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Atomizer/optimization_engine/research_agent.py
Anto01 0a7cca9c6a feat: Complete Phase 2.5-2.7 - Intelligent LLM-Powered Workflow Analysis 
This commit implements three major architectural improvements to transform
Atomizer from static pattern matching to intelligent AI-powered analysis.

## Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅

Created intelligent system that understands existing capabilities before
requesting examples:

**New Files:**
- optimization_engine/codebase_analyzer.py (379 lines)
  Scans Atomizer codebase for existing FEA/CAE capabilities

- optimization_engine/workflow_decomposer.py (507 lines, v0.2.0)
  Breaks user requests into atomic workflow steps
  Complete rewrite with multi-objective, constraints, subcase targeting

- optimization_engine/capability_matcher.py (312 lines)
  Matches workflow steps to existing code implementations

- optimization_engine/targeted_research_planner.py (259 lines)
  Creates focused research plans for only missing capabilities

**Results:**
- 80-90% coverage on complex optimization requests
- 87-93% confidence in capability matching
- Fixed expression reading misclassification (geometry vs result_extraction)

## Phase 2.6: Intelligent Step Classification ✅

Distinguishes engineering features from simple math operations:

**New Files:**
- optimization_engine/step_classifier.py (335 lines)

**Classification Types:**
1. Engineering Features - Complex FEA/CAE needing research
2. Inline Calculations - Simple math to auto-generate
3. Post-Processing Hooks - Middleware between FEA steps

## Phase 2.7: LLM-Powered Workflow Intelligence ✅

Replaces static regex patterns with Claude AI analysis:

**New Files:**
- optimization_engine/llm_workflow_analyzer.py (395 lines)
  Uses Claude API for intelligent request analysis
  Supports both Claude Code (dev) and API (production) modes

- .claude/skills/analyze-workflow.md
  Skill template for LLM workflow analysis integration

**Key Breakthrough:**
- Detects ALL intermediate steps (avg, min, normalization, etc.)
- Understands engineering context (CBUSH vs CBAR, directions, metrics)
- Distinguishes OP2 extraction from part expression reading
- Expected 95%+ accuracy with full nuance detection

## Test Coverage

**New Test Files:**
- tests/test_phase_2_5_intelligent_gap_detection.py (335 lines)
- tests/test_complex_multiobj_request.py (130 lines)
- tests/test_cbush_optimization.py (130 lines)
- tests/test_cbar_genetic_algorithm.py (150 lines)
- tests/test_step_classifier.py (140 lines)
- tests/test_llm_complex_request.py (387 lines)

All tests include:
- UTF-8 encoding for Windows console
- atomizer environment (not test_env)
- Comprehensive validation checks

## Documentation

**New Documentation:**
- docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md (254 lines)
- docs/PHASE_2_7_LLM_INTEGRATION.md (227 lines)
- docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md (252 lines)

**Updated:**
- README.md - Added Phase 2.5-2.7 completion status
- DEVELOPMENT_ROADMAP.md - Updated phase progress

## Critical Fixes

1. **Expression Reading Misclassification** (lines cited in session summary)
   - Updated codebase_analyzer.py pattern detection
   - Fixed workflow_decomposer.py domain classification
   - Added capability_matcher.py read_expression mapping

2. **Environment Standardization**
   - All code now uses 'atomizer' conda environment
   - Removed test_env references throughout

3. **Multi-Objective Support**
   - WorkflowDecomposer v0.2.0 handles multiple objectives
   - Constraint extraction and validation
   - Subcase and direction targeting

## Architecture Evolution

**Before (Static & Dumb):**
User Request → Regex Patterns → Hardcoded Rules → Missed Steps ❌

**After (LLM-Powered & Intelligent):**
User Request → Claude AI Analysis → Structured JSON →
├─ Engineering (research needed)
├─ Inline (auto-generate Python)
├─ Hooks (middleware scripts)
└─ Optimization (config) ✅

## LLM Integration Strategy

**Development Mode (Current):**
- Use Claude Code directly for interactive analysis
- No API consumption or costs
- Perfect for iterative development

**Production Mode (Future):**
- Optional Anthropic API integration
- Falls back to heuristics if no API key
- For standalone batch processing

## Next Steps

- Phase 2.8: Inline Code Generation
- Phase 2.9: Post-Processing Hook Generation
- Phase 3: MCP Integration for automated documentation research

