INTRODUCTION
In the rapidly evolving landscape of electronic design automation, artificial intelligence has begun to transform how engineers approach circuit design and component procurement. This article explores the development of an intelligent LLM-powered agent that bridges the gap between conceptual circuit requirements and practical implementation by generating ASCII-based circuit diagrams and creating comprehensive bills of materials with real-world sourcing information.
The core challenge in electronic prototyping often lies not just in designing the circuit itself, but in the subsequent procurement process. Engineers frequently find themselves spending considerable time researching component availability, comparing prices across different suppliers, and ensuring compatibility between parts from various manufacturers. Our proposed LLM agent addresses this workflow by integrating circuit design generation with intelligent parts sourcing, creating a seamless experience from initial concept to component ordering.
SYSTEM ARCHITECTURE AND CORE COMPONENTS
The LLM Circuit Agent operates as a conversational system that guides users through the entire process of circuit design and procurement. The architecture consists of several interconnected modules that work together to deliver a comprehensive solution. The conversation manager handles user interactions and maintains context throughout the design process. The circuit generator creates ASCII representations of electronic circuits based on user requirements. The web search integration module queries online retailers and component databases to find suitable parts. The BOM generator compiles detailed parts lists with pricing and availability information.
The agent begins each interaction by understanding the user's circuit requirements through natural language processing. This initial phase is crucial because it establishes the foundation for all subsequent operations. The system must parse technical specifications, understand component preferences, and identify any constraints or special requirements the user might have.
Let me demonstrate the conversation manager implementation, which serves as the entry point for all user interactions:
class CircuitDesignAgent:
def __init__(self):
self.conversation_state = {
'circuit_type': None,
'specifications': {},
'preferred_suppliers': [],
'country': None,
'design_choices': {},
'current_phase': 'initial'
}
self.circuit_generator = ASCIICircuitGenerator()
self.parts_searcher = PartsSearcher()
self.bom_generator = BOMGenerator()
def process_user_input(self, user_message):
if self.conversation_state['current_phase'] == 'initial':
return self.handle_initial_request(user_message)
elif self.conversation_state['current_phase'] == 'supplier_selection':
return self.handle_supplier_selection(user_message)
elif self.conversation_state['current_phase'] == 'design_choices':
return self.handle_design_choices(user_message)
else:
return self.handle_general_query(user_message)
def handle_initial_request(self, user_message):
circuit_specs = self.parse_circuit_requirements(user_message)
self.conversation_state['specifications'] = circuit_specs
self.conversation_state['current_phase'] = 'supplier_selection'
response = f"I understand you want to build a {circuit_specs['type']} circuit. "
response += "To help you source the components, please let me know your preferred suppliers. "
response += "You can specify online retailers like Digi-Key, Mouser, or Amazon, "
response += "or simply tell me your country and I'll suggest local suppliers."
return response
This conversation manager implementation demonstrates how the agent maintains state throughout the interaction. The system tracks the current phase of the conversation and routes user inputs to appropriate handlers. The initial request handler extracts circuit specifications from natural language descriptions and transitions the conversation to the supplier selection phase.
USER INTERACTION FLOW AND REQUIREMENT PARSING
The user interaction begins when someone describes their circuit needs in natural language. The agent must interpret these requirements and extract technical specifications that can be used for circuit generation. This process involves understanding both explicit requirements like voltage levels and implicit constraints like power consumption or physical size limitations.
The requirement parsing system uses a combination of keyword extraction and contextual understanding to identify circuit parameters. When a user says "I need a simple LED driver for a 12V supply that can handle 500mA", the system must extract the supply voltage, current requirement, and circuit type. Additionally, it should infer related specifications like the need for current regulation and thermal considerations.
