1import numpy as np
2from typing import Dict, List, Tuple, Any
3from dataclasses import dataclass
4import torch
5import torch.nn as nn
6from sklearn.gaussian_process import GaussianProcessRegressor
7from sklearn.gaussian_process.kernels import Matern
8
9@dataclass
10class RAGConfiguration:
11    """RAG system configuration parameters"""
12    chunk_size: int
13    overlap_ratio: float
14    embedding_model: str
15    retrieval_k: int
16    reranking_model: str
17    temperature: float
18    search_type: str  # 'dense', 'sparse', 'hybrid'
19    
20class AutoRAG:
21    """Auto-optimizing RAG system"""
22    
23    def __init__(self):
24        self.config_history: List[Tuple[RAGConfiguration, float]] = []
25        self.gp_model = GaussianProcessRegressor(
26            kernel=Matern(nu=2.5),
27            alpha=1e-6,
28            normalize_y=True,
29            n_restarts_optimizer=10
30        )
31        self.meta_learner = MetaLearner()
32        
33    def optimize_configuration(self, 
34                              query_type: str,
35                              performance_history: List[float]) -> RAGConfiguration:
36        """Automatic configuration tuning via Bayesian optimization"""
37        
38        # Initial configuration based on query type
39        base_config = self._get_base_config(query_type)
40        
41        if len(self.config_history) < 10:
42            # Initial exploration phase
43            return self._exploration_config(base_config)
44        
45        # Bayesian optimization
46        X = self._configs_to_array([c[0] for c in self.config_history])
47        y = np.array([c[1] for c in self.config_history])
48        
49        self.gp_model.fit(X, y)
50        
51        # Determine next trial point
52        next_config = self._acquisition_function(base_config)
53        
54        return next_config
55    
56    def _acquisition_function(self, base_config: RAGConfiguration) -> RAGConfiguration:
57        """Determine next trial point via acquisition function"""
58        candidates = self._generate_candidates(base_config, n=100)
59        
60        X_candidates = self._configs_to_array(candidates)
61        
62        # Predicted mean and variance
63        mu, sigma = self.gp_model.predict(X_candidates, return_std=True)
64        
65        # Upper Confidence Bound (UCB)
66        beta = 2.0
67        ucb = mu + beta * sigma
68        
69        best_idx = np.argmax(ucb)
70        return candidates[best_idx]
71    
72    def _generate_candidates(self, 
73                            base_config: RAGConfiguration, 
74                            n: int) -> List[RAGConfiguration]:
75        """Generate candidate configurations"""
76        candidates = []
77        
78        for _ in range(n):
79            config = RAGConfiguration(
80                chunk_size=int(np.random.normal(base_config.chunk_size, 100)),
81                overlap_ratio=np.clip(np.random.normal(base_config.overlap_ratio, 0.05), 0, 0.5),
82                embedding_model=base_config.embedding_model,
83                retrieval_k=int(np.random.normal(base_config.retrieval_k, 2)),
84                reranking_model=base_config.reranking_model,
85                temperature=np.clip(np.random.normal(base_config.temperature, 0.1), 0, 1),
86                search_type=np.random.choice(['dense', 'sparse', 'hybrid'])
87            )
88            candidates.append(config)
89        
90        return candidates
91
92class MetaLearner(nn.Module):
93    """Meta-learner for query classification and RAG strategy selection"""
94    
95    def __init__(self, input_dim=768, hidden_dim=256, num_strategies=5):
96        super().__init__()
97        self.encoder = nn.Sequential(
98            nn.Linear(input_dim, hidden_dim),
99            nn.ReLU(),
100            nn.Dropout(0.2),
101            nn.Linear(hidden_dim, hidden_dim),
102            nn.ReLU(),
103            nn.Dropout(0.2)
104        )
105        
106        self.strategy_head = nn.Linear(hidden_dim, num_strategies)
107        self.performance_predictor = nn.Linear(hidden_dim + num_strategies, 1)
108        
109    def forward(self, query_embedding: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
110        """Predict optimal strategy from query"""
111        features = self.encoder(query_embedding)
112        strategy_logits = self.strategy_head(features)
113        strategy_probs = torch.softmax(strategy_logits, dim=-1)
114        
115        # Predict expected performance for each strategy
116        strategy_features = torch.cat([features, strategy_probs], dim=-1)
117        expected_performance = self.performance_predictor(strategy_features)
118        
119        return strategy_probs, expected_performance