🚀 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-16 13:35:41 -05:00

1385 lines
54 KiB
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"""
Research Agent for Autonomous Learning and Feature Generation
This module enables Atomizer to autonomously research unknown domains,
learn patterns from examples and documentation, and generate new features.
Philosophy:
-----------
When encountering a request for functionality that doesn't exist:
1. Detect the knowledge gap by searching the feature registry
2. Plan research strategy: User examples → NX MCP → Web docs
3. Execute interactive research (ask user for examples first)
4. Learn patterns and schemas from gathered information
5. Generate new features following learned patterns
6. Test and validate with user confirmation
7. Document and integrate into knowledge base
This creates a self-extending system that grows more capable over time.
Example Workflow:
-----------------
User: "Create NX material XML for titanium Ti-6Al-4V"
ResearchAgent:
1. identify_knowledge_gap() → No 'material_generator' feature found
2. create_research_plan() → Ask user for example XML first
3. execute_interactive_research() → User provides steel_material.xml
4. synthesize_knowledge() → Extract XML schema, material properties
5. design_feature() → Generate nx_material_generator.py
6. validate_with_user() → User confirms generated XML works
7. document_session() → Save to knowledge_base/research_sessions/
Author: Atomizer Development Team
Version: 0.1.0 (Phase 2)
Last Updated: 2025-01-16
"""
import json
import os
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Any
import xml.etree.ElementTree as ET
class KnowledgeGap:
"""Represents a detected gap in Atomizer's current capabilities."""
def __init__(
self,
missing_features: List[str],
missing_knowledge: List[str],
user_request: str,
confidence: float
):
"""
Initialize knowledge gap.
Args:
missing_features: Feature IDs that don't exist in registry
missing_knowledge: Domains we don't have knowledge about
user_request: Original user request that triggered detection
confidence: How confident we are this is a true gap (0.0-1.0)
"""
self.missing_features = missing_features
self.missing_knowledge = missing_knowledge
self.user_request = user_request
self.confidence = confidence
self.research_needed = confidence < 0.8
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization."""
return {
'missing_features': self.missing_features,
'missing_knowledge': self.missing_knowledge,
'user_request': self.user_request,
'confidence': self.confidence,
'research_needed': self.research_needed
}
class ResearchPlan:
"""A step-by-step plan for researching a knowledge gap."""
def __init__(self, steps: List[Dict[str, Any]]):
"""
Initialize research plan.
Args:
steps: List of research steps, each with:
- step: Step number (1, 2, 3...)
- action: Type of action ('ask_user', 'query_mcp', 'web_search')
- priority: Priority level ('high', 'medium', 'low')
- details: Action-specific details (query string, etc.)
"""
self.steps = steps
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization."""
return {'steps': self.steps}
class ResearchFindings:
"""Results gathered from executing a research plan."""
def __init__(
self,
sources: Dict[str, Any],
raw_data: Dict[str, Any],
confidence_scores: Dict[str, float]
):
"""
Initialize research findings.
Args:
sources: Dictionary mapping source type to source details
raw_data: Raw data gathered from each source
confidence_scores: Confidence score for each source (0.0-1.0)
"""
self.sources = sources
self.raw_data = raw_data
self.confidence_scores = confidence_scores
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization."""
return {
'sources': self.sources,
'raw_data': self.raw_data,
'confidence_scores': self.confidence_scores
}
class SynthesizedKnowledge:
"""Knowledge synthesized from multiple research sources."""
def __init__(
self,
schema: Optional[Dict[str, Any]],
patterns: List[Dict[str, Any]],
examples: List[Dict[str, Any]],
confidence: float,
synthesis_notes: str
):
"""
Initialize synthesized knowledge.
Args:
schema: Extracted schema (e.g., XML structure, API signatures)
patterns: Identified reusable patterns
examples: Concrete examples demonstrating usage
confidence: Overall confidence in synthesized knowledge (0.0-1.0)
synthesis_notes: Explanation of synthesis process
"""
self.schema = schema
self.patterns = patterns
self.examples = examples
self.confidence = confidence
self.synthesis_notes = synthesis_notes
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for JSON serialization."""
return {
'schema': self.schema,
'patterns': self.patterns,
'examples': self.examples,
'confidence': self.confidence,
'synthesis_notes': self.synthesis_notes
}
class ResearchAgent:
"""
Autonomous research system for learning new capabilities.
The ResearchAgent enables Atomizer to:
- Detect when it lacks knowledge to fulfill a user request
- Plan and execute multi-source research
- Learn patterns and schemas from examples and documentation
- Generate new features based on learned knowledge
- Persist knowledge for future use
Attributes:
feature_registry_path: Path to feature_registry.json
knowledge_base_path: Path to knowledge_base/ directory
min_confidence_threshold: Minimum confidence to generate code (default: 0.70)
"""
def __init__(
self,
feature_registry_path: Optional[Path] = None,
knowledge_base_path: Optional[Path] = None,
min_confidence_threshold: float = 0.70
):
"""
Initialize ResearchAgent.
Args:
feature_registry_path: Path to feature registry JSON
knowledge_base_path: Path to knowledge base directory
min_confidence_threshold: Min confidence to generate code (0.0-1.0)
"""
# Determine paths
if feature_registry_path is None:
atomizer_root = Path(__file__).parent.parent
feature_registry_path = atomizer_root / "optimization_engine" / "feature_registry.json"
