The rise of AI-powered code completion tools has revolutionized how developers write code. Let's explore how to build a basic code completion system using modern AI technologies.
Understanding the Foundation
AI code completion tools typically use Large Language Models (LLMs) trained on vast amounts of code. The key components include:
- Token Generation
- Context Understanding
- Code Pattern Recognition
- Syntax Awareness
Building the Basic Architecture
1. Setting Up the Environment
First, let's set up our Python environment with the necessary dependencies:
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
class CodeCompleter:
def __init__(self, model_name="codegen-350M-mono"):
self.tokenizer = AutoTokenizer.from_pretrained(f"Salesforce/{model_name}")
self.model = AutoModelForCausalLM.from_pretrained(f"Salesforce/{model_name}")
2. Implementing the Core Logic
Let's create the basic completion function:
def generate_completion(self, code_context, max_length=50):
inputs = self.tokenizer(code_context, return_tensors="pt")
with torch.no_grad():
outputs = self.model.generate(
inputs["input_ids"],
max_length=max_length,
num_return_sequences=1,
temperature=0.7,
top_p=0.95,
do_sample=True
)
completed_code = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return completed_code
Adding Context Understanding
1. Language Detection
Implement automatic programming language detection:
def detect_language(self, code_snippet):
language_patterns = {
'python': ['.py', 'def ', 'import ', 'class '],
'javascript': ['const ', 'let ', 'function ', '=>'],
'java': ['public class', 'private ', 'void ', 'String[]']
}
for lang, patterns in language_patterns.items():
if any(pattern in code_snippet for pattern in patterns):
return lang
return 'unknown'
2. Context Window Management
Maintain a sliding context window for better completions:
class ContextManager:
def __init__(self, max_context_length=1000):
self.max_length = max_context_length
self.context = []
def update_context(self, new_code):
self.context.append(new_code)
# Maintain context window size
while len(''.join(self.context)) > self.max_length:
self.context.pop(0)
def get_current_context(self):
return ''.join(self.context)
Implementing Advanced Features
1. Syntax Validation
Add syntax checking to ensure generated code is valid:
import ast
import esprima # for JavaScript
class SyntaxValidator:
def validate_python(self, code):
try:
ast.parse(code)
return True
except SyntaxError:
return False
def validate_javascript(self, code):
try:
esprima.parseScript(code)
return True
except:
return False
2. Type Inference
Add basic type inference capabilities:
class TypeInferer:
def infer_python_types(self, code_context):
type_hints = {}
def analyze_assignment(node):
if isinstance(node, ast.Assign):
if isinstance(node.value, ast.Num):
return 'int' if isinstance(node.value.n, int) else 'float'
elif isinstance(node.value, ast.Str):
return 'str'
elif isinstance(node.value, ast.List):
return 'list'
return None
try:
tree = ast.parse(code_context)
for node in ast.walk(tree):
inferred_type = analyze_assignment(node)
if inferred_type:
type_hints[node.targets[0].id] = inferred_type
except:
pass
return type_hints
Enhancing Completion Quality
1. Code Style Enforcement
Implement style checking and formatting:
from black import format_str, FileMode # for Python
from prettier import format_code # for JavaScript
class StyleEnforcer:
def format_python(self, code):
try:
return format_str(code, mode=FileMode())
except:
return code
def format_javascript(self, code):
try:
return format_code(code)
except:
return code
2. Semantic Analysis
Add basic semantic understanding:
class SemanticAnalyzer:
def __init__(self):
self.variable_scope = {}
self.function_definitions = {}
def analyze_scope(self, code):
tree = ast.parse(code)
for node in ast.walk(tree):
if isinstance(node, ast.Name):
self.variable_scope[node.id] = self.get_scope_level(node)
elif isinstance(node, ast.FunctionDef):
self.function_definitions[node.name] = {
'args': [arg.arg for arg in node.args.args],
'returns': self.analyze_return_type(node)
}
def get_scope_level(self, node):
scope_level = 0
parent = node
while hasattr(parent, 'parent'):
parent = parent.parent
scope_level += 1
return scope_level
Putting It All Together
Here's how to combine all components into a complete code completion system:
class AICodeCompletion:
def __init__(self):
self.completer = CodeCompleter()
self.context_manager = ContextManager()
self.validator = SyntaxValidator()
self.type_inferer = TypeInferer()
self.style_enforcer = StyleEnforcer()
self.semantic_analyzer = SemanticAnalyzer()
def complete_code(self, code_context):
# Update context
self.context_manager.update_context(code_context)
# Generate completion
completion = self.completer.generate_completion(
self.context_manager.get_current_context()
)
# Validate syntax
language = self.completer.detect_language(code_context)
if language == 'python':
is_valid = self.validator.validate_python(completion)
elif language == 'javascript':
is_valid = self.validator.validate_javascript(completion)
if not is_valid:
return self.complete_code(code_context) # Try again
# Apply style formatting
if language == 'python':
completion = self.style_enforcer.format_python(completion)
elif language == 'javascript':
completion = self.style_enforcer.format_javascript(completion)
return completion
Best Practices and Considerations
- Performance Optimization
- Use caching for frequent completions
- Implement batch processing for multiple completions
- Optimize context window management
- Error Handling
- Implement graceful fallbacks
- Provide meaningful error messages
- Log completion failures for analysis
- Security Considerations
- Sanitize input code
- Implement rate limiting
- Avoid executing generated code directly
Conclusion
Building an AI-powered code completion tool is a complex but rewarding project. While this implementation is basic compared to commercial solutions, it provides a solid foundation for understanding how these systems work and can be extended with more advanced features like:
- Multi-language support
- Learning from user corrections
- Project-specific context awareness
- Integration with development environments
Remember to continuously update and improve the model based on user feedback and new developments in AI technology.