1class SelfImprovingRAG:
2    """RAG system with self-improvement capabilities"""
3    
4    def __init__(self):
5        self.auto_rag = AutoRAG()
6        self.feedback_buffer = FeedbackBuffer(capacity=1000)
7        self.improvement_scheduler = ImprovementScheduler()
8        
9    def process_with_learning(self, query: str) -> dict:
10        """Process query while learning"""
11        
12        # 1. Classify query type
13        query_type = self._classify_query(query)
14        
15        # 2. Select optimal configuration
16        config = self.auto_rag.optimize_configuration(
17            query_type, 
18            self.feedback_buffer.get_recent_performance()
19        )
20        
21        # 3. Execute RAG processing
22        result = self._execute_rag(query, config)
23        
24        # 4. Automatic evaluation
25        quality_score = self._evaluate_quality(result)
26        
27        # 5. Record feedback
28        self.feedback_buffer.add(query, result, quality_score, config)
29        
30        # 6. Periodic improvement
31        if self.improvement_scheduler.should_improve():
32            self._trigger_improvement()
33        
34        return result
35    
36    def _evaluate_quality(self, result: dict) -> float:
37        """Automatic evaluation of answer quality"""
38        scores = []
39        
40        # 1. Relevance score
41        relevance = self._calculate_relevance(
42            result['query'], 
43            result['retrieved_docs'], 
44            result['answer']
45        )
46        scores.append(relevance)
47        
48        # 2. Consistency score
49        consistency = self._calculate_consistency(result['answer'])
50        scores.append(consistency)
51        
52        # 3. Completeness score
53        completeness = self._calculate_completeness(
54            result['query'], 
55            result['answer']
56        )
57        scores.append(completeness)
58        
59        # 4. Confidence score
60        confidence = self._calculate_confidence(result['metadata'])
61        scores.append(confidence)
62        
63        return np.mean(scores)
64    
65    def _trigger_improvement(self):
66        """Trigger improvement process"""
67        # Analyze feedback data
68        analysis = self.feedback_buffer.analyze()
69        
70        # Identify problem patterns
71        issues = self._identify_issues(analysis)
72        
73        # Determine improvement strategies
74        improvements = self._plan_improvements(issues)
75        
76        # Execute improvements
77        for improvement in improvements:
78            self._apply_improvement(improvement)

1import dspy
2from dspy import Signature, Module, ChainOfThought
3
4class RAGSignature(Signature):
5    """RAG task signature definition"""
6    question = dspy.InputField(desc="User's question")
7    context = dspy.InputField(desc="Retrieved relevant documents")
8    answer = dspy.OutputField(desc="Generated answer")
9    confidence = dspy.OutputField(desc="Answer confidence score")
10
11class OptimizedRAG(Module):
12    """DSPy-optimized RAG module"""
13    
14    def __init__(self):
15        super().__init__()
16        self.retrieve = dspy.Retrieve(k=5)
17        self.generate_answer = ChainOfThought(RAGSignature)
18        
19    def forward(self, question):
20        # Retrieve context
21        context = self.retrieve(question).passages
22        
23        # Generate answer
24        prediction = self.generate_answer(
25            question=question,
26            context=context
27        )
28        
29        return dspy.Prediction(
30            answer=prediction.answer,
31            confidence=prediction.confidence,
32            context=context
33        )
34
35# Automatic optimization with DSPy
36from dspy.teleprompt import BootstrapFewShot
37
38def train_optimized_rag(train_data, val_data):
39    """Automatic RAG optimization with DSPy"""
40    
41    # Initialize model
42    rag = OptimizedRAG()
43    
44    # Define evaluation metrics
45    def rag_metric(example, pred, trace=None):
46        answer_match = example.answer.lower() in pred.answer.lower()
47        confidence_valid = float(pred.confidence) >= 0.7
48        return answer_match and confidence_valid
49    
50    # Optimization via BootstrapFewShot
51    teleprompter = BootstrapFewShot(
52        metric=rag_metric,
53        max_bootstrapped_demos=4,
54        max_labeled_demos=16
55    )
56    
57    # Execute optimization
58    optimized_rag = teleprompter.compile(
59        rag,
60        trainset=train_data,
61        valset=val_data
62    )
63    
64    return optimized_rag

