Prompt tuning for generation and validation step
This commit is contained in:
ValueOn AG
parent
a7c9e0221f
commit
259ccabbe3
18 changed files with 821 additions and 1259 deletions
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@ -1,242 +0,0 @@
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"""
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Script to analyze codebase for snake_case naming violations that should be camelStyle.
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Excludes routes (decorated endpoint functions) and JSON field names.
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"""
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import ast
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import os
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import re
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from collections import defaultdict
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from pathlib import Path
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from typing import Dict, List, Tuple
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import csv
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# Patterns to exclude (external library interfaces, etc.)
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EXCLUDE_PATTERNS = [
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r'@.*\.(get|post|put|delete|patch|options|head)', # FastAPI route decorators
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r'self\.(db|db_|model|orm)', # Database ORM attributes
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r'\.(objects|query|filter|get|all)', # ORM methods
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r'(request|response|response_model|status_code)', # FastAPI params
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r'(snake_case|kebab-case)', # String literals
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]
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# External library attribute patterns (should not be changed)
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EXTERNAL_LIB_ATTRIBUTES = {
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'pydantic', 'fastapi', 'sqlalchemy', 'psycopg', 'requests',
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'aiohttp', 'azure', 'google', 'openai', 'anthropic', 'reportlab',
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'docx', 'pptx', 'openpyxl', 'json', 'logging', 'datetime', 'typing'
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}
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def isRouteFile(filePath: str) -> bool:
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"""Check if file is a route file"""
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return 'routes' in filePath or 'route' in os.path.basename(filePath).lower()
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def shouldExcludeName(name: str, context: str = "") -> bool:
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"""Check if a name should be excluded from analysis"""
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# Skip if it's a builtin or external library attribute
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if name.startswith('__') and name.endswith('__'):
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return True
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# Skip if context suggests external library usage
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for pattern in EXCLUDE_PATTERNS:
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if re.search(pattern, context, re.IGNORECASE):
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return True
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return False
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def isSnakeCase(name: str) -> bool:
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"""Check if a name is snake_case"""
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if not name or name.startswith('_'):
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return False
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# Check if contains underscore and is not all caps
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return '_' in name and not name.isupper()
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def analyzeFile(filePath: str) -> Dict[str, List[str]]:
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"""Analyze a Python file for naming violations"""
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violations = {
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'functions': [],
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'parameters': [],
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'variables': []
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}
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try:
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with open(filePath, 'r', encoding='utf-8') as f:
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content = f.read()
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tree = ast.parse(content, filename=filePath)
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except (SyntaxError, UnicodeDecodeError):
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return violations
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# Track current context
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currentClass = None
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inRouteDecorator = False
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class NamingAnalyzer(ast.NodeVisitor):
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def __init__(self):
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self.violations = violations
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self.currentClass = None
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self.inRouteDecorator = False
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self.functionDefs = []
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def visit_FunctionDef(self, node):
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# Check if this is a route endpoint (has FastAPI decorator)
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isRouteEndpoint = False
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for decorator in node.decorator_list:
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if isinstance(decorator, ast.Attribute):
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if decorator.attr in ['get', 'post', 'put', 'delete', 'patch', 'options', 'head']:
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isRouteEndpoint = True
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break
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elif isinstance(decorator, ast.Call):
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if isinstance(decorator.func, ast.Attribute):
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if decorator.func.attr in ['get', 'post', 'put', 'delete', 'patch', 'options', 'head']:
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isRouteEndpoint = True
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break
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# Skip route endpoint function names
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# But we still need to check their parameters and variables
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funcName = node.name
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if not isRouteEndpoint and isSnakeCase(funcName) and not shouldExcludeName(funcName):
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self.violations['functions'].append(f"{funcName} (line {node.lineno})")
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# Analyze parameters
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for arg in node.args.args:
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if arg.arg != 'self' and arg.arg != 'cls':
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paramName = arg.arg
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if isSnakeCase(paramName) and not shouldExcludeName(paramName):
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self.violations['parameters'].append(f"{paramName} in {funcName} (line {node.lineno})")
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# Analyze function body for local variables
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for stmt in node.body:
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self.visit(stmt)
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def visit_ClassDef(self, node):
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oldClass = self.currentClass
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self.currentClass = node.name
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self.generic_visit(node)
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self.currentClass = oldClass
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def visit_Assign(self, node):
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for target in node.targets:
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if isinstance(target, ast.Name):
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varName = target.id
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# Skip constants (ALL_CAPS), builtins, and private (_xxx)
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if varName.isupper() or varName.startswith('_'):
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continue
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# Local variables should be camelStyle
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if isSnakeCase(varName) and not shouldExcludeName(varName):
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self.violations['variables'].append(f"{varName} (line {node.lineno})")
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def visit_For(self, node):
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if isinstance(node.target, ast.Name):
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varName = node.target.id
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if isSnakeCase(varName) and not shouldExcludeName(varName):
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self.violations['variables'].append(f"{varName} (line {node.lineno})")
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self.generic_visit(node)
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def visit_With(self, node):
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if node.items:
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for item in node.items:
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if item.optional_vars:
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if isinstance(item.optional_vars, ast.Name):
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varName = item.optional_vars.id
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if isSnakeCase(varName) and not shouldExcludeName(varName):
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self.violations['variables'].append(f"{varName} (line {node.lineno})")
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self.generic_visit(node)
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analyzer = NamingAnalyzer()
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analyzer.visit(tree)
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return violations
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def analyzeCodebase(rootDir: str = 'gateway') -> Dict[str, Dict[str, int]]:
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"""Analyze entire codebase"""
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results = defaultdict(lambda: {
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'functions': 0,
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'parameters': 0,
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'variables': 0,
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'details': {
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'functions': [],
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'parameters': [],
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'variables': []
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}
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})
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# Handle both absolute and relative paths
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rootPath = Path(rootDir)
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if not rootPath.exists():
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# Try relative to current directory
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rootPath = Path('.').resolve() / rootDir
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if not rootPath.exists():
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# Try just current directory if we're already in gateway
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rootPath = Path('.')
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# Find all Python files
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for pyFile in rootPath.rglob('*.py'):
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# Skip route files for function name analysis (but analyze their internals)
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filePath = str(pyFile.relative_to(rootPath))
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# Skip test files and special scripts
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if 'test' in filePath.lower() or 'tool_' in filePath or '__pycache__' in filePath:
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continue
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violations = analyzeFile(str(pyFile))
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# Check if there are any violations
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totalViolations = len(violations['functions']) + len(violations['parameters']) + len(violations['variables'])
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if totalViolations > 0:
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moduleName = filePath.replace('\\', '/')
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results[moduleName]['functions'] = len(violations['functions'])
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results[moduleName]['parameters'] = len(violations['parameters'])
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results[moduleName]['variables'] = len(violations['variables'])
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results[moduleName]['details'] = violations
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return results
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def generateCSV(results: Dict[str, Dict[str, int]], outputFile: str = 'naming_violations.csv'):
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"""Generate CSV report"""
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with open(outputFile, 'w', newline='', encoding='utf-8') as f:
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writer = csv.writer(f)
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writer.writerow(['Module', 'Function Names', 'Parameter Names', 'Variable Names', 'Total'])
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# Sort by total violations
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sortedResults = sorted(
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results.items(),
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key=lambda x: x[1]['functions'] + x[1]['parameters'] + x[1]['variables'],
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reverse=True
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)
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rowsWritten = 0
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for module, stats in sortedResults:
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total = stats['functions'] + stats['parameters'] + stats['variables']
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if total > 0:
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writer.writerow([
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module,
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stats['functions'],
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stats['parameters'],
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stats['variables'],
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total
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])
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rowsWritten += 1
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if rowsWritten == 0:
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print("WARNING: No rows written to CSV despite finding violations!")
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print(f"CSV report generated: {outputFile}")
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print(f"Total modules analyzed: {len(results)}")
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# Print summary
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totalFuncs = sum(r['functions'] for r in results.values())
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totalParams = sum(r['parameters'] for r in results.values())
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totalVars = sum(r['variables'] for r in results.values())
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print(f"\nSummary:")
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print(f" Function names: {totalFuncs}")
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print(f" Parameter names: {totalParams}")
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print(f" Variable names: {totalVars}")
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print(f" Total violations: {totalFuncs + totalParams + totalVars}")
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if __name__ == '__main__':
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print("Analyzing codebase for naming violations...")
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results = analyzeCodebase('gateway')
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# Write CSV to gateway directory
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outputPath = Path('gateway') / 'naming_violations_report.csv'
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generateCSV(results, str(outputPath))
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@ -1,254 +0,0 @@
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# Complete Function Call Diagram
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```mermaid
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graph TB
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subgraph AI_Service["AI Service Modules"]
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MA[mainServiceAi<br/>AiService]
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SC[subCoreAi<br/>SubCoreAi]
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SDG[subDocumentGeneration<br/>SubDocumentGeneration]
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SDP[subDocumentProcessing<br/>SubDocumentProcessing]
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SU[subSharedAiUtils<br/>Utilities]
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end
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subgraph EXT_Service["Extraction Service Modules"]
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MSE[mainServiceExtraction<br/>ExtractionService]
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SPE[subPromptBuilderExtraction<br/>buildExtractionPrompt]
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SP[subPipeline<br/>runExtraction]
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end
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subgraph GEN_Service["Generation Service Modules"]
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MSG[mainServiceGeneration<br/>GenerationService]
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SPG[subPromptBuilderGeneration<br/>buildGenerationPrompt]
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SJ[subJsonSchema<br/>Schemas]
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end
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%% subCoreAi calls
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SC -->|_buildGenerationPrompt| SPG
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SC -->|callAiDocuments| SDP
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SC -->|sanitizePromptContent| SU
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%% subDocumentGeneration calls
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SDG -->|processDocumentsWithContinuation| SDP
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SDG -->|buildGenerationPrompt| SPG
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SDG -->|renderReport| MSG
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SDG -->|sanitizePromptContent| SU
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%% subDocumentProcessing calls
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SDP -->|extractContent 3x| MSE
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SDP -->|_applyMerging 3x| SP
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SDP -->|readImage| SC
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%% mainServiceExtraction calls
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MSE -->|runExtraction| SP
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%% subPromptBuilderExtraction calls
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SPE -->|get_document_subJsonSchema| SJ