Here's the implementation of the requirement parsing system:
import re
from typing import Dict, List, Optional
class CircuitRequirementParser:
def __init__(self):
self.voltage_pattern = re.compile(r'(\d+(?:\.\d+)?)\s*[Vv](?:olts?)?')
self.current_pattern = re.compile(r'(\d+(?:\.\d+)?)\s*([mM]?[Aa])(?:mps?)?')
self.frequency_pattern = re.compile(r'(\d+(?:\.\d+)?)\s*([kKmMgG]?[Hh]z)')
self.circuit_types = {
'led driver': 'LED_DRIVER',
'amplifier': 'AMPLIFIER',
'power supply': 'POWER_SUPPLY',
'oscillator': 'OSCILLATOR',
'filter': 'FILTER',
'voltage regulator': 'VOLTAGE_REGULATOR'
}
def parse_requirements(self, user_input: str) -> Dict:
requirements = {
'type': self.identify_circuit_type(user_input),
'voltage_supply': self.extract_voltage(user_input),
'current_requirement': self.extract_current(user_input),
'frequency': self.extract_frequency(user_input),
'special_requirements': self.extract_special_requirements(user_input)
}
return {k: v for k, v in requirements.items() if v is not None}
def identify_circuit_type(self, text: str) -> Optional[str]:
text_lower = text.lower()
for keyword, circuit_type in self.circuit_types.items():
if keyword in text_lower:
return circuit_type
return None
def extract_voltage(self, text: str) -> Optional[float]:
matches = self.voltage_pattern.findall(text)
if matches:
return float(matches[0])
return None
def extract_current(self, text: str) -> Optional[Dict]:
matches = self.current_pattern.findall(text)
if matches:
value, unit = matches[0]
multiplier = 0.001 if unit.lower().startswith('m') else 1.0
return {
'value': float(value) * multiplier,
'unit': 'A'
}
return None
This parser implementation shows how the system extracts technical specifications from natural language input. The regular expressions identify numerical values with their units, while the circuit type identification uses keyword matching. The parser returns a structured dictionary that subsequent modules can use for circuit generation and component selection.
ASCII CIRCUIT DIAGRAM GENERATION
Once the system understands the circuit requirements, it generates an ASCII representation of the circuit diagram. ASCII circuit diagrams provide a text-based visualization that can be easily shared, version-controlled, and integrated into documentation. The challenge lies in creating diagrams that are both technically accurate and visually comprehensible.
The ASCII circuit generator uses a component library approach where each electronic component has a predefined ASCII representation. Resistors are typically shown as zigzag lines, capacitors as parallel lines with gaps, and integrated circuits as rectangular blocks with labeled pins. The generator places these components on a virtual grid and connects them with ASCII line characters.
The circuit generation process begins by selecting an appropriate topology for the requested circuit type. For an LED driver, the system might choose a current source topology or a simple resistive driver depending on the specifications. The component values are calculated based on the electrical requirements, and then the ASCII representation is constructed.
class ASCIICircuitGenerator:
def __init__(self):
self.component_library = {
'resistor': {
'horizontal': '--[###]--',
'vertical': ['|', '[', '#', '#', '#', ']', '|']
},
'capacitor': {
'horizontal': '--||--',
'vertical': ['|', '|', '|', ' ', '|', '|', '|']
},
'led': {
'horizontal': '--[>|]--',
'vertical': ['|', '[', '>', '|', ']', '|']
},
'voltage_source': {
'symbol': '(+)',
'connections': ['positive', 'negative']
}
}
self.grid_width = 80
self.grid_height = 20
def generate_led_driver_circuit(self, specs: Dict) -> str:
supply_voltage = specs.get('voltage_supply', 12)
led_current = specs.get('current_requirement', {}).get('value', 0.02)
# Calculate resistor value for current limiting
led_forward_voltage = 2.0 # Typical red LED
resistor_value = (supply_voltage - led_forward_voltage) / led_current
# Create circuit layout
circuit_lines = []
circuit_lines.append("LED Driver Circuit")
circuit_lines.append("=" * 50)
circuit_lines.append("")
circuit_lines.append(f"Supply: {supply_voltage}V")
circuit_lines.append(f"LED Current: {led_current*1000:.0f}mA")
circuit_lines.append(f"Current Limiting Resistor: {resistor_value:.0f} Ohms")
circuit_lines.append("")
circuit_lines.append("Circuit Diagram:")
circuit_lines.append("")
# ASCII circuit representation
circuit_lines.append(" +12V")
circuit_lines.append(" |")
circuit_lines.append(" |")
circuit_lines.append(f" [###] R1 = {resistor_value:.0f}Ω")
circuit_lines.append(" |")
circuit_lines.append(" |")
circuit_lines.append(" [>|] LED1")
circuit_lines.append(" |")
circuit_lines.append(" |")
circuit_lines.append(" GND")
return '\n'.join(circuit_lines)
def generate_circuit(self, circuit_type: str, specifications: Dict) -> str:
if circuit_type == 'LED_DRIVER':
return self.generate_led_driver_circuit(specifications)
elif circuit_type == 'VOLTAGE_REGULATOR':
return self.generate_voltage_regulator_circuit(specifications)
else:
return self.generate_generic_circuit(circuit_type, specifications)
This circuit generator demonstrates how the system creates ASCII representations based on circuit specifications. The LED driver example shows a simple current-limiting circuit with calculated component values. The generator includes the electrical calculations alongside the visual representation, providing both the schematic and the engineering rationale.
