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"""
Binary rescoring implementation para Pinecone/Weaviate/Qdrant.
Combina velocidad de binary search con accuracy de float32 rescoring.
"""
import numpy as np
from typing import List, Tuple, Dict
import time


class BinaryRescoringSearch:
    """
    Implementa binary search + float32 rescoring para optimal speed/accuracy.
    
    Performance:
    - Binary search: 8-15ms para 250M vectors
    - Rescoring: +12ms para top 100 candidates
    - Total: ~20-27ms vs 150ms+ float32 alone
    """

    def __init__(self, vector_db_client, rescore_multiplier: int = 4):
        """
        Args:
            vector_db_client: Cliente Pinecone/Weaviate/Qdrant
            rescore_multiplier: Cuántos candidatos recuperar vs top_k final
                            (4 = recuperar 40 para devolver top 10)
        """
        self.db = vector_db_client
        self.rescore_multiplier = rescore_multiplier

        # Tracking de performance
        self.metrics = {
            'binary_search_ms': [],
            'rescoring_ms': [],
            'total_ms': [],
            'accuracy_improvement': []
        }

    def search(
        self,
        query_vector: List[float],
        top_k: int = 10,
        filter_metadata: Dict = None
    ) -> List[Dict]:
        """
        Búsqueda híbrida: binary fast search + float32 rescoring.
        
        Args:
            query_vector: Query embedding (float32)
            top_k: Número de resultados finales
            filter_metadata: Filtros metadata (ej: {'category': 'electronics'})
        
        Returns:
            Lista de resultados rescored con scores mejorados
        """
        start_total = time.time()

        # Step 1: Binary search para candidatos
        binary_start = time.time()

        # Quantizar query a binary
        query_binary = self._quantize_binary(query_vector)

        # Búsqueda en índice binary (Hamming distance)
        num_candidates = top_k * self.rescore_multiplier
        binary_results = self.db.query(
            vector=query_binary,
            top_k=num_candidates,
            filter=filter_metadata,
            include_metadata=True,
            include_values=True  # Necesario para rescoring
        )

        binary_time = (time.time() - binary_start) * 1000  # ms
        self.metrics['binary_search_ms'].append(binary_time)

        # Step 2: Rescore con float32 original embeddings
        rescore_start = time.time()

        rescored_results = []
        for match in binary_results['matches']:
            # Recuperar embedding float32 original (stored en metadata o separate index)
            float32_embedding = self._get_float32_embedding(match['id'])

            # Calcular cosine similarity exacto
            float32_score = self._cosine_similarity(query_vector, float32_embedding)

            rescored_results.append({
                'id': match['id'],
                'binary_score': match['score'],
                'float32_score': float32_score,  # Score mejorado
                'metadata': match['metadata']
            })

        # Ordenar por float32 score
        rescored_results.sort(key=lambda x: x['float32_score'], reverse=True)

        rescore_time = (time.time() - rescore_start) * 1000  # ms
        self.metrics['rescoring_ms'].append(rescore_time)

        total_time = (time.time() - start_total) * 1000
        self.metrics['total_ms'].append(total_time)

        # Retornar top K final
        final_results = rescored_results[:top_k]

        print(f"⚡ Binary search: {binary_time:.1f}ms | Rescoring: {rescore_time:.1f}ms | Total: {total_time:.1f}ms")

        return final_results

    def _quantize_binary(self, vector: List[float]) -> List[int]:
        """Quantiza float32 vector a binary (1 bit por dim)."""
        return [1 if val > 0 else 0 for val in vector]

    def _cosine_similarity(self, vec1: List[float], vec2: List[float]) -> float:
        """Calcula cosine similarity entre dos vectores."""
        vec1_np = np.array(vec1)
        vec2_np = np.array(vec2)

        dot_product = np.dot(vec1_np, vec2_np)
        norm1 = np.linalg.norm(vec1_np)
        norm2 = np.linalg.norm(vec2_np)

        return dot_product / (norm1 * norm2)

    def _get_float32_embedding(self, doc_id: str) -> List[float]:
        """
        Recupera embedding float32 original.
        