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SPE -->|sanitizePromptContent| SU
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%% mainServiceGeneration calls utilities
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MSG -->|utility functions| SU
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%% subCoreAi detailed calls
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SC -.->|aiObjects.call| AI_Interface["AiObjects Interface"]
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SDP -.->|aiObjects.call| AI_Interface
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%% Style
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classDef aiClass fill:#e1f5ff,stroke:#0066cc,stroke-width:2px
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classDef extClass fill:#fff5e1,stroke:#cc6600,stroke-width:2px
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classDef genClass fill:#e1ffe1,stroke:#006600,stroke-width:2px
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classDef utilClass fill:#f0f0f0,stroke:#666,stroke-width:2px
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classDef interfaceClass fill:#ffe1f5,stroke:#cc0066,stroke-width:2px
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class MA,SC,SDG,SDP,SU aiClass
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class MSE,SPE,SP extClass
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class MSG,SPG,SJ genClass
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class AI_Interface interfaceClass
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```
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## Detailed Call Map with Function Names
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```mermaid
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graph LR
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%% Nodes
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SC[subCoreAi]
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SDG[subDocumentGeneration]
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SDP[subDocumentProcessing]
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SU[subSharedAiUtils]
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SPE[subPromptBuilderExtraction]
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SPG[subPromptBuilderGeneration]
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MSE[mainServiceExtraction]
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MSG[mainServiceGeneration]
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SP[subPipeline]
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SJ[subJsonSchema]
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%% subCoreAi function calls
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SC -->|"_buildGenerationPrompt()<br/>calls"| SPG
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SC -->|"callAiDocuments()<br/>calls callAiText()"| SDP
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SC -->|"sanitizePromptContent()"| SU
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%% subDocumentGeneration function calls
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SDG -->|"_processDocumentsUnified()<br/>calls"| SDP
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SDG -->|"_processDocument()<br/>calls"| SPG
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SDG -->|"_processDocument()<br/>calls"| MSG
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SDG -->|"sanitizePromptContent()"| SU
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%% subDocumentProcessing function calls
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SDP -->|"extractContent()"| MSE
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SDP -->|"_mergePartResults()<br/>_convertPartResultsToJson()<br/>_mergeChunkResultsJson()<br/>all call"| SP
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SDP -->|"_processChunksWithMapping()<br/>calls readImage()"| SC
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%% Extraction service calls
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MSE -->|"extractContent()<br/>calls"| SP
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%% Prompt builder calls
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SPE -->|"get_document_subJsonSchema()"| SJ
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SPE -->|"sanitizePromptContent()"| SU
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%% Generation service calls
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MSG -->|"uses utility functions"| SU
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classDef aiModule fill:#e1f5ff,stroke:#0066cc
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classDef extModule fill:#fff5e1,stroke:#cc6600
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classDef genModule fill:#e1ffe1,stroke:#006600
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class SC,SDG,SDP,SU aiModule
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class MSE,SPE,SP extModule
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class MSG,SPG,SJ genModule
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```
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## Call Flow by Module
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### 1. subCoreAi (SubCoreAi Class)
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**Calls Out:**
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- `buildGenerationPrompt()` → subPromptBuilderGeneration (line 363-366)
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- `callAiText()` → subDocumentProcessing (line 453)
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- `renderReport()` → mainServiceGeneration (line 478-482)
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- `sanitizePromptContent()` → subSharedAiUtils (line 61, via services.ai)
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**Called By:**
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- mainServiceAi (creates instance)
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- subDocumentProcessing._processChunksWithMapping (calls readImage at line 672-675)
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---
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### 2. subDocumentGeneration (SubDocumentGeneration Class)
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**Calls Out:**
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- `processDocumentsWithContinuation()` → subDocumentProcessing (line 110)
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- `buildGenerationPrompt()` → subPromptBuilderGeneration (line 330)
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- `renderReport()` → mainServiceGeneration (line 392)
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- `sanitizePromptContent()` → subSharedAiUtils (line 466)
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**Called By:**
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- mainServiceAi (creates instance)
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---
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### 3. subDocumentProcessing (SubDocumentProcessing Class)
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**Calls Out:**
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- `extractContent()` → mainServiceExtraction (lines 78, 131, 220)
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- `_applyMerging()` → subPipeline (lines 1044, 1095, 1232, 1293, 1345)
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- `readImage()` → subCoreAi (line 672-675)
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- `sanitizePromptContent()` → subSharedAiUtils (via self.services.ai)
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**Called By:**
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- mainServiceAi (creates instance)
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- subCoreAi.callAiDocuments (calls callAiText at line 453)
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- subDocumentGeneration._processDocumentsUnified (calls processDocumentsWithContinuation)
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---
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### 4. mainServiceExtraction (ExtractionService Class)
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**Calls Out:**
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- `runExtraction()` → subPipeline (line 61)
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- Uses ExtractorRegistry from subRegistry
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**Called By:**
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- subDocumentProcessing.extractContent (3 times)
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---
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### 5. subPromptBuilderExtraction
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**Calls Out:**
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- `get_document_subJsonSchema()` → subJsonSchema (line 172)
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- `sanitizePromptContent()` → subSharedAiUtils (via services.ai)
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**Called By:**
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- mainServiceGeneration (indirectly via getAdaptiveExtractionPrompt)
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---
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### 6. mainServiceGeneration (GenerationService Class)
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**Calls Out:**
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- `get_renderer()` → renderers.registry (line 501)
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- Utility functions from subDocumentUtility
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- Uses modelRegistry (external)
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**Called By:**
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- subCoreAi.callAiDocuments (calls renderReport)
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- subDocumentGeneration._processDocument (calls renderReport)
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---
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### 7. subPromptBuilderGeneration
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**Calls Out:**
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- Returns prompt template string
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**Called By:**
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- subCoreAi._buildGenerationPrompt (line 363-366)
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- subDocumentGeneration._processDocument (line 330)
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---
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### 8. subPipeline
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**Calls Out:**
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- Creates IntelligentTokenAwareMerger from subMerger (line 96)
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- Uses mergers from merging submodules
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**Called By:**
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- mainServiceExtraction.extractContent (calls runExtraction)
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- subDocumentProcessing (calls _applyMerging 5 times)
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---
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### 9. subSharedAiUtils
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**Functions Provided:**
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- `buildPromptWithPlaceholders()`
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- `sanitizePromptContent()`
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- `extractTextFromContentParts()`
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- `reduceText()`
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- `determineCallType()`
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**Called By:**
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- subCoreAi (imports and calls functions)
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- subDocumentGeneration (via services.ai.sanitizePromptContent)
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- subPromptBuilderExtraction (via services.ai.sanitizePromptContent)
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---
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### 10. subJsonSchema
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**Functions Provided:**
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- `get_document_subJsonSchema()`
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- `get_multi_document_subJsonSchema()`
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**Called By:**
|
||||
- subPromptBuilderExtraction.buildExtractionPrompt (line 172)
|
||||
|
||||
---
|
||||
|
||||
## Circular Dependencies
|
||||
|
||||
**AI Service Loop:**
|
||||
1. subDocumentProcessing → subCoreAi.readImage() (for image processing)
|
||||
2. subDocumentProcessing → mainServiceExtraction (for extraction)
|
||||
3. mainServiceExtraction → subPipeline (for processing)
|
||||
4. subPipeline creates IntelligentTokenAwareMerger
|
||||
|
||||
**Flow:**
|
||||
```
|
||||
subDocumentProcessing.extractContent()
|
||||
→ mainServiceExtraction.extractContent()
|
||||
→ subPipeline.runExtraction()
|
||||
→ returns ContentExtracted
|
||||
→ processed by subDocumentProcessing
|
||||
→ calls subPipeline._applyMerging()
|
||||
```
|
||||
|
|
@ -70,6 +70,12 @@ class ModelSelector:
|
|||
promptSize = len(prompt.encode("utf-8"))
|
||||
contextSize = len(context.encode("utf-8"))
|
||||
totalSize = promptSize + contextSize
|
||||
# Convert bytes to approximate tokens (1 token ≈ 4 bytes)
|
||||
promptTokens = promptSize / 4
|
||||
contextTokens = contextSize / 4
|
||||
totalTokens = totalSize / 4
|
||||
|
||||
logger.debug(f"Request sizes - Prompt: {promptTokens:.0f} tokens ({promptSize} bytes), Context: {contextTokens:.0f} tokens ({contextSize} bytes), Total: {totalTokens:.0f} tokens ({totalSize} bytes)")
|
||||
|
||||
# Step 1: Filter by operation type (MUST match) - check if model has this operation type
|
||||
operationFiltered = []
|
||||
|
|
@ -80,10 +86,32 @@ class ModelSelector:
|
|||
operationFiltered.append(model)
|
||||
logger.debug(f"After operation type filtering: {len(operationFiltered)} models")
|
||||
|
||||
if operationFiltered:
|
||||
logger.debug(f"Models with {options.operationType.value}: {[m.name for m in operationFiltered]}")
|
||||
|
||||
# Step 2: Filter by prompt size (MUST be <= 80% of context size)
|
||||
promptFiltered = [m for m in operationFiltered if m.contextLength == 0 or promptSize <= m.contextLength * 0.8]
|
||||
# Note: contextLength is in tokens, so we need to compare tokens with tokens
|
||||
promptFiltered = []
|
||||
for model in operationFiltered:
|
||||
if model.contextLength == 0:
|
||||
# No context length limit - always pass
|
||||
promptFiltered.append(model)
|
||||
else:
|
||||
maxAllowedTokens = model.contextLength * 0.8
|
||||
# Compare prompt tokens (not bytes) with model's token limit
|
||||
if promptTokens <= maxAllowedTokens:
|
||||
promptFiltered.append(model)
|
||||
else:
|
||||
logger.debug(f"Model {model.name} filtered out: promptSize={promptTokens:.0f} tokens > maxAllowed={maxAllowedTokens:.0f} tokens (80% of {model.contextLength} tokens)")
|
||||
|
||||
logger.debug(f"After prompt size filtering: {len(promptFiltered)} models")
|
||||
|
||||
if not promptFiltered and operationFiltered:
|
||||
logger.warning(f"All {len(operationFiltered)} models with {options.operationType.value} were filtered out due to prompt size. Prompt: {promptTokens:.0f} tokens. Available models:")
|
||||
for model in operationFiltered:
|
||||
maxAllowed = model.contextLength * 0.8 / 4 if model.contextLength > 0 else "unlimited"
|
||||
logger.warning(f" - {model.name}: contextLength={model.contextLength} tokens, maxAllowed={maxAllowed} tokens")
|
||||
|
||||
# Step 3: Calculate scores for each model
|
||||
scoredModels = []
|
||||
for model in promptFiltered:
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ from modules.datamodels.datamodelAi import (
|
|||
AiModelCall,
|
||||
AiModelResponse,
|
||||
)
|
||||
from modules.datamodels.datamodelExtraction import ContentPart
|
||||
from modules.datamodels.datamodelExtraction import ContentPart, MergeStrategy
|
||||
|
||||
|
||||
# Dynamic model registry - models are now loaded from connectors via aicore system
|
||||
|
|
@ -73,7 +73,8 @@ class AiObjects:
|
|||
logger.info(f"Selected model: {selectedModel.name} ({selectedModel.displayName})")
|
||||
return selectedModel.name
|
||||
|
||||
# AI for Extraction and Text Generation
|
||||
|
||||
# AI for Extraction, Processing, Generation
|
||||
async def call(self, request: AiCallRequest) -> AiCallResponse:
|
||||
"""Call AI model for text generation with model-aware chunking."""
|
||||
# Handle content parts (unified path)
|
||||
|
|
@ -366,7 +367,6 @@ class AiObjects:
|
|||
content_parts.append(content_part)
|
||||
|
||||
# Use existing merging system
|
||||
from modules.datamodels.datamodelExtraction import MergeStrategy
|
||||
merge_strategy = MergeStrategy(
|
||||
useIntelligentMerging=True,
|
||||
groupBy="typeGroup",
|
||||
|
|
@ -374,8 +374,7 @@ class AiObjects:
|
|||
mergeType="concatenate"
|
||||
)
|
||||
|
||||
from modules.services.serviceExtraction.subPipeline import _applyMerging
|
||||
merged_parts = _applyMerging(content_parts, merge_strategy)
|
||||
merged_parts = applyMerging(content_parts, merge_strategy)
|
||||
|
||||
# Convert merged parts back to final string
|
||||
final_content = "\n\n".join([part.data for part in merged_parts])
|
||||
|
|
@ -413,7 +412,6 @@ class AiObjects:
|
|||
content_parts.append(content_part)
|
||||
|
||||
# Use existing merging system
|
||||
from modules.datamodels.datamodelExtraction import MergeStrategy
|
||||
merge_strategy = MergeStrategy(
|
||||
useIntelligentMerging=True,
|
||||
groupBy="typeGroup",
|
||||
|
|
@ -421,8 +419,7 @@ class AiObjects:
|
|||
mergeType="concatenate"
|
||||
)
|
||||
|
||||
from modules.services.serviceExtraction.subPipeline import _applyMerging
|
||||
merged_parts = _applyMerging(content_parts, merge_strategy)
|
||||
merged_parts = applyMerging(content_parts, merge_strategy)
|
||||
|
||||
# Convert merged parts back to final string
|
||||
final_content = "\n\n".join([part.data for part in merged_parts])
|
||||
|
|
@ -505,82 +502,6 @@ class AiObjects:
|
|||
)
|
||||
|
||||
|
||||
# AI for Image Generation
|
||||
async def generateImage(self, prompt: str, size: str = "1024x1024", quality: str = "standard", style: str = "vivid", options: AiCallOptions = None) -> AiCallResponse:
|
||||
"""Generate an image using AI."""
|
||||
|
||||
if options is None:
|
||||
options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_GENERATE)
|
||||
|
||||
# Calculate input bytes
|
||||
inputBytes = len(prompt.encode("utf-8"))
|
||||
|
||||
try:
|
||||
# Select the best model for image generation
|
||||
modelName = self._selectModel(prompt, "", options)
|
||||
selectedModel = modelRegistry.getModel(modelName)
|
||||
|
||||
if not selectedModel:
|
||||
raise ValueError(f"Selected model {modelName} not found in registry")
|
||||
|
||||
# Get the connector for this model
|
||||
connector = modelRegistry.getConnectorForModel(modelName)
|
||||
if not connector:
|
||||
raise ValueError(f"No connector found for model {modelName}")
|
||||
|
||||
# Start timing
|
||||
startTime = time.time()
|
||||
|
||||
# Create standardized call object for image generation
|
||||
modelCall = AiModelCall(
|
||||
messages=[{"role": "user", "content": prompt}],
|
||||
model=selectedModel,
|
||||
options=AiCallOptions(size=size, quality=quality, style=style)
|
||||
)
|
||||
|
||||
# Call the model with standardized interface
|
||||
if selectedModel.functionCall:
|
||||
modelResponse = await selectedModel.functionCall(modelCall)
|
||||
|
||||
# Extract content from standardized response
|
||||
if not modelResponse.success:
|
||||
raise ValueError(f"Model call failed: {modelResponse.error}")
|
||||
content = modelResponse.content
|
||||
else:
|
||||
raise ValueError(f"Model {modelName} has no function call defined")
|
||||
|
||||
# Calculate timing and output bytes
|
||||
endTime = time.time()
|
||||
processingTime = endTime - startTime
|
||||
outputBytes = len(content.encode("utf-8"))
|
||||
|
||||
# Calculate price using model's own price calculation method
|
||||
priceUsd = selectedModel.calculatePriceUsd(processingTime, inputBytes, outputBytes)
|
||||
|
||||
logger.info(f"✅ Image generation successful with model: {modelName}")
|
||||
return AiCallResponse(
|
||||
success=True,
|
||||
content=content,
|
||||
modelName=modelName,
|
||||
processingTime=processingTime,
|
||||
priceUsd=priceUsd,
|
||||
bytesSent=inputBytes,
|
||||
bytesReceived=outputBytes,
|
||||
errorCount=0
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Image generation failed with model {modelName}: {str(e)}")
|
||||
return AiCallResponse(
|
||||
content=f"Image generation failed: {str(e)}",
|
||||
modelName=modelName,
|
||||
priceUsd=0.0,
|
||||
processingTime=0.0,
|
||||
bytesSent=inputBytes,
|
||||
bytesReceived=0,
|
||||
errorCount=1
|
||||
)
|
||||
|
||||
# Utility methods
|
||||
async def listAvailableModels(self, connectorType: str = None) -> List[Dict[str, Any]]:
|
||||
"""List available models, optionally filtered by connector type."""
|
||||
|
|
@ -601,3 +522,65 @@ class AiObjects:
|
|||
models = modelRegistry.getModelsByTag(tag)
|
||||
return [model.name for model in models]
|
||||
|
||||
|
||||
def applyMerging(parts: List[ContentPart], strategy: MergeStrategy) -> List[ContentPart]:
|
||||
"""Apply merging strategy to parts with intelligent token-aware merging."""