if knowledge_base_path is None:
atomizer_root = Path(__file__).parent.parent
knowledge_base_path = atomizer_root / "knowledge_base"
self.feature_registry_path = Path(feature_registry_path)
self.knowledge_base_path = Path(knowledge_base_path)
self.min_confidence_threshold = min_confidence_threshold
# Load feature registry
self.feature_registry = self._load_feature_registry()
def _load_feature_registry(self) -> Dict[str, Any]:
"""Load feature registry from JSON file."""
if not self.feature_registry_path.exists():
return {'feature_registry': {'version': '0.2.0', 'categories': {}}}
with open(self.feature_registry_path, 'r') as f:
return json.load(f)
def identify_knowledge_gap(self, user_request: str) -> KnowledgeGap:
"""
Analyze user request and identify what we don't know.
This method searches the feature registry to determine if we have
the necessary features to fulfill the user's request. If not, it
identifies what's missing and returns a KnowledgeGap.
Args:
user_request: The user's natural language request
Returns:
KnowledgeGap object containing:
- missing_features: List of feature IDs we don't have
- missing_knowledge: List of domains we lack knowledge in
- research_needed: Whether research is required
- confidence: How confident we are in this assessment
Example:
>>> agent = ResearchAgent()
>>> gap = agent.identify_knowledge_gap(
... "Create NX material XML for titanium"
... )
>>> gap.missing_features
['material_xml_generator']
>>> gap.research_needed
True
"""
# Convert request to lowercase for case-insensitive matching
request_lower = user_request.lower()
# Define keywords that indicate different domains
domain_keywords = {
'material': ['material', 'material xml', 'physical material', 'alloy', 'steel', 'titanium', 'aluminum'],
'geometry': ['geometry', 'fillet', 'chamfer', 'thickness', 'dimension', 'sketch', 'feature'],
'loads_bc': ['load', 'boundary condition', 'constraint', 'force', 'pressure', 'fixed', 'displacement'],
'mesh': ['mesh', 'element', 'refinement', 'element size', 'mesh quality'],
'analysis': ['analysis', 'modal', 'thermal', 'fatigue', 'buckling', 'nonlinear'],
'reporting': ['report', 'visualization', 'plot', 'chart', 'summary', 'dashboard'],
'optimization': ['optimize', 'minimize', 'maximize', 'pareto', 'multi-objective']
}
# Search feature registry for matching features
matched_features = []
registry = self.feature_registry.get('feature_registry', {})
categories = registry.get('categories', {})
for category_name, category_data in categories.items():
subcategories = category_data.get('subcategories', {})
for subcat_name, subcat_data in subcategories.items():
for feature_id, feature_data in subcat_data.items():
if isinstance(feature_data, dict):
# Check natural language mappings
usage_examples = feature_data.get('usage_examples', [])
for example in usage_examples:
natural_lang = example.get('natural_language', [])
for phrase in natural_lang:
if phrase.lower() in request_lower:
matched_features.append(feature_id)
break
# Identify missing domains
missing_domains = []
for domain, keywords in domain_keywords.items():
for keyword in keywords:
if keyword in request_lower:
# Check if we have features for this domain
domain_covered = False
for category_name, category_data in categories.items():
subcategories = category_data.get('subcategories', {})
for subcat_name in subcategories.keys():
if domain in subcat_name or subcat_name in domain:
domain_covered = True
break
if domain_covered:
break
if not domain_covered:
missing_domains.append(domain)
break
# Check knowledge base for existing knowledge
existing_knowledge = self.search_knowledge_base(request_lower)
# Determine confidence based on matches
if matched_features and not missing_domains:
# We have features and domain knowledge
confidence = 0.9
missing_features = []
elif matched_features and missing_domains:
# We have some features but missing domain knowledge
confidence = 0.6
missing_features = []
elif not matched_features and not missing_domains:
# No matches but domain seems covered (might need new feature)
confidence = 0.4
missing_features = ['unknown_feature_needed']
else:
# No matches and missing domain knowledge
confidence = 0.2
missing_features = ['new_feature_required']
# Adjust confidence if we have existing knowledge
if existing_knowledge and existing_knowledge.get('confidence', 0) > 0.7:
confidence = max(confidence, 0.8)
return KnowledgeGap(
missing_features=missing_features if not matched_features else [],
missing_knowledge=list(set(missing_domains)),
user_request=user_request,
confidence=confidence
)
def create_research_plan(self, knowledge_gap: KnowledgeGap) -> ResearchPlan:
"""
Create step-by-step research plan to fill knowledge gap.
Prioritizes research sources:
1. User examples (highest confidence)
2. NX MCP / official documentation (high confidence)
3. Web search / community docs (medium confidence)
Args:
knowledge_gap: The detected knowledge gap
Returns:
ResearchPlan with ordered steps
Example:
>>> gap = KnowledgeGap(
... missing_features=['material_generator'],
... missing_knowledge=['NX material XML format'],
... user_request="Create material XML",
... confidence=0.2
... )
>>> plan = agent.create_research_plan(gap)
>>> plan.steps[0]['action']
'ask_user_for_example'
"""
steps = []
# Determine what topics we need to research
topics = knowledge_gap.missing_knowledge if knowledge_gap.missing_knowledge else ['general approach']
primary_topic = topics[0]
# Step 1: ALWAYS ask user for examples first (highest confidence source)
steps.append({
'step': 1,
'action': 'ask_user_for_example',
'priority': 'high',
'source_type': 'user_validated',
'expected_confidence': CONFIDENCE_LEVELS['user_validated'],
'details': {
'prompt': self._generate_user_prompt(knowledge_gap),
'topic': primary_topic,
'file_types': self._infer_file_types(primary_topic)
