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+# AI Service - DispatchAI Platform
+
+**FastAPI-based AI Agent Service** for intelligent call handling, customer information extraction, and service scheduling.
+
+## 🎯 Overview
+
+The AI Service is the brain of the DispatchAI platform, handling all AI-powered interactions with customers during phone calls. It uses LangGraph-based multi-step agents to collect customer information, understand service requests, and schedule appointments.
+
+## 🏗️ Architecture
+
+### Core Components
+
+```
+apps/ai/
+├── app/
+│   ├── main.py                 # FastAPI application entry point
+│   ├── config.py               # Configuration & settings
+│   ├── api/                    # API endpoints
+│   │   ├── health.py          # Health check
+│   │   ├── chat.py            # Chat endpoints
+│   │   ├── call.py            # Call conversation handling
+│   │   ├── summary.py         # Call summaries
+│   │   ├── email.py           # Email sending
+│   │   └── dispatch.py        # Service dispatch & calendar
+│   ├── services/              # Core business logic
+│   │   ├── call_handler.py   # Main conversation orchestrator
+│   │   ├── dialog_manager.py # Multi-turn conversation state
+│   │   ├── llm_service.py    # LLM integration
+│   │   ├── llm_speech_corrector.py  # Speech-to-text correction
+│   │   ├── call_summary.py   # Post-call summaries
+│   │   ├── redis_service.py  # Redis interactions
+│   │   ├── ses_email.py      # Email service (AWS SES)
+│   │   ├── ics_lib.py        # ICS calendar file generation
+│   │   └── retrieve/         # Information extractors
+│   │       ├── customer_info_extractors.py
+│   │       └── time_extractor.py
+│   ├── models/                # Data models
+│   │   ├── call.py           # CallSkeleton, Message, UserInfo
+│   │   └── chat.py           # Chat models
+│   ├── client/                # External service clients
+│   │   └── mcp_client.py     # MCP (Model Context Protocol) client
+│   ├── custom_types/         # Custom type definitions
+│   │   └── customer_service_types.py
+│   ├── infrastructure/       # Infrastructure clients
+│   │   └── redis_client.py  # Redis async client
+│   └── utils/                # Utilities
+│       ├── mcp_parse.py     # MCP response parsing
+│       ├── prompts/         # LLM prompts
+│       └── validators/      # Data validators
+├── tests/                    # Test suite
+├── pyproject.toml           # Python dependencies (uv)
+├── Dockerfile.dev          # Development Docker image
+├── Dockerfile.uat          # UAT Docker image
+└── Makefile                # Build automation
+```
+
+## 🤖 AI Workflow - 8-Step Conversation Agent
+
+The AI service implements a sophisticated **8-step LangGraph workflow** to handle customer service conversations:
+
+### Workflow Steps
+
+1. **Name Collection** → Collect customer's full name
+2. **Phone Collection** → Verify contact phone number
+3. **Address Collection** → Get service address with address validation
+4. **Service Selection** → Help customer choose from available services
+5. **Time Selection** → Schedule preferred appointment time
+6. **Booking Confirmation** → Confirm all details
+7. **Dispatch** → Send email + calendar invite
+8. **Completion** → Wrap up conversation
+
+### State Management
+
+The agent maintains conversation state in **Redis** using `CallSkeleton` format:
+
+```python
+{
+    "callSid": "CAxxx...",           # Unique call ID
+    "company": {...},                # Company info
+    "user": {
+        "userInfo": {                # Collected customer info
+            "name": "...",
+            "phone": "...",
+            "email": "...",
+            "address": "..."
+        },
+        "service": {...}             # Selected service
+    },
+    "services": [...],               # Available services list
+    "history": [...]                 # Message history
+}
+```
+
+## 🔌 API Endpoints
+
+### Core Endpoints
+
+**Base URL**: `http://localhost:8000/api`
+
+#### `/ai/conversation` (POST)
+Main conversation endpoint for call handling.
+
+**Request**:
+```json
+{
+    "callSid": "CA1234567890",
+    "customerMessage": {
+        "message": "Hi, I need a cleaning service",
+        "speaker": "customer",
+        "timestamp": "2024-01-01T12:00:00Z"
+    }
+}
+```
+
+**Response**:
+```json
+{
+    "assistantMessage": {
+        "message": "I'd be happy to help you with a cleaning service. May I get your name please?",
+        "speaker": "assistant",
+        "timestamp": "2024-01-01T12:00:01Z"
+    },
+    "userInfo": {...},              # Updated customer info
+    "conversationComplete": false,  # Booking status
+    "currentStep": "collect_name"   # Current workflow step
+}
+```
+
+#### `/dispatch/send-email-and-calendar` (POST)
+Send email with calendar integration (Google/Outlook/iCal).