1class PromptOptimizer:
2    """Automatic prompt optimization system"""
3    
4    def __init__(self):
5        self.prompt_templates = []
6        self.performance_history = {}
7        
8    def optimize_prompt(self, 
9                       task_description: str,
10                       examples: List[dict],
11                       current_prompt: str) -> str:
12        """Prompt optimization via genetic algorithm"""
13        
14        population_size = 50
15        generations = 20
16        mutation_rate = 0.1
17        
18        # Generate initial population
19        population = self._generate_initial_population(
20            current_prompt, 
21            population_size
22        )
23        
24        for generation in range(generations):
25            # Evaluate each prompt
26            fitness_scores = []
27            for prompt in population:
28                score = self._evaluate_prompt(prompt, examples)
29                fitness_scores.append(score)
30            
31            # Selection
32            selected = self._selection(population, fitness_scores)
33            
34            # Crossover
35            offspring = self._crossover(selected)
36            
37            # Mutation
38            mutated = self._mutation(offspring, mutation_rate)
39            
40            # Generate next generation
41            population = mutated
42        
43        # Return best prompt
44        best_idx = np.argmax(fitness_scores)
45        return population[best_idx]
46    
47    def _evaluate_prompt(self, prompt: str, examples: List[dict]) -> float:
48        """Evaluate prompt performance"""
49        scores = []
50        
51        for example in examples:
52            # Generate prediction with prompt
53            prediction = self._generate_with_prompt(
54                prompt, 
55                example['input']
56            )
57            
58            # Compare with expected output
59            score = self._calculate_similarity(
60                prediction, 
61                example['expected_output']
62            )
63            scores.append(score)
64        
65        return np.mean(scores)

1class FinancialAutoRAG:
2    """Autonomous RAG system for finance"""
3    
4    def __init__(self):
5        self.market_analyzer = MarketDataAnalyzer()
6        self.auto_optimizer = AutoRAG()
7        self.compliance_checker = ComplianceChecker()
8        
9    def process_financial_query(self, query: str) -> dict:
10        # Automatic market data retrieval
11        market_context = self.market_analyzer.get_relevant_data(query)
12        
13        # Compliance check
14        if not self.compliance_checker.is_compliant(query):
15            return {"error": "Compliance violation detected"}
16        
17        # Auto-optimized RAG processing
18        config = self.auto_optimizer.optimize_configuration(
19            query_type="financial",
20            performance_history=self.get_recent_performance()
21        )
22        
23        # Execute processing
24        result = self.execute_rag_with_config(query, market_context, config)
25        
26        # Automatic learning
27        self.learn_from_result(query, result)
28        
29        return result

1class MultiAgentRAG:
2    """Multi-agent collaborative RAG system"""
3    
4    def __init__(self):
5        self.agents = {
6            'retrieval': RetrievalAgent(),
7            'ranking': RankingAgent(),
8            'generation': GenerationAgent(),
9            'evaluation': EvaluationAgent(),
10            'optimization': OptimizationAgent()
11        }
12        self.coordinator = AgentCoordinator()
13    
14    def collaborative_process(self, query: str) -> dict:
15        # Inter-agent collaborative processing
16        plan = self.coordinator.create_execution_plan(query)
17        
18        results = {}
19        for step in plan:
20            agent = self.agents[step['agent']]
21            result = agent.execute(step['task'], results)
22            results[step['name']] = result
23            
24            # Feedback to other agents
25            self.coordinator.broadcast_result(step['name'], result)
26        
27        return results['final_answer']

1class EvolvingRAG:
2    """Continuously evolving RAG system"""
3    
4    def __init__(self):
5        self.evolution_engine = EvolutionEngine()
6        self.fitness_evaluator = FitnessEvaluator()
7        
8    def evolve(self):
9        """Continuous system evolution"""
10        while True:
11            # Evaluate current performance
12            current_fitness = self.fitness_evaluator.evaluate()
13            
14            # Generate variants
15            variants = self.evolution_engine.generate_variants()
16            
17            # A/B testing
18            for variant in variants:
19                variant_fitness = self.test_variant(variant)
20                
21                if variant_fitness > current_fitness:
22                    self.adopt_variant(variant)
23                    current_fitness = variant_fitness
24            
25            # Adaptive learning
26            self.adaptive_learning()