        Opciones de implementación:
        1. Stored en metadata (si DB lo soporta)
        2. Separate index float32 (dual indexing)
        3. S3/Redis cache para hot vectors
        """
        # Implementación depende de tu architecture
        # Ejemplo: dual indexing en Pinecone
        float32_result = self.db.fetch(ids=[doc_id])
        return float32_result['vectors'][doc_id]['values']

    def get_performance_report(self) -> Dict:
        """Reporte de performance rescoring."""
        if not self.metrics['total_ms']:
            return {}

        return {
            'avg_binary_search_ms': np.mean(self.metrics['binary_search_ms']),
            'avg_rescoring_ms': np.mean(self.metrics['rescoring_ms']),
            'avg_total_ms': np.mean(self.metrics['total_ms']),
            'p95_total_ms': np.percentile(self.metrics['total_ms'], 95),
            'p99_total_ms': np.percentile(self.metrics['total_ms'], 99),
            'total_searches': len(self.metrics['total_ms'])
        }


# Ejemplo de uso con Pinecone
if __name__ == "__main__":
    from pinecone import Pinecone

    pc = Pinecone(api_key="YOUR_API_KEY")
    index = pc.Index("your-binary-index")

    # Inicializar rescoring search
    search_engine = BinaryRescoringSearch(
        vector_db_client=index,
        rescore_multiplier=4  # Recuperar 4x candidatos para rescore
    )

    # Query de prueba
    query = [0.1, -0.2, 0.3, -0.4, ...]  # Tu query embedding

    results = search_engine.search(
        query_vector=query,
        top_k=10,
        filter_metadata={'category': 'legal_contracts'}
    )

    print("\n🔍 Top 10 resultados rescored:")
    for i, r in enumerate(results, 1):
        print(f"{i}. Score: {r['float32_score']:.4f} (binary: {r['binary_score']:.4f}) - {r['id']}")

    # Performance report
    perf = search_engine.get_performance_report()
    print(f"\n⚡ Performance:")
    print(f" Avg total latency: {perf['avg_total_ms']:.1f}ms")
    print(f" P95: {perf['p95_total_ms']:.1f}ms | P99: {perf['p99_total_ms']:.1f}ms") 

"""
Usando Matryoshka truncation con OpenAI text-embedding-3.
Reduce storage 6x (1536→256 dims) con 

"""
Training custom Matryoshka embedding model con Sentence Transformers.
Permite truncation flexible a 128, 256, 512, 768, 1024 dims.
"""
from sentence_transformers import SentenceTransformer, InputExample, losses
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from torch.utils.data import DataLoader

# Inicializar modelo base
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')

# Datos de entrenamiento (pares de textos similares)
train_examples = [
    InputExample(texts=['Contrato legal', 'Documento jurídico'], label=0.9),
    InputExample(texts=['Política GDPR', 'Regulación privacidad'], label=0.85),
    # ... más ejemplos
]

train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)

# Definir Matryoshka Loss con múltiples dimensiones
matryoshka_dimensions = [128, 256, 512, 768]  # Nested dimensions

train_loss = losses.MatryoshkaLoss(
    model=model,
    loss=losses.CosineSimilarityLoss(model),
    matryoshka_dims=matryoshka_dimensions
)

# Entrenar modelo
model.fit(
    train_objectives=[(train_dataloader, train_loss)],
    epochs=1,
    warmup_steps=100
)

# Guardar modelo
model.save('matryoshka-legal-embeddings')

# Testing: Generar embeddings a diferentes dimensiones
test_text = "Acuerdo de confidencialidad empresarial"

for dims in matryoshka_dimensions:
    # Generar embedding completo y truncar
    full_embedding = model.encode(test_text)
    truncated_embedding = full_embedding[:dims]

    print(f"{dims} dims: {len(truncated_embedding)} dimensiones, "
        f"storage: {len(truncated_embedding)*4} bytes")

# Output:
# 128 dims: 128 dimensiones, storage: 512 bytes
# 256 dims: 256 dimensiones, storage: 1024 bytes
# 512 dims: 512 dimensiones, storage: 2048 bytes
# 768 dims: 768 dimensiones, storage: 3072 bytes

"""
Product Quantization implementation con Qdrant para 100M+ vectores.
Compresión 64x con 

"""
Implementación production-ready de voyage-context-3 con LangChain.
Incluye fallback a OpenAI, monitoring de costes, y A/B testing.
"""
from langchain_voyageai import VoyageAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone, ServerlessSpec
import os
from typing import List, Dict, Optional
import time


class VoyageContextRAG:
    """
    Sistema RAG optimizado usando voyage-context-3 para reducción costes.
    