INTERNET SEARCH INTEGRATION FOR COMPONENT SOURCING
After generating the circuit diagram, the agent must find real components that match the calculated specifications. This involves searching electronic component distributors and online retailers to find parts that meet the technical requirements while considering factors like availability, pricing, and shipping options.
The parts search system integrates with various APIs and web scraping techniques to gather component information from multiple sources. The search process must handle component tolerances, package types, and manufacturer variations. For a 470-ohm resistor, the system might find options from different manufacturers in various package sizes and tolerance ratings.
The search integration also considers the user's geographic location and preferred suppliers. A user in Europe might prefer Farnell or RS Components, while someone in North America might favor Digi-Key or Mouser. The system adapts its search strategy based on these preferences and provides relevant sourcing options.
import requests
from typing import List, Dict
import json
class PartsSearcher:
def __init__(self):
self.supplier_apis = {
'digikey': {
'base_url': 'https://api.digikey.com/v1',
'search_endpoint': '/products/search',
'api_key': None # Would be configured with actual API key
},
'mouser': {
'base_url': 'https://api.mouser.com/api/v1',
'search_endpoint': '/search/partnumber',
'api_key': None
}
}
self.component_categories = {
'resistor': 'Resistors',
'capacitor': 'Capacitors',
'led': 'LEDs',
'ic': 'Integrated Circuits'
}
def search_component(self, component_type: str, specifications: Dict,
preferred_suppliers: List[str] = None) -> List[Dict]:
results = []
if preferred_suppliers is None:
preferred_suppliers = ['digikey', 'mouser']
for supplier in preferred_suppliers:
if supplier in self.supplier_apis:
supplier_results = self.search_supplier(supplier, component_type, specifications)
results.extend(supplier_results)
return self.rank_search_results(results, specifications)
def search_supplier(self, supplier: str, component_type: str, specs: Dict) -> List[Dict]:
# This would implement actual API calls to component distributors
# For demonstration, we'll simulate the search results
if component_type == 'resistor':
return self.simulate_resistor_search(supplier, specs)
elif component_type == 'led':
return self.simulate_led_search(supplier, specs)
else:
return []
def simulate_resistor_search(self, supplier: str, specs: Dict) -> List[Dict]:
target_resistance = specs.get('resistance', 470)
tolerance = specs.get('tolerance', 0.05) # 5% default
# Simulate finding resistors with different characteristics
simulated_results = [
{
'supplier': supplier,
'part_number': f'RES-{target_resistance}-1',
'manufacturer': 'Yageo',
'description': f'{target_resistance}Ω ±5% 1/4W Resistor',
'resistance': target_resistance,
'tolerance': 0.05,
'power_rating': 0.25,
'package': 'Axial',
'price': 0.12,
'stock': 5000,
'datasheet_url': f'https://example.com/datasheet-{target_resistance}.pdf'
},
{
'supplier': supplier,
'part_number': f'RES-{target_resistance}-2',
'manufacturer': 'Vishay',
'description': f'{target_resistance}Ω ±1% 1/2W Resistor',
'resistance': target_resistance,
'tolerance': 0.01,
'power_rating': 0.5,
'package': 'Axial',
'price': 0.18,
'stock': 2500,
'datasheet_url': f'https://example.com/datasheet-{target_resistance}-precision.pdf'
}
]
return simulated_results
def rank_search_results(self, results: List[Dict], specs: Dict) -> List[Dict]:
# Rank results based on specification match, price, and availability
def score_component(component):
score = 0
# Availability score
if component['stock'] > 1000:
score += 30
elif component['stock'] > 100:
score += 20
else:
score += 10
# Price score (lower price = higher score)
if component['price'] < 0.15:
score += 25
elif component['price'] < 0.25:
score += 15
else:
score += 5
# Specification match score
if 'tolerance' in specs and 'tolerance' in component:
if component['tolerance'] <= specs['tolerance']:
score += 20
return score
results.sort(key=score_component, reverse=True)
return results
This parts search implementation demonstrates how the system queries multiple suppliers and ranks results based on various criteria. The ranking algorithm considers availability, pricing, and specification matching to present the most suitable options to the user. The simulated search results show the type of information the system would gather from real component distributors.
