```
import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import logging
# Core data structures
@dataclass
class OptimizationParameters:
verifiability_threshold: float
specificity_bounds: Tuple[float, float]
confidence_limits: Tuple[float, float]
type_1_threshold: float
type_2_threshold: float
confidence_interval: float
local_optima_threshold: float
global_search_depth: int
iteration_limit: int
@dataclass
class Solution:
content: str
confidence: float
verification_path: List[str]
bias_assessment: Dict[str, float]
robustness_score: float
class BiasType(Enum):
TRAINING = "training_data"
SAMPLING = "sampling"
SELECTION = "selection"
CONFIRMATION = "confirmation"
class ResponseOptimizer:
def __init__(self, params: OptimizationParameters):
self.params = params
self.logger = logging.getLogger(__name__)
def analyze_input(self, query: str) -> Dict:
"""Parse and analyze input query."""
tokens = self._tokenize(query)
assumptions = self._identify_assumptions(tokens)
constraints = self._map_constraints(assumptions)
completeness = self._evaluate_completeness(tokens, constraints)
return {
"tokens": tokens,
"assumptions": assumptions,
"constraints": constraints,
"completeness": completeness
}
def generate_solution_space(self,
query: str,
analysis: Dict) -> List[Solution]:
"""Generate initial solution candidates."""
basis_vectors = self._generate_basis_vectors(analysis)
feasible_region = self._project_query(query, basis_vectors)
candidates = []
for i in range(self.params.global_search_depth):
solution = self._generate_candidate(feasible_region, analysis)
if self._meets_constraints(solution):
candidates.append(solution)
return candidates
def test_solutions(self,
candidates: List[Solution]) -> List[Solution]:
"""Apply adversarial testing to candidates."""
tested_solutions = []
for solution in candidates:
# Apply Socratic questioning
assumptions_valid = self._test_assumptions(solution)
edge_cases_valid = self._test_edge_cases(solution)
logical_gaps = self._identify_logical_gaps(solution)
# Test against inverse problems
negation_set = self._generate_negation_set(solution)
contradictions = self._find_contradictions(solution, negation_set)
# Calculate robustness score
robustness = min(
self._evaluate_logical_consistency(solution),
self._evaluate_verifiability(solution),
self._evaluate_completeness(solution)
)
solution.robustness_score = robustness
tested_solutions.append(solution)
return tested_solutions
def optimize_response(self,
candidates: List[Solution]) -> Solution:
"""Iteratively refine solutions."""
iterations = 0
best_solution = max(candidates, key=lambda x: x.robustness_score)
while iterations < self.params.iteration_limit:
variations = self._generate_variations(best_solution)
for variant in variations:
variant = self._detect_and_adjust_bias(variant)
if variant.robustness_score > best_solution.robustness_score:
best_solution = variant
if self._check_convergence(best_solution):
break
iterations += 1
return best_solution
def _detect_and_adjust_bias(self, solution: Solution) -> Solution:
"""Detect and adjust for various types of bias."""
biases = {}
for bias_type in BiasType:
bias_score = self._evaluate_bias(solution, bias_type)
biases[bias_type.value] = bias_score
# Adjust confidence based on detected bias
total_bias = sum(biases.values()) / len(biases)
solution.confidence *= (1 - total_bias)
solution.bias_assessment = biases
return solution
def generate_final_response(self, solution: Solution) -> Dict:
"""Assemble final response with metadata."""
response = {
"content": solution.content,
"metadata": {
"confidence": solution.confidence,
"verification_path": solution.verification_path,
"bias_assessment": solution.bias_assessment,
"robustness_score": solution.robustness_score
}
}
# Final quality checks
if not self._verify_final_response(response):
self.logger.warning("Final response failed verification")
response["metadata"]["warning"] = "Response may not meet all quality criteria"
return response
def _verify_final_response(self, response: Dict) -> bool:
"""Perform final verification of response."""
checks = [
self._verify_optimization_parameters(response),
self._verify_confidence_bounds(response),
self._verify_internal_consistency(response),
self._verify_specificity(response)
]
return all(checks)
def _calibrate_response(self,
response: Dict,
user_context: Dict) -> Dict:
"""Calibrate response based on user context."""
response["content"] = self._adjust_specificity(
response["content"],
user_context.get("expertise_level", "general")
)
response["content"] = self._adjust_technical_level(
response["content"],
user_context.get("technical_level", "medium")
)
depth_ratio = self._calculate_depth_ratio(
response["content"],
user_context.get("time_constraint", float('inf'))
)
if depth_ratio > 1.0:
response["content"] = self._compress_content(response["content"])
return response
def handle_error(self, error_type: str, details: Dict) -> Dict:
"""Handle various types of errors in response generation."""
if error_type == "ambiguity":
clarifying_questions = self._generate_clarifying_questions(details)
return {"type": "clarification_needed", "questions": clarifying_questions}
elif error_type == "logical_inconsistency":
resolution = self._attempt_logical_resolution(details)
if not resolution["resolved"]:
return {"type": "clarification_needed", "contradiction_points": resolution["points"]}
return resolution
elif error_type == "knowledge_gap":
return {
"type": "knowledge_boundary",
"limitation": details["gap_description"],
"alternatives": self._suggest_alternatives(details)
}
return {"type": "unknown_error", "details": details}
```