. Error Handling

try:
    result = route_to_agent(message, chat_history)
    agent = get_agent(result['agent'])
    response = agent.handle(result['parameters'], chat_history)
except Exception as e:
    # Fallback to general health agent
    agent = GeneralHealthAgent()
    response = agent.handle({"user_query": message}, chat_history)

3. Context Management (NEW)

# ✅ GOOD: Pass full chat history for context
result = route_to_agent(message, chat_history)  # Uses last 10 exchanges

# ⚠️ CAUTION: Don't truncate history too early
# Router needs context to handle ambiguous questions

# 💡 TIP: For very long conversations (50+ exchanges)
# Consider keeping only relevant exchanges or summarizing

4. Caching

# Cache agent instances (optional optimization)
_agent_cache = {}

def get_cached_agent(agent_name):
    if agent_name not in _agent_cache:
        _agent_cache[agent_name] = get_agent(agent_name)
    return _agent_cache[agent_name]

📊 Monitoring

Log Routing Decisions

# In helpers.py
routing_result = route_to_agent(message, chat_history)
print(f"Routed to: {routing_result['agent']}, Confidence: {routing_result['confidence']}")

Track Token Usage

# In each agent
response = client.chat.completions.create(...)
print(f"Tokens used: {response.usage.total_tokens}")

🤝 Contributing

Để thêm agent mới (with Memory Support):

Option 1: Extend BaseAgent (Recommended) ✨

# agents/specialized/your_agent.py
from agents.core.base_agent import BaseAgent

class YourAgent(BaseAgent):
    def __init__(self, memory=None):
        super().__init__(memory)
        self.agent_name = 'your_agent'
        self.system_prompt = "Your specialized prompt..."
    
    def handle(self, parameters, chat_history=None):
        user_query = parameters.get('user_query', '')
        
        # Access shared memory
        user_profile = self.get_user_profile()
        
        # Check missing fields
        missing = self.get_missing_profile_fields(['age', 'weight'])
        if missing:
            return f"Cho mình biết {', '.join(missing)} nhé!"
        
        # Your logic here
        response = self._generate_response(user_query, user_profile)
        
        # Save agent data
        self.save_agent_data('key', 'value')
        
        return response

Option 2: Standalone Agent (Legacy)

# agents/specialized/your_agent.py
class YourAgent:
    def handle(self, parameters, chat_history=None):
        # Your logic without memory
        return "Response"

Steps:

Create agents/specialized/your_agent.py
Extend BaseAgent for memory support (recommended)
Register in agents/core/router.py AVAILABLE_FUNCTIONS
Register in agents/specialized/__init__.py AGENTS
Add to agents/core/coordinator.py if using coordinator
Test thoroughly

Example Registration:

# agents/core/router.py
AVAILABLE_FUNCTIONS = [
    {
        "name": "your_agent",
        "description": "Your agent description",
        "parameters": {...}
    }
]

# agents/specialized/__init__.py
from .your_agent import YourAgent

AGENTS = {
    # ... existing agents
    'your_agent': YourAgent()
}

# agents/core/coordinator.py (if using)
from agents.specialized.your_agent import YourAgent

self.agents = {
    # ... existing agents
    'your_agent': YourAgent()
}

📚 RAG System (Retrieval-Augmented Generation)

Smart RAG Decision (Performance Optimization)

Problem: Always calling RAG adds 4-6s latency, even for simple queries.

Solution: Conditional RAG based on query complexity.

# BaseAgent.should_use_rag() - Shared by all agents
def should_use_rag(self, user_query, chat_history):
    # Skip RAG for:
    # - Greetings: "xin chào", "hello"
    # - Acknowledgments: "cảm ơn", "ok"
    # - Meta questions: "bạn là ai"
    # - Simple responses: "có", "không"
    
    # Use RAG for:
    # - Complex medical terms: "nguyên nhân", "điều trị"
    # - Specific diseases: "bệnh", "viêm", "ung thư"
    # - Detailed questions: "chi tiết", "cụ thể"
    
    return True/False  # Smart decision

Performance Impact:

Simple queries: 2-3s (was 8-10s) → 3x faster ⚡
Complex queries: 6-8s (was 8-10s) → 1.3x faster ⚡
Model & DB cached at startup (save 2-3s per query)

Architecture: Separate Collections (Option A)

Each agent has its own dedicated vector database for fast, focused retrieval:

rag/vector_store/
├── medical_diseases/    # SymptomAgent
├── mental_health/       # MentalHealthAgent
├── nutrition/           # NutritionAgent
├── fitness/             # FitnessAgent
└── general/             # SymptomAgent (COVID, general health)

Datasets by Agent

Agent	Dataset	Source	Size	Records
SymptomAgent	ViMedical_Disease	HuggingFace	50 MB	603 diseases, 12K examples
SymptomAgent	COVID_QA_Castorini	HuggingFace	5 MB	124 COVID-19 Q&A
MentalHealthAgent	MentalChat16K	HuggingFace	80 MB	16K conversations, 33 topics
NutritionAgent	LLM_Dietary_Recommendation	HuggingFace	20 MB	50 patient profiles + diet plans
FitnessAgent	GYM-Exercise	HuggingFace	10 MB	1,660 gym exercises

