40 KiB
40 KiB
AI Assistant Development
You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natural language understanding, context management, and seamless integrations.
Context
The user needs to develop an AI assistant or chatbot with natural language capabilities, intelligent responses, and practical functionality. Focus on creating production-ready assistants that provide real value to users.
Requirements
$ARGUMENTS
Instructions
1. AI Assistant Architecture
Design comprehensive assistant architecture:
Assistant Architecture Framework
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from abc import ABC, abstractmethod
import asyncio
@dataclass
class ConversationContext:
"""Maintains conversation state and context"""
user_id: str
session_id: str
messages: List[Dict[str, Any]]
user_profile: Dict[str, Any]
conversation_state: Dict[str, Any]
metadata: Dict[str, Any]
class AIAssistantArchitecture:
def __init__(self, config: Dict[str, Any]):
self.config = config
self.components = self._initialize_components()
def design_architecture(self):
"""Design comprehensive AI assistant architecture"""
return {
'core_components': {
'nlu': self._design_nlu_component(),
'dialog_manager': self._design_dialog_manager(),
'response_generator': self._design_response_generator(),
'context_manager': self._design_context_manager(),
'integration_layer': self._design_integration_layer()
},
'data_flow': self._design_data_flow(),
'deployment': self._design_deployment_architecture(),
'scalability': self._design_scalability_features()
}
def _design_nlu_component(self):
"""Natural Language Understanding component"""
return {
'intent_recognition': {
'model': 'transformer-based classifier',
'features': [
'Multi-intent detection',
'Confidence scoring',
'Fallback handling'
],
'implementation': '''
class IntentClassifier:
def __init__(self, model_path: str, *, config: Optional[Dict[str, Any]] = None):
self.model = self.load_model(model_path)
self.intents = self.load_intent_schema()
default_config = {"threshold": 0.65}
self.config = {**default_config, **(config or {})}
async def classify(self, text: str) -> Dict[str, Any]:
# Preprocess text
processed = self.preprocess(text)
# Get model predictions
predictions = await self.model.predict(processed)
# Extract intents with confidence
intents = []
for intent, confidence in predictions:
if confidence > self.config['threshold']:
intents.append({
'name': intent,
'confidence': confidence,
'parameters': self.extract_parameters(text, intent)
})
return {
'intents': intents,
'primary_intent': intents[0] if intents else None,
'requires_clarification': len(intents) > 1
}
'''
},
'entity_extraction': {
'model': 'NER with custom entities',
'features': [
'Domain-specific entities',
'Contextual extraction',
'Entity resolution'
]
},
'sentiment_analysis': {
'model': 'Fine-tuned sentiment classifier',
'features': [
'Emotion detection',
'Urgency classification',
'User satisfaction tracking'
]
}
}
def _design_dialog_manager(self):
"""Dialog management system"""
return '''
class DialogManager:
"""Manages conversation flow and state"""
def __init__(self):
self.state_machine = ConversationStateMachine()
self.policy_network = DialogPolicy()
async def process_turn(self,
context: ConversationContext,
nlu_result: Dict[str, Any]) -> Dict[str, Any]:
# Determine current state
current_state = self.state_machine.get_state(context)
# Apply dialog policy
action = await self.policy_network.select_action(
current_state,
nlu_result,
context
)
# Execute action
result = await self.execute_action(action, context)
# Update state
new_state = self.state_machine.transition(
current_state,
action,
result
)
return {
'action': action,
'new_state': new_state,
'response_data': result
}
async def execute_action(self, action: str, context: ConversationContext):
"""Execute dialog action"""
action_handlers = {
'greet': self.handle_greeting,
'provide_info': self.handle_information_request,
'clarify': self.handle_clarification,
'confirm': self.handle_confirmation,
'execute_task': self.handle_task_execution,