|
||||
logger.debug(f"applyMerging called with {len(parts)} parts")
|
||||
|
||||
# Import merging dependencies
|
||||
from modules.services.serviceExtraction.merging.mergerText import TextMerger
|
||||
from modules.services.serviceExtraction.merging.mergerTable import TableMerger
|
||||
from modules.services.serviceExtraction.merging.mergerDefault import DefaultMerger
|
||||
from modules.services.serviceExtraction.subMerger import IntelligentTokenAwareMerger
|
||||
|
||||
# Check if intelligent merging is enabled
|
||||
if strategy.useIntelligentMerging:
|
||||
modelCapabilities = strategy.capabilities or {}
|
||||
subMerger = IntelligentTokenAwareMerger(modelCapabilities)
|
||||
|
||||
# Use intelligent merging for all parts
|
||||
merged = subMerger.mergeChunksIntelligently(parts, strategy.prompt or "")
|
||||
|
||||
# Calculate and log optimization stats
|
||||
stats = subMerger.calculateOptimizationStats(parts, merged)
|
||||
logger.info(f"🧠 Intelligent merging stats: {stats}")
|
||||
logger.debug(f"Intelligent merging: {stats['original_ai_calls']} → {stats['optimized_ai_calls']} calls ({stats['reduction_percent']}% reduction)")
|
||||
|
||||
return merged
|
||||
|
||||
# Fallback to traditional merging
|
||||
textMerger = TextMerger()
|
||||
tableMerger = TableMerger()
|
||||
defaultMerger = DefaultMerger()
|
||||
|
||||
# Group by typeGroup
|
||||
textParts = [p for p in parts if p.typeGroup == "text"]
|
||||
tableParts = [p for p in parts if p.typeGroup == "table"]
|
||||
structureParts = [p for p in parts if p.typeGroup == "structure"]
|
||||
otherParts = [p for p in parts if p.typeGroup not in ("text", "table", "structure")]
|
||||
|
||||
logger.debug(f"Grouped - text: {len(textParts)}, table: {len(tableParts)}, structure: {len(structureParts)}, other: {len(otherParts)}")
|
||||
|
||||
merged: List[ContentPart] = []
|
||||
|
||||
if textParts:
|
||||
textMerged = textMerger.merge(textParts, strategy)
|
||||
logger.debug(f"TextMerger merged {len(textParts)} parts into {len(textMerged)} parts")
|
||||
merged.extend(textMerged)
|
||||
if tableParts:
|
||||
tableMerged = tableMerger.merge(tableParts, strategy)
|
||||
logger.debug(f"TableMerger merged {len(tableParts)} parts into {len(tableMerged)} parts")
|
||||
merged.extend(tableMerged)
|
||||
if structureParts:
|
||||
# For now, treat structure like text
|
||||
structureMerged = textMerger.merge(structureParts, strategy)
|
||||
logger.debug(f"StructureMerger merged {len(structureParts)} parts into {len(structureMerged)} parts")
|
||||
merged.extend(structureMerged)
|
||||
if otherParts:
|
||||
otherMerged = defaultMerger.merge(otherParts, strategy)
|
||||
logger.debug(f"DefaultMerger merged {len(otherParts)} parts into {len(otherMerged)} parts")
|
||||
merged.extend(otherMerged)
|
||||
|
||||
logger.debug(f"applyMerging returning {len(merged)} parts")
|
||||
return merged
|
||||
|
||||
|
|
|
|||
|
|
@ -302,6 +302,7 @@ Respond with ONLY a JSON object in this exact format:
|
|||
# Done - build final result
|
||||
if operationId:
|
||||
self.services.workflow.progressLogUpdate(operationId, 0.95, f"Generation complete ({iteration} iterations, {len(allSections)} sections)")
|
||||
logger.info(f"Generation complete after {iteration} iterations: {len(allSections)} sections")
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -385,7 +386,7 @@ Respond with ONLY a JSON object in this exact format:
|
|||
rawResponse: str = None
|
||||
) -> bool:
|
||||
"""
|
||||
Determine if generation should continue based on JSON completeness and complete_response flag.
|
||||
Determine if generation should continue based on JSON completeness, complete_response flag, and task completion.
|
||||
Returns True if we should continue, False if done.
|
||||
"""
|
||||
if len(allSections) == 0:
|
||||
|
|
@ -395,13 +396,22 @@ Respond with ONLY a JSON object in this exact format:
|
|||
if rawResponse:
|
||||
import re
|
||||
if re.search(r'"complete_response"\s*:\s*true', rawResponse, re.IGNORECASE):
|
||||
logger.info(f"Iteration {iteration}: AI marked response as complete (complete_response flag detected)")
|
||||
return False
|
||||
|
||||
# If JSON was complete (and no complete_response flag), we're done
|
||||
# If JSON was broken and repaired, continue to get more content
|
||||
# If JSON was complete, stop (AI should have set complete_response if task is done)
|
||||
# For continuation iterations (iteration > 1), if JSON is complete but no flag was set,
|
||||
# stop to prevent infinite loops - AI had a chance to set the flag
|
||||
if wasJsonComplete:
|
||||
if iteration > 1:
|
||||
# Continuation mode: JSON complete without flag means we're likely done
|
||||
# Stop to prevent infinite loops
|
||||
logger.info(f"Iteration {iteration}: JSON complete without complete_response flag - stopping")
|
||||
return False
|
||||
# First iteration with complete JSON - done
|
||||
return False
|
||||
else:
|
||||
# JSON was incomplete/broken - continue
|
||||
return True
|
||||
|
||||
def _buildFinalResultFromSections(
|
||||
|
|
@ -523,25 +533,61 @@ Respond with ONLY a JSON object in this exact format:
|
|||
)
|
||||
|
||||
try:
|
||||
# Ensure AI connectors are initialized before delegating to generator
|
||||
if hasattr(self.services, 'ai') and hasattr(self.services.ai, '_ensureAiObjectsInitialized'):
|
||||
await self.services.ai._ensureAiObjectsInitialized()
|
||||
if options is None or (hasattr(options, 'operationType') and options.operationType is None):
|
||||
# Use AI to determine parameters ONLY when truly needed (options=None OR operationType=None)
|
||||
self.services.workflow.progressLogUpdate(aiOperationId, 0.1, "Analyzing prompt parameters")
|
||||
options = await self._analyzePromptAndCreateOptions(prompt)
|
||||
|
||||
# Route image-generation requests directly to image pipeline to avoid JSON loop
|
||||
imgFormats = {"png", "jpg", "jpeg", "webp", "image", "base64"}
|
||||
# Handle image generation requests directly via generic path
|
||||
opType = getattr(options, "operationType", None)
|
||||
fmt = (outputFormat or "").lower() if outputFormat else None
|
||||
isImageRequest = (opType == OperationTypeEnum.IMAGE_GENERATE) or (fmt in imgFormats)
|
||||
isImageRequest = (opType == OperationTypeEnum.IMAGE_GENERATE)
|
||||
|
||||
if isImageRequest:
|
||||
# Image generation uses generic call path but bypasses document generation pipeline
|
||||
self.services.workflow.progressLogUpdate(aiOperationId, 0.4, "Calling AI for image generation")
|
||||
imageResponse = await self.generateImage(prompt, options=options)
|
||||
|
||||
# Call via generic path (no looping for images)
|
||||
request = AiCallRequest(
|
||||
prompt=prompt,
|
||||
context="",
|
||||
options=options
|
||||
)
|
||||
|
||||
response = await self.aiObjects.call(request)
|
||||
|
||||
# Extract image data from response
|
||||
if response.content:
|
||||
# For base64 format, return in expected format
|
||||
if outputFormat == "base64":
|
||||
result = {
|
||||
"success": True,
|
||||
"image_data": response.content,
|
||||
"documents": [{
|
||||
"documentName": "generated_image.png",
|
||||
"documentData": response.content,
|
||||
"mimeType": "image/png",
|
||||
"title": title or "Generated Image"
|
||||
}]
|
||||
}
|
||||
else:
|
||||
# Return raw content for other formats
|
||||
result = response.content
|
||||
|
||||
# Emit stats for image generation
|
||||
self.services.workflow.storeWorkflowStat(
|
||||
self.services.currentWorkflow,
|
||||
response,
|
||||
f"ai.generate.image"
|
||||
)
|
||||
|
||||
self.services.workflow.progressLogUpdate(aiOperationId, 0.9, "Image generated")
|
||||
self.services.workflow.progressLogFinish(aiOperationId, True)
|
||||
return imageResponse
|
||||
return result
|
||||
else:
|
||||
errorMsg = f"No image data returned: {response.content}"
|
||||
logger.error(f"Error in AI image generation: {errorMsg}")
|
||||
self.services.workflow.progressLogFinish(aiOperationId, False)
|
||||
return {"success": False, "error": errorMsg}
|
||||
|
||||
# CRITICAL: For document generation with JSON templates, NEVER compress the prompt
|
||||
# Compressing would truncate the template structure and confuse the AI
|
||||
|
|
@ -656,102 +702,6 @@ Respond with ONLY a JSON object in this exact format:
|
|||
self.services.workflow.progressLogFinish(aiOperationId, False)
|
||||
raise
|
||||
|
||||
# AI Image Analysis
|
||||
async def readImage(
|
||||
self,
|
||||
prompt: str,
|
||||
imageData: Union[str, bytes],
|
||||
mimeType: str = None,
|
||||
options: Optional[AiCallOptions] = None,
|
||||
) -> str:
|
||||
"""Call AI for image analysis using interface.call() with contentParts."""
|
||||
await self._ensureAiObjectsInitialized()
|
||||
|
||||
try:
|
||||
# Check if imageData is valid
|
||||
if not imageData:
|
||||
error_msg = "No image data provided"
|
||||
logger.error(f"Error in AI image analysis: {error_msg}")
|
||||
return f"Error: {error_msg}"
|
||||
|
||||
|
||||
# Always use IMAGE_ANALYSE operation type for image processing
|
||||
if options is None:
|
||||
options = AiCallOptions(operationType=OperationTypeEnum.IMAGE_ANALYSE)
|
||||
else:
|
||||
# Override the operation type to ensure image analysis
|
||||
options.operationType = OperationTypeEnum.IMAGE_ANALYSE
|
||||
|
||||
# Create content parts with image data
|
||||
from modules.datamodels.datamodelExtraction import ContentPart
|
||||
import base64
|
||||
|
||||
# ContentPart.data must be a string - convert bytes to base64 if needed
|
||||
if isinstance(imageData, bytes):
|
||||
imageDataStr = base64.b64encode(imageData).decode('utf-8')
|
||||
else:
|
||||
# Already a base64 string
|
||||
imageDataStr = imageData
|
||||
|
||||
imagePart = ContentPart(
|
||||
id="image_0",
|
||||
parentId=None,
|
||||
label="Image",
|
||||
typeGroup="image",
|
||||
mimeType=mimeType or "image/jpeg",
|
||||
data=imageDataStr, # Must be a string (base64 encoded)
|
||||
metadata={"imageAnalysis": True}
|
||||
)
|
||||
|
||||
# Create request with content parts
|
||||
request = AiCallRequest(
|
||||
prompt=prompt,
|
||||
context="",
|
||||
options=options,
|
||||
contentParts=[imagePart]
|
||||
)
|
||||
|
||||
response = await self.aiObjects.call(request)
|
||||
result = response.content
|
||||
|
||||
# Check if result is valid
|
||||
if not result or (isinstance(result, str) and not result.strip()):
|
||||
error_msg = f"No response from AI image analysis (result: {repr(result)})"
|
||||
logger.error(f"Error in AI image analysis: {error_msg}")
|
||||
return f"Error: {error_msg}"
|
||||
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(f"Error in AI image analysis: {str(e)}")
|
||||
return f"Error: {str(e)}"
|
||||
|
||||
# AI Image Generation
|
||||
async def generateImage(
|
||||
self,
|
||||
prompt: str,
|
||||
size: str = "1024x1024",
|
||||
quality: str = "standard",
|
||||
style: str = "vivid",
|
||||
options: Optional[AiCallOptions] = None,
|
||||
) -> Dict[str, Any]:
|
||||
"""Generate an image using AI using interface.generateImage()."""
|
||||
await self._ensureAiObjectsInitialized()
|
||||
|
||||
try:
|
||||
response = await self.aiObjects.generateImage(prompt, size, quality, style, options)
|
||||
|
||||
# Emit stats for image generation
|
||||
self.services.workflow.storeWorkflowStat(
|
||||
self.services.currentWorkflow,
|
||||
response,
|
||||
f"ai.generate.image"
|
||||
)
|
||||
|
||||
return response
|
||||
except Exception as e:
|
||||
logger.error(f"Error in AI image generation: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
async def callAiText(
|
||||
self,
|
||||
prompt: str,
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ import time
|
|||
import asyncio
|
||||
|
||||
from .subRegistry import ExtractorRegistry, ChunkerRegistry
|
||||
from .subPipeline import runExtraction, _applyMerging
|
||||
from .subPipeline import runExtraction
|
||||
from modules.datamodels.datamodelExtraction import ContentExtracted, ContentPart, MergeStrategy, ExtractionOptions, PartResult
|
||||
from modules.datamodels.datamodelChat import ChatDocument
|
||||
from modules.datamodels.datamodelAi import AiCallResponse, AiCallRequest, AiCallOptions, OperationTypeEnum
|
||||
|
|
@ -14,6 +14,9 @@ from modules.aicore.aicoreModelRegistry import modelRegistry
|
|||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Rebuild ExtractionOptions to resolve forward references after all imports are complete
|
||||
ExtractionOptions.model_rebuild()
|
||||
|
||||
|
||||
class ExtractionService:
|
||||
def __init__(self, services: Optional[Any] = None):
|
||||
|
|
@ -649,7 +652,8 @@ class ExtractionService:
|
|||
|
||||
|
||||
# Apply existing merging logic using the sophisticated merging system
|
||||
merged_parts = _applyMerging(content_parts, merge_strategy)
|
||||
from modules.interfaces.interfaceAiObjects import applyMerging
|
||||
merged_parts = applyMerging(content_parts, merge_strategy)
|
||||
|
||||
# Convert merged parts back to final string
|
||||
final_content = "\n\n".join([part.data for part in merged_parts])
|
||||
|
|
|
|||
|
|
@ -4,55 +4,11 @@ import logging
|
|||
from modules.datamodels.datamodelExtraction import ContentExtracted, ContentPart, ExtractionOptions, MergeStrategy
|
||||
from .subUtils import makeId
|
||||
from .subRegistry import ExtractorRegistry, ChunkerRegistry
|
||||
from .merging.mergerText import TextMerger
|
||||
from .merging.mergerTable import TableMerger
|
||||
from .merging.mergerDefault import DefaultMerger
|
||||
from .subMerger import IntelligentTokenAwareMerger
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _mergeParts(parts: List[ContentPart], mergeStrategy: MergeStrategy) -> List[ContentPart]:
|
||||
"""Merge parts based on the provided strategy."""