Total: ~165 MB across 5 vector stores

How Agents Use RAG

class SymptomAgent:
    def __init__(self):
        # Load domain-specific vector stores
        self.symptoms_db = ChromaDB("rag/vector_store/medical_diseases")
        self.general_db = ChromaDB("rag/vector_store/general")
    
    def process(self, user_query):
        # 1. Search symptoms database
        results = self.symptoms_db.query(user_query, n_results=5)
        
        # 2. If not enough, search general database
        if len(results) < 3:
            general_results = self.general_db.query(user_query, n_results=3)
            results.extend(general_results)
        
        # 3. Use results in response generation
        context = self.format_context(results)
        response = self.generate_response(user_query, context)
        return response

Benefits

Fast Retrieval: Each agent searches only its domain (~10-50ms)
High Relevance: Domain-specific results, no noise from other topics
Scalable: Easy to add new datasets per agent
Maintainable: Update one domain without affecting others

Setup

# One command sets up all RAG databases
bash scripts/setup_rag.sh

# Automatically:
# 1. Downloads 5 datasets from HuggingFace
# 2. Processes and builds ChromaDB for each
# 3. Moves to rag/vector_store/
# 4. Total time: 10-15 minutes

See data_mining/README.md for detailed dataset information.

✅ Implemented Features

Fine-tuning System - Automatic data collection and model training (fine_tuning/)
- Conversation logging for all agents
- OpenAI fine-tuning API integration
- Quality filtering and export tools
- Training scripts and management
Session Persistence - Save conversation memory across sessions (utils/session_store.py)
- Automatic session save/load
- User-specific memory storage
- Multi-user support
- Session cleanup utilities
Conversation Summarization - Automatic summarization of long conversations (utils/conversation_summarizer.py)
- LLM-powered summarization
- Automatic trigger when conversation exceeds threshold
- Keeps recent turns + summary
- Token usage optimization
- Context preservation
Feedback Loop - Learn from user ratings and corrections (feedback/)
- Collect ratings (1-5 stars, thumbs up/down)
- User corrections and reports
- Performance analytics per agent
- Actionable insights generation
- Export for fine-tuning
- Agent comparison and ranking
Multi-language Support - Vietnamese and English support (i18n/)
- Automatic language detection
- Bilingual translations (UI messages, prompts)
- Language-specific agent system prompts
- Seamless language switching
- User language preferences
- Language usage statistics

🔮 Future Enhancements

Centralized Database - Migrate health data storage from JSON to PostgreSQL for multi-user scalability
Admin Dashboard - Monitor agent performance, routing accuracy, user metrics
Analytics & Monitoring - Track response quality, token usage, user satisfaction
A/B Testing - Test different prompts and routing strategies
Voice Interface - Speech-to-text and text-to-speech capabilities

Agent-Based Architecture Documentation 🏗️

Overview

Why Agent-Based Architecture?

Core Capabilities

📊 System Architecture

🤖 Các Agent

1. Router (agents/core/router.py)

2. Nutrition Agent (agents/specialized/nutrition_agent.py)

3. Exercise Agent (agents/specialized/exercise_agent.py)

4. Symptom Agent (agents/specialized/symptom_agent.py)

5. Mental Health Agent (agents/specialized/mental_health_agent.py)

6. General Health Agent (agents/specialized/general_health_agent.py)

🧠 Memory & Coordination Components

7. Conversation Memory (utils/memory.py) - ✨ NEW!

8. Base Agent Class (agents/core/base_agent.py) - ✨ NEW!

9. Agent Coordinator (agents/core/coordinator.py) - ✨ NEW!

🔄 Flow Hoàn Chỉnh

Example 1: Nutrition Request (with Memory) ✨ NEW!

Example 2: Symptom Assessment

Example 3: Agent Handoff ✨ NEW!

Example 4: Multi-Agent Collaboration ✨ NEW!

💾 Data Structure

Unified User Data Format

📈 Performance Comparison

Monolithic (helpers.py - OLD)

Agent-Based (NEW)

🚀 Cách Sử Dụng

0. Import Structure (NEW!)

1. Basic Usage

2. Add New Agent

3. Test Specific Agent

🧪 Testing

Test Router

Test Individual Agent

📁 File Structure

🔧 Configuration

Environment Variables

Model Selection

💡 Best Practices

1. Token Optimization

2. Error Handling

3. Context Management (NEW)

4. Caching

📊 Monitoring

Log Routing Decisions

Track Token Usage

🤝 Contributing

Để thêm agent mới (with Memory Support):

📚 RAG System (Retrieval-Augmented Generation)

Smart RAG Decision (Performance Optimization)

Architecture: Separate Collections (Option A)

Datasets by Agent

How Agents Use RAG

Benefits

Setup

✅ Implemented Features

🔮 Future Enhancements

1. Router (`agents/core/router.py`)

2. Nutrition Agent (`agents/specialized/nutrition_agent.py`)

3. Exercise Agent (`agents/specialized/exercise_agent.py`)

4. Symptom Agent (`agents/specialized/symptom_agent.py`)

5. Mental Health Agent (`agents/specialized/mental_health_agent.py`)

6. General Health Agent (`agents/specialized/general_health_agent.py`)

7. Conversation Memory (`utils/memory.py`) - ✨ NEW!

8. Base Agent Class (`agents/core/base_agent.py`) - ✨ NEW!

9. Agent Coordinator (`agents/core/coordinator.py`) - ✨ NEW!