'end_conversation': self.handle_conversation_end
}
handler = action_handlers.get(action, self.handle_unknown)
return await handler(context)
'''
2. Natural Language Processing
Implement advanced NLP capabilities:
NLP Pipeline Implementation
class NLPPipeline:
def __init__(self):
self.tokenizer = self._initialize_tokenizer()
self.embedder = self._initialize_embedder()
self.models = self._load_models()
async def process_message(self, message: str, context: ConversationContext):
"""Process user message through NLP pipeline"""
# Tokenization and preprocessing
tokens = self.tokenizer.tokenize(message)
# Generate embeddings
embeddings = await self.embedder.embed(tokens)
# Parallel processing of NLP tasks
tasks = [
self.detect_intent(embeddings),
self.extract_entities(tokens, embeddings),
self.analyze_sentiment(embeddings),
self.detect_language(tokens),
self.check_spelling(tokens)
]
results = await asyncio.gather(*tasks)
return {
'intent': results[0],
'entities': results[1],
'sentiment': results[2],
'language': results[3],
'corrections': results[4],
'original_message': message,
'processed_tokens': tokens
}
async def detect_intent(self, embeddings):
"""Advanced intent detection"""
# Multi-label classification
intent_scores = await self.models['intent_classifier'].predict(embeddings)
# Hierarchical intent detection
primary_intent = self.get_primary_intent(intent_scores)
sub_intents = self.get_sub_intents(primary_intent, embeddings)
return {
'primary': primary_intent,
'secondary': sub_intents,
'confidence': max(intent_scores.values()),
'all_scores': intent_scores
}
def extract_entities(self, tokens, embeddings):
"""Extract and resolve entities"""
# Named Entity Recognition
entities = self.models['ner'].extract(tokens, embeddings)
# Entity linking and resolution
resolved_entities = []
for entity in entities:
resolved = self.resolve_entity(entity)
resolved_entities.append({
'text': entity['text'],
'type': entity['type'],
'resolved_value': resolved['value'],
'confidence': resolved['confidence'],
'alternatives': resolved.get('alternatives', [])
})
return resolved_entities
def build_semantic_understanding(self, nlu_result, context):
"""Build semantic representation of user intent"""
return {
'user_goal': self.infer_user_goal(nlu_result, context),
'required_information': self.identify_missing_info(nlu_result),
'constraints': self.extract_constraints(nlu_result),
'preferences': self.extract_preferences(nlu_result, context)
}
3. Conversation Flow Design
Design intelligent conversation flows:
Conversation Flow Engine
class ConversationFlowEngine:
def __init__(self):
self.flows = self._load_conversation_flows()
self.state_tracker = StateTracker()
def design_conversation_flow(self):
"""Design multi-turn conversation flows"""
return {
'greeting_flow': {
'triggers': ['hello', 'hi', 'greetings'],
'nodes': [
{
'id': 'greet_user',
'type': 'response',
'content': self.personalized_greeting,
'next': 'ask_how_to_help'
},
{
'id': 'ask_how_to_help',
'type': 'question',
'content': "How can I assist you today?",
'expected_intents': ['request_help', 'ask_question'],
'timeout': 30,
'timeout_action': 'offer_suggestions'
}
]
},
'task_completion_flow': {
'triggers': ['task_request'],
'nodes': [
{
'id': 'understand_task',
'type': 'nlu_processing',
'extract': ['task_type', 'parameters'],
'next': 'check_requirements'
},
{
'id': 'check_requirements',
'type': 'validation',
'validate': self.validate_task_requirements,
'on_success': 'confirm_task',
'on_missing': 'request_missing_info'
},
{
'id': 'request_missing_info',
'type': 'slot_filling',
'slots': self.get_required_slots,
'prompts': self.get_slot_prompts,
'next': 'confirm_task'
},
{
'id': 'confirm_task',
'type': 'confirmation',
'content': self.generate_task_summary,
'on_confirm': 'execute_task',
'on_deny': 'clarify_task'
}
]
}
}
async def execute_flow(self, flow_id: str, context: ConversationContext):
"""Execute a conversation flow"""
flow = self.flows[flow_id]
current_node = flow['nodes'][0]
while current_node:
result = await self.execute_node(current_node, context)