|
||||
if not parts or not mergeStrategy:
|
||||
return parts
|
||||
|
||||
groupBy = mergeStrategy.groupBy
|
||||
orderBy = mergeStrategy.orderBy
|
||||
|
||||
# Group parts by the specified field
|
||||
groups = {}
|
||||
for part in parts:
|
||||
key = getattr(part, groupBy, "unknown")
|
||||
if key not in groups:
|
||||
groups[key] = []
|
||||
groups[key].append(part)
|
||||
|
||||
# Merge each group
|
||||
merged_parts = []
|
||||
for group_key, group_parts in groups.items():
|
||||
if len(group_parts) == 1:
|
||||
merged_parts.extend(group_parts)
|
||||
else:
|
||||
# Sort by orderBy field if specified
|
||||
if orderBy:
|
||||
group_parts.sort(key=lambda p: getattr(p, orderBy, ""))
|
||||
|
||||
# Use appropriate merger based on type
|
||||
type_group = group_parts[0].typeGroup if group_parts else "unknown"
|
||||
|
||||
if type_group == "text":
|
||||
merger = TextMerger()
|
||||
elif type_group == "table":
|
||||
merger = TableMerger()
|
||||
else:
|
||||
merger = DefaultMerger()
|
||||
|
||||
# Merge the group
|
||||
merged = merger.merge(group_parts, mergeStrategy)
|
||||
merged_parts.extend(merged)
|
||||
|
||||
return merged_parts
|
||||
# REMOVED: _mergeParts function - unused, functionality replaced by applyMerging in interfaceAiObjects.py
|
||||
|
||||
|
||||
def runExtraction(extractorRegistry: ExtractorRegistry, chunkerRegistry: ChunkerRegistry, documentBytes: bytes, fileName: str, mimeType: str, options: ExtractionOptions) -> ContentExtracted:
|
||||
|
|
@ -78,69 +34,12 @@ def runExtraction(extractorRegistry: ExtractorRegistry, chunkerRegistry: Chunker
|
|||
|
||||
# Apply merging strategy if provided (preserve existing logic)
|
||||
if options.mergeStrategy:
|
||||
parts = _applyMerging(parts, options.mergeStrategy)
|
||||
from modules.interfaces.interfaceAiObjects import applyMerging
|
||||
parts = applyMerging(parts, options.mergeStrategy)
|
||||
|
||||
return ContentExtracted(id=makeId(), parts=parts)
|
||||
|
||||
|
||||
# REMOVED: poolAndLimit function - chunking now handled in AI call phase
|
||||
|
||||
|
||||
def _applyMerging(parts: List[ContentPart], strategy: MergeStrategy) -> List[ContentPart]:
|
||||
"""Apply merging strategy to parts with intelligent token-aware merging."""
|
||||
logger.debug(f"_applyMerging called with {len(parts)} parts")
|
||||
|
||||
# Check if intelligent merging is enabled
|
||||
if strategy.useIntelligentMerging:
|
||||
model_capabilities = strategy.capabilities or {}
|
||||
subMerger = IntelligentTokenAwareMerger(model_capabilities)
|
||||
|
||||
# Use intelligent merging for all parts
|
||||
merged = subMerger.mergeChunksIntelligently(parts, strategy.prompt or "")
|
||||
|
||||
# Calculate and log optimization stats
|
||||
stats = subMerger.calculateOptimizationStats(parts, merged)
|
||||
logger.info(f"🧠 Intelligent merging stats: {stats}")
|
||||
logger.debug(f"Intelligent merging: {stats['original_ai_calls']} → {stats['optimized_ai_calls']} calls ({stats['reduction_percent']}% reduction)")
|
||||
|
||||
return merged
|
||||
|
||||
# Fallback to traditional merging
|
||||
textMerger = TextMerger()
|
||||
tableMerger = TableMerger()
|
||||
defaultMerger = DefaultMerger()
|
||||
|
||||
# Group by typeGroup
|
||||
textParts = [p for p in parts if p.typeGroup == "text"]
|
||||
tableParts = [p for p in parts if p.typeGroup == "table"]
|
||||
structureParts = [p for p in parts if p.typeGroup == "structure"]
|
||||
otherParts = [p for p in parts if p.typeGroup not in ("text", "table", "structure")]
|
||||
|
||||
logger.debug(f"Grouped - text: {len(textParts)}, table: {len(tableParts)}, structure: {len(structureParts)}, other: {len(otherParts)}")
|
||||
|
||||
merged: List[ContentPart] = []
|
||||
|
||||
if textParts:
|
||||
textMerged = textMerger.merge(textParts, strategy)
|
||||
logger.debug(f"TextMerger merged {len(textParts)} parts into {len(textMerged)} parts")
|
||||
merged.extend(textMerged)
|
||||
if tableParts:
|
||||
tableMerged = tableMerger.merge(tableParts, strategy)
|
||||
logger.debug(f"TableMerger merged {len(tableParts)} parts into {len(tableMerged)} parts")
|
||||
merged.extend(tableMerged)
|
||||
if structureParts:
|
||||
# For now, treat structure like text
|
||||
structureMerged = textMerger.merge(structureParts, strategy)
|
||||
logger.debug(f"StructureMerger merged {len(structureParts)} parts into {len(structureMerged)} parts")
|
||||
merged.extend(structureMerged)
|
||||
if otherParts:
|
||||
otherMerged = defaultMerger.merge(otherParts, strategy)
|
||||
logger.debug(f"DefaultMerger merged {len(otherParts)} parts into {len(otherMerged)} parts")
|
||||
merged.extend(otherMerged)
|
||||
|
||||
logger.debug(f"_applyMerging returning {len(merged)} parts")
|
||||
return merged
|
||||
|
||||
|
||||
# REMOVED: _applySizeLimit function - no longer needed after removing poolAndLimit
|
||||
# REMOVED: applyMerging function - moved to interfaceAiObjects.py for proper interface-level access
|
||||
|
||||
|
|
|
|||
|
|
@ -61,27 +61,58 @@ class RendererImage(BaseRenderer):
|
|||
# Save image generation prompt to debug
|
||||
aiService.services.utils.writeDebugFile(imagePrompt, "image_generation_prompt")
|
||||
|
||||
# Generate image using AI
|
||||
imageResult = await aiService.aiObjects.generateImage(
|
||||
# Format prompt as JSON with image generation parameters
|
||||
from modules.datamodels.datamodelAi import AiCallPromptImage, AiCallOptions, OperationTypeEnum
|
||||
import json
|
||||
|
||||
promptModel = AiCallPromptImage(
|
||||
prompt=imagePrompt,
|
||||
size="1024x1024",
|
||||
quality="standard",
|
||||
style="vivid"
|
||||
)
|
||||
promptJson = promptModel.model_dump_json(exclude_none=True, indent=2)
|
||||
|
||||
# Use generic path via callAiDocuments
|
||||
options = AiCallOptions(
|
||||
operationType=OperationTypeEnum.IMAGE_GENERATE,
|
||||
resultFormat="base64"
|
||||
)
|
||||
|
||||
# Call via generic path
|
||||
imageResult = await aiService.callAiDocuments(
|
||||
prompt=promptJson,
|
||||
documents=None,
|
||||
options=options,
|
||||
outputFormat="base64"
|
||||
)
|
||||
|
||||
# Save image generation response to debug
|
||||
aiService.services.utils.writeDebugFile(str(imageResult), "image_generation_response")
|
||||
|
||||
# Extract base64 image data from result
|
||||
if imageResult and imageResult.get("success", False):
|
||||
# The generic path returns a dict with documents array for base64 format
|
||||
if isinstance(imageResult, dict):
|
||||
if imageResult.get("success", False):
|
||||
# Check if it's the new format with documents array
|
||||
documents = imageResult.get("documents", [])
|
||||
if documents and len(documents) > 0:
|
||||
imageData = documents[0].get("documentData", "")
|
||||
if imageData:
|
||||
return imageData
|
||||
# Fallback: check for image_data field
|
||||
imageData = imageResult.get("image_data", "")
|
||||
if imageData:
|
||||
return imageData
|
||||
else:
|
||||
raise ValueError("No image data returned from AI")
|
||||
else:
|
||||
errorMsg = imageResult.get("error", "Unknown error") if imageResult else "No result"
|
||||
errorMsg = imageResult.get("error", "Unknown error")
|
||||
raise ValueError(f"AI image generation failed: {errorMsg}")
|
||||
elif isinstance(imageResult, str):
|
||||
# If it's just a string, it might be base64 data directly
|
||||
return imageResult
|
||||
else:
|
||||
raise ValueError(f"Unexpected image generation result format: {type(imageResult)}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error generating AI image: {str(e)}")
|
||||
|
|
|
|||
|
|
@ -93,10 +93,10 @@ Instructions:
|
|||
- Arrays must contain ONLY JSON values; do not include comments or ellipses.
|
||||
- Use ONLY the element structures shown in the template.
|
||||
- Continue from where it stopped — add NEW items only; do not repeat existing items.
|
||||
- Generate all remaining content needed to complete the user request.
|
||||
- Generate remaining content to complete the user request.
|
||||
- Fill with actual content (no placeholders or instructional text such as "Add more...").
|
||||
- When fully complete, add "complete_response": true at root level.
|
||||
- Output JSON only; no markdown fences or extra text before/after.
|
||||
- When the request is fully satisfied, add "complete_response": true at root level.
|
||||
- Output JSON only; no markdown fences or extra text.
|
||||
|
||||
Continue generating:
|
||||
"""
|
||||
|
|
@ -110,14 +110,12 @@ JSON structure template:
|
|||
{jsonTemplate}
|
||||
|
||||
Instructions:
|
||||
- Start your response with {{"metadata": ...}} — return COMPLETE, STRICT JSON.
|
||||
- Return ONLY valid JSON (strict). No comments of any kind (no //, /* */, or #). No trailing commas. Strings must use double quotes.
|
||||
- Arrays must contain ONLY JSON values; do not include comments or ellipses.
|
||||
- Do NOT reuse the example section IDs from the template; create your own.
|
||||
- Use ONLY the element structures shown in the template.
|
||||
- Start with {{"metadata": ...}} — return COMPLETE, STRICT JSON.
|
||||
- Return ONLY valid JSON (strict). No comments. No trailing commas. Use double quotes.
|
||||
- Do NOT reuse example section IDs; create your own.
|
||||
- Generate complete content based on the user request.
|
||||
- When fully complete, add "complete_response": true at root level.
|
||||
- Output JSON only; no markdown fences or any additional text.
|
||||
- When the request is fully satisfied, add "complete_response": true at root level.
|
||||
- Output JSON only; no markdown fences or extra text.
|
||||
|
||||
Generate your complete response starting from {{"metadata": ...}}:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@ class ProgressLogger:
|
|||
self.workflowService = workflowService
|
||||
self.workflow = workflow
|
||||
self.activeOperations = {}
|
||||
self.finishedOperations = set() # Track finished operations to avoid repeated warnings
|
||||
|
||||
def startOperation(self, operationId: str, serviceName: str, actionName: str, context: str = ""):
|
||||
"""Start a new long-running operation.
|
||||
|
|
@ -33,6 +34,9 @@ class ProgressLogger:
|
|||
actionName: Name of the action being performed
|
||||
context: Additional context information
|
||||
"""
|
||||
# Remove from finished operations if it was there (for restart scenarios)
|
||||
self.finishedOperations.discard(operationId)
|
||||
|
||||
self.activeOperations[operationId] = {
|
||||
'service': serviceName,
|
||||
'action': actionName,
|
||||
|
|
@ -51,7 +55,14 @@ class ProgressLogger:
|
|||
statusUpdate: Additional status information
|
||||
"""
|
||||
if operationId not in self.activeOperations:
|
||||
logger.warning(f"Operation {operationId} not found for progress update")
|
||||
# Only warn once per operation ID if it was already finished
|
||||
if operationId in self.finishedOperations:
|
||||
# Operation already finished - silently ignore subsequent updates
|
||||
return
|
||||
else:
|
||||
# Operation never started - log warning once and mark as problematic
|
||||
logger.warning(f"Operation {operationId} not found for progress update (operation never started)")
|
||||
self.finishedOperations.add(operationId) # Prevent repeated warnings
|
||||
return
|
||||
|
||||
op = self.activeOperations[operationId]
|
||||
|
|
@ -67,7 +78,10 @@ class ProgressLogger:
|
|||
success: Whether the operation completed successfully
|
||||
"""
|
||||
if operationId not in self.activeOperations:
|
||||
logger.warning(f"Operation {operationId} not found for completion")
|
||||
# Only warn once if operation was already finished
|
||||
if operationId not in self.finishedOperations:
|
||||
logger.warning(f"Operation {operationId} not found for completion (operation never started or already finished)")
|
||||
self.finishedOperations.add(operationId)
|
||||
return
|
||||
|
||||
op = self.activeOperations[operationId]
|
||||
|
|
@ -84,6 +98,9 @@ class ProgressLogger:
|
|||
# Remove from active operations AFTER creating the log
|
||||
del self.activeOperations[operationId]
|
||||
|
||||
# Mark as finished to prevent repeated warnings from updateOperation calls
|
||||
self.finishedOperations.add(operationId)
|
||||
|
||||
def _logProgress(self, operationId: str, progress: float, status: str):
|
||||
"""Create standardized ChatLog entry.
|
||||
|
||||
|
|
@ -122,4 +139,5 @@ class ProgressLogger:
|
|||
def clearAllOperations(self):
|
||||
"""Clear all active operations (for cleanup)."""
|
||||
self.activeOperations.clear()
|
||||
self.finishedOperations.clear()
|
||||
logger.debug("Cleared all active operations")
|
||||
|
|
|
|||
|
|
@ -121,6 +121,15 @@ class MethodAi(MethodBase):
|
|||
mimeType=d.get("mimeType") or output_mime_type
|
||||
))
|
||||
|
||||
# Preserve structured content field for validation (if it exists)
|
||||
# This allows validator to see the actual structured data, not just rendered output
|
||||
if "content" in result and result["content"] and isinstance(result["content"], (dict, list)):
|
||||
action_documents.append(ActionDocument(
|
||||
documentName="structured_content.json",
|
||||
documentData=result["content"],
|
||||
mimeType="application/json"
|
||||
))
|
||||
|
||||
final_documents = action_documents
|
||||
else:
|
||||
extension = output_extension.lstrip('.')
