Dialogue System Architecture

# implement update_dialogue_state, get_missing_slots
# Key operations: results display, prompt construction, API interaction
import json
from openai import OpenAI

client = OpenAI()

# Define the dialogue state schema
BOOKING_SCHEMA = {
 "cuisine": None,
 "party_size": None,
 "date": None,
 "time": None,
 "location": None,
 "special_requests": None
}

def update_dialogue_state(current_state: dict, user_message: str,
 conversation_history: list) -> dict:
 """Use an LLM to extract slot values from the user message."""

 system_prompt = """You are a dialogue state tracker for a restaurant booking system.
Given the conversation history and the current user message, extract any new
information that fills the following slots:
- cuisine: type of food (e.g., Italian, Japanese, Mexican)
- party_size: number of people (integer)
- date: reservation date (YYYY-MM-DD format)
- time: reservation time (HH:MM format)
- location: city or neighborhood
- special_requests: any dietary needs or preferences

Return ONLY a JSON object with the slots that have new values.
If no new information is found, return an empty JSON object {}.
Do not include slots that were not mentioned."""

 response = client.chat.completions.create(
 model="gpt-4o",
 messages=[
 {"role": "system", "content": system_prompt},
 *conversation_history,
 {"role": "user", "content": user_message}
 ],
 response_format={"type": "json_object"},
 temperature=0.0
 )

 # Parse extracted slots
 new_slots = json.loads(response.choices[0].message.content)

 # Merge with current state (new values override old ones)
 updated_state = {**current_state, **new_slots}
 return updated_state

def get_missing_slots(state: dict) -> list:
 """Identify which required slots still need to be filled."""
 required = ["cuisine", "party_size", "date", "time"]
 return [slot for slot in required if state.get(slot) is None]

# Example usage
state = BOOKING_SCHEMA.copy()
history = []

# Simulate a conversation turn
user_msg = "I'd like to book a table for 4 people at an Italian place"
state = update_dialogue_state(state, user_msg, history)
missing = get_missing_slots(state)
print(f"State: {json.dumps(state, indent=2)}")
print(f"Missing: {missing}")

# Define DialoguePhase, DialogueManager; implement get_next_action, update_slot, handle_correction
# Key operations: prompt construction
from dataclasses import dataclass, field
from typing import Optional
from enum import Enum

class DialoguePhase(Enum):
 COLLECTING = "collecting"
 CONFIRMING = "confirming"
 EXECUTING = "executing"
 COMPLETED = "completed"

@dataclass
class DialogueManager:
 """Manages dialogue flow for task-oriented conversations."""

 state: dict = field(default_factory=lambda: {
 "cuisine": None, "party_size": None,
 "date": None, "time": None,
 "location": None, "special_requests": None
 })
 phase: DialoguePhase = DialoguePhase.COLLECTING
 required_slots: list = field(
 default_factory=lambda: ["cuisine", "party_size", "date", "time"]
 )
 turn_count: int = 0

 def get_next_action(self) -> dict:
 """Determine the next system action based on current state."""
 self.turn_count += 1

 if self.phase == DialoguePhase.COLLECTING:
 missing = [s for s in self.required_slots
 if self.state.get(s) is None]
 if missing:
 return {
 "action": "request_slot",
 "slot": missing[0],
 "missing_count": len(missing)
 }
 else:
 self.phase = DialoguePhase.CONFIRMING
 return {"action": "confirm", "state": self.state}

 elif self.phase == DialoguePhase.CONFIRMING:
 self.phase = DialoguePhase.EXECUTING
 return {"action": "execute", "state": self.state}

 elif self.phase == DialoguePhase.EXECUTING:
 self.phase = DialoguePhase.COMPLETED
 return {"action": "complete", "state": self.state}

 return {"action": "done"}

 def update_slot(self, slot: str, value) -> None:
 """Update a single slot value with validation."""
 if slot == "party_size":
 value = int(value)
 if value < 1 or value > 20:
 raise ValueError("Party size must be between 1 and 20")
 self.state[slot] = value

 def handle_correction(self, slot: str, new_value) -> None:
 """Handle user corrections to previously filled slots."""
 self.update_slot(slot, new_value)
 if self.phase == DialoguePhase.CONFIRMING:
 # Go back to collecting to re-confirm
 self.phase = DialoguePhase.COLLECTING

# Slot-specific prompt templates
SLOT_PROMPTS = {
 "cuisine": "What type of cuisine are you in the mood for?",
 "party_size": "How many people will be dining?",
 "date": "What date would you like to book? (e.g., March 15, next Friday)",
 "time": "What time works best for your reservation?",
 "location": "Any preferred neighborhood or area?",
}

# Implementation example
# Key operations: agent orchestration, tool integration, prompt construction
CUSTOMER_SUPPORT_PROMPT = """You are Aria, a customer support specialist for TechFlow,
an electronics retailer. Follow these guidelines precisely.

