# Basic RAG Flow
query = "What is machine learning?"
relevant_docs = retrieve(query, top_k=5)
context = concatenate(relevant_docs)
response = generate(query, context)

# Advanced RAG Flow
expanded_query = query_expansion(query)
hybrid_results = hybrid_search(expanded_query)  # Semantic + Keyword
reranked_docs = rerank(hybrid_results)
filtered_docs = filter_relevance(reranked_docs)
response = generate(query, filtered_docs)

class ModularRAG:
    def __init__(self):
        self.query_module = QueryProcessor()
        self.retrieval_module = MultiSourceRetriever()
        self.generation_module = ContextualGenerator()
        self.memory_module = ConversationMemory()
    
    def process(self, query, context=None):
        processed_query = self.query_module.process(query)
        docs = self.retrieval_module.retrieve(processed_query)
        response = self.generation_module.generate(query, docs, context)
        self.memory_module.update(query, response)
        return response

class AgenticRAG:
    def __init__(self):
        self.tools = [WebSearch(), Calculator(), DatabaseQuery()]
        self.planner = ActionPlanner()
        self.executor = ToolExecutor()
        self.reflector = SelfReflector()
    
    def solve(self, query):
        plan = self.planner.create_plan(query)
        for step in plan:
            result = self.executor.execute(step, self.tools)
            if not self.reflector.validate(result):
                plan = self.planner.revise_plan(plan, result)
        return self.synthesize_results(plan.results)

class SelfRAG:
    def generate_with_reflection(self, query):
        # Decide whether to retrieve
        if self.should_retrieve(query):
            docs = self.retrieve(query)
            
            # Assess relevance
            relevant_docs = []
            for doc in docs:
                if self.is_relevant(query, doc):
                    relevant_docs.append(doc)
            
            response = self.generate(query, relevant_docs)
            
            # Self-critique
            if self.is_supported(response, relevant_docs):
                return response
            else:
                return self.refine_response(query, relevant_docs)
        else:
            return self.generate_without_retrieval(query)

class CorrectiveRAG:
    def process_with_correction(self, query):
        docs = self.retrieve(query)
        confidence = self.evaluate_retrieval_quality(docs)
        
        if confidence < LOW_THRESHOLD:
            # Trigger web search or external sources
            external_docs = self.web_search(query)
            docs.extend(external_docs)
        elif confidence < MEDIUM_THRESHOLD:
            # Decompose and re-retrieve
            sub_queries = self.decompose_query(query)
            for sub_query in sub_queries:
                sub_docs = self.retrieve(sub_query)
                docs.extend(sub_docs)
        
        # Generate with corrected knowledge
        return self.generate(query, docs)

class AdaptiveRAG:
    def __init__(self):
        self.query_classifier = QueryClassifier()
        self.strategies = {
            'simple': SimpleGeneration(),
            'factual': SingleStepRAG(),
            'complex': MultiStepRAG(),
            'conversational': DialogueRAG()
        }
    
    def process(self, query):
        query_type = self.query_classifier.classify(query)
        strategy = self.strategies[query_type]
        return strategy.process(query)

class HyDERAG:
    def retrieve_with_hyde(self, query):
        # Generate hypothetical document
        hypothetical_doc = self.generate_hypothetical_answer(query)
        
        # Embed hypothetical content
        hyp_embedding = self.embed(hypothetical_doc)
        
        # Retrieve similar real documents
        similar_docs = self.vector_search(hyp_embedding)
        
        # Generate final answer using real documents
        return self.generate(query, similar_docs)
    
    def generate_hypothetical_answer(self, query):
        prompt = f"Write a detailed answer to: {query}"
        return self.llm.generate(prompt)

class FusionRAG:
    def __init__(self):
        self.retrievers = [
            SemanticRetriever(),
            KeywordRetriever(),
            HybridRetriever()
        ]
        self.fusion_method = ReciprocalRankFusion()
    
    def retrieve_and_fuse(self, query):
        all_results = []
        
        # Multiple query variations
        query_variations = self.generate_query_variations(query)
        
        for variation in query_variations:
            for retriever in self.retrievers:
                results = retriever.retrieve(variation)
                all_results.append(results)
        