BILL OF MATERIALS CREATION AND OPTIMIZATION
The Bill of Materials generation represents the culmination of the circuit design and parts sourcing process. The BOM must include all necessary components with their specifications, quantities, supplier information, and pricing. The system also considers practical factors like minimum order quantities, shipping costs, and component availability.
The BOM generator takes the circuit design and search results to create a comprehensive parts list. It optimizes the selection by considering total cost, shipping consolidation opportunities, and component compatibility. When multiple suppliers offer the same component, the system selects based on the user's preferences and overall cost optimization.
The BOM also includes additional items that might not be obvious from the circuit diagram itself. For a simple LED driver, this might include a breadboard for prototyping, jumper wires for connections, and basic tools if the user is a beginner. The system can ask clarifying questions to determine the user's experience level and adjust the BOM accordingly.
from datetime import datetime
from typing import List, Dict, Tuple
class BOMGenerator:
def __init__(self):
self.additional_items = {
'prototyping': [
{
'description': 'Breadboard - Half Size',
'quantity': 1,
'category': 'prototyping',
'estimated_price': 3.50
},
{
'description': 'Jumper Wire Kit',
'quantity': 1,
'category': 'prototyping',
'estimated_price': 8.99
}
],
'tools': [
{
'description': 'Digital Multimeter',
'quantity': 1,
'category': 'tools',
'estimated_price': 25.00
}
]
}
def generate_bom(self, circuit_components: List[Dict],
search_results: Dict, user_preferences: Dict) -> Dict:
bom_items = []
total_cost = 0.0
# Process main circuit components
for component in circuit_components:
best_option = self.select_best_component_option(
component, search_results.get(component['type'], [])
)
if best_option:
bom_item = self.create_bom_item(component, best_option)
bom_items.append(bom_item)
total_cost += bom_item['total_price']
# Add additional items based on user preferences
if user_preferences.get('include_prototyping', True):
for item in self.additional_items['prototyping']:
bom_items.append(self.create_additional_bom_item(item))
total_cost += item['estimated_price']
# Generate BOM summary
bom_summary = {
'items': bom_items,
'total_cost': total_cost,
'generated_date': datetime.now().isoformat(),
'suppliers': list(set([item['supplier'] for item in bom_items if 'supplier' in item])),
'estimated_shipping': self.estimate_shipping_cost(bom_items, user_preferences)
}
return bom_summary
def select_best_component_option(self, component_spec: Dict,
available_options: List[Dict]) -> Dict:
if not available_options:
return None
# Select based on ranking from search results
# The search results are already ranked, so take the first available option
for option in available_options:
if option['stock'] > 0:
return option
return available_options[0] # Fallback to first option even if out of stock
def create_bom_item(self, component_spec: Dict, selected_option: Dict) -> Dict:
quantity = component_spec.get('quantity', 1)
unit_price = selected_option['price']
return {
'reference': component_spec.get('reference', 'Unknown'),
'description': selected_option['description'],
'manufacturer': selected_option['manufacturer'],
'part_number': selected_option['part_number'],
'supplier': selected_option['supplier'],
'quantity': quantity,
'unit_price': unit_price,
'total_price': quantity * unit_price,
'stock_status': 'In Stock' if selected_option['stock'] > 0 else 'Out of Stock',
'datasheet_url': selected_option.get('datasheet_url', ''),
'specifications': self.extract_key_specifications(selected_option)
}
def format_bom_output(self, bom_data: Dict) -> str:
output_lines = []
output_lines.append("BILL OF MATERIALS")
output_lines.append("=" * 50)
output_lines.append(f"Generated: {bom_data['generated_date']}")
output_lines.append(f"Total Cost: ${bom_data['total_cost']:.2f}")