    Features:
    - Fallback automático a OpenAI si Voyage falla
    - Tracking de costes por embedding
    - Support para binary quantization en Pinecone
    - A/B testing capability para validar accuracy
    """

    def __init__(
        self,
        voyage_api_key: str,
        openai_api_key: str,
        pinecone_api_key: str,
        index_name: str = "voyage-context-3",
        dimension: int = 512,  # Usar 512 dims para optimal cost/performance
        use_binary: bool = True
    ):
        self.voyage_api_key = voyage_api_key
        self.openai_api_key = openai_api_key
        self.dimension = dimension
        self.use_binary = use_binary

        # Inicializar Voyage embeddings (primary)
        self.voyage_embedder = VoyageAIEmbeddings(
            voyage_api_key=voyage_api_key,
            model="voyage-context-3",
            truncation_dimension=dimension  # Matryoshka truncation
        )

        # OpenAI embeddings como fallback
        self.openai_embedder = OpenAIEmbeddings(
            openai_api_key=openai_api_key,
            model="text-embedding-3-small",
            dimensions=dimension  # Match Voyage dims
        )

        # Inicializar Pinecone
        pc = Pinecone(api_key=pinecone_api_key)

        # Crear índice si no existe
        if index_name not in [idx.name for idx in pc.list_indexes()]:
            pc.create_index(
                name=index_name,
                dimension=dimension,
                metric="cosine",
                spec=ServerlessSpec(
                    cloud="aws",
                    region="us-west-2"
                )
            )
            print(f"✅ Índice '{index_name}' creado")

        self.index = pc.Index(index_name)

        # Tracking de costes (estimados)
        self.cost_tracker = {
            'voyage_embeddings': 0,
            'openai_embeddings': 0,
            'total_tokens': 0
        }

    def embed_documents(
        self,
        documents: List[str],
        metadata: Optional[List[Dict]] = None,
        use_fallback_on_error: bool = True
    ) -> List[List[float]]:
        """
        Genera embeddings usando voyage-context-3 con fallback a OpenAI.
        
        Args:
            documents: Lista de textos a embedder
            metadata: Metadata contextual para cada documento
            use_fallback_on_error: Si True, usa OpenAI si Voyage falla
        
        Returns:
            Lista de embeddings (vectores float)
        """
        try:
            start_time = time.time()

            # Voyage-context-3 acepta metadata contextual
            if metadata:
                # Concatenar metadata relevante al documento
                enriched_docs = [
                    f"{doc}\n\nContext: {meta.get('type', '')}, "
                    f"{meta.get('jurisdiction', '')}, {meta.get('year', '')}"
                    for doc, meta in zip(documents, metadata)
                ]
            else:
                enriched_docs = documents

            embeddings = self.voyage_embedder.embed_documents(enriched_docs)

            # Tracking de costes (estimado: $0.008 per 1M tokens)
            total_tokens = sum(len(doc.split()) * 1.3 for doc in documents)  # Aproximación
            cost = (total_tokens / 1_000_000) * 0.008
            self.cost_tracker['voyage_embeddings'] += cost
            self.cost_tracker['total_tokens'] += total_tokens

            elapsed = time.time() - start_time
            print(f"✅ {len(documents)} embeddings generados con Voyage en {elapsed:.2f}s (${cost:.4f})")

            return embeddings

        except Exception as e:
            print(f"⚠️ Error con Voyage embeddings: {e}")

            if use_fallback_on_error:
                print("🔄 Fallback a OpenAI embeddings...")

                embeddings = self.openai_embedder.embed_documents(documents)

                # Tracking costes OpenAI ($0.02 per 1M tokens para text-embedding-3-small)
                total_tokens = sum(len(doc.split()) * 1.3 for doc in documents)
                cost = (total_tokens / 1_000_000) * 0.02
                self.cost_tracker['openai_embeddings'] += cost
                self.cost_tracker['total_tokens'] += total_tokens

                print(f"✅ Fallback completado (${cost:.4f})")

                return embeddings
            else:
                raise

    def store_vectors(
        self,
        documents: List[str],
        metadata: List[Dict],
        batch_size: int = 100
    ):
        """
        Almacena vectores en Pinecone con binary quantization opcional.
        
        Args:
            documents: Textos a almacenar
            metadata: Metadata para cada documento
            batch_size: Tamaño de batch para upserts
        """
        print(f"📤 Almacenando {len(documents)} documentos en Pinecone...")

        for i in range(0, len(documents), batch_size):
            batch_docs = documents[i:i+batch_size]
            batch_meta = metadata[i:i+batch_size]

            # Generar embeddings
            embeddings = self.embed_documents(batch_docs, batch_meta)

            # Preparar vectores para upsert
            vectors = []
            for j, (doc, emb, meta) in enumerate(zip(batch_docs, embeddings, batch_meta)):
                vector_id = f"doc_{i+j}"

                # Si usamos binary quantization, convertir embeddings
                if self.use_binary:
                    # Threshold a 0 para binary (1 si > 0, 0 si