# Determine next node
if result.get('user_input'):
next_node_id = self.determine_next_node(
current_node,
result['user_input'],
context
)
else:
next_node_id = current_node.get('next')
current_node = self.get_node(flow, next_node_id)
# Update context
context.conversation_state.update(result.get('state_updates', {}))
return context
4. Response Generation
Create intelligent response generation:
Response Generator
class ResponseGenerator:
def __init__(self, llm_client=None):
self.llm = llm_client
self.templates = self._load_response_templates()
self.personality = self._load_personality_config()
async def generate_response(self,
intent: str,
context: ConversationContext,
data: Dict[str, Any]) -> str:
"""Generate contextual responses"""
# Select response strategy
if self.should_use_template(intent):
response = self.generate_from_template(intent, data)
elif self.should_use_llm(intent, context):
response = await self.generate_with_llm(intent, context, data)
else:
response = self.generate_hybrid_response(intent, context, data)
# Apply personality and tone
response = self.apply_personality(response, context)
# Ensure response appropriateness
response = self.validate_response(response, context)
return response
async def generate_with_llm(self, intent, context, data):
"""Generate response using LLM"""
# Construct prompt
prompt = self.build_llm_prompt(intent, context, data)
# Set generation parameters
params = {
'temperature': self.get_temperature(intent),
'max_tokens': 150,
'stop_sequences': ['\n\n', 'User:', 'Human:']
}
# Generate response
response = await self.llm.generate(prompt, **params)
# Post-process response
return self.post_process_llm_response(response)
def build_llm_prompt(self, intent, context, data):
"""Build context-aware prompt for LLM"""
return f"""
You are a helpful AI assistant with the following characteristics:
{self.personality.description}
Conversation history:
{self.format_conversation_history(context.messages[-5:])}
User intent: {intent}
Relevant data: {json.dumps(data, indent=2)}
Generate a helpful, concise response that:
1. Addresses the user's intent
2. Uses the provided data appropriately
3. Maintains conversation continuity
4. Follows the personality guidelines
Response:"""
def generate_from_template(self, intent, data):
"""Generate response from templates"""
template = self.templates.get(intent)
if not template:
return self.get_fallback_response()
# Select template variant
variant = self.select_template_variant(template, data)
# Fill template slots
response = variant
for key, value in data.items():
response = response.replace(f"{{{key}}}", str(value))
return response
def apply_personality(self, response, context):
"""Apply personality traits to response"""
# Add personality markers
if self.personality.get('friendly'):
response = self.add_friendly_markers(response)
if self.personality.get('professional'):
response = self.ensure_professional_tone(response)
# Adjust based on user preferences
if context.user_profile.get('prefers_brief'):
response = self.make_concise(response)
return response
5. Context Management
Implement sophisticated context management:
Context Management System
class ContextManager:
def __init__(self):
self.short_term_memory = ShortTermMemory()
self.long_term_memory = LongTermMemory()
self.working_memory = WorkingMemory()
async def manage_context(self,
new_input: Dict[str, Any],
current_context: ConversationContext) -> ConversationContext:
"""Manage conversation context"""
# Update conversation history
current_context.messages.append({
'role': 'user',
'content': new_input['message'],
'timestamp': datetime.now(),
'metadata': new_input.get('metadata', {})
})
# Resolve references
resolved_input = await self.resolve_references(new_input, current_context)
# Update working memory
self.working_memory.update(resolved_input, current_context)
# Detect topic changes
topic_shift = self.detect_topic_shift(resolved_input, current_context)
if topic_shift:
current_context = self.handle_topic_shift(topic_shift, current_context)