|
||||
|
|
@ -222,74 +231,3 @@ class MethodAi(MethodBase):
|
|||
except:
|
||||
pass
|
||||
return ActionResult.isFailure(error=str(e))
|
||||
|
||||
|
||||
@action
|
||||
async def generateImage(self, parameters: Dict[str, Any]) -> ActionResult:
|
||||
"""
|
||||
GENERAL:
|
||||
- Purpose: Generate images using AI based on text prompts.
|
||||
- Input requirements: prompt (required); optional size, quality, style.
|
||||
- Output format: Base64 encoded image data.
|
||||
|
||||
Parameters:
|
||||
- prompt (str, required): Text description of the image to generate.
|
||||
- size (str, optional): Image size. Options: 1024x1024, 1792x1024, 1024x1792. Default: 1024x1024.
|
||||
- quality (str, optional): Image quality. Options: standard, hd. Default: standard.
|
||||
- style (str, optional): Image style. Options: vivid, natural. Default: vivid.
|
||||
"""
|
||||
try:
|
||||
prompt = parameters.get("prompt")
|
||||
if not prompt:
|
||||
return ActionResult.isFailure(error="Image prompt is required")
|
||||
|
||||
# Extract optional parameters
|
||||
size = parameters.get("size", "1024x1024")
|
||||
quality = parameters.get("quality", "standard")
|
||||
style = parameters.get("style", "vivid")
|
||||
|
||||
# Build AI call options for image generation
|
||||
options = AiCallOptions(
|
||||
operationType=OperationTypeEnum.IMAGE_GENERATE,
|
||||
resultFormat="base64"
|
||||
)
|
||||
|
||||
# Create structured prompt using Pydantic model
|
||||
promptModel = AiCallPromptImage(
|
||||
prompt=prompt,
|
||||
size=size,
|
||||
quality=quality,
|
||||
style=style
|
||||
)
|
||||
|
||||
# Convert to JSON string for prompt
|
||||
promptJson = promptModel.model_dump_json(exclude_none=True, indent=2)
|
||||
|
||||
# Call AI service through unified path
|
||||
result = await self.services.ai.callAiDocuments(
|
||||
prompt=promptJson,
|
||||
documents=None,
|
||||
options=options,
|
||||
outputFormat="base64"
|
||||
)
|
||||
|
||||
# Create meaningful filename
|
||||
meaningfulName = self._generateMeaningfulFileName(
|
||||
base_name="generated_image",
|
||||
extension="png",
|
||||
action_name="generate"
|
||||
)
|
||||
|
||||
from modules.datamodels.datamodelChat import ActionDocument
|
||||
actionDocument = ActionDocument(
|
||||
documentName=meaningfulName,
|
||||
documentData=result,
|
||||
mimeType="image/png"
|
||||
)
|
||||
|
||||
return ActionResult.isSuccess(documents=[actionDocument])
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in image generation: {str(e)}")
|
||||
return ActionResult.isFailure(error=str(e))
|
||||
|
||||
|
|
|
|||
266
modules/workflows/processing/adaptive/VALIDATOR_ANALYSIS.md
Normal file
266
modules/workflows/processing/adaptive/VALIDATOR_ANALYSIS.md
Normal file
|
|
@ -0,0 +1,266 @@
|
|||
# Content Validator - Deep Analysis & Target Design
|
||||
|
||||
## CURRENT STATE ANALYSIS
|
||||
|
||||
### How Validator Currently Works
|
||||
|
||||
#### 1. **Document Input Flow**
|
||||
```
|
||||
ActionResult.documents (List[ActionDocument])
|
||||
→ modeReact.py extracts "structured content" with hardcoded checks
|
||||
→ Creates SimpleNamespace objects with wrapped documentData
|
||||
→ Passes to ContentValidator.validateContent()
|
||||
```
|
||||
|
||||
#### 2. **Current Problems in modeReact.py (Lines 99-136)**
|
||||
- ❌ **Hardcoded document name checks**: `docName == "structured_content.json"`
|
||||
- ❌ **Hardcoded mimeType checks**: `mimeType == "application/json"`
|
||||
- ❌ **Hardcoded structure checks**: `'content' in docData or 'documents' in docData or 'sections' in docData`
|
||||
- ❌ **Single document selection**: `break` after first match - ignores other documents
|
||||
- ❌ **Non-generic logic**: Specific to certain document structures
|
||||
- ❌ **Workaround approach**: Trying to find structured content in various ways
|
||||
|
||||
#### 3. **Current Problems in contentValidator.py**
|
||||
|
||||
**`_extractContent()` method (Lines 21-41)**:
|
||||
- ❌ **Inconsistent handling**: Checks for `dict with 'content'` but then also handles raw `data`
|
||||
- ❌ **Silent failures**: Returns empty string on any exception
|
||||
- ❌ **Size limit hardcoded**: 10KB threshold is arbitrary
|
||||
- ❌ **No format awareness**: Doesn't check if document is binary/base64 before extracting
|
||||
- ❌ **No document type detection**: Doesn't distinguish text vs binary vs structured data
|
||||
|
||||
**`_validateWithAI()` method (Lines 60-200)**:
|
||||
- ❌ **Forces all content to string**: `content[:2000]` truncation assumes text
|
||||
- ❌ **No document metadata passed**: Only name and content, no size, format, mimeType info
|
||||
- ❌ **No binary/base64 handling**: Will fail or show garbage for binary documents
|
||||
- ❌ **Multiple JSON extraction strategies**: Indicates unreliable AI response parsing
|
||||
- ❌ **Size limits inconsistent**: 10KB in extraction, 2KB in prompt - why different?
|
||||
|
||||
#### 4. **Missing Capabilities**
|
||||
- ❌ No document size reporting to validator
|
||||
- ❌ No format validation (txt vs md vs pdf vs docx)
|
||||
- ❌ No binary data handling (images, PDFs, etc.)
|
||||
- ❌ No document count/summary statistics
|
||||
- ❌ No distinction between document types for validation
|
||||
|
||||
---
|
||||
|
||||
## TARGET DESIGN
|
||||
|
||||
### Core Principles
|
||||
1. **GENERIC**: No hardcoded document names, types, or structures
|
||||
2. **DOCUMENT-AWARE**: Handle all document types (text, binary, base64, structured)
|
||||
3. **SIZE-CONSCIOUS**: Never pass full large documents to AI
|
||||
4. **METADATA-RICH**: Pass document metadata (name, size, format, mimeType) to validator
|
||||
5. **FORMAT-FLEXIBLE**: Allow format flexibility (md ≈ text, but pdf ≠ docx)
|
||||
|
||||
### Target Architecture
|
||||
|
||||
```
|
||||
Documents Input (List[ActionDocument])
|
||||
↓
|
||||
Document Analyzer (generic)
|
||||
- Extract metadata (name, size, mimeType, format)
|
||||
- Determine content type (text/binary/base64/structured)
|
||||
- Create preview/summary for large documents
|
||||
↓
|
||||
Document Summary (for AI validation)
|
||||
- Metadata only for binary/base64
|
||||
- Preview/sample for large text documents
|
||||
- Full content for small text/structured documents
|
||||
↓
|
||||
Validation Prompt Builder (generic)
|
||||
- Include document summaries (not full content)
|
||||
- Include document metadata
|
||||
- Include format validation rules (generic)
|
||||
↓
|
||||
AI Validator
|
||||
- Validates against task objective (generic)
|
||||
- Validates format compliance (flexible)
|
||||
- Validates document count/size
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## REQUIRED CHANGES
|
||||
|
||||
### 1. **Remove All Hardcoded Checks from modeReact.py**
|
||||
- ❌ Remove document name checks
|
||||
- ❌ Remove mimeType-specific logic
|
||||
- ❌ Remove structure-specific checks
|
||||
- ✅ Pass ALL documents to validator (let validator decide what to validate)
|
||||
- ✅ Keep it simple: `validationDocs = result.documents`
|
||||
|
||||
### 2. **Redesign contentValidator.py - New Structure**
|
||||
|
||||
#### New Method: `_analyzeDocuments(documents)`
|
||||
```python
|
||||
def _analyzeDocuments(self, documents: List[Any]) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Generic document analysis - extract metadata and create summaries.
|
||||
Returns list of document summaries ready for validation prompt.
|
||||
"""
|
||||
summaries = []
|
||||
for doc in documents:
|
||||
summary = {
|
||||
"name": getattr(doc, 'documentName', 'Unknown'),
|
||||
"mimeType": getattr(doc, 'mimeType', 'unknown'),
|
||||
"format": self._detectFormat(doc),
|
||||
"size": self._calculateSize(doc),
|
||||
"type": self._detectContentType(doc), # text/binary/base64/structured
|
||||
"preview": self._createPreview(doc), # None for binary, sample for large text
|
||||
"isAccessible": self._isContentAccessible(doc) # Can we read content?
|
||||
}
|
||||
summaries.append(summary)
|
||||
return summaries
|
||||
```
|
||||
|
||||
#### New Method: `_detectFormat(doc)`
|
||||
- Extract from filename extension or mimeType
|
||||
- Generic mapping: `text/plain` → `txt`, `text/markdown` → `md`, etc.
|
||||
- Return format string (txt, md, pdf, docx, json, etc.)
|
||||
|
||||
#### New Method: `_calculateSize(doc)`
|
||||
- Calculate document size in bytes
|
||||
- Handle string, dict, list, bytes, base64
|
||||
- Return: `{"bytes": int, "readable": "1.5 MB"}`
|
||||
|
||||
#### New Method: `_detectContentType(doc)`
|
||||
- `text`: Readable text content
|
||||
- `structured`: JSON/dict/list structures
|
||||
- `binary`: Binary data (PDF, images, etc.)
|
||||
- `base64`: Base64-encoded data
|
||||
- Return content type string
|
||||
|
||||
#### New Method: `_createPreview(doc)`
|
||||
- **Binary/Base64**: Return `None` (metadata only)
|
||||
- **Large text (>50KB)**: Return first 1KB + size indicator
|
||||
- **Small text (≤50KB)**: Return full content
|
||||
- **Structured data**: Return JSON string (truncated if large)
|
||||
|
||||
#### New Method: `_isContentAccessible(doc)`
|
||||
- Check if document content can be extracted for validation
|
||||
- Binary/base64 documents: `False` (validate by metadata only)
|
||||
- Text/structured documents: `True`
|
||||
|
||||
### 3. **Redesign Validation Prompt (Generic)**
|
||||
|
||||
```python
|
||||
validationPrompt = f"""TASK VALIDATION
|
||||
|
||||
USER REQUEST: '{intent.get('primaryGoal', 'Unknown')}'
|
||||
EXPECTED DATA TYPE: {intent.get('dataType', 'unknown')}
|
||||
EXPECTED FORMAT: {intent.get('expectedFormat', 'unknown')}
|
||||
SUCCESS CRITERIA ({criteriaCount} items): {successCriteria}
|
||||
|
||||
DELIVERED DOCUMENTS ({len(documentSummaries)} items):
|
||||
{json.dumps(documentSummaries, indent=2)}
|
||||
|
||||
VALIDATION RULES:
|
||||
1. Check if delivered documents match expected data type
|
||||
2. Check if delivered formats are compatible with expected format
|
||||
(Note: text formats like txt/md are compatible; pdf ≠ docx but both are documents)
|
||||
3. Verify each success criterion is met based on document content/metadata
|
||||
4. Check document sizes are reasonable for the task
|
||||
5. Rate overall quality (0.0-1.0)
|
||||
6. Identify specific gaps
|
||||
7. Suggest next steps
|
||||
|
||||
OUTPUT FORMAT - JSON ONLY (no prose):
|
||||
{{
|
||||
"overallSuccess": false,
|
||||
"qualityScore": 0.0,
|
||||
"dataTypeMatch": false,
|
||||
"formatMatch": false,
|
||||
"documentCount": {len(documentSummaries)},
|
||||
"successCriteriaMet": {[False] * criteriaCount},
|
||||
"gapAnalysis": "Specific gaps found",
|
||||
"improvementSuggestions": ["NEXT STEP: Action 1"],
|
||||
"validationDetails": [
|
||||
{{
|
||||
"documentName": "document.ext",
|
||||
"issues": ["Issue 1"],
|
||||
"suggestions": ["NEXT STEP: Fix 1"]
|
||||
}}
|
||||
]
|
||||
}}
|
||||
"""
|
||||
```
|
||||
|
||||
### 4. **Format Validation Logic (Generic & Flexible)**
|
||||
|
||||
```python
|
||||
def _isFormatCompatible(self, deliveredFormat: str, expectedFormat: str) -> bool:
|
||||
"""
|
||||
Generic format compatibility check.
|
||||
- txt/md/html are text formats (compatible with each other)
|
||||
- pdf/docx/xlsx are document formats (not compatible with each other)
|
||||
- json/xml are structured formats
|
||||
- images are image formats
|
||||
"""
|
||||
# Text formats are interchangeable
|
||||
textFormats = ['txt', 'md', 'html', 'text', 'plain']
|
||||
if deliveredFormat.lower() in textFormats and expectedFormat.lower() in textFormats:
|
||||
return True
|
||||
|
||||
# Exact match
|
||||
if deliveredFormat.lower() == expectedFormat.lower():
|
||||
return True
|
||||
|
||||
# Structured formats
|
||||
if deliveredFormat.lower() in ['json', 'xml'] and expectedFormat.lower() in ['json', 'xml']:
|
||||
return True # Could be made more flexible
|
||||
|
||||
return False
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## IMPLEMENTATION PLAN
|
||||
|
||||
### Phase 1: Clean Up modeReact.py
|
||||
- Remove all hardcoded checks
|
||||
- Simply pass `result.documents` to validator
|
||||
|
||||
### Phase 2: Redesign Document Analysis
|
||||
- Implement `_analyzeDocuments()`
|
||||
- Implement helper methods: `_detectFormat()`, `_calculateSize()`, `_detectContentType()`, `_createPreview()`
|
||||
|
||||
### Phase 3: Redesign Validation Prompt
|
||||
- Generic prompt with document summaries
|
||||
- Include metadata, not full content
|
||||
- Size-aware handling
|
||||
|
||||
### Phase 4: Implement Format Validation
|
||||
- Generic format compatibility logic
|
||||
- Flexible matching (text formats, document formats, etc.)