        # Fuse results using reciprocal rank fusion
        fused_results = self.fusion_method.fuse(all_results)
        return fused_results

class GraphRAG:
    def __init__(self):
        self.knowledge_graph = KnowledgeGraph()
        self.entity_extractor = EntityExtractor()
        self.graph_traverser = GraphTraverser()
    
    def retrieve_with_graph(self, query):
        # Extract entities from query
        entities = self.entity_extractor.extract(query)
        
        # Find related entities in graph
        related_entities = []
        for entity in entities:
            neighbors = self.graph_traverser.get_neighbors(entity)
            related_entities.extend(neighbors)
        
        # Retrieve documents about related entities
        docs = []
        for entity in related_entities:
            entity_docs = self.retrieve_by_entity(entity)
            docs.extend(entity_docs)
        
        return self.generate(query, docs)

# Phase 1: Basic RAG
class BasicRAG:
    def __init__(self):
        self.retriever = SimpleRetriever()
        self.generator = SimpleGenerator()

# Phase 2: Enhanced RAG
class EnhancedRAG(BasicRAG):
    def __init__(self):
        super().__init__()
        self.query_enhancer = QueryEnhancer()
        self.reranker = CrossEncoderReranker()

# Phase 3: Production RAG
class ProductionRAG(EnhancedRAG):
    def __init__(self):
        super().__init__()
        self.cache = RedisCache()
        self.monitor = PerformanceMonitor()
        self.safety = SafetyFilter()

def preprocess_documents(documents):
    processed = []
    for doc in documents:
        # Clean text
        clean_text = clean_html_and_formatting(doc['content'])
        
        # Chunk long documents
        chunks = smart_chunking(clean_text, max_length=512, overlap=50)
        
        # Add metadata
        for i, chunk in enumerate(chunks):
            processed.append({
                'id': f"{doc['id']}_chunk_{i}",
                'content': chunk,
                'source': doc['title'],
                'chunk_index': i,
                'metadata': doc.get('metadata', {})
            })
    
    return processed

# Use appropriate models for your domain
EMBEDDING_MODELS = {
    'general': 'all-MiniLM-L6-v2',
    'scientific': 'allenai/scibert_scivocab_uncased',
    'legal': 'nlpaueb/legal-bert-base-uncased',
    'multilingual': 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
}

class HybridRetriever:
    def __init__(self, alpha=0.7):
        self.semantic_retriever = SemanticRetriever()
        self.keyword_retriever = KeywordRetriever()
        self.alpha = alpha  # Weight for semantic vs keyword
    
    def retrieve(self, query, top_k=10):
        semantic_results = self.semantic_retriever.retrieve(query, top_k)
        keyword_results = self.keyword_retriever.retrieve(query, top_k)
        
        # Combine scores
        combined_results = self.combine_scores(
            semantic_results, keyword_results, self.alpha
        )
        
        return combined_results[:top_k]

def enhance_query(query):
    # Expand with synonyms
    expanded = add_synonyms(query)
    
    # Add domain-specific terms
    enhanced = add_domain_terms(expanded)
    
    # Generate related questions
    related_queries = generate_related_queries(query)
    
    return {
        'original': query,
        'expanded': expanded,
        'enhanced': enhanced,
        'related': related_queries
    }

def prepare_context(retrieved_docs, max_tokens=4000):
    # Sort by relevance
    sorted_docs = sorted(retrieved_docs, key=lambda x: x['score'], reverse=True)
    
    context_parts = []
    token_count = 0
    
    for doc in sorted_docs:
        doc_tokens = count_tokens(doc['content'])
        if token_count + doc_tokens <= max_tokens:
            context_parts.append(f"Source: {doc['source']}\n{doc['content']}")
            token_count += doc_tokens
        else:
            break
    
    return '\n\n'.join(context_parts)

def create_rag_prompt(query, context, system_message=None):
    system_msg = system_message or """
    You are a helpful assistant that answers questions based on the provided context.
    Use only the information in the context to answer questions.
    If the context doesn't contain enough information, say so clearly.
    Cite your sources when possible.
    """
    
    prompt = f"""
    Context:
    {context}
    
    Question: {query}
    
    Instructions:
    - Provide a comprehensive answer based on the context
    - Include relevant details and examples
    - If uncertain, acknowledge limitations
    - Cite sources when making specific claims
    