output_lines.append(f"Estimated Shipping: ${bom_data['estimated_shipping']:.2f}")
output_lines.append("")
# Header
header = f"{'Ref':<8} {'Description':<30} {'Mfr':<12} {'Part Number':<15} {'Qty':<5} {'Price':<8} {'Total':<8}"
output_lines.append(header)
output_lines.append("-" * len(header))
# Items
for item in bom_data['items']:
line = f"{item['reference']:<8} "
line += f"{item['description'][:29]:<30} "
line += f"{item['manufacturer'][:11]:<12} "
line += f"{item['part_number'][:14]:<15} "
line += f"{item['quantity']:<5} "
line += f"${item['unit_price']:<7.2f} "
line += f"${item['total_price']:<7.2f}"
output_lines.append(line)
output_lines.append("")
output_lines.append("SUPPLIER SUMMARY:")
for supplier in bom_data['suppliers']:
supplier_total = sum([item['total_price'] for item in bom_data['items']
if item.get('supplier') == supplier])
output_lines.append(f"{supplier}: ${supplier_total:.2f}")
return '\n'.join(output_lines)
This BOM generator implementation shows how the system creates comprehensive parts lists with pricing and supplier information. The formatted output provides a clear overview of all required components, making it easy for users to proceed with ordering. The supplier summary helps users understand how their order might be split across different vendors.
HANDLING DESIGN CHOICES AND USER DECISIONS
During the circuit design process, the agent often encounters situations where multiple valid design approaches exist. For example, when designing a voltage regulator, the system might choose between a linear regulator for simplicity or a switching regulator for efficiency. These decisions significantly impact the final circuit performance, cost, and complexity.
The decision handling system presents these choices to users in an understandable format, explaining the trade-offs involved. The agent describes the implications of each choice in terms that match the user's apparent expertise level. For a beginner, it might emphasize ease of assembly and cost, while for an experienced engineer, it might focus on efficiency and thermal considerations.
The system maintains context about previous decisions to ensure consistency throughout the design process. If a user chooses a low-cost approach for one component, the agent will consider this preference when making subsequent recommendations. This contextual awareness creates a more coherent design experience.
class DesignDecisionHandler:
def __init__(self):
self.decision_templates = {
'voltage_regulator_type': {
'question': 'For your voltage regulator, I can suggest two approaches:',
'options': {
'linear': {
'description': 'Linear regulator (simple, low noise, less efficient)',
'pros': ['Simple circuit', 'Low noise', 'Easy to implement'],
'cons': ['Lower efficiency', 'Heat generation', 'Limited input voltage range'],
'cost_impact': 'Lower',
'complexity': 'Beginner'
},
'switching': {
'description': 'Switching regulator (efficient, complex, more expensive)',
'pros': ['High efficiency', 'Wide input range', 'Less heat'],
'cons': ['More complex', 'Potential noise', 'Requires more components'],
'cost_impact': 'Higher',
'complexity': 'Intermediate'
}
}
},
'led_current_control': {
'question': 'For LED current control, I can implement:',
'options': {
'resistor': {
'description': 'Simple resistor current limiting',
'pros': ['Very simple', 'Cheap', 'Reliable'],
'cons': ['Wastes power', 'Current varies with voltage', 'Not precise'],
'cost_impact': 'Lowest',
'complexity': 'Beginner'
},
'constant_current': {
'description': 'Constant current driver IC',
'pros': ['Precise current', 'Efficient', 'Stable'],
'cons': ['More expensive', 'Additional components', 'More complex'],
'cost_impact': 'Medium',
'complexity': 'Intermediate'
}
}
}
}
def present_decision(self, decision_type: str, context: Dict) -> str:
if decision_type not in self.decision_templates:
return "I need to make a design choice, but I don't have enough information."