}
})
# Step 2: Search existing knowledge base
steps.append({
'step': 2,
'action': 'search_knowledge_base',
'priority': 'high',
'source_type': 'internal',
'expected_confidence': 0.8,
'details': {
'query': primary_topic,
'search_path': self.knowledge_base_path / 'research_sessions'
}
})
# Step 3: Query NX MCP if available (for NX-specific topics)
if any(kw in primary_topic.lower() for kw in ['nx', 'nastran', 'material', 'geometry', 'load', 'mesh']):
steps.append({
'step': 3,
'action': 'query_nx_mcp',
'priority': 'medium',
'source_type': 'nx_mcp_official',
'expected_confidence': CONFIDENCE_LEVELS['nx_mcp_official'],
'details': {
'query': f"NX {primary_topic} API documentation",
'fallback': True # Skip if MCP not available
}
})
# Step 4: Web search for documentation and examples
steps.append({
'step': 4,
'action': 'web_search',
'priority': 'low',
'source_type': 'web_generic',
'expected_confidence': CONFIDENCE_LEVELS['web_generic'],
'details': {
'query': f"Siemens NX {primary_topic} documentation examples",
'fallback_queries': [
f"NXOpen {primary_topic} API",
f"{primary_topic} NX automation"
]
}
})
# Step 5: Search NXOpenTSE (community examples)
steps.append({
'step': 5,
'action': 'search_nxopen_tse',
'priority': 'low',
'source_type': 'nxopen_tse',
'expected_confidence': CONFIDENCE_LEVELS['nxopen_tse'],
'details': {
'query': f"{primary_topic} example code",
'site': 'nxopen.tse.de'
}
})
return ResearchPlan(steps)
def execute_interactive_research(
self,
plan: ResearchPlan,
user_responses: Optional[Dict[int, Any]] = None
) -> ResearchFindings:
"""
Execute research plan, gathering information from multiple sources.
This method executes each step in the research plan, starting with
asking the user for examples. It collects data from all sources and
assigns confidence scores based on source reliability.
Args:
plan: The research plan to execute
user_responses: Optional dict mapping step number to user response
Returns:
ResearchFindings with gathered data and confidence scores
Example:
>>> plan = agent.create_research_plan(gap)
>>> findings = agent.execute_interactive_research(
... plan,
... user_responses={1: 'steel_material.xml'}
... )
>>> findings.sources
{'user_example': 'steel_material.xml', ...}
"""
sources = {}
raw_data = {}
confidence_scores = {}
user_responses = user_responses or {}
# Execute each step in the plan
for step in plan.steps:
step_num = step['step']
action = step['action']
source_type = step.get('source_type', 'unknown')
expected_confidence = step.get('expected_confidence', 0.5)
# Step 1: Ask user for example
if action == 'ask_user_for_example':
if step_num in user_responses:
user_input = user_responses[step_num]
# Handle file path
if isinstance(user_input, (str, Path)):
file_path = Path(user_input)
if file_path.exists():
file_content = file_path.read_text(encoding='utf-8')
sources['user_example'] = str(file_path)
raw_data['user_example'] = file_content
confidence_scores['user_example'] = CONFIDENCE_LEVELS['user_validated']
else:
# User provided content directly as string
sources['user_example'] = 'user_provided_content'
raw_data['user_example'] = user_input
confidence_scores['user_example'] = CONFIDENCE_LEVELS['user_validated']
# Handle dict/object
elif isinstance(user_input, dict):
sources['user_example'] = 'user_provided_data'
raw_data['user_example'] = user_input
confidence_scores['user_example'] = CONFIDENCE_LEVELS['user_validated']
# Step 2: Search knowledge base
elif action == 'search_knowledge_base':
existing_knowledge = self.search_knowledge_base(step['details']['query'])
if existing_knowledge:
sources['knowledge_base'] = f"research_sessions/{existing_knowledge.get('session_id')}"
raw_data['knowledge_base'] = existing_knowledge
confidence_scores['knowledge_base'] = existing_knowledge.get('confidence', 0.8)
# Step 3: Query NX MCP (placeholder for future implementation)
elif action == 'query_nx_mcp':
# TODO: Implement NX MCP query when MCP server is available
# For now, skip this step
pass
# Step 4: Web search
elif action == 'web_search':
# Perform web search for NXOpen documentation
query = step['details']['query']
try:
# In a real LLM integration, this would call WebSearch tool
# For now, we'll mark that web search would happen here
# and store placeholder data
sources['web_search'] = f"Web search: {query}"
raw_data['web_search'] = {
'query': query,
'note': 'Web search integration requires LLM tool access',
'implementation_status': 'placeholder'
}
confidence_scores['web_search'] = CONFIDENCE_LEVELS['web_generic']
except Exception as e:
# Silently skip if web search fails
pass
# Step 5: Search NXOpenTSE
elif action == 'search_nxopen_tse':
# Search NXOpenTSE community examples
query = step['details']['query']
try:
# In a real implementation, this would scrape/search nxopen.tse.de
# For now, mark as placeholder
sources['nxopen_tse'] = f"NXOpenTSE: {query}"
raw_data['nxopen_tse'] = {
'query': query,
'site': 'nxopen.tse.de',
'note': 'NXOpenTSE search integration requires web scraping',
'implementation_status': 'placeholder'
}
confidence_scores['nxopen_tse'] = CONFIDENCE_LEVELS['nxopen_tse']
except Exception:
# Silently skip if search fails
pass
return ResearchFindings(
sources=sources,
raw_data=raw_data,
confidence_scores=confidence_scores
)
def synthesize_knowledge(
self,
findings: ResearchFindings
) -> SynthesizedKnowledge:
"""
Combine findings from multiple sources into actionable knowledge.
This method analyzes raw data from research findings, extracts
patterns and schemas, and creates a coherent knowledge representation
that can be used for feature generation.
Args:
findings: Research findings from multiple sources
Returns:
SynthesizedKnowledge with:
- schema: Extracted structure/format
- patterns: Reusable patterns identified
- examples: Concrete usage examples
- confidence: Overall confidence score
Example:
>>> knowledge = agent.synthesize_knowledge(findings)
>>> knowledge.schema['root_element']
'PhysicalMaterial'
>>> knowledge.confidence
0.85
"""
# Initialize synthesis structures
schema = {}
patterns = []
examples = []
synthesis_notes = []
# Calculate weighted confidence from sources
total_confidence = 0.0
total_weight = 0
for source_type, confidence in findings.confidence_scores.items():
# Weight based on source type
weight = CONFIDENCE_LEVELS.get(source_type, 0.5)
total_confidence += confidence * weight
total_weight += weight
overall_confidence = total_confidence / total_weight if total_weight > 0 else 0.5
# Process each source's raw data
for source_type, raw_data in findings.raw_data.items():
synthesis_notes.append(f"Processing {source_type}...")
# Handle XML data (e.g., NX material files)
if isinstance(raw_data, str) and raw_data.strip().startswith('<?xml'):
xml_schema = self._extract_xml_schema(raw_data)
if xml_schema:
schema['xml_structure'] = xml_schema
patterns.append({
'type': 'xml_generation',
'root_element': xml_schema.get('root_element'),
'required_fields': xml_schema.get('required_fields', []),
'source': source_type
})
synthesis_notes.append(f" ✓ Extracted XML schema with root: {xml_schema.get('root_element')}")
# Handle Python code
elif isinstance(raw_data, str) and ('def ' in raw_data or 'class ' in raw_data):
code_patterns = self._extract_code_patterns(raw_data)
patterns.extend(code_patterns)
synthesis_notes.append(f" ✓ Extracted {len(code_patterns)} code patterns")
# Handle dictionary/JSON data
elif isinstance(raw_data, dict):
schema.update(raw_data)
examples.append({
'type': 'structured_data',
'data': raw_data,
'source': source_type
})
synthesis_notes.append(f" ✓ Incorporated structured data from {source_type}")
# Handle file path (read and process file)
elif isinstance(raw_data, (str, Path)) and Path(raw_data).exists():
file_path = Path(raw_data)
file_content = file_path.read_text()
if file_path.suffix == '.xml':
xml_schema = self._extract_xml_schema(file_content)
if xml_schema:
schema['xml_structure'] = xml_schema
patterns.append({
'type': 'xml_generation',
'template_file': str(file_path),
'schema': xml_schema
})
synthesis_notes.append(f" ✓ Learned XML schema from {file_path.name}")
elif file_path.suffix == '.py':
code_patterns = self._extract_code_patterns(file_content)
patterns.extend(code_patterns)
synthesis_notes.append(f" ✓ Learned {len(code_patterns)} patterns from {file_path.name}")
# Create final synthesis notes
notes = "\n".join(synthesis_notes)
notes += f"\n\nOverall confidence: {overall_confidence:.2f}"
notes += f"\nTotal patterns extracted: {len(patterns)}"
notes += f"\nSchema elements identified: {len(schema)}"
return SynthesizedKnowledge(
schema=schema if schema else None,
patterns=patterns,
examples=examples,
confidence=overall_confidence,
synthesis_notes=notes
)
def _extract_xml_schema(self, xml_content: str) -> Optional[Dict[str, Any]]:
"""
Extract schema information from XML content.
Args:
xml_content: XML string content
Returns:
Dictionary with root_element, required_fields, optional_fields, attributes
"""
try:
root = ET.fromstring(xml_content)
# Extract root element info
schema = {
'root_element': root.tag,
'attributes': dict(root.attrib),
'required_fields': [],
'optional_fields': [],
'structure': {}
}
# Analyze child elements
for child in root:
field_info = {
'name': child.tag,
'attributes': dict(child.attrib),
'text_content': child.text.strip() if child.text else None
}
# Determine if field is likely required (has content)
if child.text and child.text.strip():
schema['required_fields'].append(child.tag)
else:
schema['optional_fields'].append(child.tag)
schema['structure'][child.tag] = field_info
return schema
except ET.ParseError:
return None
def _extract_code_patterns(self, code_content: str) -> List[Dict[str, Any]]:
"""
Extract reusable patterns from Python code.
Args:
code_content: Python code string
Returns:
List of identified patterns (functions, classes, imports)
"""
patterns = []
# Extract function definitions
import re
func_pattern = r'def\s+(\w+)\s*\((.*?)\):'
for match in re.finditer(func_pattern, code_content):
func_name = match.group(1)
params = match.group(2)
patterns.append({
'type': 'function',
'name': func_name,
'parameters': params,
'reusable': True
})
# Extract class definitions
class_pattern = r'class\s+(\w+)(?:\((.*?)\))?:'
for match in re.finditer(class_pattern, code_content):
class_name = match.group(1)
base_classes = match.group(2) if match.group(2) else None
patterns.append({
'type': 'class',
'name': class_name,
'base_classes': base_classes,
'reusable': True
})
# Extract import statements
import_pattern = r'(?:from\s+([\w.]+)\s+)?import\s+([\w\s,*]+)'
for match in re.finditer(import_pattern, code_content):
module = match.group(1) if match.group(1) else None
imports = match.group(2)
patterns.append({
'type': 'import',
'module': module,
'items': imports,
'reusable': True
})
return patterns
def design_feature(
self,
synthesized_knowledge: SynthesizedKnowledge,
feature_name: str
) -> Dict[str, Any]:
"""
Create feature specification from synthesized knowledge.
This method takes learned knowledge and designs a new feature
that follows Atomizer's feature registry schema.
Args:
synthesized_knowledge: Knowledge learned from research
feature_name: Name for the new feature
Returns:
Feature specification dict following feature_registry.json schema
Example:
>>> feature_spec = agent.design_feature(
... knowledge,
... 'nx_material_generator'
... )
>>> feature_spec['feature_id']
'nx_material_generator'
"""
# Extract category from feature name or patterns
category = self._infer_category(feature_name, synthesized_knowledge)
subcategory = self._infer_subcategory(feature_name, synthesized_knowledge)
# Create base feature specification
feature_spec = {
'feature_id': feature_name,