|
||||
|
||||
### Phase 5: Testing
|
||||
- Test with text documents (small & large)
|
||||
- Test with binary documents (PDF, images)
|
||||
- Test with base64 documents
|
||||
- Test with structured data (JSON)
|
||||
|
||||
---
|
||||
|
||||
## KEY DESIGN DECISIONS
|
||||
|
||||
1. **Pass ALL documents**: Validator decides what to validate, not the caller
|
||||
2. **Metadata over content**: For large/binary documents, pass metadata only
|
||||
3. **Preview samples**: For large text documents, pass preview + size info
|
||||
4. **Generic prompts**: No task-specific or format-specific logic
|
||||
5. **Flexible format matching**: Text formats compatible, document formats strict
|
||||
6. **Size limits**: 50KB threshold for full content (configurable)
|
||||
7. **Content type detection**: Explicit type detection (text/binary/base64/structured)
|
||||
|
||||
---
|
||||
|
||||
## BENEFITS OF TARGET DESIGN
|
||||
|
||||
✅ **Generic**: Works with any document type without hardcoding
|
||||
✅ **Scalable**: Handles large documents without issues
|
||||
✅ **Flexible**: Format validation is flexible where appropriate
|
||||
✅ **Maintainable**: Clear separation of concerns
|
||||
✅ **Robust**: Handles edge cases (binary, base64, large files)
|
||||
✅ **Testable**: Each component can be tested independently
|
||||
|
||||
|
|
@ -1,14 +1,22 @@
|
|||
# contentValidator.py
|
||||
# Content validation for adaptive React mode
|
||||
# Generic, document-aware validation system
|
||||
|
||||
import logging
|
||||
import json
|
||||
from typing import List, Dict, Any
|
||||
import base64
|
||||
import re
|
||||
from typing import List, Dict, Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Configuration constants
|
||||
MAX_CONTENT_SIZE_FOR_FULL_PREVIEW = 50 * 1024 # 50KB threshold
|
||||
PREVIEW_SAMPLE_SIZE = 1024 # 1KB preview for large documents
|
||||
|
||||
|
||||
class ContentValidator:
|
||||
"""Validates delivered content against user intent"""
|
||||
"""Validates delivered content against user intent - generic and document-aware"""
|
||||
|
||||
def __init__(self, services=None, learningEngine=None):
|
||||
self.services = services
|
||||
|
|
@ -18,78 +26,277 @@ class ContentValidator:
|
|||
"""Validates delivered content against user intent using AI (single attempt; parse-or-fail)"""
|
||||
return await self._validateWithAI(documents, intent)
|
||||
|
||||
def _extractContent(self, doc: Any) -> str:
|
||||
"""Extracts content from a document with size protection for large documents"""
|
||||
def _analyzeDocuments(self, documents: List[Any]) -> List[Dict[str, Any]]:
|
||||
"""Generic document analysis - create simple summaries with metadata."""
|
||||
summaries = []
|
||||
for doc in documents:
|
||||
try:
|
||||
if hasattr(doc, 'documentData'):
|
||||
data = doc.documentData
|
||||
if isinstance(data, dict) and 'content' in data:
|
||||
content = data['content']
|
||||
# For large content, check size before converting to string
|
||||
if hasattr(content, '__len__') and len(str(content)) > 10000: # 10KB threshold
|
||||
# For very large content, return a size indicator instead
|
||||
return f"[Large document content - {len(str(content))} characters - truncated for validation]"
|
||||
return str(content)
|
||||
data = getattr(doc, 'documentData', None)
|
||||
name = getattr(doc, 'documentName', 'Unknown')
|
||||
mimeType = getattr(doc, 'mimeType', 'unknown')
|
||||
formatExt = self._detectFormat(doc)
|
||||
sizeInfo = self._calculateSize(doc)
|
||||
|
||||
# Simple preview: if it's dict/list, dump JSON; otherwise use string
|
||||
preview = None
|
||||
if data is not None:
|
||||
if isinstance(data, (dict, list)):
|
||||
preview = json.dumps(data, indent=2, ensure_ascii=False)
|
||||
# Truncate if too large
|
||||
if len(preview) > MAX_CONTENT_SIZE_FOR_FULL_PREVIEW:
|
||||
preview = preview[:PREVIEW_SAMPLE_SIZE] + f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
|
||||
else:
|
||||
content = data
|
||||
# For large content, check size before converting to string
|
||||
if hasattr(content, '__len__') and len(str(content)) > 10000: # 10KB threshold
|
||||
return f"[Large document content - {len(str(content))} characters - truncated for validation]"
|
||||
return str(content)
|
||||
return ""
|
||||
except Exception:
|
||||
return ""
|
||||
text = str(data)
|
||||
if len(text) > MAX_CONTENT_SIZE_FOR_FULL_PREVIEW:
|
||||
preview = text[:PREVIEW_SAMPLE_SIZE] + f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
|
||||
else:
|
||||
preview = text
|
||||
|
||||
# Removed schema fallback creator to keep failures explicit
|
||||
summary = {
|
||||
"name": name,
|
||||
"mimeType": mimeType,
|
||||
"format": formatExt,
|
||||
"size": sizeInfo["readable"],
|
||||
"preview": preview
|
||||
}
|
||||
summaries.append(summary)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error analyzing document {getattr(doc, 'documentName', 'Unknown')}: {str(e)}")
|
||||
summaries.append({
|
||||
"name": getattr(doc, 'documentName', 'Unknown'),
|
||||
"mimeType": getattr(doc, 'mimeType', 'unknown'),
|
||||
"format": "unknown",
|
||||
"size": "0 B",
|
||||
"preview": None,
|
||||
"error": str(e)
|
||||
})
|
||||
return summaries
|
||||
|
||||
def _isValidJsonResponse(self, response: str) -> bool:
|
||||
"""Checks if response contains valid JSON structure"""
|
||||
def _calculateAvailablePromptSpace(self, basePromptSizeBytes: int) -> int:
|
||||
"""Calculate available space for document summaries based on model context length."""
|
||||
try:
|
||||
import re
|
||||
# Look for JSON with expected structure
|
||||
json_match = re.search(r'\{[^{}]*"overallSuccess"[^{}]*\}', response, re.DOTALL)
|
||||
if json_match:
|
||||
json.loads(json_match.group(0))
|
||||
return True
|
||||
return False
|
||||
except:
|
||||
return False
|
||||
from modules.aicore.aicoreModelRegistry import modelRegistry
|
||||
from modules.aicore.aicoreModelSelector import modelSelector
|
||||
from modules.datamodels.datamodelAi import AiCallOptions, OperationTypeEnum
|
||||
|
||||
# Removed text-based fallback extraction to avoid hiding issues
|
||||
# Get available models
|
||||
availableModels = modelRegistry.getAvailableModels()
|
||||
|
||||
# Create options for PLAN operation (what validation uses)
|
||||
options = AiCallOptions(
|
||||
operationType=OperationTypeEnum.PLAN,
|
||||
priority=None,
|
||||
processingMode=None
|
||||
)
|
||||
|
||||
# Get failover model list to find the model that will be used
|
||||
failoverModels = modelSelector.getFailoverModelList("", "", options, availableModels)
|
||||
|
||||
if not failoverModels:
|
||||
# Fallback: assume 16K tokens context (conservative)
|
||||
logger.warning("No models available for space calculation, using fallback: 16K tokens")
|
||||
maxBytes = 16 * 1024 * 4 # 16K tokens * 4 bytes per token
|
||||
else:
|
||||
# Use the first (best) model
|
||||
model = failoverModels[0]
|
||||
# Calculate 80% of context length in bytes (tokens * 4 bytes per token)
|
||||
maxBytes = int(model.contextLength * 0.8 * 4)
|
||||
|
||||
# Available space = max - base prompt - safety margin (10%)
|
||||
availableBytes = int((maxBytes - basePromptSizeBytes) * 0.9)
|
||||
|
||||
# Ensure minimum available space (at least 1KB)
|
||||
availableBytes = max(availableBytes, 1024)
|
||||
|
||||
logger.debug(f"Prompt space calculation: base={basePromptSizeBytes} bytes, max={maxBytes} bytes, available={availableBytes} bytes")
|
||||
|
||||
return availableBytes
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error calculating available prompt space: {str(e)}, using fallback: 8KB")
|
||||
# Fallback: assume 8KB available
|
||||
return 8 * 1024
|
||||
|
||||
def _analyzeDocumentsWithSizeLimit(self, documents: List[Any], maxTotalBytes: int) -> List[Dict[str, Any]]:
|
||||
"""Analyze documents with size limit, dividing available space evenly among documents."""
|
||||
if not documents:
|
||||
return []
|
||||
|
||||
# Reserve space for JSON structure overhead (approximately 200 bytes per document)
|
||||
jsonOverheadPerDoc = 200
|
||||
reservedOverhead = len(documents) * jsonOverheadPerDoc
|
||||
availableForContent = max(0, maxTotalBytes - reservedOverhead)
|
||||
|
||||
# Divide available space evenly among documents
|
||||
bytesPerDoc = availableForContent // len(documents) if documents else 0
|
||||
# Ensure minimum space per document (at least 100 bytes)
|
||||
bytesPerDoc = max(bytesPerDoc, 100)
|
||||
|
||||
logger.debug(f"Document summary space: total={maxTotalBytes} bytes, available={availableForContent} bytes, perDoc={bytesPerDoc} bytes")
|
||||
|
||||
summaries = []
|
||||
for doc in documents:
|
||||
try:
|
||||
data = getattr(doc, 'documentData', None)
|
||||
name = getattr(doc, 'documentName', 'Unknown')
|
||||
mimeType = getattr(doc, 'mimeType', 'unknown')
|
||||
formatExt = self._detectFormat(doc)
|
||||
sizeInfo = self._calculateSize(doc)
|
||||
|
||||
# Create preview with size limit
|
||||
preview = None
|
||||
if data is not None:
|
||||
if isinstance(data, (dict, list)):
|
||||
preview = json.dumps(data, indent=2, ensure_ascii=False)
|
||||
else:
|
||||
preview = str(data)
|
||||
|
||||
# Truncate preview to fit within bytesPerDoc (accounting for JSON structure)
|
||||
# Estimate: preview takes ~70% of document summary space
|
||||
maxPreviewBytes = int(bytesPerDoc * 0.7)
|
||||
previewBytes = len(preview.encode('utf-8'))
|
||||
|
||||
if previewBytes > maxPreviewBytes:
|
||||
# Truncate to fit
|
||||
truncated = preview.encode('utf-8')[:maxPreviewBytes]
|
||||
# Try to decode safely
|
||||
try:
|
||||
preview = truncated.decode('utf-8', errors='ignore')
|
||||
except:
|
||||
preview = truncated[:maxPreviewBytes-50].decode('utf-8', errors='ignore')
|
||||
preview += f"\n\n[Truncated - {self._formatBytes(sizeInfo['bytes'])} total]"
|
||||
|
||||
summary = {
|
||||
"name": name,
|
||||
"mimeType": mimeType,
|
||||
"format": formatExt,
|
||||
"size": sizeInfo["readable"],
|
||||
"preview": preview
|
||||
}
|
||||
summaries.append(summary)
|
||||
except Exception as e:
|
||||
logger.warning(f"Error analyzing document {getattr(doc, 'documentName', 'Unknown')}: {str(e)}")
|
||||
summaries.append({
|
||||
"name": getattr(doc, 'documentName', 'Unknown'),
|
||||
"mimeType": getattr(doc, 'mimeType', 'unknown'),
|
||||
"format": "unknown",
|
||||
"size": "0 B",
|
||||
"preview": None,
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
return summaries
|
||||
|
||||
def _detectFormat(self, doc: Any) -> str:
|
||||
"""Extract format from filename extension (always use extension)"""
|
||||
try:
|
||||
docName = getattr(doc, 'documentName', '')
|
||||
|
||||
# Extract from filename extension
|
||||
if docName and '.' in docName:
|
||||
ext = docName.rsplit('.', 1)[1].lower()
|
||||
return ext
|
||||
|
||||
return 'unknown'
|
||||
except Exception as e:
|
||||
logger.warning(f"Error detecting format: {str(e)}")
|
||||
return 'unknown'
|
||||
|
||||
def _calculateSize(self, doc: Any) -> Dict[str, Any]:
|
||||
"""Calculate document size in bytes and human-readable format"""
|
||||
try:
|
||||
if not hasattr(doc, 'documentData') or doc.documentData is None:
|
||||
return {"bytes": 0, "readable": "0 B"}
|
||||
|
||||
data = doc.documentData
|
||||
size_bytes = 0
|
||||
|
||||
if isinstance(data, str):
|
||||
size_bytes = len(data.encode('utf-8'))
|
||||
elif isinstance(data, bytes):
|
||||
size_bytes = len(data)
|
||||
elif isinstance(data, (dict, list)):
|
||||
# Estimate JSON size
|
||||
try:
|
||||
json_str = json.dumps(data)
|
||||
size_bytes = len(json_str.encode('utf-8'))
|
||||
except:
|
||||
size_bytes = len(str(data).encode('utf-8'))
|
||||
else:
|
||||
size_bytes = len(str(data).encode('utf-8'))
|
||||
|
||||
# Convert to human-readable format
|
||||
readable = self._formatBytes(size_bytes)
|
||||
|
||||
return {"bytes": size_bytes, "readable": readable}
|
||||
except Exception as e:
|
||||
logger.warning(f"Error calculating size: {str(e)}")
|
||||
return {"bytes": 0, "readable": "0 B"}
|
||||
|
||||
def _formatBytes(self, bytes: int) -> str:
|
||||
"""Format bytes to human-readable string"""
|
||||
for unit in ['B', 'KB', 'MB', 'GB']:
|
||||
if bytes < 1024.0:
|
||||
return f"{bytes:.1f} {unit}"
|
||||
bytes /= 1024.0
|
||||
return f"{bytes:.1f} TB"
|
||||
|
||||
|
||||
def _isFormatCompatible(self, deliveredFormat: str, expectedFormat: str) -> bool:
|
||||
"""
|
||||
Generic format compatibility check.