template = self.decision_templates[decision_type]
presentation = []
presentation.append(template['question'])
presentation.append("")
for option_key, option_data in template['options'].items():
presentation.append(f"Option {option_key.upper()}: {option_data['description']}")
presentation.append(f" Advantages: {', '.join(option_data['pros'])}")
presentation.append(f" Disadvantages: {', '.join(option_data['cons'])}")
presentation.append(f" Cost Impact: {option_data['cost_impact']}")
presentation.append(f" Complexity Level: {option_data['complexity']}")
presentation.append("")
presentation.append("Which approach would you prefer? Please specify the option name.")
return '\n'.join(presentation)
def process_decision(self, decision_type: str, user_choice: str, context: Dict) -> Dict:
if decision_type not in self.decision_templates:
return {'error': 'Unknown decision type'}
template = self.decision_templates[decision_type]
user_choice_lower = user_choice.lower().strip()
selected_option = None
for option_key, option_data in template['options'].items():
if option_key in user_choice_lower or option_key.upper() in user_choice:
selected_option = option_key
break
if not selected_option:
return {
'error': 'Could not understand your choice. Please specify one of the available options.',
'available_options': list(template['options'].keys())
}
return {
'decision_type': decision_type,
'selected_option': selected_option,
'option_data': template['options'][selected_option],
'impact_on_design': self.calculate_design_impact(decision_type, selected_option, context)
}
def calculate_design_impact(self, decision_type: str, selected_option: str, context: Dict) -> Dict:
impact = {
'component_changes': [],
'cost_change': 0.0,
'complexity_change': 0
}
if decision_type == 'voltage_regulator_type':
if selected_option == 'switching':
impact['component_changes'] = [
'Add switching controller IC',
'Add inductor',
'Add Schottky diode',
'Add input/output capacitors'
]
impact['cost_change'] = 5.00
impact['complexity_change'] = 2
else: # linear
impact['component_changes'] = [
'Add linear regulator IC',
'Add input/output capacitors'
]
impact['cost_change'] = 1.50
impact['complexity_change'] = 1
return impact
This decision handling system demonstrates how the agent presents technical choices in an accessible format. The system explains trade-offs clearly and tracks the impact of decisions on the overall design. This approach helps users make informed choices while maintaining technical accuracy.
COMPLETE INTEGRATION AND RUNNING EXAMPLE
To demonstrate the complete system in action, let's trace through a full interaction where a user requests an LED driver circuit. This example shows how all the components work together to deliver a complete solution from initial request to final BOM.
The interaction begins when a user describes their needs. The agent parses the requirements, asks clarifying questions about suppliers, generates the circuit diagram, searches for components, handles any design decisions, and finally produces a comprehensive BOM. This end-to-end process showcases the system's capability to bridge the gap between conceptual requirements and practical implementation.
class CompleteCircuitAgent:
def __init__(self):
self.conversation_state = {
'circuit_type': None,
'specifications': {},
'preferred_suppliers': [],
'country': None,
'design_choices': {},
'current_phase': 'initial',
'generated_circuit': None,
'component_search_results': {},
'final_bom': None
}
self.parser = CircuitRequirementParser()
self.circuit_generator = ASCIICircuitGenerator()
self.parts_searcher = PartsSearcher()
self.bom_generator = BOMGenerator()
self.decision_handler = DesignDecisionHandler()
def run_complete_example(self):
print("=== COMPLETE CIRCUIT AGENT DEMONSTRATION ===")
print()
# Simulate user input
user_request = "I need an LED driver circuit for a 12V supply that can handle 20mA for a red LED"
print(f"User: {user_request}")
print()
# Phase 1: Parse requirements
response1 = self.handle_initial_request(user_request)
print(f"Agent: {response1}")
print()
# Simulate supplier selection
supplier_choice = "I'm in the US, please suggest Digi-Key and Mouser"
print(f"User: {supplier_choice}")
print()
# Phase 2: Handle supplier selection
response2 = self.handle_supplier_selection(supplier_choice)
print(f"Agent: {response2}")
print()
# Simulate design choice
design_choice = "I prefer the simple resistor approach"
print(f"User: {design_choice}")
print()
# Phase 3: Handle design choice and generate circuit
response3 = self.handle_design_choices(design_choice)
print(f"Agent: {response3}")
print()
# Phase 4: Generate final BOM
final_response = self.generate_final_bom()
print(f"Agent: {final_response}")
def handle_initial_request(self, user_message):
specs = self.parser.parse_requirements(user_message)
self.conversation_state['specifications'] = specs
self.conversation_state['current_phase'] = 'supplier_selection'
return ("I understand you want to build an LED driver circuit. "
"To help you source components, please tell me your preferred suppliers "
"or your country so I can suggest local options.")