'name': feature_name.replace('_', ' ').title(),
'description': f'Auto-generated feature for {feature_name.replace("_", " ")}',
'category': category,
'subcategory': subcategory,
'lifecycle_stage': self._infer_lifecycle_stage(feature_name),
'abstraction_level': 'primitive', # Start as primitive, can be composed later
'implementation': {
'file_path': f'optimization_engine/custom_functions/{feature_name}.py',
'function_name': feature_name,
'entry_point': f'from optimization_engine.custom_functions.{feature_name} import {feature_name}'
},
'interface': {
'inputs': self._extract_inputs_from_knowledge(synthesized_knowledge),
'outputs': self._extract_outputs_from_knowledge(synthesized_knowledge)
},
'dependencies': {
'features': [],
'libraries': self._extract_libraries_from_knowledge(synthesized_knowledge),
'nx_version': '2412' # Default to current version
},
'usage_examples': [{
'description': f'Use {feature_name} for automated task',
'natural_language': [
feature_name.replace('_', ' '),
f'generate {feature_name.split("_")[0]}'
]
}],
'metadata': {
'author': 'Research Agent (Auto-generated)',
'created': datetime.now().strftime('%Y-%m-%d'),
'status': 'experimental',
'tested': False,
'confidence': synthesized_knowledge.confidence
}
}
# Add schema information if available
if synthesized_knowledge.schema:
feature_spec['learned_schema'] = synthesized_knowledge.schema
# Add patterns if available
if synthesized_knowledge.patterns:
feature_spec['learned_patterns'] = synthesized_knowledge.patterns
return feature_spec
def _infer_category(self, feature_name: str, knowledge: SynthesizedKnowledge) -> str:
"""Infer feature category from name and knowledge."""
name_lower = feature_name.lower()
if any(kw in name_lower for kw in ['extract', 'stress', 'displacement', 'metric']):
return 'engineering'
elif any(kw in name_lower for kw in ['optimize', 'solver', 'runner']):
return 'software'
elif any(kw in name_lower for kw in ['chart', 'dashboard', 'visualize']):
return 'ui'
else:
return 'engineering' # Default
def _infer_subcategory(self, feature_name: str, knowledge: SynthesizedKnowledge) -> str:
"""Infer feature subcategory from name and knowledge."""
name_lower = feature_name.lower()
if 'extractor' in name_lower:
return 'extractors'
elif 'generator' in name_lower or 'material' in name_lower:
return 'generators'
elif 'solver' in name_lower or 'runner' in name_lower:
return 'optimization'
else:
return 'custom'
def _infer_lifecycle_stage(self, feature_name: str) -> str:
"""Infer lifecycle stage from feature name."""
name_lower = feature_name.lower()
if 'extract' in name_lower:
return 'post_extraction'
elif 'solver' in name_lower or 'run' in name_lower:
return 'solve'
elif 'update' in name_lower or 'prepare' in name_lower:
return 'pre_solve'
else:
return 'all'
def _extract_inputs_from_knowledge(self, knowledge: SynthesizedKnowledge) -> List[Dict]:
"""Extract input parameters from synthesized knowledge."""
inputs = []
# Check if XML schema exists
if knowledge.schema and 'xml_structure' in knowledge.schema:
xml_schema = knowledge.schema['xml_structure']
for field in xml_schema.get('required_fields', []):
inputs.append({
'name': field.lower(),
'type': 'float', # Assume numeric for now
'required': True,
'description': f'{field} parameter from learned schema'
})
# If no inputs found, add generic parameter
if not inputs:
inputs.append({
'name': 'parameters',
'type': 'dict',
'required': True,
'description': 'Feature parameters'
})
return inputs
def _extract_outputs_from_knowledge(self, knowledge: SynthesizedKnowledge) -> List[Dict]:
"""Extract output parameters from synthesized knowledge."""
# Default output structure
return [{
'name': 'result',
'type': 'dict',
'description': 'Generated result from feature'
}]
def _extract_libraries_from_knowledge(self, knowledge: SynthesizedKnowledge) -> List[str]:
"""Extract required libraries from code patterns."""
libraries = []
for pattern in knowledge.patterns:
if pattern['type'] == 'import':
module = pattern.get('module')
if module:
libraries.append(module)
return list(set(libraries)) # Remove duplicates
def validate_with_user(self, feature_spec: Dict[str, Any]) -> bool:
"""
Confirm feature specification with user before implementation.
Args:
feature_spec: The designed feature specification
Returns:
True if user approves, False otherwise
"""
# TODO: Implement user validation workflow
# This will be interactive in actual implementation
return True
def generate_feature_code(
self,
feature_spec: Dict[str, Any],
synthesized_knowledge: SynthesizedKnowledge
) -> str:
"""
Generate Python code for a feature from learned templates and patterns.
Args:
feature_spec: Feature specification from design_feature()
synthesized_knowledge: Knowledge synthesized from research
Returns:
Generated Python code as string
Example:
>>> code = agent.generate_feature_code(feature_spec, knowledge)
>>> # code contains working Python implementation
"""
feature_name = feature_spec['feature_id']
feature_description = feature_spec['description']
# Start building the code
code_lines = []
# Add header
code_lines.append('"""')
code_lines.append(f'{feature_name}')
code_lines.append('')
code_lines.append(f'{feature_description}')
code_lines.append('')
code_lines.append('Auto-generated by Research Agent')
code_lines.append(f'Created: {datetime.now().strftime("%Y-%m-%d")}')
code_lines.append(f'Confidence: {synthesized_knowledge.confidence:.2f}')
code_lines.append('"""')
code_lines.append('')
# Add imports
code_lines.append('from pathlib import Path')
code_lines.append('from typing import Dict, Any, Optional')
code_lines.append('')
# Add imports from learned patterns
for pattern in synthesized_knowledge.patterns:
if pattern['type'] == 'import':
module = pattern.get('module')
items = pattern.get('items', '')
if module:
code_lines.append(f'from {module} import {items}')