# Maintain entity state
current_context = self.update_entity_state(resolved_input, current_context)
# Prune old context if needed
if len(current_context.messages) > self.config['max_context_length']:
current_context = self.prune_context(current_context)
return current_context
async def resolve_references(self, input_data, context):
"""Resolve pronouns and references"""
text = input_data['message']
# Pronoun resolution
pronouns = self.extract_pronouns(text)
for pronoun in pronouns:
referent = self.find_referent(pronoun, context)
if referent:
text = text.replace(pronoun['text'], referent['resolved'])
# Temporal reference resolution
temporal_refs = self.extract_temporal_references(text)
for ref in temporal_refs:
resolved_time = self.resolve_temporal_reference(ref, context)
text = text.replace(ref['text'], str(resolved_time))
input_data['resolved_message'] = text
return input_data
def maintain_entity_state(self):
"""Track entity states across conversation"""
return '''
class EntityStateTracker:
def __init__(self):
self.entities = {}
def update_entity(self, entity_id: str, updates: Dict[str, Any]):
"""Update entity state"""
if entity_id not in self.entities:
self.entities[entity_id] = {
'id': entity_id,
'type': updates.get('type'),
'attributes': {},
'history': []
}
# Record history
self.entities[entity_id]['history'].append({
'timestamp': datetime.now(),
'updates': updates
})
# Apply updates
self.entities[entity_id]['attributes'].update(updates)
def get_entity_state(self, entity_id: str) -> Optional[Dict[str, Any]]:
"""Get current entity state"""
return self.entities.get(entity_id)
def query_entities(self, entity_type: str = None, **filters):
"""Query entities by type and attributes"""
results = []
for entity in self.entities.values():
if entity_type and entity['type'] != entity_type:
continue
matches = True
for key, value in filters.items():
if entity['attributes'].get(key) != value:
matches = False
break
if matches:
results.append(entity)
return results
'''
6. Integration with LLMs
Integrate with various LLM providers:
LLM Integration Layer
class LLMIntegrationLayer:
def __init__(self):
self.providers = {
'openai': OpenAIProvider(),
'anthropic': AnthropicProvider(),
'local': LocalLLMProvider()
}
self.current_provider = None
async def setup_llm_integration(self, provider: str, config: Dict[str, Any]):
"""Setup LLM integration"""
self.current_provider = self.providers[provider]
await self.current_provider.initialize(config)
return {
'provider': provider,
'capabilities': self.current_provider.get_capabilities(),
'rate_limits': self.current_provider.get_rate_limits()
}
async def generate_completion(self,
prompt: str,
system_prompt: str = None,
**kwargs):
"""Generate completion with fallback handling"""
try:
# Primary attempt
response = await self.current_provider.complete(
prompt=prompt,
system_prompt=system_prompt,
**kwargs
)
# Validate response
if self.is_valid_response(response):
return response
else:
return await self.handle_invalid_response(prompt, response)
except RateLimitError:
# Switch to fallback provider
return await self.use_fallback_provider(prompt, system_prompt, **kwargs)
except Exception as e:
# Log error and use cached response if available
return self.get_cached_response(prompt) or self.get_default_response()
def create_function_calling_interface(self):
"""Create function calling interface for LLMs"""
return '''
class FunctionCallingInterface:
def __init__(self):
self.functions = {}
def register_function(self,
name: str,
func: callable,
description: str,
parameters: Dict[str, Any]):
"""Register a function for LLM to call"""
self.functions[name] = {
'function': func,
'description': description,
'parameters': parameters
}
async def process_function_call(self, llm_response):
"""Process function calls from LLM"""
if 'function_call' not in llm_response:
return llm_response
function_name = llm_response['function_call']['name']
arguments = llm_response['function_call']['arguments']
if function_name not in self.functions:
return {'error': f'Unknown function: {function_name}'}