+
+**Request**:
+```json
+{
+    "to": "customer@example.com",
+    "subject": "Service Booking Confirmation",
+    "body": "Your appointment is confirmed...",
+    "summary": "Cleaning Service",
+    "start": "2024-01-15T10:00:00+10:00",
+    "end": "2024-01-15T12:00:00+10:00",
+    "calendarapp": "google",       # "none", "google", "outlook"
+    "access_token": "...",         # OAuth token
+    "calendar_id": "primary"
+}
+```
+
+#### `/summary/generate` (POST)
+Generate AI-powered call summary after conversation ends.
+
+#### `/email/send` (POST)
+Send simple email without calendar.
+
+## 🧠 Key Services
+
+### CustomerServiceLangGraph (`call_handler.py`)
+
+Main orchestrator for conversation workflow.
+
+**Key Methods**:
+- `process_conversation(state, message)` - Process user message through workflow
+- `_collect_name()` - Name collection logic
+- `_collect_phone()` - Phone validation & collection
+- `_collect_address()` - Address extraction with validation
+- `_select_service()` - Service recommendation & selection
+- `_select_time()` - Time slot scheduling
+- `_complete_booking()` - Final confirmation & dispatch
+
+**Features**:
+- Multi-attempt collection (3 max attempts per field)
+- Speech correction for addresses
+- Service price & description display
+- Natural conversation flow
+- Context-aware responses
+
+### Information Extractors (`services/retrieve/`)
+
+Specialized extractors for structured data:
+
+- **`customer_info_extractors.py`**: Name, phone, email, address extraction
+- **`time_extractor.py`**: Natural language time parsing with timezone handling
+
+**Usage**:
+```python
+from services.retrieve.customer_info_extractors import (
+    extract_name_from_conversation,
+    extract_phone_from_conversation,
+    extract_address_from_conversation,
+)
+
+name = extract_name_from_conversation(message_history)
+phone = extract_phone_from_conversation(message_history)
+address = extract_address_from_conversation(message_history)
+```
+
+### LLM Service (`llm_service.py`)
+
+Abstraction layer for OpenAI LLM calls.
+
+**Features**:
+- OpenAI GPT-4 integration
+- Streaming support
+- Custom system prompts
+- Context management
+- Error handling & retries
+
+### Speech Corrector (`llm_speech_corrector.py`)
+
+Corrects common speech-to-text errors for addresses.
+
+**Example**:
+```
+Input:  "1 twenty five Johnson street"
+Output: "1/25 Johnson Street"
+```
+
+### Call Summary (`call_summary.py`)
+
+Generates structured summaries of completed calls:
+- Key information extracted
+- Service requested
+- Booking details
+- Customer sentiment
+
+### Email Service (`ses_email.py`)
+
+AWS SES integration for sending emails:
+- Plain text & HTML emails
+- ICS calendar attachments
+- OAuth-based calendar integration
+- Email templates
+
+## 🧪 Testing
+
+### Run Tests
+
+```bash
+cd apps/ai
+
+# Install dependencies
+make sync
+
+# Run all tests with coverage
+make test
+
+# Run tests in verbose mode
+uv run pytest tests/ -v
+
+# Run specific test file
+uv run pytest tests/test_smoke.py -v
+
+# Coverage report
+uv run pytest --cov=app --cov-report=html
+```
+
+### Code Quality
+
+```bash
+# Lint with Ruff
+make lint
+
+# Auto-fix linting issues
+make lint-fix
+
+# Format code
+make format
+
+# Type check with MyPy
+make typecheck
+
+# Run all checks
+make check-all
+```
+
+### Manual Testing
+
+```bash
+# Start AI service
+docker compose up ai
+
+# Test health endpoint
+curl http://localhost:8000/api/health
+
+# View API docs
+open http://localhost:8000/docs
+```
+
+## 🔧 Configuration
+
+### Environment Variables
+
+**Required**:
+```bash
+OPENAI_API_KEY=sk-...              # OpenAI API key
+OPENAI_MODEL=gpt-4o-mini           # LLM model name
+REDIS_HOST=redis                   # Redis host
+REDIS_PORT=6379                    # Redis port
+```
+
+**Optional**:
+```bash
+REDIS_URL=redis://localhost:6379   # Full Redis URL
+API_PREFIX=/api                    # API prefix
+DEBUG=true                         # Debug mode
+MAX_ATTEMPTS=3                     # Max collection attempts
+OPENAI_MAX_TOKENS=2500             # Max response tokens
+OPENAI_TEMPERATURE=0.0             # Temperature (0=deterministic)
+CORS_ORIGINS=["*"]                 # CORS allowed origins
+```
+
+### Supported Services
+
+Services are configured via `config.py`:
+
+```python
+supported_services = [
+    "clean", "cleaning",