    Answer:
    """
    
    return system_msg, prompt

class RAGEvaluator:
    def __init__(self):
        self.metrics = {
            'retrieval': ['precision@k', 'recall@k', 'mrr'],
            'generation': ['bleu', 'rouge', 'bert_score'],
            'end_to_end': ['human_rating', 'factual_accuracy']
        }
    
    def evaluate_retrieval(self, queries, ground_truth):
        results = {}
        for query, expected_docs in zip(queries, ground_truth):
            retrieved = self.rag_system.retrieve(query)
            results[query] = {
                'precision@5': self.precision_at_k(retrieved, expected_docs, 5),
                'recall@5': self.recall_at_k(retrieved, expected_docs, 5),
                'mrr': self.mean_reciprocal_rank(retrieved, expected_docs)
            }
        return results

import redis
from functools import wraps

class RAGCache:
    def __init__(self):
        self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
        self.ttl = 3600  # 1 hour
    
    def cache_embeddings(self, text_hash, embedding):
        self.redis_client.setex(f"emb:{text_hash}", self.ttl, embedding.tobytes())
    
    def get_cached_embedding(self, text_hash):
        cached = self.redis_client.get(f"emb:{text_hash}")
        return np.frombuffer(cached) if cached else None

def cached_retrieval(func):
    @wraps(func)
    def wrapper(self, query, *args, **kwargs):
        cache_key = f"retrieval:{hash(query)}"
        cached_result = self.cache.get(cache_key)
        
        if cached_result:
            return cached_result
        
        result = func(self, query, *args, **kwargs)
        self.cache.set(cache_key, result, ttl=300)  # 5 minutes
        return result
    
    return wrapper

import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor

class AsyncRAG:
    def __init__(self):
        self.executor = ThreadPoolExecutor(max_workers=4)
    
    async def parallel_retrieve(self, query):
        # Run multiple retrievers in parallel
        tasks = [
            self.run_retriever(self.semantic_retriever, query),
            self.run_retriever(self.keyword_retriever, query),
            self.run_retriever(self.hybrid_retriever, query)
        ]
        
        results = await asyncio.gather(*tasks)
        return self.merge_results(results)
    
    async def run_retriever(self, retriever, query):
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(self.executor, retriever.retrieve, query)

import faiss
import numpy as np

class OptimizedVectorStore:
    def __init__(self, dimension, use_gpu=False):
        self.dimension = dimension
        
        # Use product quantization for memory efficiency
        self.index = faiss.IndexIVFPQ(
            faiss.IndexFlatL2(dimension),
            dimension,
            n_centroids=100,  # Number of clusters
            m=8,  # Number of subquantizers
            nbits=8  # Bits per subquantizer
        )
        
        if use_gpu and faiss.get_num_gpus() > 0:
            self.index = faiss.index_cpu_to_gpu(
                faiss.StandardGpuResources(), 0, self.index
            )
    
    def add_vectors(self, vectors):
        # Train the index first
        if not self.index.is_trained:
            self.index.train(vectors.astype('float32'))
        
        self.index.add(vectors.astype('float32'))
    
    def search(self, query_vector, k=10):
        distances, indices = self.index.search(
            query_vector.astype('float32').reshape(1, -1), k
        )
        return distances[0], indices[0]

# retrieval_service.py
from flask import Flask, request, jsonify

app = Flask(__name__)
retriever = VectorRetriever()

@app.route('/retrieve', methods=['POST'])
def retrieve():
    data = request.json
    query = data['query']
    top_k = data.get('top_k', 10)
    
    results = retriever.retrieve(query, top_k)
    return jsonify(results)

# generation_service.py
from flask import Flask, request, jsonify

app = Flask(__name__)
generator = LLMGenerator()

@app.route('/generate', methods=['POST'])
def generate():
    data = request.json
    query = data['query']
    context = data['context']
    
    response = generator.generate(query, context)
    return jsonify({'response': response})

# Integration with monitoring tools
import wandb
from prometheus_client import Counter, Histogram, start_http_server

class RAGMonitor:
    def __init__(self):
        # Prometheus metrics
        self.query_counter = Counter('rag_queries_total', 'Total RAG queries')
        self.latency_histogram = Histogram('rag_latency_seconds', 'RAG latency')
        self.error_counter = Counter('rag_errors_total', 'RAG errors')
        
        # Weights & Biases logging
        wandb.init(project="rag-system")
    
    def log_query(self, query, response, latency, sources):
        self.query_counter.inc()
        self.latency_histogram.observe(latency)
        
        wandb.log({
            'query_length': len(query),
            'response_length': len(response),
            'latency': latency,
            'num_sources': len(sources)
        })