## Identity and Tone
- Professional yet warm; use the customer's name when available
- Empathetic but efficient; acknowledge frustration, then move to solutions
- Never sarcastic, condescending, or dismissive
- Use simple language; avoid technical jargon unless the customer uses it first

## Capabilities
You CAN:
- Look up order status using the check_order tool
- Process returns and exchanges for orders within 30 days
- Apply discount codes and promotional offers
- Update shipping addresses before an order ships
- Provide product information and recommendations

You CANNOT:
- Modify payment methods or access credit card information
- Override manager-level pricing decisions
- Make promises about delivery dates beyond what the system shows
- Discuss competitor products or pricing

## Response Format
- Keep responses under 3 sentences unless a detailed explanation is needed
- Use bullet points for multi-step instructions
- Always end with a clear next step or question

## Escalation Rules
Transfer to a human agent when:
- The customer requests a human three or more times
- The issue involves a billing dispute over $100
- The customer expresses extreme distress or uses threatening language
- You cannot resolve the issue within 5 exchanges

## Safety and Compliance
- Never share other customers' information
- Do not speculate about internal company policies
- If unsure about a policy, say "Let me check on that" and escalate
"""

# Define ConversationTurn, DialoguePipeline; implement __init__, process_turn, _update_state
# Key operations: prompt construction, API interaction
from dataclasses import dataclass, field
from typing import Optional
from openai import OpenAI
import json

client = OpenAI()

@dataclass
class ConversationTurn:
 role: str
 content: str
 metadata: dict = field(default_factory=dict)

class DialoguePipeline:
 """End-to-end dialogue pipeline combining state tracking,
 policy, and generation."""

 def __init__(self, system_prompt: str, state_schema: dict):
 self.system_prompt = system_prompt
 self.state = state_schema.copy()
 self.history: list[ConversationTurn] = []
 self.max_history = 20 # Keep last N turns

 def process_turn(self, user_message: str) -> str:
 """Process one turn of dialogue and return the response."""

 # 1. Add user message to history
 self.history.append(
 ConversationTurn(role="user", content=user_message)
 )

 # 2. Update dialogue state
 self._update_state(user_message)

 # 3. Determine next action
 action = self._get_policy_action()

 # 4. Generate response using LLM
 response = self._generate_response(action)

 # 5. Add assistant response to history
 self.history.append(
 ConversationTurn(
 role="assistant", content=response,
 metadata={"action": action, "state": self.state.copy()}
 )
 )

 # 6. Trim history if needed
 if len(self.history) > self.max_history:
 self.history = self.history[-self.max_history:]

 return response

 def _update_state(self, user_message: str):
 """Extract slot values from user message."""
 messages = [
 {"role": "system", "content": (
 "Extract slot values from the user message. "
 f"Current state: {json.dumps(self.state)}. "
 "Return JSON with only newly mentioned slots."
 )},
 {"role": "user", "content": user_message}
 ]
 resp = client.chat.completions.create(
 model="gpt-4o-mini", messages=messages,
 response_format={"type": "json_object"}, temperature=0
 )
 new_values = json.loads(resp.choices[0].message.content)
 self.state.update(new_values)

 def _get_policy_action(self) -> dict:
 """Simple rule-based policy for next action."""
 required = ["cuisine", "party_size", "date", "time"]
 missing = [s for s in required if not self.state.get(s)]

 if missing:
 return {"type": "request", "slot": missing[0]}
 return {"type": "confirm"}

 def _generate_response(self, action: dict) -> str:
 """Generate natural language response for the chosen action."""
 context = (
 f"\n\nCurrent booking state: {json.dumps(self.state)}"
 f"\nNext action: {json.dumps(action)}"
 )
 messages = [
 {"role": "system",
 "content": self.system_prompt + context},
 *[{"role": t.role, "content": t.content}
 for t in self.history[-10:]]
 ]
 resp = client.chat.completions.create(
 model="gpt-4o", messages=messages, temperature=0.7
 )
 return resp.choices[0].message.content

# Define ConversationMode, HybridDialogueRouter; implement classify_intent, __init__, handle_message
# Key operations: RAG pipeline, agent orchestration, prompt construction
from enum import Enum

class ConversationMode(Enum):
 TASK = "task"
 CHITCHAT = "chitchat"
 FAQ = "faq"
 ESCALATION = "escalation"

def classify_intent(user_message: str, context: list) -> ConversationMode:
 """Route user message to the appropriate dialogue mode."""

 classification_prompt = """Classify the user's intent into one of these categories:
- TASK: User wants to perform a specific action (book, order, cancel, modify, check status)
- CHITCHAT: Casual conversation, greetings, small talk, opinions
- FAQ: Asking a question about products, policies, or general information
- ESCALATION: Requesting a human agent, expressing frustration, threatening

Recent context: {context}
User message: {message}

Respond with exactly one word: TASK, CHITCHAT, FAQ, or ESCALATION"""

 response = client.chat.completions.create(
 model="gpt-4o-mini",
 messages=[{
 "role": "user",
 "content": classification_prompt.format(
 context=context[-3:] if context else "None",
 message=user_message
 )
 }],
 temperature=0,
 max_tokens=10
 )

 label = response.choices[0].message.content.strip().upper()
 return ConversationMode(label.lower())

class HybridDialogueRouter:
 """Routes conversations between task and freeform modes."""

 def __init__(self):
 self.task_pipeline = None # TaskDialoguePipeline instance
 self.chitchat_pipeline = None # OpenDomainPipeline instance
 self.faq_pipeline = None # RAG-based Q&A
 self.active_mode = None

 def handle_message(self, user_message: str, history: list) -> str:
 mode = classify_intent(user_message, history)

 # If already in a task, stay in task mode unless explicitly leaving
 if (self.active_mode == ConversationMode.TASK
 and mode == ConversationMode.CHITCHAT):
 # Check if the task is still active
 if self.task_pipeline and not self.task_pipeline.is_complete:
 mode = ConversationMode.TASK # Stay in task mode

 self.active_mode = mode

 if mode == ConversationMode.TASK:
 return self.task_pipeline.process(user_message)
 elif mode == ConversationMode.FAQ:
 return self.faq_pipeline.answer(user_message)
 elif mode == ConversationMode.ESCALATION:
 return self._handle_escalation(user_message)
 else:
 return self.chitchat_pipeline.respond(user_message)

 def _handle_escalation(self, message: str) -> str:
 return ("I understand your concern. Let me connect you with "
 "a human agent who can help further. Please hold.")