Custom Implementation Guide

from atlas_inference import ATLASInference
import torch

# Initialize models
teacher_model = "Arc-Intelligence/ATLAS-8B-Thinking"
student_model = "meta-llama/Llama-3.2-8B-Instruct"

# Create ATLAS instance
atlas = ATLASInference(
    teacher_model_name=teacher_model,
    student_model_name=student_model,
    device="cuda" if torch.cuda.is_available() else "cpu"
)

# Run the two-pass protocol
task = "Your domain-specific task here"
result = atlas.run_full_protocol(task)

# Access results
baseline = result['baseline_response']     # Student alone
enhanced = result['guided_response']       # Student with teaching
improvement = result['improvement_score']  # Performance gain

class CodeGenerationATLAS:
    """
    ATLAS for code generation tasks
    """

    def __init__(self):
        self.atlas = ATLASInference(
            teacher_model_name="Arc-Intelligence/ATLAS-8B-Instruct",
            student_model_name="codellama/CodeLlama-7b-Instruct"
        )

    def enhance_code_generation(self, specification):
        """
        Generate code with teaching enhancement
        """
        # Add context for better teaching
        prompt = f"""
        Task: {specification}
        Requirements:
        - Include error handling
        - Follow best practices
        - Add type hints
        - Include docstrings
        """

        result = self.atlas.run_full_protocol(prompt)

        # Post-process to extract code
        code = self.extract_code_blocks(result['guided_response'])

        # Validate syntax
        if self.validate_syntax(code):
            return code
        else:
            # Retry with more specific guidance
            return self.retry_with_corrections(specification, code)

    def validate_syntax(self, code):
        """Check if generated code is syntactically valid"""
        try:
            compile(code, '<string>', 'exec')
            return True
        except SyntaxError:
            return False

class DataAnalysisATLAS:
    """
    ATLAS for data analysis tasks
    """

    def enhance_analysis(self, data_description, question):
        """
        Enhance analytical reasoning
        """
        # Structure the task for optimal teaching
        task = f"""
        Data Context: {data_description}
        Analysis Question: {question}

        Required approach:
        1. State assumptions
        2. Choose appropriate methods
        3. Interpret results
        4. Address limitations
        """

        result = self.atlas.run_full_protocol(task)

        # Extract structured insights
        analysis = self.parse_analysis(result['guided_response'])

        return {
            'assumptions': analysis.get('assumptions', []),
            'methodology': analysis.get('methods', ''),
            'findings': analysis.get('findings', ''),
            'limitations': analysis.get('limitations', []),
            'confidence': self.assess_confidence(analysis)
        }

class DebuggingATLAS:
    """
    ATLAS for systematic debugging
    """

    def debug_issue(self, error_description, context):
        """
        Systematic debugging with teaching
        """
        # Structure for systematic investigation
        task = f"""
        Error: {error_description}
        System Context: {context}

        Debug systematically:
        1. Identify error pattern
        2. Form hypotheses
        3. Design tests
        4. Find root cause
        """

        result = self.atlas.run_full_protocol(task)

        # Parse debugging steps
        debug_plan = self.parse_debug_plan(result['guided_response'])

        return {
            'hypotheses': debug_plan['hypotheses'],
            'tests': debug_plan['test_commands'],
            'root_cause': debug_plan['root_cause'],
            'fix': debug_plan['suggested_fix'],
            'prevention': debug_plan['prevention_measures']
        }

from functools import lru_cache
import hashlib

class CachedATLAS:
    """
    ATLAS with intelligent caching
    """

    def __init__(self):
        self.atlas = ATLASInference(...)
        self.guidance_cache = {}
        self.ttl = 3600  # 1 hour

    def get_cached_guidance(self, task):
        """
        Cache teaching guidance for similar tasks
        """
        # Create semantic hash
        task_hash = self.semantic_hash(task)

        # Check cache
        if task_hash in self.guidance_cache:
            cached = self.guidance_cache[task_hash]
            if time.time() - cached['timestamp'] < self.ttl:
                return cached['guidance']

        # Generate new guidance
        result = self.atlas.diagnostic_probe(task)
        guidance = self.atlas.generate_guidance(task, result)

        # Cache it
        self.guidance_cache[task_hash] = {
            'guidance': guidance,
            'timestamp': time.time()
        }

        return guidance

    def semantic_hash(self, task):
        """
        Create hash based on semantic content
        """
        # Extract key features
        features = {
            'length': len(task),
            'keywords': self.extract_keywords(task),
            'complexity': self.estimate_complexity(task)
        }

        # Create stable hash
        feature_str = json.dumps(features, sort_keys=True)
        return hashlib.md5(feature_str.encode()).hexdigest()

async def batch_enhance(tasks, batch_size=4):
    """
    Process multiple tasks efficiently
    """
    results = []

    for i in range(0, len(tasks), batch_size):
        batch = tasks[i:i + batch_size]

        # Process batch in parallel
        batch_results = await asyncio.gather(*[
            atlas.run_full_protocol(task) for task in batch
        ])

        results.extend(batch_results)

    return results

class ATLASAnalytics:
    """
    Track ATLAS performance in production
    """

    def __init__(self):
        self.metrics = {
            'total_requests': 0,
            'improvements': [],
            'degradations': [],
            'teaching_strategies': {},
            'average_latency': 0
        }

    def track_result(self, result):
        """
        Track enhancement results
        """
        self.metrics['total_requests'] += 1

        improvement = result['improvement_score']
        if improvement > 0:
            self.metrics['improvements'].append(improvement)
        elif improvement < 0:
            self.metrics['degradations'].append(improvement)

        strategy = result.get('teaching_strategy', 'unknown')
        self.metrics['teaching_strategies'][strategy] = \
            self.metrics['teaching_strategies'].get(strategy, 0) + 1

    def get_summary(self):
        """
        Generate performance summary
        """
        return {
            'requests': self.metrics['total_requests'],
            'avg_improvement': np.mean(self.metrics['improvements']),
            'non_degradation_rate': len(self.metrics['improvements']) /
                                   max(1, len(self.metrics['improvements']) +
                                       len(self.metrics['degradations'])),
            'strategy_distribution': self.metrics['teaching_strategies']
        }