# Validate arguments
validated_args = self.validate_arguments(
function_name,
arguments
)
# Execute function
result = await self.functions[function_name]['function'](**validated_args)
# Return result for LLM to process
return {
'function_result': result,
'function_name': function_name
}
'''
7. Testing Conversational AI
Implement comprehensive testing:
Conversation Testing Framework
class ConversationTestFramework:
def __init__(self):
self.test_suites = []
self.metrics = ConversationMetrics()
def create_test_suite(self):
"""Create comprehensive test suite"""
return {
'unit_tests': self._create_unit_tests(),
'integration_tests': self._create_integration_tests(),
'conversation_tests': self._create_conversation_tests(),
'performance_tests': self._create_performance_tests(),
'user_simulation': self._create_user_simulation()
}
def _create_conversation_tests(self):
"""Test multi-turn conversations"""
return '''
class ConversationTest:
async def test_multi_turn_conversation(self):
"""Test complete conversation flow"""
assistant = AIAssistant()
context = ConversationContext(user_id="test_user")
# Conversation script
conversation = [
{
'user': "Hello, I need help with my order",
'expected_intent': 'order_help',
'expected_action': 'ask_order_details'
},
{
'user': "My order number is 12345",
'expected_entities': [{'type': 'order_id', 'value': '12345'}],
'expected_action': 'retrieve_order'
},
{
'user': "When will it arrive?",
'expected_intent': 'delivery_inquiry',
'should_use_context': True
}
]
for turn in conversation:
# Send user message
response = await assistant.process_message(
turn['user'],
context
)
# Validate intent detection
if 'expected_intent' in turn:
assert response['intent'] == turn['expected_intent']
# Validate entity extraction
if 'expected_entities' in turn:
self.validate_entities(
response['entities'],
turn['expected_entities']
)
# Validate context usage
if turn.get('should_use_context'):
assert 'order_id' in response['context_used']
def test_error_handling(self):
"""Test error scenarios"""
error_cases = [
{
'input': "askdjfkajsdf",
'expected_behavior': 'fallback_response'
},
{
'input': "I want to [REDACTED]",
'expected_behavior': 'safety_response'
},
{
'input': "Tell me about " + "x" * 1000,
'expected_behavior': 'length_limit_response'
}
]
for case in error_cases:
response = assistant.process_message(case['input'])
assert response['behavior'] == case['expected_behavior']
'''
def create_automated_testing(self):
"""Automated conversation testing"""
return '''
class AutomatedConversationTester:
def __init__(self):
self.test_generator = TestCaseGenerator()
self.evaluator = ResponseEvaluator()
async def run_automated_tests(self, num_tests: int = 100):
"""Run automated conversation tests"""
results = {
'total_tests': num_tests,
'passed': 0,
'failed': 0,
'metrics': {}
}
for i in range(num_tests):
# Generate test case
test_case = self.test_generator.generate()
# Run conversation
conversation_log = await self.run_conversation(test_case)
# Evaluate results
evaluation = self.evaluator.evaluate(
conversation_log,
test_case['expectations']
)
if evaluation['passed']:
results['passed'] += 1
else:
results['failed'] += 1
# Collect metrics
self.update_metrics(results['metrics'], evaluation['metrics'])
return results
def generate_adversarial_tests(self):
"""Generate adversarial test cases"""
return [
# Ambiguous inputs
"I want that thing we discussed",
# Context switching
"Actually, forget that. Tell me about the weather",
# Multiple intents
"Cancel my order and also update my address",
# Incomplete information
"Book a flight",
# Contradictions
"I want a vegetarian meal with bacon"
]
'''
8. Deployment and Scaling
Deploy and scale AI assistants:
Deployment Architecture
class AssistantDeployment:
def create_deployment_architecture(self):
"""Create scalable deployment architecture"""
return {
'containerization': '''
# Dockerfile for AI Assistant
FROM python:3.11-slim
WORKDIR /app
# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy application
COPY . .