|
||||
- txt/md/html are text formats (compatible with each other)
|
||||
- pdf/docx/xlsx are document formats (not compatible with each other)
|
||||
- json/xml are structured formats
|
||||
- images are image formats
|
||||
"""
|
||||
deliveredLower = deliveredFormat.lower()
|
||||
expectedLower = expectedFormat.lower()
|
||||
|
||||
# Exact match
|
||||
if deliveredLower == expectedLower:
|
||||
return True
|
||||
|
||||
# Text formats are interchangeable
|
||||
textFormats = ['txt', 'md', 'html', 'text', 'plain']
|
||||
if deliveredLower in textFormats and expectedLower in textFormats:
|
||||
return True
|
||||
|
||||
# Structured formats
|
||||
if deliveredLower in ['json', 'xml'] and expectedLower in ['json', 'xml']:
|
||||
return True
|
||||
|
||||
# Document formats are NOT compatible with each other
|
||||
documentFormats = ['pdf', 'docx', 'xlsx', 'pptx']
|
||||
if deliveredLower in documentFormats and expectedLower in documentFormats:
|
||||
return False # pdf ≠ docx
|
||||
|
||||
return False
|
||||
|
||||
async def _validateWithAI(self, documents: List[Any], intent: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""AI-based comprehensive validation - single main function"""
|
||||
"""AI-based comprehensive validation - generic approach"""
|
||||
try:
|
||||
if not hasattr(self, 'services') or not self.services or not hasattr(self.services, 'ai'):
|
||||
return self._createFailedValidationResult("AI service not available")
|
||||
|
||||
# Extract content from all documents
|
||||
documentContents = []
|
||||
for doc in documents:
|
||||
content = self._extractContent(doc)
|
||||
documentContents.append({
|
||||
"name": getattr(doc, 'documentName', 'Unknown'),
|
||||
"content": content[:2000] # Limit content for AI processing
|
||||
})
|
||||
|
||||
# Create structured AI validation prompt
|
||||
# Build prompt base WITHOUT document summaries first
|
||||
successCriteria = intent.get('successCriteria', [])
|
||||
criteriaCount = len(successCriteria)
|
||||
|
||||
validationPrompt = f"""TASK VALIDATION
|
||||
promptBase = f"""TASK VALIDATION
|
||||
|
||||
USER REQUEST: '{intent.get('primaryGoal', 'Unknown')}'
|
||||
EXPECTED TYPE: {intent.get('dataType', 'unknown')}
|
||||
EXPECTED DATA TYPE: {intent.get('dataType', 'unknown')}
|
||||
EXPECTED FORMAT: {intent.get('expectedFormat', 'unknown')}
|
||||
SUCCESS CRITERIA ({criteriaCount} items): {successCriteria}
|
||||
|
||||
VALIDATION RULES:
|
||||
1. Check if content matches expected data type
|
||||
2. Check if content matches expected format
|
||||
3. Verify each success criterion is met
|
||||
4. Rate overall quality (0.0-1.0)
|
||||
5. Identify specific gaps
|
||||
6. Suggest next steps
|
||||
1. Check if delivered documents match expected data type
|
||||
2. Check if delivered formats are compatible with expected format
|
||||
3. Verify each success criterion is met based on document content/metadata
|
||||
4. Check document sizes are reasonable for the task
|
||||
5. Rate overall quality (0.0-1.0)
|
||||
6. Identify specific gaps based on what the user requested
|
||||
|
||||
OUTPUT FORMAT - JSON ONLY (no prose):
|
||||
{{
|
||||
|
|
@ -97,31 +304,45 @@ OUTPUT FORMAT - JSON ONLY (no prose):
|
|||
"qualityScore": 0.0,
|
||||
"dataTypeMatch": false,
|
||||
"formatMatch": false,
|
||||
"documentCount": {len(documents)},
|
||||
"successCriteriaMet": {[False] * criteriaCount},
|
||||
"gapAnalysis": "Specific gaps found",
|
||||
"improvementSuggestions": ["NEXT STEP: Action 1", "NEXT STEP: Action 2"],
|
||||
"gapAnalysis": "Describe what is missing or incorrect",
|
||||
"improvementSuggestions": ["General action to improve overall result"],
|
||||
"validationDetails": [
|
||||
{{
|
||||
"documentName": "Document Name",
|
||||
"issues": ["Issue 1", "Issue 2"],
|
||||
"suggestions": ["NEXT STEP: Fix 1", "NEXT STEP: Fix 2"]
|
||||
"documentName": "document.ext",
|
||||
"issues": ["Specific problem with this document"],
|
||||
"suggestions": ["Specific fix for this document's issues"]
|
||||
}}
|
||||
]
|
||||
}}
|
||||
|
||||
DELIVERED CONTENT TO CHECK:
|
||||
{json.dumps(documentContents, indent=2)}
|
||||
Field explanations:
|
||||
- "improvementSuggestions": Overall actions to improve the entire result (general, high-level)
|
||||
- "validationDetails[].suggestions": Specific fixes for each document's individual issues (document-specific, detailed)
|
||||
- Do NOT use prefixes like "NEXT STEP:" - describe actions directly
|
||||
|
||||
DELIVERED DOCUMENTS ({len(documents)} items):
|
||||
"""
|
||||
|
||||
# Calculate available space for document summaries
|
||||
# Get the model that will be used for validation
|
||||
basePromptSize = len(promptBase.encode('utf-8'))
|
||||
availableBytes = self._calculateAvailablePromptSpace(basePromptSize)
|
||||
|
||||
# Analyze documents with size constraints
|
||||
documentSummaries = self._analyzeDocumentsWithSizeLimit(documents, availableBytes)
|
||||
|
||||
# Build final prompt with summaries at the end
|
||||
documentsJson = json.dumps(documentSummaries, indent=2)
|
||||
validationPrompt = promptBase + documentsJson
|
||||
|
||||
# Call AI service for validation
|
||||
response = await self.services.ai.callAiPlanning(
|
||||
prompt=validationPrompt,
|
||||
placeholders=None
|
||||
)
|
||||
|
||||
# No retries or correction prompts here; parse-or-fail below
|
||||
|
||||
if not response or not response.strip():
|
||||
logger.warning("AI validation returned empty response")
|
||||
raise ValueError("AI validation failed - empty response")
|
||||
|
|
@ -131,8 +352,6 @@ DELIVERED CONTENT TO CHECK:
|
|||
logger.debug(f"AI validation response length: {len(result)}")
|
||||
|
||||
# Try to find JSON in the response with multiple strategies
|
||||
import re
|
||||
|
||||
# Strategy 1: Look for JSON in markdown code blocks
|
||||
json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', result, re.DOTALL)
|
||||
if json_match:
|
||||
|
|
@ -168,6 +387,7 @@ DELIVERED CONTENT TO CHECK:
|
|||
normalized = {
|
||||
"overallSuccess": overall if isinstance(overall, bool) else None,
|
||||
"qualityScore": float(quality) if isinstance(quality, (int, float)) else None,
|
||||
"documentCount": len(documentSummaries),
|
||||
"validationDetails": details if isinstance(details, list) else [{
|
||||
"documentName": "AI Validation",
|
||||
"gapAnalysis": gap,
|
||||
|
|
@ -198,3 +418,21 @@ DELIVERED CONTENT TO CHECK:
|
|||
except Exception as e:
|
||||
logger.error(f"AI validation failed: {str(e)}")
|
||||
raise
|
||||
|
||||
def _createFailedValidationResult(self, errorMessage: str) -> Dict[str, Any]:
|
||||
"""Create a standardized failed validation result"""
|
||||
return {
|
||||
"overallSuccess": False,
|
||||
"qualityScore": 0.0,
|
||||
"dataTypeMatch": False,
|
||||
"formatMatch": False,
|
||||
"documentCount": 0,
|
||||
"successCriteriaMet": [],
|
||||
"gapAnalysis": errorMessage,
|
||||
"improvementSuggestions": [],
|
||||
"validationDetails": [],
|
||||
"schemaCompliant": True,
|
||||
"originalType": "error",
|
||||
"missingFields": [],
|
||||
"error": errorMessage
|
||||
}
|
||||
|
|
|
|||
|
|
@ -96,14 +96,10 @@ class ReactMode(BaseMode):
|
|||
# Attach deterministic label for clarity
|
||||
observation['resultLabel'] = result.resultLabel
|
||||
|
||||
# NEW: Add content validation (against original cleaned user prompt / workflow intent)
|
||||
# Content validation (against original cleaned user prompt / workflow intent)
|
||||
if getattr(self, 'workflowIntent', None) and result.documents:
|
||||
# Validate ONLY the produced JSON (structured content), not rendered files
|
||||
from types import SimpleNamespace
|
||||
validationDocs = []
|
||||
if hasattr(result, 'content') and result.content:
|
||||
validationDocs.append(SimpleNamespace(documentName='generated.json', documentData={'content': result.content}))
|
||||
validationResult = await self.contentValidator.validateContent(validationDocs, self.workflowIntent)
|
||||
# Pass ALL documents to validator - validator decides what to validate (generic approach)
|
||||
validationResult = await self.contentValidator.validateContent(result.documents, self.workflowIntent)
|
||||
observation['contentValidation'] = validationResult
|
||||
quality_score = validationResult.get('qualityScore', 0.0)
|
||||
if quality_score is None:
|
||||
|
|
|
|||
|
|
@ -1,107 +0,0 @@
|
|||
Module,Function Names,Parameter Names,Variable Names,Total
|
||||
modules/workflows/methods/methodSharepoint.py,0,2,211,213
|
||||
modules/workflows/methods/methodOutlook.py,0,3,131,134
|
||||
modules/services/serviceAi/subDocumentProcessing.py,0,0,104,104
|
||||
modules/features/syncDelta/mainSyncDelta.py,1,10,88,99
|
||||
modules/shared/jsonUtils.py,0,3,88,91
|
||||
modules/services/serviceGeneration/renderers/rendererDocx.py,3,8,79,90
|
||||
modules/services/serviceWorkflow/mainServiceWorkflow.py,0,3,85,88
|
||||
modules/services/serviceGeneration/renderers/rendererPptx.py,2,7,73,82
|
||||
modules/services/serviceGeneration/renderers/rendererPdf.py,3,8,50,61
|
||||
modules/connectors/connectorVoiceGoogle.py,1,2,52,55
|
||||
modules/services/serviceGeneration/renderers/rendererHtml.py,3,6,46,55
|
||||
modules/services/serviceGeneration/renderers/rendererBaseTemplate.py,3,21,27,51
|
||||
modules/shared/configuration.py,2,17,30,49
|
||||
modules/services/serviceExtraction/subMerger.py,2,5,31,38
|
||||
modules/connectors/connectorDbPostgre.py,0,14,20,34
|
||||
modules/interfaces/interfaceDbAppObjects.py,0,8,26,34
|
||||
modules/routes/routeSecurityGoogle.py,0,0,32,32
|
||||
modules/shared/attributeUtils.py,3,4,25,32
|
||||
modules/interfaces/interfaceDbChatObjects.py,0,4,27,31
|
||||
modules/routes/routeSecurityAdmin.py,0,2,28,30
|
||||
modules/services/serviceNeutralization/subProcessList.py,7,0,22,29
|
||||
modules/services/serviceGeneration/renderers/rendererText.py,3,7,19,29
|
||||
modules/routes/routeSecurityMsft.py,0,0,27,27
|
||||
modules/services/serviceGeneration/renderers/rendererMarkdown.py,3,7,17,27
|
||||
modules/services/serviceGeneration/renderers/rendererXlsx.py,3,0,24,27
|
||||
modules/services/serviceGeneration/renderers/rendererImage.py,3,2,21,26
|
||||
modules/security/tokenManager.py,4,7,14,25
|
||||
modules/workflows/workflowManager.py,0,0,25,25
|
||||
modules/services/serviceGeneration/renderers/rendererCsv.py,3,5,17,25
|
||||
modules/shared/auditLogger.py,5,16,3,24
|
||||
modules/shared/debugLogger.py,0,0,24,24
|
||||
modules/workflows/processing/shared/placeholderFactory.py,0,0,24,24
|
||||
modules/interfaces/interfaceDbAppAccess.py,0,2,21,23
|
||||
modules/connectors/connectorTicketsJira.py,0,0,22,22
|
||||
modules/services/serviceGeneration/renderers/registry.py,7,3,12,22
|
||||
modules/routes/routeDataConnections.py,1,1,19,21
|
||||
modules/security/tokenRefreshService.py,0,2,19,21
|
||||
modules/services/serviceExtraction/extractors/extractorPptx.py,0,1,16,17
|
||||
modules/routes/routeSecurityLocal.py,0,0,16,16
|
||||
modules/workflows/methods/methodBase.py,0,4,12,16
|
||||
modules/services/serviceGeneration/mainServiceGeneration.py,0,4,11,15
|
||||
modules/services/serviceUtils/mainServiceUtils.py,0,14,1,15
|
||||
modules/features/neutralizePlayground/mainNeutralizePlayground.py,8,5,2,15
|
||||
modules/interfaces/interfaceTicketObjects.py,0,5,9,14
|
||||
modules/services/serviceNeutralization/subParseString.py,7,0,6,13
|
||||
modules/workflows/processing/modes/modeReact.py,0,1,11,12
|
||||
modules/interfaces/interfaceDbComponentAccess.py,0,2,9,11
|
||||
modules/services/serviceAi/subCoreAi.py,0,0,11,11
|
||||
modules/services/serviceExtraction/subRegistry.py,0,0,11,11
|
||||
modules/services/serviceNeutralization/mainServiceNeutralization.py,0,2,9,11
|
||||
modules/interfaces/interfaceAiObjects.py,0,0,10,10
|
||||
modules/services/serviceAi/subSharedAiUtils.py,0,3,7,10
|
||||
modules/connectors/connectorDbJson.py,0,3,6,9