def handle_supplier_selection(self, user_message):
# Extract supplier preferences
if 'digi-key' in user_message.lower() or 'digikey' in user_message.lower():
self.conversation_state['preferred_suppliers'].append('digikey')
if 'mouser' in user_message.lower():
self.conversation_state['preferred_suppliers'].append('mouser')
# Check if we need to make design decisions
circuit_type = self.conversation_state['specifications'].get('type')
if circuit_type == 'LED_DRIVER':
self.conversation_state['current_phase'] = 'design_choices'
return self.decision_handler.present_decision('led_current_control',
self.conversation_state['specifications'])
else:
return self.proceed_to_circuit_generation()
def handle_design_choices(self, user_message):
decision_result = self.decision_handler.process_decision(
'led_current_control', user_message, self.conversation_state['specifications']
)
if 'error' in decision_result:
return decision_result['error']
self.conversation_state['design_choices']['led_current_control'] = decision_result
return self.proceed_to_circuit_generation()
def proceed_to_circuit_generation(self):
# Generate circuit diagram
circuit_type = self.conversation_state['specifications']['type']
circuit_diagram = self.circuit_generator.generate_circuit(
circuit_type, self.conversation_state['specifications']
)
self.conversation_state['generated_circuit'] = circuit_diagram
# Search for components
self.search_for_components()
response = "Here's your circuit design:\n\n"
response += circuit_diagram
response += "\n\nI'm now searching for components from your preferred suppliers..."
return response
def search_for_components(self):
# Determine required components based on circuit type and design choices
required_components = self.determine_required_components()
search_results = {}
for component in required_components:
results = self.parts_searcher.search_component(
component['type'],
component['specifications'],
self.conversation_state['preferred_suppliers']
)
search_results[component['type']] = results
self.conversation_state['component_search_results'] = search_results
def determine_required_components(self):
components = []
specs = self.conversation_state['specifications']
if specs.get('type') == 'LED_DRIVER':
# Calculate resistor value
supply_voltage = specs.get('voltage_supply', 12)
led_current = specs.get('current_requirement', {}).get('value', 0.02)
led_forward_voltage = 2.0
resistor_value = (supply_voltage - led_forward_voltage) / led_current
components.append({
'type': 'resistor',
'reference': 'R1',
'specifications': {
'resistance': resistor_value,
'power_rating': (supply_voltage - led_forward_voltage) * led_current,
'tolerance': 0.05
},
'quantity': 1
})
components.append({
'type': 'led',
'reference': 'LED1',
'specifications': {
'color': 'red',
'forward_voltage': 2.0,
'forward_current': led_current
},
'quantity': 1
})
return components
def generate_final_bom(self):
required_components = self.determine_required_components()
bom_data = self.bom_generator.generate_bom(
required_components,
self.conversation_state['component_search_results'],
{'include_prototyping': True}
)
self.conversation_state['final_bom'] = bom_data
response = "Component search complete! Here's your Bill of Materials:\n\n"
response += self.bom_generator.format_bom_output(bom_data)
response += "\n\nYou can now proceed to order these components from the specified suppliers."
return response
# Run the complete example
if __name__ == "__main__":
agent = CompleteCircuitAgent()
agent.run_complete_example()
This complete integration demonstrates how all the system components work together to provide a seamless user experience. The agent maintains conversation state throughout the interaction, making informed decisions based on user preferences and technical requirements.
ERROR HANDLING AND EDGE CASES
Real-world implementation of the circuit agent requires robust error handling to manage various failure scenarios. Component searches might return no results, APIs might be unavailable, or users might provide ambiguous requirements. The system must gracefully handle these situations while providing helpful feedback to users.