# Load models at build time
RUN python -m app.model_loader
# Expose port
EXPOSE 8080
# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
CMD python -m app.health_check
# Run application
CMD ["gunicorn", "--worker-class", "uvicorn.workers.UvicornWorker", \
"--workers", "4", "--bind", "0.0.0.0:8080", "app.main:app"]
''',
'kubernetes_deployment': '''
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-assistant
spec:
replicas: 3
selector:
matchLabels:
app: ai-assistant
template:
metadata:
labels:
app: ai-assistant
spec:
containers:
- name: assistant
image: ai-assistant:latest
ports:
- containerPort: 8080
resources:
requests:
memory: "2Gi"
cpu: "1000m"
limits:
memory: "4Gi"
cpu: "2000m"
env:
- name: MODEL_CACHE_SIZE
value: "1000"
- name: MAX_CONCURRENT_SESSIONS
value: "100"
livenessProbe:
httpGet:
path: /health
port: 8080
periodSeconds: 10
readinessProbe:
httpGet:
path: /ready
port: 8080
periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
name: ai-assistant-service
spec:
selector:
app: ai-assistant
ports:
- port: 80
targetPort: 8080
type: LoadBalancer
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
name: ai-assistant-hpa
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: ai-assistant
minReplicas: 3
maxReplicas: 10
metrics:
- type: Resource
resource:
name: cpu
target:
type: Utilization
averageUtilization: 70
- type: Resource
resource:
name: memory
target:
type: Utilization
averageUtilization: 80
''',
'caching_strategy': self._design_caching_strategy(),
'load_balancing': self._design_load_balancing()
}
def _design_caching_strategy(self):
"""Design caching for performance"""
return '''
class AssistantCache:
def __init__(self):
self.response_cache = ResponseCache()
self.model_cache = ModelCache()
self.context_cache = ContextCache()
async def get_cached_response(self,
message: str,
context_hash: str) -> Optional[str]:
"""Get cached response if available"""
cache_key = self.generate_cache_key(message, context_hash)
# Check response cache
cached = await self.response_cache.get(cache_key)
if cached and not self.is_expired(cached):
return cached['response']
return None
def cache_response(self,
message: str,
context_hash: str,
response: str,
ttl: int = 3600):
"""Cache response with TTL"""
cache_key = self.generate_cache_key(message, context_hash)
self.response_cache.set(
cache_key,
{
'response': response,
'timestamp': datetime.now(),
'ttl': ttl
}
)
def preload_model_cache(self):
"""Preload frequently used models"""
models_to_cache = [
'intent_classifier',
'entity_extractor',
'response_generator'
]
for model_name in models_to_cache:
model = load_model(model_name)
self.model_cache.store(model_name, model)
'''
9. Monitoring and Analytics
Monitor assistant performance:
Assistant Analytics System
class AssistantAnalytics:
def __init__(self):
self.metrics_collector = MetricsCollector()
self.analytics_engine = AnalyticsEngine()
def create_monitoring_dashboard(self):
"""Create monitoring dashboard configuration"""
return {
'real_time_metrics': {
'active_sessions': 'gauge',
'messages_per_second': 'counter',
'response_time_p95': 'histogram',
'intent_accuracy': 'gauge',
'fallback_rate': 'gauge'
},
'conversation_metrics': {
'avg_conversation_length': 'gauge',
'completion_rate': 'gauge',
'user_satisfaction': 'gauge',
'escalation_rate': 'gauge'
},
'system_metrics': {
'model_inference_time': 'histogram',
'cache_hit_rate': 'gauge',
'error_rate': 'counter',
'resource_utilization': 'gauge'
},
'alerts': [
{
'name': 'high_fallback_rate',
'condition': 'fallback_rate > 0.2',
'severity': 'warning'
},
{
'name': 'slow_response_time',
'condition': 'response_time_p95 > 2000',
'severity': 'critical'
}
]
}
def analyze_conversation_quality(self):
"""Analyze conversation quality metrics"""
return '''
class ConversationQualityAnalyzer:
def analyze_conversations(self, time_range: str):
"""Analyze conversation quality"""
conversations = self.fetch_conversations(time_range)
metrics = {
'intent_recognition': self.analyze_intent_accuracy(conversations),
'response_relevance': self.analyze_response_relevance(conversations),
'conversation_flow': self.analyze_conversation_flow(conversations),
'user_satisfaction': self.analyze_satisfaction(conversations),
'error_patterns': self.identify_error_patterns(conversations)
}
return self.generate_quality_report(metrics)
def identify_improvement_areas(self, analysis):
"""Identify areas for improvement"""
improvements = []
# Low intent accuracy
if analysis['intent_recognition']['accuracy'] < 0.85:
improvements.append({
'area': 'Intent Recognition',
'issue': 'Low accuracy in intent detection',
'recommendation': 'Retrain intent classifier with more examples',
'priority': 'high'
})
# High fallback rate
if analysis['conversation_flow']['fallback_rate'] > 0.15:
improvements.append({
'area': 'Coverage',
'issue': 'High fallback rate',
'recommendation': 'Expand training data for uncovered intents',
'priority': 'medium'
})
return improvements
'''
10. Continuous Improvement
Implement continuous improvement cycle:
Improvement Pipeline
class ContinuousImprovement:
def create_improvement_pipeline(self):
"""Create continuous improvement pipeline"""
return {
'data_collection': '''
class ConversationDataCollector:
async def collect_feedback(self, session_id: str):
"""Collect user feedback"""
feedback_prompt = {
'satisfaction': 'How satisfied were you with this conversation? (1-5)',
'resolved': 'Was your issue resolved?',
'improvements': 'How could we improve?'