+    "garden", "gardening",
+    "plumber", "plumbing",
+    "electric", "electrical",
+    "repair"
+]
+```
+
+### Supported Time Keywords
+
+Natural language time parsing:
+
+```python
+supported_time_keywords = [
+    "tomorrow", "morning", "afternoon", "evening",
+    "monday", "tuesday", "wednesday", "thursday",
+    "friday", "saturday", "sunday"
+]
+```
+
+## 🚀 Development
+
+### Local Development
+
+1. **Install dependencies**:
+   ```bash
+   cd apps/ai
+   make sync
+   ```
+
+2. **Run locally**:
+   ```bash
+   # With uv
+   uv run uvicorn app.main:app --reload --port 8000
+
+   # Or with Docker
+   docker compose up ai
+   ```
+
+3. **Access service**:
+   - API: http://localhost:8000
+   - Docs: http://localhost:8000/docs
+   - Health: http://localhost:8000/api/health
+
+### Dependency Management
+
+The AI service uses **uv** for fast Python package management:
+
+```bash
+# Install new dependency
+uv add package-name
+
+# Add dev dependency
+uv add --dev package-name
+
+# Update dependencies
+uv sync --upgrade
+
+# Remove dependency
+uv remove package-name
+```
+
+### Adding New Extractors
+
+1. Create extractor in `app/services/retrieve/customer_info_extractors.py`:
+   ```python
+   def extract_new_field_from_conversation(message_history: list) -> str:
+       # Extract logic
+       return extracted_value
+   ```
+
+2. Import in `call_handler.py`:
+   ```python
+   from .retrieve.customer_info_extractors import extract_new_field_from_conversation
+   ```
+
+3. Use in workflow step:
+   ```python
+   value = extract_new_field_from_conversation(state["message_history"])
+   ```
+
+### Adding New Workflow Steps
+
+1. Add step to `CustomerServiceState` in `custom_types/customer_service_types.py`
+2. Implement step handler in `call_handler.py`
+3. Add step transition logic in `process_conversation()`
+4. Update prompts in `utils/prompts/`
+
+## 📊 Monitoring & Debugging
+
+### Logs
+
+```bash
+# View AI service logs
+docker logs dispatchai-ai -f
+
+# Filter logs
+docker logs dispatchai-ai 2>&1 | grep "CONVERSATION"
+```
+
+### Redis Inspection
+
+```bash
+# Connect to Redis
+docker exec -it dispatchai-redis redis-cli
+
+# List all call skeletons
+KEYS callskeleton:*
+
+# Get specific call skeleton
+GET callskeleton:CA1234567890
+
+# Check message history
+LRANGE history:CA1234567890 0 -1
+```
+
+### Debugging Conversation Flow
+
+Add debug logs in `call_handler.py`:
+
+```python
+print(f"🔍 [STEP] {state['current_step']}")
+print(f"🔍 [INFO] Name: {state['name']}, Phone: {state['phone']}")
+print(f"🔍 [COMPLETE] All fields: {all_complete}")
+```
+
+## 🔗 Integration Points
+
+### With Backend
+
+**Redis**: CallSkeleton storage & message history
+```python
+from services.redis_service import get_call_skeleton, update_user_info
+```
+
+**MongoDB**: Not directly accessed, all via Backend API
+
+### With Frontend
+
+**HTTP**: Frontend calls `/api/ai/conversation` with user messages
+
+**WebSocket/SSE**: Not currently used (can be added for real-time)
+
+### With External Services
+
+**OpenAI**: LLM inference
+**AWS SES**: Email sending
+**Google Calendar**: OAuth + Calendar API
+**Outlook Calendar**: Microsoft Graph API
+
+## 🐛 Common Issues
+
+### Issue: "CallSkeleton not found"
+
+**Cause**: Redis doesn't have call data
+**Fix**: Ensure Backend created CallSkeleton before AI service called
+
+### Issue: "Address validation failed"
+
+**Cause**: Invalid address format or speech-to-text error
+**Fix**: Check speech corrector, add more patterns
+
+### Issue: "Service not in available list"
+
+**Cause**: Customer requesting unsupported service
+**Fix**: Update `supported_services` in config or add service mapping
+
+### Issue: "LLM timeout"
+
+**Cause**: Slow response from OpenAI or network issue
+**Fix**: Increase timeout, add retry logic, check API key
+
+## 📚 Additional Resources
+
+- **FastAPI Docs**: https://fastapi.tiangolo.com
+- **LangGraph Docs**: https://langchain-ai.github.io/langgraph/
+- **OpenAI API**: https://platform.openai.com/docs
+- **Redis Python**: https://redis.readthedocs.io
+- **uv Package Manager**: https://github.com/astral-sh/uv
+
+## 🤝 Contributing
+
+When adding new features:
+1. Update tests in `tests/`
+2. Add type hints
+3. Run `make check-all`
+4. Update this README
+5. Document new API endpoints
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