else:
code_lines.append(f'import {items}')
if any(p['type'] == 'import' for p in synthesized_knowledge.patterns):
code_lines.append('')
# Add XML ElementTree if we have XML schema
if synthesized_knowledge.schema and 'xml_structure' in synthesized_knowledge.schema:
code_lines.append('import xml.etree.ElementTree as ET')
code_lines.append('')
# Generate main function
code_lines.append(f'def {feature_name}(')
# Add function parameters from feature spec
inputs = feature_spec['interface']['inputs']
for i, input_param in enumerate(inputs):
param_name = input_param['name']
param_type = input_param.get('type', 'Any')
required = input_param.get('required', True)
# Map types to Python type hints
type_map = {
'str': 'str',
'int': 'int',
'float': 'float',
'bool': 'bool',
'dict': 'Dict[str, Any]',
'list': 'list',
'Path': 'Path'
}
py_type = type_map.get(param_type, 'Any')
if not required:
py_type = f'Optional[{py_type}]'
default = ' = None'
else:
default = ''
comma = ',' if i < len(inputs) - 1 else ''
code_lines.append(f' {param_name}: {py_type}{default}{comma}')
code_lines.append(') -> Dict[str, Any]:')
code_lines.append(' """')
code_lines.append(f' {feature_description}')
code_lines.append('')
code_lines.append(' Args:')
for input_param in inputs:
code_lines.append(f' {input_param["name"]}: {input_param.get("description", "")}')
code_lines.append('')
code_lines.append(' Returns:')
code_lines.append(' Dictionary with generated results')
code_lines.append(' """')
code_lines.append('')
# Generate function body based on learned patterns
if synthesized_knowledge.schema and 'xml_structure' in synthesized_knowledge.schema:
# XML generation code
xml_schema = synthesized_knowledge.schema['xml_structure']
root_element = xml_schema['root_element']
code_lines.append(' # Generate XML from learned schema')
code_lines.append(f' root = ET.Element("{root_element}")')
code_lines.append('')
code_lines.append(' # Add attributes if any')
if xml_schema.get('attributes'):
for attr_name, attr_value in xml_schema['attributes'].items():
code_lines.append(f' root.set("{attr_name}", "{attr_value}")')
code_lines.append('')
code_lines.append(' # Add child elements from parameters')
for field in xml_schema.get('required_fields', []):
field_lower = field.lower()
code_lines.append(f' if {field_lower} is not None:')
code_lines.append(f' elem = ET.SubElement(root, "{field}")')
code_lines.append(f' elem.text = str({field_lower})')
code_lines.append('')
code_lines.append(' # Convert to string')
code_lines.append(' xml_str = ET.tostring(root, encoding="unicode")')
code_lines.append('')
code_lines.append(' return {')
code_lines.append(' "xml_content": xml_str,')
code_lines.append(' "root_element": root.tag,')
code_lines.append(' "success": True')
code_lines.append(' }')
else:
# Generic implementation
code_lines.append(' # TODO: Implement feature logic')
code_lines.append(' # This is a placeholder implementation')
code_lines.append(' result = {')
code_lines.append(' "status": "generated",')
code_lines.append(f' "feature": "{feature_name}",')
code_lines.append(' "note": "This is an auto-generated placeholder"')
code_lines.append(' }')
code_lines.append('')
code_lines.append(' return result')
code_lines.append('')
code_lines.append('')
code_lines.append('# Example usage')
code_lines.append('if __name__ == "__main__":')
code_lines.append(f' result = {feature_name}(')
# Add example parameter values
for input_param in inputs:
param_name = input_param['name']
code_lines.append(f' {param_name}=None, # TODO: Provide example value')
code_lines.append(' )')
code_lines.append(' print(result)')
code_lines.append('')
return '\n'.join(code_lines)
def document_session(
self,
topic: str,
knowledge_gap: KnowledgeGap,
findings: ResearchFindings,
knowledge: SynthesizedKnowledge,
generated_files: List[str]
) -> Path:
"""
Save research session to knowledge base for future reference.
Creates a dated folder in knowledge_base/research_sessions/ with:
- user_question.txt: Original user request
- sources_consulted.txt: List of sources with confidence scores
- findings.md: What was learned from each source
- decision_rationale.md: Why this approach was chosen
Args:
topic: Short topic name (e.g., 'nx_materials')
knowledge_gap: The original knowledge gap
findings: Research findings gathered
knowledge: Synthesized knowledge
generated_files: List of files generated from this research
Returns:
Path to created session folder
Example:
>>> session_path = agent.document_session(
... 'nx_materials',
... gap, findings, knowledge,
... ['nx_material_generator.py']
... )
>>> session_path
PosixPath('knowledge_base/research_sessions/2025-01-16_nx_materials')
"""
# Create session folder
date_str = datetime.now().strftime('%Y-%m-%d')
session_name = f"{date_str}_{topic}"
session_path = self.knowledge_base_path / "research_sessions" / session_name
session_path.mkdir(parents=True, exist_ok=True)
# Save user question
with open(session_path / "user_question.txt", 'w', encoding='utf-8') as f:
f.write(knowledge_gap.user_request)
# Save sources consulted
with open(session_path / "sources_consulted.txt", 'w', encoding='utf-8') as f:
f.write("Sources Consulted\n")
f.write("=" * 50 + "\n\n")
for source, score in findings.confidence_scores.items():
f.write(f"- {source}: {findings.sources.get(source, 'N/A')} "
f"(confidence: {score:.2f})\n")
# Save findings
with open(session_path / "findings.md", 'w', encoding='utf-8') as f:
f.write(f"# Research Findings: {topic}\n\n")
f.write(f"**Date**: {date_str}\n\n")
f.write("## Knowledge Synthesized\n\n")
f.write(knowledge.synthesis_notes + "\n\n")
f.write(f"**Overall Confidence**: {knowledge.confidence:.2f}\n\n")
f.write("## Generated Files\n\n")
for file_path in generated_files:
f.write(f"- `{file_path}`\n")
# Save decision rationale
with open(session_path / "decision_rationale.md", 'w', encoding='utf-8') as f:
f.write(f"# Decision Rationale: {topic}\n\n")
f.write(f"**Confidence Score**: {knowledge.confidence:.2f}\n\n")
f.write("## Why This Approach\n\n")
f.write(knowledge.synthesis_notes + "\n\n")
f.write("## Alternative Approaches Considered\n\n")
f.write("(To be filled by implementation)\n")
return session_path
def search_knowledge_base(self, query: str) -> Optional[Dict[str, Any]]:
"""
Search existing knowledge base for relevant information.
Before starting new research, check if we already have knowledge
about this topic from past research sessions.
Args:
query: Search query (topic or keywords)
Returns:
Dict with existing knowledge if found, None otherwise
Example:
>>> existing = agent.search_knowledge_base("material XML")
>>> if existing and existing['confidence'] > 0.8:
... # Use existing knowledge
... template = load_template(existing['template_path'])
"""
query_lower = query.lower()
research_sessions_path = self.knowledge_base_path / "research_sessions"
if not research_sessions_path.exists():
return None
# Search through all research sessions
best_match = None
best_score = 0.0
for session_dir in research_sessions_path.iterdir():
if not session_dir.is_dir():
continue
# Calculate relevance score based on folder name and contents
folder_name = session_dir.name.lower()
relevance_score = 0.0
# Check folder name for keywords
query_words = query_lower.split()
for word in query_words:
# Special handling for important short words (NX, AI, ML, etc.)
min_length = 1 if word in ['nx', 'ai', 'ml', 'ui'] else 2
if len(word) > min_length and word in folder_name:
relevance_score += 0.3
# Check user_question.txt
user_question_file = session_dir / "user_question.txt"
if user_question_file.exists():
try:
question_content = user_question_file.read_text(encoding='utf-8').lower()
for word in query_words:
min_length = 1 if word in ['nx', 'ai', 'ml', 'ui'] else 2
if len(word) > min_length and word in question_content:
relevance_score += 0.2
except Exception:
pass
# Check findings.md for relevant content
findings_file = session_dir / "findings.md"
if findings_file.exists():
try:
findings_content = findings_file.read_text(encoding='utf-8').lower()
for word in query_words:
min_length = 1 if word in ['nx', 'ai', 'ml', 'ui'] else 2
if len(word) > min_length and word in findings_content:
relevance_score += 0.1
except Exception:
pass
# Update best match if this session is more relevant
if relevance_score > best_score and relevance_score > 0.5: # Threshold
best_score = relevance_score
best_match = {
'session_id': session_dir.name,
'session_path': session_dir,
'relevance_score': relevance_score,
'confidence': min(0.9, relevance_score) # Cap at 0.9
}
# Try to extract confidence from findings
if findings_file.exists():
try:
findings_content = findings_file.read_text(encoding='utf-8')
# Look for confidence score in findings
import re
conf_match = re.search(r'confidence[:\s]+([0-9.]+)', findings_content.lower())
if conf_match:
extracted_conf = float(conf_match.group(1))
best_match['confidence'] = extracted_conf
except Exception:
pass
# Load schema if available (from findings or decision_rationale)
try:
if findings_file.exists():
findings_content = findings_file.read_text(encoding='utf-8')
# Try to extract schema information
if 'schema' in findings_content.lower() or 'xml' in findings_content.lower():
best_match['has_schema'] = True
except Exception:
pass
return best_match
def _generate_user_prompt(self, knowledge_gap: KnowledgeGap) -> str:
"""
Generate user-friendly prompt asking for examples.
Args:
knowledge_gap: The detected knowledge gap
Returns:
Formatted prompt string
"""
topic = knowledge_gap.missing_knowledge[0] if knowledge_gap.missing_knowledge else "this feature"
file_types = self._infer_file_types(topic)
prompt = f"I don't currently have knowledge about {topic}.\n\n"
prompt += f"To help me learn, could you provide an example file?\n"
prompt += f"Suggested file types: {', '.join(file_types)}\n\n"
prompt += f"Once you provide an example, I'll:\n"
prompt += f"1. Analyze its structure and patterns\n"
prompt += f"2. Extract reusable templates\n"
prompt += f"3. Generate the feature you requested\n"
prompt += f"4. Save the knowledge for future use"
return prompt
def _infer_file_types(self, topic: str) -> List[str]:
"""
Infer expected file types based on topic.
Args:
topic: The topic or domain
Returns:
List of suggested file extensions
"""
topic_lower = topic.lower()
# Material-related topics
if any(kw in topic_lower for kw in ['material', 'physical property', 'alloy']):
return ['.xml', '.mat', '.txt']
# Geometry-related topics
elif any(kw in topic_lower for kw in ['geometry', 'fillet', 'chamfer', 'sketch']):
return ['.prt', '.py', '.txt']
# Load/BC-related topics
elif any(kw in topic_lower for kw in ['load', 'boundary condition', 'constraint', 'force']):
return ['.py', '.txt', '.sim']
# Python/code-related topics
elif any(kw in topic_lower for kw in ['function', 'script', 'automation', 'journal']):
return ['.py', '.txt']
# XML/data-related topics
elif any(kw in topic_lower for kw in ['xml', 'config', 'settings']):
return ['.xml', '.json', '.txt']
# Default: accept common file types
else:
return ['.xml', '.py', '.txt', '.json']
# Confidence score reference
CONFIDENCE_LEVELS = {
'user_validated': 0.95, # User confirmed it works
'nx_mcp_official': 0.85, # Official NX documentation
'nxopen_tse': 0.70, # Community-verified (NXOpenTSE)
'web_generic': 0.50 # Generic web search results
}
def get_confidence_description(score: float) -> str:
"""
Get human-readable confidence description.
Args:
score: Confidence score (0.0-1.0)
Returns:
Description like "HIGH", "MEDIUM", "LOW"
"""
if score >= 0.8:
return "HIGH"
elif score >= 0.6:
return "MEDIUM"
elif score >= 0.4:
return "LOW"
else:
return "VERY LOW"
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