The error handling strategy includes validation at each step of the process, fallback mechanisms when primary data sources are unavailable, and clear communication about limitations or problems. When a specific component cannot be found, the system suggests alternatives or asks the user for guidance on acceptable substitutions.
Network connectivity issues represent another common challenge. The agent should cache component data when possible and provide offline functionality for basic circuit generation. When real-time pricing is unavailable, the system can use historical data or estimated prices while clearly indicating the uncertainty.
class ErrorHandler:
def __init__(self):
self.error_messages = {
'no_components_found': "I couldn't find suitable components for your specifications. Would you like me to suggest alternatives or broaden the search criteria?",
'api_unavailable': "The component database is temporarily unavailable. I can generate the circuit diagram and provide estimated component values, but real-time pricing isn't available.",
'ambiguous_requirements': "Your circuit requirements are unclear. Could you provide more specific details about voltage levels, current requirements, or intended application?",
'unsupported_circuit': "I don't currently support this type of circuit. I can help with LED drivers, voltage regulators, amplifiers, and basic filters."
}
def handle_component_search_failure(self, component_type: str, specifications: Dict) -> Dict:
# Suggest alternative specifications or components
alternatives = self.suggest_alternatives(component_type, specifications)
return {
'error_type': 'component_not_found',
'message': f"Could not find {component_type} with exact specifications.",
'alternatives': alternatives,
'suggested_action': 'Consider alternative specifications or substitute components'
}
def suggest_alternatives(self, component_type: str, specs: Dict) -> List[Dict]:
alternatives = []
if component_type == 'resistor':
target_resistance = specs.get('resistance', 0)
# Suggest standard resistor values near the target
standard_values = [330, 470, 680, 1000, 1500, 2200, 3300, 4700]
closest_standard = min(standard_values, key=lambda x: abs(x - target_resistance))
alternatives.append({
'type': 'resistor',
'resistance': closest_standard,
'note': f'Closest standard value to calculated {target_resistance:.0f}Ω'
})
return alternatives
FUTURE ENHANCEMENTS AND CONSIDERATIONS
The circuit design agent represents a foundation that can be extended in numerous directions. Machine learning integration could improve component selection by learning from user preferences and successful designs. The system could develop expertise in specific application domains like automotive electronics or IoT devices.
Advanced simulation capabilities would allow the agent to verify circuit functionality before recommending components. Integration with SPICE simulators could provide performance predictions and identify potential issues early in the design process. This would significantly increase confidence in the generated designs.
The agent could also evolve to handle more complex circuits with multiple stages and sophisticated control systems. Support for microcontroller-based designs would open up possibilities for smart circuits with programmable functionality. The system could generate both the hardware design and basic firmware templates.
Collaboration features would enable multiple users to work on circuit designs together, with the agent facilitating design reviews and component optimization across teams. Integration with version control systems would support design iteration and change tracking.
The economic optimization capabilities could be enhanced to consider bulk purchasing, lead times, and supply chain risks. The agent could suggest design modifications that improve manufacturability or reduce costs for production quantities.
CONCLUSION
The development of an intelligent circuit design agent represents a significant step toward democratizing electronic design and streamlining the prototyping process. By combining natural language understanding, circuit generation, and intelligent component sourcing, the system bridges the gap between conceptual ideas and practical implementation.
The agent's ability to generate ASCII circuit diagrams provides an accessible visualization format that works across different platforms and tools. The integration with real-world component databases ensures that designs can be immediately translated into actionable procurement plans. The decision handling system empowers users to make informed choices about design trade-offs while maintaining technical accuracy.
The modular architecture allows for continuous improvement and extension of capabilities. As component databases expand and new suppliers emerge, the system can adapt to provide broader sourcing options. The conversation-based interface makes advanced circuit design concepts accessible to users with varying levels of technical expertise.
This approach to automated circuit design and procurement represents a practical application of artificial intelligence that delivers immediate value to engineers, hobbyists, and students. The system reduces the time and expertise required to move from circuit concept to component ordering, accelerating the innovation cycle in electronic design.
The implementation demonstrates how modern AI systems can augment human expertise rather than replace it, providing intelligent assistance while preserving user control over critical design decisions. This collaborative approach between human creativity and machine efficiency points toward a future where complex technical tasks become more accessible and efficient.
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