|
||||
modules/workflows/methods/methodAi.py,0,0,9,9
|
||||
modules/services/serviceExtraction/subPromptBuilderExtraction.py,0,0,9,9
|
||||
modules/services/serviceGeneration/subDocumentUtility.py,0,3,6,9
|
||||
modules/services/serviceNeutralization/subProcessCommon.py,7,2,0,9
|
||||
modules/services/serviceNeutralization/subProcessText.py,5,0,4,9
|
||||
modules/interfaces/interfaceDbChatAccess.py,0,2,6,8
|
||||
modules/security/auth.py,0,1,7,8
|
||||
modules/aicore/aicorePluginAnthropic.py,0,0,7,7
|
||||
modules/security/tokenRefreshMiddleware.py,0,2,4,6
|
||||
modules/services/serviceGeneration/renderers/rendererJson.py,3,0,3,6
|
||||
analyze_naming_violations.py,5,0,0,5
|
||||
modules/aicore/aicorePluginOpenai.py,0,0,5,5
|
||||
modules/routes/routeVoiceGoogle.py,0,0,5,5
|
||||
modules/shared/eventManagement.py,2,3,0,5
|
||||
modules/workflows/processing/adaptive/intentAnalyzer.py,0,0,5,5
|
||||
modules/workflows/processing/shared/executionState.py,0,5,0,5
|
||||
modules/services/serviceGeneration/subJsonSchema.py,0,0,5,5
|
||||
modules/services/serviceNeutralization/subPatterns.py,5,0,0,5
|
||||
modules/services/serviceNeutralization/subProcessBinary.py,4,0,1,5
|
||||
modules/services/serviceExtraction/extractors/extractorXlsx.py,0,0,5,5
|
||||
modules/interfaces/interfaceDbComponentObjects.py,0,3,1,4
|
||||
modules/routes/routeDataNeutralization.py,0,0,4,4
|
||||
modules/routes/routeWorkflows.py,0,0,4,4
|
||||
modules/shared/timezoneUtils.py,3,1,0,4
|
||||
modules/workflows/processing/adaptive/contentValidator.py,0,0,4,4
|
||||
modules/workflows/processing/core/messageCreator.py,0,0,4,4
|
||||
modules/services/serviceSharepoint/mainServiceSharepoint.py,0,0,4,4
|
||||
modules/routes/routeDataUsers.py,0,0,3,3
|
||||
modules/services/serviceExtraction/subPipeline.py,0,0,3,3
|
||||
app.py,0,0,2,2
|
||||
modules/datamodels/datamodelChat.py,0,1,1,2
|
||||
modules/routes/routeAttributes.py,0,0,2,2
|
||||
modules/routes/routeDataPrompts.py,0,0,2,2
|
||||
modules/security/csrf.py,0,1,1,2
|
||||
modules/security/jwtService.py,0,0,2,2
|
||||
modules/workflows/processing/adaptive/learningEngine.py,0,0,2,2
|
||||
modules/workflows/processing/modes/modeActionplan.py,0,0,2,2
|
||||
modules/workflows/processing/shared/methodDiscovery.py,0,0,2,2
|
||||
modules/services/serviceNormalization/mainServiceNormalization.py,0,0,2,2
|
||||
modules/services/serviceExtraction/extractors/extractorImage.py,0,0,2,2
|
||||
modules/aicore/aicoreBase.py,0,0,1,1
|
||||
modules/aicore/aicoreModelSelector.py,0,0,1,1
|
||||
modules/connectors/connectorTicketsClickup.py,0,0,1,1
|
||||
modules/datamodels/datamodelDocument.py,0,1,0,1
|
||||
modules/datamodels/datamodelSecurity.py,0,0,1,1
|
||||
modules/routes/routeAdmin.py,0,0,1,1
|
||||
modules/routes/routeDataFiles.py,0,0,1,1
|
||||
modules/workflows/processing/workflowProcessor.py,0,0,1,1
|
||||
modules/workflows/processing/adaptive/adaptiveLearningEngine.py,0,0,1,1
|
||||
modules/workflows/processing/core/actionExecutor.py,0,0,1,1
|
||||
modules/workflows/processing/core/taskPlanner.py,0,0,1,1
|
||||
modules/workflows/processing/modes/modeBase.py,0,0,1,1
|
||||
modules/services/serviceAi/subDocumentGeneration.py,0,0,1,1
|
||||
|
|
|
@ -1,184 +0,0 @@
|
|||
# Analysis: `processDocumentsWithContinuation` and Subfunctions Usage
|
||||
|
||||
## Executive Summary
|
||||
|
||||
**FINDING**: The function `processDocumentsWithContinuation` in `subDocumentProcessing.py` is **NOT USED** anywhere in the active codebase. The continuation chain was only referenced by the deleted `subDocumentGeneration.py` module.
|
||||
|
||||
---
|
||||
|
||||
## Main Function: `processDocumentsWithContinuation`
|
||||
|
||||
**Location**: `gateway/modules/services/serviceAi/subDocumentProcessing.py:303`
|
||||
|
||||
**Status**: ❌ **NOT USED**
|
||||
|
||||
### Usage Search Results
|
||||
|
||||
- ❌ No actual code calls to `.processDocumentsWithContinuation(`
|
||||
- ⚠️ Only mentioned in documentation files:
|
||||
- `wiki/poweron/appdoc/doc_system_function_relationship_ai.md` (documentation)
|
||||
- `gateway/callAiWithDocumentGeneration_usage_analysis.md` (previous analysis - noted it was called by deleted code)
|
||||
|
||||
### Why It's Not Used
|
||||
|
||||
The only caller was `subDocumentGeneration._processDocumentsUnified()` which we just deleted. The current active codebase uses `subCoreAi.callAiDocuments()` which has its own continuation logic via `_callAiWithLooping()`.
|
||||
|
||||
---
|
||||
|
||||
## Function Call Chain Analysis
|
||||
|
||||
```
|
||||
processDocumentsWithContinuation (line 303) - ❌ NOT USED
|
||||
├─> _buildContinuationPrompt (line 319, 324) - ❌ ONLY USED HERE
|
||||
└─> _processWithContinuationLoop (line 322, 373) - ❌ ONLY USED HERE
|
||||
├─> _buildContinuationIterationPrompt (line 393, 459) - ❌ ONLY USED HERE
|
||||
└─> processDocumentsPerChunkJsonWithPrompt (line 402) - ✅ USED ELSEWHERE
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Subfunction Analysis
|
||||
|
||||
### 1. `_buildContinuationPrompt`
|
||||
**Location**: Line 324-371
|
||||
**Status**: ✅ **USED** (but only internally)
|
||||
**Called by**: `processDocumentsWithContinuation` (line 319)
|
||||
**Effectively**: ❌ **UNUSED** (because parent function is unused)
|
||||
|
||||
**Internal Usage**:
|
||||
- Called from `processDocumentsWithContinuation` at line 319
|
||||
|
||||
**Functionality**:
|
||||
- Builds a prompt with continuation instructions
|
||||
- Adds JSON structure requirements with `"continue": true/false` flag
|
||||
- Adds `continuation_context` field specification
|
||||
|
||||
**Note**: This uses a different continuation pattern than `SubCoreAi._callAiWithLooping()`:
|
||||
- This uses `"continue": true/false + "continuation_context"` for document sections
|
||||
- SubCoreAi uses `buildContinuationContext()` with `last_raw_json`
|
||||
|
||||
---
|
||||
|
||||
### 2. `_processWithContinuationLoop`
|
||||
**Location**: Line 373-457
|
||||
**Status**: ✅ **USED** (but only internally)
|
||||
**Called by**: `processDocumentsWithContinuation` (line 322)
|
||||
**Effectively**: ❌ **UNUSED** (because parent function is unused)
|
||||
|
||||
**Internal Usage**:
|
||||
- Called from `processDocumentsWithContinuation` at line 322
|
||||
|
||||
**External Dependencies**:
|
||||
- Calls `self._buildContinuationIterationPrompt()` (line 393)
|
||||
- Calls `self.processDocumentsPerChunkJsonWithPrompt()` (line 402)
|
||||
|
||||
**Functionality**:
|
||||
- Implements continuation loop (max 10 iterations)
|
||||
- Accumulates sections across iterations
|
||||
- Checks `continue` flag and `continuation_context` to determine if more iterations needed
|
||||
- Builds final result with accumulated sections
|
||||
|
||||
---
|
||||
|
||||
### 3. `_buildContinuationIterationPrompt`
|
||||
**Location**: Line 459-498
|
||||
**Status**: ✅ **USED** (but only internally)
|
||||
**Called by**: `_processWithContinuationLoop` (line 393)
|
||||
**Effectively**: ❌ **UNUSED** (because parent chain is unused)
|
||||
|
||||
**Internal Usage**:
|
||||
- Called from `_processWithContinuationLoop` at line 393 (in loop, conditionally)
|
||||
|
||||
**Functionality**:
|
||||
- Builds a prompt for continuation iteration with context
|
||||
- Includes summary of previously generated content (last 3 sections)
|
||||
- Includes continuation instructions with last section ID, element index, remaining requirements
|
||||
|
||||
---
|
||||
|
||||
### 4. `processDocumentsPerChunkJsonWithPrompt`
|
||||
**Location**: Line 219-301
|
||||
**Status**: ✅ **USED ELSEWHERE**
|
||||
**Called by**:
|
||||
- `_processWithContinuationLoop` (line 402)
|
||||
- Also referenced in backup files (not active code)
|
||||
|
||||
**Internal Usage**:
|
||||
- Called from `_processWithContinuationLoop` at line 402
|
||||
|
||||
**External Usage Search**:
|
||||
- ✅ Used internally by continuation loop
|
||||
- ⚠️ Referenced in `local/backup/backup_mainServiceAi.py.txt` (backup file, not active)
|
||||
- ❌ Not used by any other active code
|
||||
|
||||
**Functionality**:
|
||||
- Processes documents with per-chunk AI calls
|
||||
- Uses a custom prompt instead of default extraction prompt
|
||||
- Returns merged JSON document
|
||||
|
||||
**Note**: This function itself is only used by the continuation loop. However, it's a more general function that could be useful, so it's not "dead code" - it's just currently only used by unused code.
|
||||
|
||||
---
|
||||
|
||||
## Summary Table
|
||||
|
||||
| Function | Line | Status | Called By | Effectively Used? |
|
||||
|----------|------|--------|-----------|-------------------|
|
||||
| `processDocumentsWithContinuation` | 303 | ❌ Not used | (external) | ❌ No |
|
||||
| `_buildContinuationPrompt` | 324 | ✅ Used internally | `processDocumentsWithContinuation:319` | ❌ No |
|
||||
| `_processWithContinuationLoop` | 373 | ✅ Used internally | `processDocumentsWithContinuation:322` | ❌ No |
|
||||
| `_buildContinuationIterationPrompt` | 459 | ✅ Used internally | `_processWithContinuationLoop:393` | ❌ No |
|
||||
| `processDocumentsPerChunkJsonWithPrompt` | 219 | ✅ Used internally | `_processWithContinuationLoop:402` | ⚠️ **ONLY USED BY UNUSED CODE** |
|
||||
|
||||
---
|
||||
|
||||
## Current Active Implementation
|
||||
|
||||
The active continuation logic is in `subCoreAi.callAiDocuments()` → `_callAiWithLooping()`:
|
||||
- Uses `buildGenerationPrompt()` with `continuationContext` parameter
|
||||
- Uses `buildContinuationContext()` to build context from sections
|
||||
- Different continuation pattern (uses `last_raw_json` instead of `continuation_context`)
|
||||
|
||||
---
|
||||
|
||||
## Dead Code Identification
|
||||
|
||||
**Completely Unused Chain** (can be safely removed):
|
||||
1. ✅ `processDocumentsWithContinuation` - entry point, not called
|
||||
2. ✅ `_buildContinuationPrompt` - only used by #1
|
||||
3. ✅ `_processWithContinuationLoop` - only used by #1
|
||||
4. ✅ `_buildContinuationIterationPrompt` - only used by #3
|
||||
|
||||
**Potentially Unused** (only used by dead code):
|
||||
- ⚠️ `processDocumentsPerChunkJsonWithPrompt` - only caller is dead code, but function is general-purpose
|
||||
|
||||
---
|
||||
|
||||
## Recommendations
|
||||
|
||||
1. **Remove Dead Code Chain**: All four functions (`processDocumentsWithContinuation`, `_buildContinuationPrompt`, `_processWithContinuationLoop`, `_buildContinuationIterationPrompt`) can be safely removed.
|
||||
|
||||
2. **For `processDocumentsPerChunkJsonWithPrompt`**:
|
||||
- **Option A**: Remove if not needed (it's only used by the dead continuation chain)
|
||||
- **Option B**: Keep if it might be useful for future custom prompt processing
|
||||
- **Recommendation**: Since it's a general-purpose function that could be useful, keep it but note that it's currently unused.
|
||||
|
||||
3. **If Keeping**: Document why this continuation logic exists but is unused, or mark as deprecated/legacy alternative to `_callAiWithLooping()`.
|
||||
|
||||
---
|
||||
|
||||
## Verification Commands
|
||||
|
||||
To verify these findings:
|
||||
|
||||
```bash
|
||||
# Search for actual function calls (should return no results for the main function)
|
||||
grep -r "\.processDocumentsWithContinuation(" gateway/ --exclude-dir=wiki --exclude-dir=local --exclude-dir=backup
|
||||
|
||||
# Search for _buildContinuationPrompt usage (should only find the definition)
|
||||
grep -r "_buildContinuationPrompt" gateway/ --exclude-dir=wiki --exclude-dir=local --exclude-dir=backup --exclude="*.md"
|
||||
|
||||
# Search for _processWithContinuationLoop usage (should only find the definition)
|
||||
grep -r "_processWithContinuationLoop" gateway/ --exclude-dir=wiki --exclude-dir=local --exclude-dir=backup --exclude="*.md"
|
||||
```
|
||||
|
||||
Loading…
Reference in a new issue