}
feedback = await self.prompt_user_feedback(
session_id,
feedback_prompt
)
# Store feedback
await self.store_feedback({
'session_id': session_id,
'timestamp': datetime.now(),
'feedback': feedback,
'conversation_metadata': self.get_session_metadata(session_id)
})
return feedback
def identify_training_opportunities(self):
"""Identify conversations for training"""
# Find low-confidence interactions
low_confidence = self.find_low_confidence_interactions()
# Find failed conversations
failed = self.find_failed_conversations()
# Find highly-rated conversations
exemplary = self.find_exemplary_conversations()
return {
'needs_improvement': low_confidence + failed,
'good_examples': exemplary
}
''',
'model_retraining': '''
class ModelRetrainer:
async def retrain_models(self, new_data):
"""Retrain models with new data"""
# Prepare training data
training_data = self.prepare_training_data(new_data)
# Validate data quality
validation_result = self.validate_training_data(training_data)
if not validation_result['passed']:
return {'error': 'Data quality check failed', 'issues': validation_result['issues']}
# Retrain models
models_to_retrain = ['intent_classifier', 'entity_extractor']
for model_name in models_to_retrain:
# Load current model
current_model = self.load_model(model_name)
# Create new version
new_model = await self.train_model(
model_name,
training_data,
base_model=current_model
)
# Evaluate new model
evaluation = await self.evaluate_model(
new_model,
self.get_test_set()
)
# Deploy if improved
if evaluation['performance'] > current_model.performance:
await self.deploy_model(new_model, model_name)
return {'status': 'completed', 'models_updated': models_to_retrain}
''',
'a_b_testing': '''
class ABTestingFramework:
def create_ab_test(self,
test_name: str,
variants: List[Dict[str, Any]],
metrics: List[str]):
"""Create A/B test for assistant improvements"""
test = {
'id': generate_test_id(),
'name': test_name,
'variants': variants,
'metrics': metrics,
'allocation': self.calculate_traffic_allocation(variants),
'duration': self.estimate_test_duration(metrics)
}
# Deploy test
self.deploy_test(test)
return test
async def analyze_test_results(self, test_id: str):
"""Analyze A/B test results"""
data = await self.collect_test_data(test_id)
results = {}
for metric in data['metrics']:
# Statistical analysis
analysis = self.statistical_analysis(
data['control'][metric],
data['variant'][metric]
)
results[metric] = {
'control_mean': analysis['control_mean'],
'variant_mean': analysis['variant_mean'],
'lift': analysis['lift'],
'p_value': analysis['p_value'],
'significant': analysis['p_value'] < 0.05
}
return results
'''
}
Output Format
- Architecture Design: Complete AI assistant architecture with components
- NLP Implementation: Natural language processing pipeline and models
- Conversation Flows: Dialog management and flow design
- Response Generation: Intelligent response creation with LLM integration
- Context Management: Sophisticated context and state management
- Testing Framework: Comprehensive testing for conversational AI
- Deployment Guide: Scalable deployment architecture
- Monitoring Setup: Analytics and performance monitoring
- Improvement Pipeline: Continuous improvement processes
Focus on creating production-ready AI assistants that provide real value through natural conversations, intelligent responses, and continuous learning from user interactions.